fwd,
csd,
reduce_rank=True,
pick_ori='max-power',
inversion='matrix',
reg=reg[subject]['dics'])
# Compute source power
stc_baseline, _ = mne.beamformer.apply_dics_csd(csd_baseline, inv)
stc_ers, _ = mne.beamformer.apply_dics_csd(csd_ers, inv)
stc_baseline.subject = fname.subject_id(subject=subject)
stc_ers.subject = fname.subject_id(subject=subject)
# Normalize with baseline power.
stc_ers /= stc_baseline
stc_ers.data = np.log(stc_ers.data)
stc_ers.save(fname.stc_dics(subject=subject))
stc_ers.save_as_volume(fname.nii_dics(subject=subject), src=fwd['src'])
fig = stc_ers.plot(subject=fname.subject_id(subject=subject),
subjects_dir=fname.subjects_dir,
src=fwd['src'])
with mne.open_report(fname.report(subject=subject)) as report:
report.add_figs_to_section(fig,
'DICS Source estimate',
'Source level',
replace=True)
report.save(fname.report_html(subject=subject),
overwrite=True,
open_browser=False)
import mne
import argparse
import numpy as np
from config import fname, events_id, subjects_with_extra_stim_artifacts, stim_artifact_sensor, n_jobs
# Handle command line arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('subject', metavar='sub###', type=int, help='The subject to process')
args = parser.parse_args()
subject = args.subject
print('Processing subject:', subject)
report = mne.open_report(fname.report(subject=subject))
raw = mne.io.read_raw_fif(fname.raw_filt(subject=subject))
ica = mne.preprocessing.read_ica(fname.ica(subject=subject))
# Create short epochs for evoked analysis
events = mne.find_events(raw, shortest_event=0.01)
epochs = mne.Epochs(raw, events, events_id, tmin=-0.2, tmax=0.5, reject=None, baseline=(-0.2, 0), preload=True)
report.add_figs_to_section(epochs.average().plot_joint(times=[0.035, 0.1]), ['Evokeds without cleaning (grads)', 'Evokeds without cleaning (mags)'], 'Sensor level', replace=True)
# Apply ICA to remove EOG and ECG artifacts
epochs = ica.apply(epochs)
# Do a first pass for fixing the stim artifact
mne.preprocessing.fix_stim_artifact(epochs)
epochs.save(fname.epochs(subject=subject), overwrite=True)
# For some subjects, there are more stim artifacts that we need to remove using ICA
if subject in subjects_with_extra_stim_artifacts:
###############################################################################
# 7) Create HTML report
blocks_duration = '<p>Block 1 Duration from %s to %s seconds.<br>'\
'Block 1 length: %s seconds<p>'\
'<p>Block 2 Duration from %s to %s seconds.<br>'\
'Block 2 length: %s seconds<p>' \
'<p>Block 2 Duration from %s to %s seconds.<br>' \
'Block 3 length: %s seconds<p>' \
'<p>Block 2 Duration from %s to %s seconds.<br>' \
'Block 4 length: %s seconds<p>' \
% (round(b1s, 2), round(b1e, 2), round(b1e - b1s, 2),
round(b2s, 2), round(b2e, 2), round(b2e - b2s, 2),
round(b3s, 2), round(b3e, 2), round(b3e - b3s, 2),
round(b4s, 2), round(b4e, 2), round(b4e - b4s, 2))
with open_report(fname.report(subject=subject)[0]) as report:
report.add_htmls_to_section(htmls=blocks_duration,
captions='Block durations',
section='Filtered data')
report.add_figs_to_section(fig,
'Blocks PSD',
section='Filtered data',
replace=True)
report.add_figs_to_section(filt_plot,
'Filtered data',
section='Filtered data',
replace=True)
report.save(fname.report(subject=subject)[1],
overwrite=True,
open_browser=False)
er_raw = mne.io.read_raw_fif(fname.ernoise, preload=True)
raw_picks = mne.pick_types(raw.info, meg=True, eeg=False)
er_raw_picks = mne.pick_types(er_raw.info, meg=True, eeg=False)
raw._data[raw_picks] += er_raw._data[er_raw_picks, :len(raw.times)]
###############################################################################
# Save everything
###############################################################################
raw.save(fname.simulated_raw(noise=config.noise, vertex=config.vertex),
overwrite=True)
###############################################################################
# Plot it!
###############################################################################
with mne.open_report(fname.report(noise=config.noise,
vertex=config.vertex)) as report:
fig = plt.figure()
plt.plot(times, generate_signal(times, freq=10))
plt.xlabel('Time (s)')
report.add_figs_to_section(fig,
'Signal time course',
section='Sensor-level',
replace=True)
fig = raw.plot()
report.add_figs_to_section(fig,
'Simulated raw',
section='Sensor-level',
replace=True)
report.save(fname.report_html(noise=config.noise, vertex=config.vertex),
overwrite=True,
# Handle command line arguments
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('subject', metavar='sub###', help='The subject to process')
args = parser.parse_args()
subject = args.subject
print('Processing subject:', subject)
# Read the epochs
print('Reading epochs...')
epochs = mne.read_epochs(fname.epo(subject=subject))
# Suppress warning about wavelet length.
warnings.simplefilter('ignore')
# Load the report to add figures to
report = mne.open_report(fname.report(subject=subject))
# Individual frequencies to estimate the CSD for
fmin = freq_bands[0][0]
fmax = freq_bands[-1][1]
frequencies = np.arange(fmin, fmax + 1, 2)
# Compute CSD matrices for each frequency and each condition.
for condition in conditions:
print('Condition:', condition)
# Remove the mean during the time interval for which we compute the CSD
epochs_baselined = epochs[condition].apply_baseline((csd_tmin, csd_tmax))
# Compute CSD for the desired time interval
csd = csd_morlet(epochs_baselined,
frequencies=frequencies,
# output path
output_path = fname.output(processing_step='task_blocks',
subject=subject,
file_type='raw.fif')
# sample down and save file
raw_bl_filt.save(output_path, overwrite=True)
###############################################################################
# 7) Create HTML report
blocks_duration = '<p>Block 1 Duration from %s to %s seconds.<br>'\
'Block 1 length: %s seconds<p>'\
'<p>Block 2 Duration from %s to %s seconds.<br>'\
'Block 2 length: %s seconds<p>' \
% (round(b1s, 2), round(b1e, 2), round(b1e - b1s, 2),
round(b2s, 2), round(b2e, 2), round(b2e - b2s, 2))
with open_report(fname.report(subject=subject)[0]) as report:
report.add_htmls_to_section(htmls=blocks_duration,
captions='Block durations',
section='Filtered data')
report.add_figs_to_section(fig, 'Blocks PSD',
section='Filtered data',
replace=True)
report.add_figs_to_section(filt_plot,
'Filtered data',
section='Filtered data',
replace=True)
report.save(fname.report(subject=subject)[1], overwrite=True,
open_browser=False)
