def mne_ransac_bad_channels(raw, overwrite=False):
bids_chan_file = tb.fileparts(raw.filenames[0], '_channels.tsv', -4)
ransacfile = tb.fileparts(raw.filenames[0], '_channels_ransac.tsv')
if not pathlib.Path.is_file(pathlib.Path(ransacfile)) or overwrite:
epochs = mne_epoch(raw).drop_bad()
epochs.load_data()
ransac = Ransac(random_state=999)
ransac.fit(epochs)
raw.info['bads'] = ransac.bad_chs_
chans = pd.read_csv(bids_chan_file, delimiter='\t')
chans.loc[chans.name.isin(ransac.bad_chs_), 'status'] = 'bad'
pd.DataFrame.to_csv(chans, ransacfile, sep='\t')
else:
chans = pd.read_csv(ransacfile, delimiter='\t')
raw.info['bads'] = list(chans.loc[chans['status'] == 'bad', 'name'])
return raw
raw.filter(preprocess_options['ica_highpass'], None,
skip_by_annotation=['boundary'])
raw.filter(None, 40, picks=['eog'])
raw.notch_filter([60, 120], picks=['eog'])
# Make ICA Epochs
epochs = mne.Epochs(raw, events, event_id=event_id,
tmin=preprocess_options['tmin'],
tmax=preprocess_options['tmax'],
baseline=(None, None), reject=None, preload=True)
epochs.set_montage(bv_montage)
# Autodetect bad channels
ransac = Ransac(verbose=False, n_jobs=4,
min_corr=.60, unbroken_time=.6)
ransac.fit(epochs.copy().filter(None, 40))
if len(ransac.bad_chs_):
for chan in ransac.bad_chs_:
epochs.info['bads'].append(chan)
print(f'RANSAC Bad Channels: {ransac.bad_chs_}')
# Save RANSAC
ransac_file = deriv_path / f'{sub}_task-{task}_ref_FCz_ransac.pkl'
with open(ransac_file, 'wb') as f:
pickle.dump(ransac, f)
# Make RANSAC json
json_info = {
'Description': 'RANSAC object computed from epoched data',
'parameteres': {
'unbroken_time': .6,
def test_ransac():
"""Some basic tests for ransac."""
event_id = {'Visual/Left': 3}
tmin, tmax = -0.2, 0.5
events = mne.find_events(raw)
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
baseline=(None, 0),
decim=8,
reject=None,
preload=True)
# normal case
picks = mne.pick_types(epochs.info,
meg='mag',
eeg=False,
stim=False,
eog=False,
exclude=[])
ransac = Ransac(picks=picks, random_state=np.random.RandomState(42))
epochs_clean = ransac.fit_transform(epochs)
assert len(epochs_clean) == len(epochs)
# Pass string instead of array of idx
picks = 'eeg'
ransac = Ransac(picks=picks)
ransac.fit(epochs[:2])
expected = mne.pick_types(epochs.info,
meg=False,
eeg=True,
stim=False,
eog=False,
exclude='bads')
assert (expected == ransac.picks).all()
# Pass numpy instead of epochs
X = epochs.get_data()
pytest.raises(AttributeError, ransac.fit, X)
# should not contain both channel types
picks = mne.pick_types(epochs.info,
meg=True,
eeg=False,
stim=False,
eog=False,
exclude=[])
ransac = Ransac(picks=picks)
pytest.raises(ValueError, ransac.fit, epochs)
# should not contain other channel types.
picks = mne.pick_types(raw.info,
meg=False,
eeg=True,
stim=True,
eog=False,
exclude=[])
ransac = Ransac(picks=picks)
pytest.raises(ValueError, ransac.fit, epochs)