def correct_baseline(freq_A, freq_B, frequency):
move = select(freq_A, freq=frequency)
rest = select(freq_B, freq=frequency)
merged = merge_datasets(move, rest)
merged = concatenate(merged, 'time')
baseline = math(merged, operator_name='mean', axis='time')
move.data[0] /= baseline.data[0][:, None, :]
rest.data[0] /= baseline.data[0][:, None, :]
return move, rest
def estimate_ieeg_prf(ieeg_file, method, freq=(60, 80)):
with ieeg_file.open('rb') as f:
data = load(f)
stimuli = data.attr['stimuli']
data = select(data, freq=freq)
data = math(data, operator_name='mean', axis='time')
data = math(data, operator_name='mean', axis='freq')
data = concatenate(data, 'trial')
compute_prf(ieeg_file, data.data[0], data.chan[0], stimuli, method)
return replace_extension(ieeg_file, 'prf.tsv')
def _reject_channels(d, elec_names, cond, minimalduration):
markers = d.read_markers()
block_beg = []
block_end = []
for mrk in markers:
if mrk['name'] in cond:
dur = (mrk['end'] - mrk['start'])
if dur >= minimalduration:
block_beg.append(mrk['start'])
block_end.append(mrk['end'])
data = d.read_data(chan=elec_names, begtime=block_beg, endtime=block_end)
data = concatenate(data, 'time')
clean_labels = reject_channels(data, 3)
return clean_labels
def test_concatenate_trial():
data1 = concatenate(data, axis='trial')
assert data1.number_of('trial') == 1
assert data1.list_of_axes == ('chan', 'time', 'trial_axis')
def test_concatenate_axis():
data1 = concatenate(data, axis='time')
assert data1.number_of(
'time')[0] == data.number_of('time')[0] * data.number_of('trial')
def merge(freq, method, frequency):
freq = select(freq, freq=frequency)
if method == '1a':
freq = concatenate(freq, axis='time')
freq = math(freq, operator_name='mean', axis='time')
freq = math(freq, operator_name='mean', axis='freq')
# only one value
out = Data(freq.data[0][:, None], freq.s_freq, chan=freq.chan[0], time=(0, ))
elif method == '1b':
freq = concatenate(freq, axis='time')
freq = math(freq, operator_name='dB')
freq = math(freq, operator_name='mean', axis='freq')
freq = math(freq, operator_name='mean', axis='time')
# only one value
out = Data(freq.data[0][:, None], freq.s_freq, chan=freq.chan[0], time=(0, ))
elif method == '1c':
freq = concatenate(freq, axis='time')
freq = math(freq, operator_name='mean', axis='freq')
freq = math(freq, operator_name='dB')
freq = math(freq, operator_name='mean', axis='time')
# only one value
out = Data(freq.data[0][:, None], freq.s_freq, chan=freq.chan[0], time=(0, ))
elif method == '1d':
freq = concatenate(freq, axis='time')
freq = math(freq, operator_name='mean', axis='freq')
freq = math(freq, operator_name='mean', axis='time')
freq = math(freq, operator_name='dB')
# only one value
out = Data(freq.data[0][:, None], freq.s_freq, chan=freq.chan[0], time=(0, ))
elif method == '2a':
freq = math(freq, operator_name='mean', axis='time')
freq = math(freq, operator_name='mean', axis='freq')
# one value per trial
out = concatenate(freq, axis='trial')
elif method == '2b':
freq = math(freq, operator_name='dB')
freq = math(freq, operator_name='mean', axis='time')
freq = math(freq, operator_name='mean', axis='freq')
# one value per trial
out = concatenate(freq, axis='trial')
elif method == '2c':
freq = math(freq, operator_name='mean', axis='time')
freq = math(freq, operator_name='dB')
freq = math(freq, operator_name='mean', axis='freq')
# one value per trial
out = concatenate(freq, axis='trial')
elif method == '2d':
freq = math(freq, operator_name='mean', axis='time')
freq = math(freq, operator_name='mean', axis='freq')
freq = math(freq, operator_name='dB')
# one value per trial
out = concatenate(freq, axis='trial')
elif method == '3a':
freq = concatenate(freq, axis='time')
# values per time point
out = math(freq, operator_name='mean', axis='freq')
elif method == '3b':
freq = concatenate(freq, axis='time')
freq = math(freq, operator_name='dB')
# values per time point
out = math(freq, operator_name='mean', axis='freq')
elif method == '3c':
freq = concatenate(freq, axis='time')
freq = math(freq, operator_name='mean', axis='freq')
# values per time point
out = math(freq, operator_name='dB')
elif method == 'dh2012':
# identical to 3b, but use log instead of dB
freq = concatenate(freq, axis='time')
freq = math(freq, operator_name='log')
# values per time point
out = math(freq, operator_name='mean', axis='freq')
return out
def timeseries_ieeg(parameters):
ieeg_dir = parameters['paths']['output'] / 'workflow' / 'ieeg'
subject = parameters['plot']['subject']
freq = parameters['ieeg']['ecog_compare']['frequency_bands'][-1]
ieeg_subj_dir = ieeg_dir / f'_subject_{subject}'
ieeg_compare_file = next(
ieeg_subj_dir.glob(
f'_frequency_{freq[0]}.{freq[1]}/ecog_compare/sub-*_compare.tsv'))
ieeg0_file = next(ieeg_subj_dir.rglob('*_task-motoractive_*_ieeg.pkl'))
ieeg1_file = next(ieeg_subj_dir.rglob('*_task-motorbaseline_*_ieeg.pkl'))
subj_dir = parameters['paths']['input'] / f'sub-{subject}'
events_ieeg_file = next(subj_dir.glob('ses-*/ieeg/*_events.tsv'))
dat0 = compute_quick_spectrogram(ieeg0_file, freq)
dat1 = compute_quick_spectrogram(ieeg1_file, freq)
dat = dat0._copy()
dat.data = concat([dat0.data, dat1.data])
dat.axis['time'] = concat([dat0.time, dat1.time])
dat.axis['chan'] = concat([dat0.chan, dat1.chan])
dat = concatenate(dat, axis='time')
ieeg_compare = read_tsv(ieeg_compare_file)
chans = ieeg_compare['channel'][ieeg_compare['measure'] > 10]
x0 = math(select(dat, chan=chans), operator_name='mean', axis='chan')
t = dat.time[0]
x = x0(trial=0)
i_t = argsort(t)
events = read_tsv(events_ieeg_file)
i_start = where(events['trial_type'] == 'rest')[0][0]
offset = events['onset'][i_start]
events['onset'] -= offset
traces = [
go.Scatter(x=t[i_t] - offset, y=x[i_t], line=dict(color='black', )),
]
layout = merge(
LAYOUT,
dict(
height=100,
width=450,
xaxis=dict(
tick0=0,
dtick=30,
range=(0, 330),
),
yaxis=dict(
dtick=0.1,
range=(0, 0.4),
),
shapes=event_shapes(events),
),
)
fig = go.Figure(data=traces, layout=layout)
return fig