def test_montage_bipolar_02():
data_wrongorder = create_data(n_trial=2, attr=[
'chan',
])
data_wrongorder.axis['chan'][1] = data_wrongorder.chan[0][-1::-1]
with raises(ValueError):
montage(data_wrongorder, bipolar=100)
def test_montage_bipolar_03():
data_wrongorder = create_data(attr=[
'chan',
])
# you should get an error if chan is not first
data_wrongorder.axis = OrderedDict([
('time', data_wrongorder.axis['time']),
('chan', data_wrongorder.axis['chan']),
])
with raises(ValueError):
montage(data_wrongorder, bipolar=100)
def hide_artifacts(parameters, data):
data_comparison = montage(data, ref_to_avg=True, method='median')
count_samples = []
bad_smp = int(data.s_freq * parameters['read']['artifacts']['window'] / 2)
for i_trl in range(data.number_of('trial')):
i_bad = abs(data_comparison.data[i_trl]) > parameters['read']['artifacts']['threshold']
# before
padded_bad = i_bad.copy()
for i_roll in range(1, bad_smp):
padded_bad |= pad(i_bad, ((0, 0), (i_roll, 0)), mode='constant', constant_values=False)[:, :-i_roll]
# after
for i_roll in range(bad_smp):
padded_bad |= pad(i_bad, ((0, 0), (0, i_roll)), mode='constant', constant_values=False)[:, i_roll:]
data.data[i_trl][padded_bad] = NaN
count_samples.append(padded_bad.sum(axis=1))
bad_smp_per_chan = sum(count_samples, axis=0) / data.s_freq
return data, bad_smp_per_chan
def read_data(parameters, filename, event_onsets, opts, continuous=False):
d = BIDSEEG(filename)
chans = d.channels[
(d.channels['status'] == 'good')
& (d.channels['type'] == 'ECOG')
]
if continuous:
data = d.read_data(
begtime=event_onsets[0] - opts['pre'],
endtime=event_onsets[-1] + opts['post'],
chan=list(chans['name']))
else:
data = d.read_data(
events=event_onsets,
pre=opts['pre'],
post=opts['post'],
chan=list(chans['name']))
if parameters['artifacts']['remove']:
data, bad_smp_per_chan = hide_artifacts(parameters, data)
lg.info(f'{filename.stem} bad points {mean(bad_smp_per_chan):.3f}s, s.d. {std(bad_smp_per_chan):.3f}s [{min(bad_smp_per_chan):.3f}-{max(bad_smp_per_chan):.3f}s]')
data = montage(data, ref_to_avg=True)
return data
def preprocess_ecog(ieeg_file, reref, duration, offset, output_dir):
"""
Parameters
----------
reref : str
'average' or 'regression'
duration : int
length of the segments
offset : bool
remove one sample for whole duration
TODO
----
labels_in_roi = find_labels_in_regions(electrodes, regions)
clean_roi_labels = [label for label in clean_labels if label in labels_in_roi]
data = select(data, chan=clean_roi_labels)
"""
with ieeg_file.open('rb') as f:
data = load(f)
data = montage(data, ref_to_avg=True, method=reref)
data = make_segments(data, duration, offset)
output_file = output_dir / replace_extension(ieeg_file.name, 'proc.pkl')
with output_file.open('wb') as f:
dump(data, f)
return output_file
def test_montage_bipolar_00():
bipol = montage(data, bipolar=100)
assert len(bipol.chan[0]) == 28
assert_array_equal(
bipol(trial=0, chan='chan00-chan01'),
data(trial=0, chan='chan00') - data(trial=0, chan='chan01'))
def test_montage_06():
reref = montage(data, ref_to_avg=True)
dat1 = reref(trial=0)
assert_array_almost_equal(sum(dat1, axis=0),
zeros((dat1.shape[1])),
decimal=4)
def test_montage_03():
CHAN = ('chan01', 'chan02')
reref = montage(data, ref_chan=CHAN)
dat1 = reref(chan=CHAN)
assert_array_almost_equal(sum(dat1[0], axis=0),
zeros((dat1[0].shape[1])),
decimal=6)
def compute_quick_spectrogram(ieeg_file, freq):
with ieeg_file.open('rb') as f:
data = load(f)
# TODO: from parameters
reref = 'average'
method = 'spectrogram'
duration = 2
