def _compute_cov(file_raw, sss, ssp_er, ssp_eog, ssp_ecg, do_ar):
subject = pp.get_subject(file_raw)
raw = mne.io.read_raw_fif(file_raw)
if DEBUG:
raw.crop(0, 30)
rawc, reject = clean_raw(
raw, subject, sss=sss, ssp_er=ssp_er, ssp_eog=ssp_eog, ssp_ecg=ssp_ecg,
do_ar=do_ar)
events = mne.make_fixed_length_events(
rawc, id=3000, start=0, duration=pp.duration)
epochs = mne.Epochs(
rawc, events, event_id=3000, tmin=0, tmax=pp.duration, proj=True,
baseline=None, reject=reject, preload=False, decim=8 if DEBUG else 1)
epochs.drop_bad()
clean_events = events[epochs.selection]
picks = mne.pick_types(rawc.info, meg=True)
covs = []
for fb in pp.fbands[:1 if DEBUG else len(pp.fbands)]:
rf = rawc.copy().load_data().filter(fb[0], fb[1])
ec = mne.Epochs(
rf, clean_events, event_id=3000, tmin=0, tmax=pp.duration,
proj=True, baseline=None, reject=None, preload=False,
decim=8 if DEBUG else 1,
picks=picks)
cov = mne.compute_covariance(ec, method='oas', rank=None)
covs.append(cov.data)
out = dict(subject=subject, kind='rest', n_events=len(events),
n_events_good=len(clean_events), covs=np.array(covs))
return out
def _run_all(file_raw):
mne.utils.set_log_level('warning')
subject = pp.get_subject(file_raw)
print(subject)
error = 'None'
result = dict()
try:
result = _compute_cov(file_raw)
except Exception as err:
error = repr(err)
print(error)
out = dict(error=error)
out.update(result)
return out