def test_baseline():
d = Dataset(micromed_file)
ev = [ev['start'] for ev in d.read_markers()][1]
chans = d.header['chan_name'][:2]
data = d.read_data(events=ev, pre=1, post=1, chan=chans)
time_interval = (-.5, -.1)
out = apply_baseline(data, time=time_interval)
mout = math(select(out, time=time_interval), operator_name='mean', axis='time')
assert_array_almost_equal(mout(trial=0), array([1, 1]))
out = apply_baseline(data, time=time_interval, baseline='zscore')
mout = math(select(out, time=time_interval), operator_name='mean', axis='time')
assert_array_almost_equal(mout(trial=0), array([0, 0]))
out = apply_baseline(data, time=time_interval, baseline='percent')
mout = math(select(out, time=time_interval), operator_name='mean', axis='time')
assert_array_almost_equal(mout(trial=0), array([0, 0]))
freq_interval = (10, 15)
freq = frequency(data)
out = apply_baseline(freq, freq=freq_interval, baseline='dB')
mout = math(select(out, freq=freq_interval), operator_name='mean', axis='freq')
assert_array_almost_equal(mout(trial=0), array([0, 0]))
out = apply_baseline(freq, freq=freq_interval, baseline='normchange')
assert out.data[0].max() <= 1
assert out.data[0].min() >= -1
def test_select_interval_invert():
data1 = select(data, time=(0.2, 0.5), invert=True)
assert data1.number_of('time')[0] == data.number_of('time')[0] - 76
assert data1.data[0].shape[1] == data.number_of('time')[0] - 76
assert data1.data[-1].shape[1] == data.number_of('time')[0] - 76
data2 = select(data1, time=(0.2, 0.5), invert=True)
assert data1.number_of('trial') == data2.number_of('trial')
def test_select_trial():
data1 = select(data, trial=(1, 2))
assert data1.number_of('trial') == 2
data1 = select(data, trial=(0, 0))
assert_array_equal(data1.data[0], data1.data[1])
assert len(data1.axis['chan']) == 2
assert data1.number_of('trial') == 2
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 test_select_oneside_interval_both():
data1 = select(data, time=(None, None))
assert len(data1.axis['time'][0]) == len(data.axis['time'][0])
assert data1.data[0].shape[1] == data.data[0].shape[1]
assert data1.axis['time'][0][0] == data.axis['time'][0][0]
assert data1.axis['time'][0][-1] == data.axis['time'][0][-1]
def test_select_oneside_interval_1():
data1 = select(data, time=(0.5, None))
assert len(data1.axis['time'][0]) * 2 == len(data.axis['time'][0])
assert data1.data[0].shape[1] * 2 == data.data[0].shape[1]
assert data1.axis['time'][0][0] >= 0.5
assert data1.axis['time'][0][-1] == data.axis['time'][0][-1]
def test_select_oneside_interval_0():
data1 = select(data, time=(None, 0.5))
assert len(data1.axis['time'][0]) * 2 == len(data.axis['time'][0])
assert data1.data[0].shape[1] * 2 == data.data[0].shape[1]
assert data1.axis['time'][0][0] == 0
assert data1.axis['time'][0][-1] < .5
def apply_common_baseline(tf, time, baseline):
"""Concatenate all the baseline periods for all the conditions. Keep it
separate for each channel and each frequency"""
tf_time = select(tf, time=time)
X = moveaxis(tf_time.data[0], 1, 2)
X = X.reshape((X.shape[0], X.shape[1], -1))
bline_mean = nanmean(X, axis=-1)
bline_std = nanstd(X, axis=-1)
if len(tf.list_of_axes) == 4:
bline_m = bline_mean[:, None, :, None]
bline_sd = bline_std[:, None, :, None]
else:
bline_m = bline_mean[:, None, :]
bline_sd = bline_std[:, None, :]
if baseline == 'zscore':
tf.data[0] = (tf.data[0] - bline_m) / bline_sd
elif baseline == 'dB':
tf.data[0] = 10 * log10(tf.data[0] / bline_m)
return tf
def select_significant_channels(data, onsets, threshold=0.0005):
assert NotImplementedError
"""Select channels that show a significant difference between the period
before onset and the period after the onset.
