extract_ts_source_tfr_grad.py

# -*- coding: utf-8 -*-
"""
Created on Mon Aug 31 10:17:09 2015

@author: mje
"""
import mne
from mne.minimum_norm import (source_induced_power, read_inverse_operator)
import numpy as np
import sys

from my_settings import (tf_folder, subjects_dir, epochs_folder, mne_folder)

subject = sys.argv[1]

# Using the same inverse operator when inspecting single trials Vs. evoked
snr = 3.0  # Standard assumption for average data but using it for single trial
lambda2 = 1.0 / snr**2
method = "MNE"  # use dSPM method (could also be MNE or sLORETA)
freqs = [8, 13]
n_cycles = 4.  # freqs / 3.
n_jobs = 1

conditions = ["ent/left", "ctl/left", "ent/right", "ctl/right"]

# Load data
# labels = mne.read_labels_from_annot(
#     subject, parc='PALS_B12_Brodmann', regexp="Bro",
#     subjects_dir=subjects_dir)
# labels_sel = labels[6], labels[7]
labels = mne.read_labels_from_annot(
    subject, parc='PALS_B12_Lobes', subjects_dir=subjects_dir)
labels_sel = [labels[9], labels[10]]
# v1_lh = mne.read_label(subjects_dir + "%s/label/lh.V1.label" % subject,
#                        subject=subject)
# v1_rh = mne.read_label(subjects_dir + "%s/label/rh.V1.label" % subject,
#                        subject=subject)
# v2_lh = mne.read_label(subjects_dir + "%s/label/lh.V2.label" % subject,
#                        subject=subject)
# v2_rh = mne.read_label(subjects_dir + "%s/label/rh.V2.label" % subject,
#                        subject=subject)
#
# vis_lh = v1_lh + v2_lh
# vis_rh = v1_rh + v2_rh
# vis_lh.name = "vis-lh"
# vis_rh.name = "vis-rh"
# labels_sel = [vis_lh, vis_rh]

inverse_operator = read_inverse_operator(mne_folder + "%s_grad-inv.fif" %
                                         subject)
src = mne.read_source_spaces(subjects_dir + "%s/bem/%s-oct-6-src.fif" %
                             (subject, subject))
epochs = mne.read_epochs(epochs_folder + "%s_trial_start-epo.fif" % subject)
# epochs.drop_bad_epochs(reject_params)
epochs.pick_types(meg="grad", eeg=False)
epochs.resample(250, n_jobs=n_jobs)

for condition in conditions:
    for label in labels_sel:
        power, itc = source_induced_power(
            epochs[condition],
            inverse_operator,
            frequencies=freqs,
            label=label,
            lambda2=lambda2,
            method=method,
            pick_ori=None,
            baseline=(-0.4, -0.1),
            baseline_mode='ratio',
            n_cycles=n_cycles,
            pca=True,
            n_jobs=n_jobs)

        power = np.mean(power, axis=1)  # average over frequencies
        itc = np.mean(itc, axis=1)  # average over frequencies

        np.save(tf_folder + "%s_%s_%s_%s_%s_source_power_snr_3.npy" %
                (subject, condition[:3], condition[4:], label.name, method),
                power)
        np.save(tf_folder + "%s_%s_%s_%s_%s_source_itc_snr_3.npy" %
                (subject, condition[:3], condition[4:], label.name, method),
                itc)