extract_ts.py

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

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

from my_settings import *

subject = sys.argv[1]

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

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]]

inverse_operator = read_inverse_operator(mne_folder +
                                         "%s-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.resample(250, n_jobs=4)

for condition in conditions:
    stcs = apply_inverse_epochs(epochs[condition],
                                inverse_operator,
                                lambda2,
                                method,
                                pick_ori="normal")

    for label in labels_sel:
        label_ts = []
        for j in range(len(stcs)):
            ts = mne.extract_label_time_course(stcs[j],
                                               labels=label,
                                               src=src,
                                               mode="pca_flip")
            ts = np.squeeze(ts)
            ts *= np.sign(ts[np.argmax(np.abs(ts))])
            label_ts.append(ts)

        label_ts = np.asarray(label_ts)
        tfr = cwt_morlet(label_ts, epochs.info["sfreq"], freqs,
                         use_fft=True, n_cycles=n_cycle)

        np.save(tf_folder + "%s_%s_%s_%s_%s-tfr" % (subject, condition[:3],
                                                    condition[4:],
                                                    label.name, method),
                tfr)
        np.save(tf_folder + "%s_%s_%s_%s_%s-ts" % (subject, condition[:3],
                                                   condition[4:],
                                                   label.name, method),
                label_ts)

    del stcs
    del tfr