07. Evoked data¶

import os.path as op

import mne
from mne.parallel import parallel_func

from library.config import meg_dir, N_JOBS, l_freq


def run_evoked(subject_id, tsss=False):
    subject = "sub%03d" % subject_id
    print("Processing subject: %s%s"
          % (subject, (' (tSSS=%d)' % tsss) if tsss else ''))

    data_path = op.join(meg_dir, subject)
    if tsss:
        fname_epo = op.join(data_path, '%s-tsss_%d-epo.fif' % (subject, tsss))
        fname_evo = op.join(data_path, '%s-tsss_%d-ave.fif' % (subject, tsss))
    else:
        fname_epo = op.join(data_path, '%s_highpass-%sHz-epo.fif'
                            % (subject, l_freq))
        fname_evo = op.join(data_path, '%s_highpass-%sHz-ave.fif'
                            % (subject, l_freq))
    print('  Creating evoked datasets')
    epochs = mne.read_epochs(fname_epo, preload=True)

    evoked_famous = epochs['face/famous'].average()
    evoked_scrambled = epochs['scrambled'].average()
    evoked_unfamiliar = epochs['face/unfamiliar'].average()

    # Simplify comment
    evoked_famous.comment = 'famous'
    evoked_scrambled.comment = 'scrambled'
    evoked_unfamiliar.comment = 'unfamiliar'

    contrast = mne.combine_evoked([evoked_famous, evoked_unfamiliar,
                                   evoked_scrambled], weights=[0.5, 0.5, -1.])
    contrast.comment = 'contrast'
    faces = mne.combine_evoked([evoked_famous, evoked_unfamiliar], 'nave')
    faces.comment = 'faces'

    # let's make trial-count-normalized ones for group statistics
    epochs_eq = epochs.copy().equalize_event_counts(['face', 'scrambled'])[0]
    evoked_faces_eq = epochs_eq['face'].average()
    evoked_scrambled_eq = epochs_eq['scrambled'].average()
    assert evoked_faces_eq.nave == evoked_scrambled_eq.nave
    evoked_faces_eq.comment = 'faces_eq'
    evoked_scrambled_eq.comment = 'scrambled_eq'

    mne.evoked.write_evokeds(fname_evo, [evoked_famous, evoked_scrambled,
                                         evoked_unfamiliar, contrast, faces,
                                         evoked_faces_eq, evoked_scrambled_eq])


parallel, run_func, _ = parallel_func(run_evoked, n_jobs=N_JOBS)
parallel(run_func(subject_id) for subject_id in range(1, 20))
if l_freq is None:
    parallel(run_func(3, tsss) for tsss in (10, 1))  # Maxwell filtered data