data = montage(data, ref_to_avg=True, method=reref)
tf = timefrequency(data, method=method, duration=duration)
dat0 = math(select(tf, freq=freq), operator_name='mean', axis='freq')
return dat0
def plot_raw_overview(filename):
event_type = 'all'
if filename.name.startswith('sub-drouwen'):
CHANS = [f'IH0{x + 1}' for x in range(8)]
elif filename.name.startswith('sub-itens'):
CHANS = [f'C0{x + 1}' for x in range(8)]
elif filename.name.startswith('sub-lemmer'):
CHANS = [f'IH{x + 1}' for x in range(8)]
elif filename.name.startswith('sub-som705'):
CHANS = [f'GA0{x + 1}' for x in range(8)] # a bit random
elif filename.name.startswith('sub-ommen'):
CHANS = ['chan1',
'chan2'] # I dont 'understand why I cannot use 'chan64'
elif filename.name.startswith('sub-vledder') or filename.name.startswith(
'sub-ommen'):
CHANS = ['chan1', 'chan64']
elif '_acq-blackrock_' in filename.name:
CHANS = ['chan1', 'chan128']
else:
print('you need to specify reference channel for this test')
return None, None
d = Dataset(filename, bids=True)
event_names, event_onsets = select_events(d, event_type)
is_ecog = d.dataset.task.channels.tsv['type'] == 'ECOG'
is_seeg = d.dataset.task.channels.tsv['type'] == 'SEEG'
chans = array(d.header['chan_name'])[is_ecog | is_seeg]
data = d.read_data(begtime=event_onsets[0],
endtime=event_onsets[-1],
chan=list(chans))
data.data[0][isnan(data.data[0])] = 0 # ignore nan
data = montage(data, ref_chan=CHANS)
freq = frequency(data, taper='hann', duration=2, overlap=0.5)
hist = make_histogram(data, max=250, step=10)
divs = []
fig = plot_hist(hist)
divs.append(to_div(fig))
bad_chans = None
if AUTOMATIC:
from sklearn.covariance import EllipticEnvelope
algorithm = EllipticEnvelope(
contamination=P['data_quality']['histogram']['contamination'])
prediction = algorithm.fit(hist.data[0]).predict(hist.data[0])
new_bad_chans = data.chan[0][prediction == -1]
print('bad channels with histogram / elliptic envelope: ' +
', '.join(new_bad_chans))
bad_chans = set(new_bad_chans)
fig = plot_outliers(hist.chan[0],
algorithm.dist_,
prediction,
yaxis_title='distance',
yaxis_type='log')
divs.append(to_div(fig))
fig = plot_freq(freq)
divs.append(to_div(fig))
if AUTOMATIC:
from sklearn.neighbors import LocalOutlierFactor
algorithm = LocalOutlierFactor(
n_neighbors=P['data_quality']['spectrum']['n_neighbors'])
prediction = algorithm.fit_predict(freq.data[0])
new_bad_chans = data.chan[0][prediction == -1]
print('bad channels with spectrum / local outlier factor: ' +
', '.join(new_bad_chans))
bad_chans |= set(new_bad_chans)
fig = plot_outliers(freq.chan[0],
algorithm.negative_outlier_factor_,
prediction,
yaxis_title='distance',
yaxis_type='linear')
divs.append(to_div(fig))
# we use again the reference channel. Ref channel was handpicked but it might have a weird spectrum
bad_chans -= set(CHANS)
return bad_chans, divs
def test_montage_bipolar_01():
data_nochan = create_data()
with raises(ValueError):
montage(data_nochan, bipolar=100)
def test_montage_regression():
reref = montage(data, ref_to_avg=True, method='regression')
assert_array_almost_equal(reref.data[0].mean(axis=0), 0)
def test_montage_05():
with raises(TypeError):
montage(data, ref_chan=['chan00'], ref_to_avg=True)
def test_montage_04():
reref = montage(data, ref_chan=[])
assert_array_equal(data.data[0], reref.data[0])
def test_montage_02():
reref = montage(data, ref_chan=['chan00'])
dat1 = reref(chan='chan00')
assert_array_equal(dat1[0], zeros(dat1[0].shape))
def test_montage_01():
with raises(TypeError):
montage(data, ref_chan='chan00')
