efel/io.py
from __future__ import annotations"""IO handler for eFEL""" """Copyright (c) 2015, EPFL/Blue Brain Project This file is part of eFEL <https://github.com/BlueBrain/eFEL> This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA."""import csvimport jsonfrom pathlib import Pathimport neoimport numpy as np def load_ascii_input( file_path: Path | str, delimiter: str = " ") -> tuple[np.ndarray, np.ndarray]: """Loads electrophysiology data from an ASCII file. Returns: A tuple containing two numpy arrays, one for time and one for voltage. """ file_path = Path(file_path) data = np.loadtxt(file_path, delimiter=delimiter) time, voltage = data[:, 0], data[:, 1] return time, voltage Function `extract_stim_times_from_neo_data` has a Cognitive Complexity of 83 (exceeds 5 allowed). Consider refactoring.def extract_stim_times_from_neo_data(blocks, stim_start, stim_end) -> tuple: """ Seeks for the stim_start and stim_end parameters inside the Neo data. Args: blocks (Neo object blocks): Description of what blocks represents. stim_start (numerical value or None): Start time of the stimulation in milliseconds. If not available, None should be used. stim_end (numerical value or None): End time of the stimulation in milliseconds. If not available, None should be used. Returns: tuple: A tuple containing: - stim_start (numerical value or None): Start time of the stimulation in milliseconds. - stim_end (numerical value or None): End time of the stimulation in milliseconds. Notes: - Epoch.name should be one of "stim", "stimulus", "stimulation", "current_injection". - First Event.name should be "stim_start", "stimulus_start", "stimulation_start", "current_injection_start". - Second Event.name should be one of "stim_end", "stimulus_end", "stimulation_end", "current_injection_end". """ # this part code aims to find informations about stimulations, if # stimulation time are None. if stim_start is None and stim_end is None: for bl in blocks: for seg in bl.segments: event_start_rescaled = False event_end_rescaled = False epoch_rescaled = False for epoch in seg.epochs:Avoid deeply nested control flow statements. if epoch.name in ( "stim", "stimulus", "stimulation", "current_injection"): if stim_start is None: epoch = epoch.rescale('ms').magnitude stim_start = epoch[0] stim_end = epoch[-1] epoch_rescaled = True else: raise ValueError( 'It seems that there are two epochs related ' 'to stimulation, the program does not know ' 'which one to chose') for event in seg.events: # Test event stim_startAvoid deeply nested control flow statements. if event.name in ( "stim_start", "stimulus_start", "stimulation_start", "current_injection_start"): # tests if not already found once if event_start_rescaled: raise ValueError( 'It seems that stimulation start time is ' 'defined more than one event.' ' The program does not know which one ' 'to choose') if epoch_rescaled: raise ValueError( 'It seems that stim_start is defined in both ' 'epoch and an event.' + ' The program does not know which one ' 'to choose') if stim_start is None: event = event.rescale('ms').magnitude try: stim_start = event[0] except BaseException: stim_start = event event_start_rescaled = True # Test event stim_end elif event.name in ( "stim_end", "stimulus_end", "stimulation_end", "current_injection_end", ): # tests if not already found once if event_end_rescaled: raise ValueError( 'It seems that stimulation end time is ' 'defined more than one event.' ' The program does not know which one ' 'to choose') if epoch_rescaled: raise ValueError( 'It seems that stim_end is defined in ' 'both epoch and an event.' ' The program does not know which one ' 'to choose') if stim_end is None: event = event.rescale('ms').magnitude try: stim_end = event[-1] except BaseException: stim_end = event event_end_rescaled = True return stim_start, stim_end Function `load_neo_file` has a Cognitive Complexity of 12 (exceeds 5 allowed). Consider refactoring.def load_neo_file(file_name: str, stim_start=None, stim_end=None, **kwargs) -> list: """ Loads a data file using neo and converts it for eFEL readability. Args: file_name (string): Path to the Dependency file location. stim_start (numerical value, optional): Start time in ms. Optional if an Epoch or two Events are in the file. stim_end (numerical value, optional): End time in ms. Optional if an Epoch or two Events are in the file. **kwargs: Additional arguments for the read() method of Neo IO class. Returns: list of Segments: Segments containing traces, formatted as [Segments_1, Segments_2, ..., Segments_n], where each Segments_i is [Traces_1, Traces_2, ..., Traces_n]. Notes: - Epoch.name should be "stim", "stimulus", "stimulation", "current_injection". - First Event.name: "stim_start", "stimulus_start", "stimulation_start", "current_injection_start". - Second Event.name: "stim_end", "stimulus_end", "stimulation_end", "current_injection_end". """ reader = neo.io.get_io(file_name) blocks = reader.read(**kwargs) stim_start, stim_end = extract_stim_times_from_neo_data( blocks, stim_start, stim_end) if stim_start is None or stim_end is None: raise ValueError( 'No stim_start or stim_end found in epochs or events. ' 'Please specify "stim_start" and "stim_end" arguments.') # Convert data for eFEL efel_blocks = [] for bl in blocks: efel_segments = [] for seg in bl.segments: traces = [] for sig in seg.analogsignals: if 'V' in sig.units.dimensionality.string: trace = { 'T': (sig.times - sig.times[0]).rescale('ms').magnitude, 'V': sig.rescale('mV').magnitude, 'stim_start': [stim_start], 'stim_end': [stim_end] } traces.append(trace) efel_segments.append(traces) efel_blocks.append(efel_segments) return efel_blocks def save_feature_to_json(feature_values, filename): """Save feature values as a JSON file.""" class NumpyEncoder(json.JSONEncoder): def default(self, o): if isinstance(o, np.integer): return int(o) elif isinstance(o, np.floating): return float(o) elif isinstance(o, np.ndarray): return o.tolist() else: return super().default(o) with open(filename, 'w') as f: json.dump(feature_values, f, cls=NumpyEncoder) def save_feature_to_csv(feature_values, filename): """Save feature values as a CSV file.""" with open(filename, 'w', newline='') as f: writer = csv.writer(f) writer.writerow(feature_values.keys()) max_list_length = max(len(value) if isinstance(value, (list, np.ndarray)) else 1 for value in feature_values.values()) for i in range(max_list_length): row_data = [value[i] if isinstance(value, (list, np.ndarray)) and i < len(value) else '' for value in feature_values.values()] writer.writerow(row_data)