import os from collections import defaultdict import numpy as np import pandas as pd # このファイルのあるディレクトリの絶対パスを取得し、そこから level ディレクトリへの絶対パスを作成 CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) LEVEL_DIR = os.path.abspath(os.path.join(CURRENT_DIR, "..", "..", "level")) def change_level(df, level="Type1_Level1", sulcus=True): """ Change the level of the given DataFrame based on specified ROI levels. Parameters: df (pd.DataFrame): The input DataFrame to be modified. level (str): The level to which the DataFrame should be changed. Default is "Type1_Level1". sulcus (bool): A flag indicating whether to include sulcus regions. Default is True. Returns: pd.DataFrame: The modified DataFrame with the specified level changes applied. """ # LEVEL_DIR を基準に CSV ファイルの絶対パスを作成 ROI_number = pd.read_csv(os.path.join(LEVEL_DIR, "Level_ROI_No.csv")) ROI_name = pd.read_csv(os.path.join(LEVEL_DIR, "Level_ROI_Name.csv")) if sulcus == False: tmp = ROI_number["Type1_Level2"] ROI_number = ROI_number[tmp != 18] ROI_number = ROI_number[tmp != 19] ROI_name = ROI_name[tmp != 18] ROI_name = ROI_name[tmp != 19] data = dict(zip(ROI_number["ROI"], ROI_number[level])) level_dict = defaultdict(list) for key, value in data.items(): level_dict[str(value)].append(key) change_df_list = [] for i, (key, value) in enumerate(level_dict.items()): name = ROI_name[level].unique()[i] change_df_list.append(df[value].sum(axis=1).rename(name)) change_df = pd.concat(change_df_list, axis=1) return change_df def make_csv(parcellation, output_dir, basename): """ Generates multiple CSV files containing volume data for different levels of parcellation. Parameters: parcellation (numpy.ndarray): The parcellation data array where each unique integer represents a different region. output_dir (str): The directory where the output CSV files will be saved. basename (str): The base name for the output CSV files. Returns: pandas.DataFrame: The DataFrame containing volume data for Type1_Level5. """ # LEVEL_DIR を基準にテキストファイルの絶対パスを作成 csv_path = os.path.join(LEVEL_DIR, "Level5.txt") df_Type1_level5 = ( pd.read_table(csv_path, names=["number", "region"]).astype("str").set_index("number") ) for i in range(1, 281): volume = np.count_nonzero(parcellation == i) df_Type1_level5.loc[str(i), basename] = volume df_Type1_level5 = df_Type1_level5.set_index("region").T.reset_index(drop=True) df_Type1_level4 = change_level(df_Type1_level5, level="Type1_Level4") df_Type1_level3 = change_level(df_Type1_level5, level="Type1_Level3") df_Type1_level2 = change_level(df_Type1_level5, level="Type1_Level2") df_Type1_level1 = change_level(df_Type1_level5, level="Type1_Level1") df_Type2_level5 = change_level(df_Type1_level5, level="Type2_Level5") df_Type2_level4 = change_level(df_Type1_level5, level="Type2_Level4") df_Type2_level3 = change_level(df_Type1_level5, level="Type2_Level3") df_Type2_level2 = change_level(df_Type1_level5, level="Type2_Level2") df_Type2_level1 = change_level(df_Type1_level5, level="Type2_Level1") os.makedirs(os.path.join(output_dir, "csv"), exist_ok=True) df_Type1_level5.to_csv( os.path.join(output_dir, f"csv/{basename}_Type1_Level5.csv"), index=False ) df_Type1_level4.to_csv( os.path.join(output_dir, f"csv/{basename}_Type1_Level4.csv"), index=False ) df_Type1_level3.to_csv( os.path.join(output_dir, f"csv/{basename}_Type1_Level3.csv"), index=False ) df_Type1_level2.to_csv( os.path.join(output_dir, f"csv/{basename}_Type1_Level2.csv"), index=False ) df_Type1_level1.to_csv( os.path.join(output_dir, f"csv/{basename}_Type1_Level1.csv"), index=False ) df_Type2_level5.to_csv( os.path.join(output_dir, f"csv/{basename}_Type2_Level5.csv"), index=False ) df_Type2_level4.to_csv( os.path.join(output_dir, f"csv/{basename}_Type2_Level4.csv"), index=False ) df_Type2_level3.to_csv( os.path.join(output_dir, f"csv/{basename}_Type2_Level3.csv"), index=False ) df_Type2_level2.to_csv( os.path.join(output_dir, f"csv/{basename}_Type2_Level2.csv"), index=False ) df_Type2_level1.to_csv( os.path.join(output_dir, f"csv/{basename}_Type2_Level1.csv"), index=False ) return df_Type1_level5