def task_fetch_data():
"""Step 00: Download the openfmri ds117 dataset files."""
for subject in subjects:
t1_fname = fname.t1(subject=subject)
sss_fnames = [fname.sss(subject=subject, run=run) for run in range(1, 7)]
flash5_fname = fname.flash5(subject=subject)
yield dict(
name=subject,
file_dep=['00_fetch_data.py'],
targets=[t1_fname, flash5_fname] + sss_fnames,
actions=['python 00_fetch_data.py %s' % subject],
)
def task_filter():
"""Step 02: Bandpass-filter the data"""
for subject in subjects:
sss_fnames = [fname.sss(subject=subject, run=run) for run in range(1, 7)]
filt_fnames = [fname.filt(subject=subject, run=run,
fmin=bandpass_fmin, fmax=bandpass_fmax)
for run in range(1, 7)]
yield dict(
name=subject,
task_dep=['anatomy'],
file_dep=sss_fnames + ['02_filter.py'],
targets=filt_fnames,
actions=['python 02_filter.py %s' % subject],
)
mne.set_log_level('INFO')
# 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)
# Keep track of PSD plots before and after filtering
figs_before = []
figs_after = []
for run in range(1, 7):
# Load the SSS transformed data.
raw = mne.io.read_raw_fif(fname.sss(subject=subject, run=run),
preload=True)
# The EOG and ECG channels have been erroneously flagged as EEG channels.
raw.set_channel_types({'EEG061': 'eog',
'EEG062': 'eog',
'EEG063': 'ecg',
'EEG064': 'misc'}) # EEG064 free-floating el.
raw.rename_channels({'EEG061': 'EOG061',
'EEG062': 'EOG062',
'EEG063': 'ECG063'})
# Drop EEG channels (they are very noisy in this dataset)
raw.pick_types(meg=True, eeg=False, eog=True, ecg=True, stim=True)
# Bandpass the data.