Parameters
----------
data : instance of wonambi.data
continuous data (already converted to z-score or dB)
onsets : array
array of
threshold : float
p-value to consider it significant
Returns
-------
list of str
list of significant array
"""
PRESTIM = 1
POSTSTIM = 1
v_pre = []
v_post = []
for on in onsets:
d = select(data, time=(on - PRESTIM, on))
v_pre.append(d(trial=0, trial_axis='trial000000').mean(axis=1))
d = select(data, time=(on, on + POSTSTIM))
v_post.append(d(trial=0, trial_axis='trial000000').mean(axis=1))
v_pre = array(v_pre)
v_post = array(v_post)
artifact = isnan(v_pre[:, 0]) | isnan(v_post[:, 0])
res = ttest_rel(v_post[~artifact, :], v_pre[~artifact, :], axis=0)
i_significant = (res.pvalue <= threshold)
significant_chan = data.chan[0][i_significant]
out = []
for i in where(i_significant)[0]:
out.append(f'{data.chan[0][i]:<6}:{res.pvalue[i]: .3f}')
lg.info('; '.join(out))
return significant_chan
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 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 test_select_typeerror():
with raises(TypeError):
select(data, trial=1)
with raises(TypeError):
select(chan='chan01')
with raises(TypeError):
select(data, time=1, chan=('chan01', ))
def apply_baseline_to_continuous(parameters, tf, onsets):
v = []
for on in onsets:
x = select(tf, time=on + parameters['spectrum']['baseline']['time'])
v.append(x.data[0])
A = concatenate(v, axis=1)
bline_m = nanmean(A, axis=1)[:, :, None]
bline_sd = nanstd(A, axis=1)[:, :, None]
if parameters['spectrum']['baseline']['type'] == 'zscore':
tf.data[0] = (tf.data[0] - bline_m) / bline_sd
elif parameters['spectrum']['baseline']['type'] == 'dB':
tf.data[0] = 10 * log10(tf.data[0] / bline_m)
return tf
def find_activity_per_finger(subject, run, event_type='cues'):
"""
TODO
----
this function could be rewritten using preexisting code
"""
data, events = load('data', subject, run, event_type=event_type)
tf = compute_timefreq(data, mean=True)
tf = get_chantime(tf)
best_time = find_max_point(tf)[1]
tf = compute_timefreq(data, mean=False)
tf = get_chan(tf, time=best_time)
values = {}
for f in FINGERS:
i_finger = create_bool(events, f)
tf_finger = select(tf, trial_axis=i_finger)
tf_finger_chan = math(tf_finger, operator_name='mean', axis='trial_axis')
values[f] = tf_finger_chan(trial=0)
v = c_[values['thumb'], values['index'], values['middle'], values['ring'], values['little']]
return v, tf.chan[0]
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
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 test_select_trials_and_string_invert():
data1 = select(data, trial=(1, 4), chan=('chan01', 'chan02'), invert=True)
assert len(data1.axis['chan']) == data.number_of('trial') - 2
assert len(data1.axis['chan'][0]) == data.number_of('chan')[0] - 2
def test_select_interval():
data1 = select(data, time=(0.2, 0.5))
assert data1.axis['time'][0].shape[0] == 76
assert data1.data[0].shape[1] == 76
assert data1.data[-1].shape[1] == 76
def test_select_trials_and_string():
data1 = select(data, trial=(1, 4), chan=('chan01', 'chan02'))
assert len(data1.axis['chan']) == 2
assert len(data1.axis['chan'][0]) == 2
def test_select_trials():
data1 = select(data, trial=(1, 4))
assert_array_equal(data.data[1], data1.data[0])
def test_select_empty_selection():
data1 = select(data, chan=[])
assert len(data1.axis['chan'][0]) == 0
assert data1.data[0].shape[0] == 0
def test_select_string_selection():
data1 = select(data, chan=['chan02'])
assert data1.axis['chan'][0][0] == 'chan02'
assert data1.data[0].shape[0] == 1
def test_select_interval_not_in_data():
data1 = select(data, time=(10.2, 10.5))
assert len(data1.axis['time'][0]) == 0
assert data1.data[0].shape[1] == 0
assert data1.data[-1].shape[1] == 0
