03-extract_events.py

"""
============================================
03. Extract events from the stimulus channel
============================================

Here, all events present in the stimulus channel indicated in
config.stim_channel are extracted.
The events are saved to the subject's MEG directory.
This is done early in the pipeline to avoid distorting event-time,
for instance by resampling.
"""

import os.path as op

import mne
import numpy as np
from mne.parallel import parallel_func

import config


def run_events(subject):
    print("Processing subject: %s" % subject)
    meg_subject_dir = op.join(config.meg_dir, subject)

    for run in config.runs:
        if config.use_maxwell_filter:
            extension = run + '_sss_raw'
        else:
            extension = run + '_filt_raw'

        raw_fname_in = op.join(meg_subject_dir,
                               config.base_fname.format(**locals()))
        eve_fname_out = op.splitext(raw_fname_in)[0] + '-eve.fif'

        raw = mne.io.read_raw_fif(raw_fname_in)

        events = mne.find_events(raw, stim_channel=config.stim_channel,
                                 consecutive=True,
                                 min_duration=config.min_event_duration,
                                 shortest_event=config.shortest_event)

        if config.trigger_time_shift:
            events = mne.event.shift_time_events(events,
                                                 np.unique(events[:, 2]),
                                                 config.trigger_time_shift,
                                                 raw.info['sfreq'])

        print("Input: ", raw_fname_in)
        print("Output: ", eve_fname_out)

        mne.write_events(eve_fname_out, events)

        if config.plot:
            # plot events
            figure = mne.viz.plot_events(events, sfreq=raw.info['sfreq'],
                                         first_samp=raw.first_samp)
            figure.show()


parallel, run_func, _ = parallel_func(run_events, n_jobs=config.N_JOBS)
parallel(run_func(subject) for subject in config.subjects_list)