Source code for neurotic.gui.epochencoder

# -*- coding: utf-8 -*-
The :mod:`neurotic.gui.epochencoder` module implements a subclass of

.. autoclass:: NeuroticWritableEpochSource

import os
import shutil
import numpy as np
import pandas as pd
from ephyviewer import WritableEpochSource

import logging
logger = logging.getLogger(__name__)

[docs]class NeuroticWritableEpochSource(WritableEpochSource): """ A subclass of :class:`ephyviewer.datasource.epochs.WritableEpochSource` for custom CSV column formatting and automatic file backup. """ def __init__(self, filename, possible_labels, color_labels=None, channel_name='', backup=True): """ Initialize a new NeuroticWritableEpochSource. """ self.filename = filename self.backup = backup WritableEpochSource.__init__(self, epoch=None, possible_labels=possible_labels, color_labels=color_labels, channel_name=channel_name) def load(self): """ Returns a dictionary containing the data for an epoch. Data is loaded from the CSV file if it exists; otherwise the superclass implementation in WritableEpochSource.load() is called to create an empty dictionary with the correct keys and types. The method returns a dictionary containing the loaded data in this form: { 'time': np.array, 'duration': np.array, 'label': np.array, 'name': string } """ if os.path.exists(self.filename): # if file already exists, load previous epoch df = pd.read_csv(self.filename, index_col=None, dtype={ 'Start (s)': 'float64', 'End (s)': 'float64', 'Type': 'U'}) epoch = {'time': df['Start (s)'].values, 'duration': df['End (s)'].values - df['Start (s)'].values, 'label': df['Type'].values, 'name': self.channel_name} else: # if file does NOT already exist, use superclass method for creating # an empty dictionary epoch = super().load() return epoch def save(self): """ Save the epoch data to a CSV file, creating a backup first if the file already exists. """ # if file already exists, make a backup copy first if self.backup and os.path.exists(self.filename): backup_filename = self.filename.split('.') backup_filename.insert(-1, 'bck') backup_filename = '.'.join(backup_filename) shutil.copy2(self.filename, backup_filename) df = pd.DataFrame() df['Start (s)'] = np.round(self.ep_times, 6) # round to nearest microsecond df['End (s)'] = np.round(self.ep_times + self.ep_durations, 6) # round to nearest microsecond df['Type'] = self.ep_labels df.sort_values(['Start (s)', 'End (s)', 'Type'], inplace=True) df.to_csv(self.filename, index=False)