FPVS_source_estimation_evoked.py

#!/imaging/local/software/miniconda/envs/mne0.20/bin/python
"""
Apply inverse operator to evoked data in FPVS.

Read evoked created by FPVS_average_epochs.py,.
read and apply inverse operator, write STCs.
==========================================

OH, May 2020
"""

import sys

from os import path as op
import numpy as np

from copy import deepcopy

from importlib import reload

import mne

import config_sweep as config
reload(config)


print(mne.__version__)

# conditions
conds = config.do_conds

def run_source_estimation_evoked(sbj_id):
    """Average epochs for one subject."""
    # path to subject's data
    sbj_path = op.join(config.data_path, config.map_subjects[sbj_id][0])

    # for STC plotting
    subject = config.mri_subjects[sbj_id]

    inv_fname = op.join(sbj_path, subject + '_EEGMEG-inv.fif')

    print('Reading EEG/MEG inverse operator: %s.' % inv_fname)
    invop = mne.minimum_norm.read_inverse_operator(inv_fname)

    # base frequencies as strings
    freqs_all = [str(ff) for ff in config.fpvs_freqs]

    morph_mat = []  # morphing matrix, one per subject

    # for evoked created with and without Notch filter for base frequency
    for do_notch in [0, 1]:

        if do_notch:  # if Notch filter at base frequency requested

            # add to epoch file name
            str_notch = '_nch'

        else:

            str_notch = ''

        for cond in conds:  # conditions

            if cond == 'face':  # hack, no frequency sweep for faces

                freqs = ['6.0']

            else:  # for all word condition, use all sweep frequencies

                freqs = freqs_all

            for freq in freqs:  # frequencies

                evo_fname = op.join(
                    sbj_path, 'AVE', '%s_f_%s_%s%s-ave.fif' %
                    (cond, config.raw_ICA_suff, ''.join(freq.split('.')),
                     str_notch))

                print('Writing evoked data to %s.' % evo_fname)
                evoked = mne.read_evokeds(evo_fname, 0)

                method = 'MNE'
                lambda2 = 1. / 3.**2

                stc = mne.minimum_norm.apply_inverse(
                    evoked, invop, lambda2=lambda2, method=method,
                    pick_ori='normal')

                stc_fname = op.join(
                    sbj_path, 'STC', '%s_f_%s_%s%s' %
                    (cond, config.raw_ICA_suff, ''.join(freq.split('.')),
                     str_notch))

                print('Saving source estimate to %s.' % stc_fname)

                stc.save(stc_fname)

                if morph_mat == []:

                    print('Computing morphing matrix for %s.' % subject)

                    morph_mat = mne.compute_source_morph(
                        src=stc, subject_from=subject,
                        subject_to=config.stc_morph,
                        subjects_dir=config.subjects_dir
                    )

                stc_mph = morph_mat.apply(stc)

                mph_fname = op.join(
                    sbj_path, 'STC', '%s_f_%s_%s%s_mph' %
                    (cond, config.raw_ICA_suff, ''.join(freq.split('.')),
                     str_notch))

                print('Saving morphed STC to %s.' % mph_fname)

                stc_mph.save(mph_fname)

    return


# get all input arguments except first
if len(sys.argv) == 1:

    sbj_ids = np.arange(0, len(config.map_subjects)) + 1

else:

    # get list of subjects IDs to process
    sbj_ids = [int(aa) for aa in sys.argv[1:]]


for ss in sbj_ids:

    # raw, psds, psds_as_evo, freqs = run_PSD_raw(ss)
    data_runs = run_source_estimation_evoked(ss)