def test_micromed_header():
d = Dataset(micromed_file)
assert d.header['chan_name'][-1] == 'EMG'
assert d.header['chan_name'][:5] == ['FP1', 'FP2', 'AF7', 'AF3', 'AFz']
assert d.header['subj_id'] == 'Tp_metrologie Tp_metrologie'
orig = d.header['orig']
assert orig['dvideo_begin'] == iinfo('u4').max
markers = d.read_markers()
assert len(markers) == 444
assert markers[0]['name'] == '1'
assert markers[0]['start'] == markers[0]['end']
data = d.read_data(chan=('FP1', 'AFz'), begsam=10, endsam=20)
assert data.data[0][1, 5] == -334.27734375
data = d.read_data(chan=('FP1', 'AFz'), begsam=-10, endsam=20)
assert all(isnan(data.data[0][1, :10]))
data = d.read_data(chan=('FP1', 'AFz'), begsam=900, endsam=1010)
assert all(isnan(data.data[0][1, -10:]))
data = d.read_data(chan=('FP1', 'AFz'), begsam=-10, endsam=-2)
assert all(isnan(data.data[0][1, :]))
with raises(OSError):
d.read_videos(10, 20)
def test_brainvision_dataset_01():
d = Dataset(brainvision_dir / 'test.vhdr')
data = d.read_data(begtime=1, endtime=2)
assert data.data[0][0, 0] == -24.0
assert len(d.read_markers()) == 12
data = d.read_data(begsam=-1, endsam=5)
assert isnan(data.data[0][0, 0])
def test_brainvision_dataset_02():
d = Dataset(brainvision_dir / 'test_old_layout_latin1_software_filter.vhdr')
data = d.read_data(begsam=1, endsam=2)
assert_almost_equal(data.data[0][0, 0], 5.1)
data = d.read_data(endsam=255)
assert isnan(data.data[0][0, -1])
data = d.read_data()
assert data.data[0].shape == (29, 251)
def test_ioeeg_eeglab():
d1 = Dataset(eeglab_1_file)
d2 = Dataset(eeglab_2_file)
data1 = d1.read_data()
data2 = d2.read_data()
assert data1.data[0][0, 0] == data2.data[0][0, 0]
assert len(d1.read_markers()) == 2
def test_ioeeg_eeglab_hdf5():
d1 = Dataset(eeglab_hdf5_1_file)
d2 = Dataset(eeglab_hdf5_2_file)
data1 = d1.read_data(begsam=100, endsam=200)
data2 = d2.read_data(begsam=100, endsam=200)
assert data1.data[0][0, 0] == data2.data[0][0, 0]
assert len(d1.read_markers()) == 2
def test_openephys_dataset_01():
d = Dataset(openephys_dir)
data = d.read_data(begtime=1, endtime=2)
d = Dataset(openephys_dir)
data = d.read_data(chan=[
'CH1',
], begsam=10, endsam=1400)
assert data.data[0][0, 0] == -132.6
mrk = d.read_markers()
assert len(mrk) == 0
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_blackrock_ns4_02():
d = Dataset(ns4_file)
d = Dataset(ns4_file)
N_SMP = d.header['n_samples']
data = d.read_data(begsam=N_SMP - 1, endsam=N_SMP + 10)
assert isnan(data.data[0][0, -10:]).all()
def test_bci2000_data():
assert bci2000_file.exists()
d = Dataset(bci2000_file)
assert len(d.read_markers()) == 0
data = d.read_data()
assert data.data[0][0, 0] == 179.702
def test_dataset_events():
d = Dataset(micromed_file)
events = [ev['start'] for ev in d.read_markers()][::30]
data = d.read_data(events=events)
assert data.time[0].shape[0] == 512
assert data.time[0].shape[0] == data.data[0].shape[1]
assert (data.number_of('time') == 512).all()
def test_write_read_fieldtrip():
data = create_data(n_trial=1, n_chan=2)
data.export(fieldtrip_file, export_format='fieldtrip')
d = Dataset(fieldtrip_file)
ftdata = d.read_data()
assert_array_equal(data.data[0], ftdata.data[0])
assert len(d.read_markers()) == 0
def test_ioeeg_mobert_end():
d = Dataset(moberg_file)
n_smp = d.header['n_samples']
data = d.read_data(begsam=n_smp - 1, endsam=n_smp + 1)
assert data(trial=0, chan='Fp1')[0] == -12302.22384929657
assert isnan(data(trial=0, chan='Fp1')[1])
assert len(d.read_markers()) == 0
def load_and_slice_data_for_feature_extraction(
edf_filepath: str,
epochstages: List[str],
bad_segments: List[int] = None,
epochoffset_secs: float = None,
end_offset: float = None,
chans_to_consider: List[str] = None,
epoch_len=pysleep_defaults.epoch_len,
stages_to_consider=pysleep_defaults.nrem_stages):
if epochoffset_secs is None:
epochoffset_secs = 0
if end_offset is not None:
last_good_epoch = int((end_offset - epochoffset_secs) / epoch_len)
epochstages = epochstages[0:last_good_epoch]
d = Dataset(edf_filepath)
eeg_data = d.read_data().data[0]
if not (1 < np.sum(np.abs(eeg_data)) / eeg_data.size < 200):
raise EEGError(
"edf data should be in mV, please rescale units in edf file")
if bad_segments is not None:
for bad_epoch in bad_segments:
epochstages[bad_epoch] = 'artifact'
epochstages = pysleep_utils.convert_epochstages_to_eegevents(
epochstages, start_offset=epochoffset_secs)
epochstages_to_consider = epochstages.loc[
epochstages['description'].isin(stages_to_consider), :]
starts = epochstages_to_consider['onset'].tolist()
ends = (epochstages_to_consider['onset'] +
epochstages_to_consider['duration']).tolist()
for i in range(len(starts) - 1, 0, -1):
if starts[i] == ends[i - 1]:
del starts[i]
del ends[i - 1]
data = d.read_data(begtime=starts, endtime=ends, chan=chans_to_consider)
data.starts = starts
data.ends = ends
return data
def read_ieeg_block(filename, electrode_file, conditions, minimalduration,
output_dir):
d = Dataset(filename, bids=True)
markers = d.read_markers()
electrodes = Electrodes(electrode_file)
elec_names = [x['name'] for x in electrodes.electrodes.tsv]
elec_names = [x for x in elec_names if x in d.header['chan_name']
] # exclude elec location that have no corresponding channel
all_conditions = [x for v in conditions.values() for x in v]
clean_labels = _reject_channels(d, elec_names, all_conditions,
minimalduration)
outputs = []
for active_baseline, data_conds in conditions.items():
block_beg = []
block_end = []
for mrk in markers:
if mrk['name'] in data_conds:
dur = (mrk['end'] - mrk['start'])
if dur >= minimalduration:
block_beg.append(mrk['start'])
block_end.append(mrk['end'])
data = d.read_data(begtime=block_beg,
endtime=block_end,
chan=clean_labels)
output_task = Task(filename)
output_task.extension = '.pkl'
output_task.task += active_baseline
output_file = output_dir / output_task.get_filename()
with output_file.open('wb') as f:
dump(data, f)
outputs.append(output_file)
return outputs
def test_wonambi_write_read():
write_wonambi(gen_data, wonambi_file, subj_id='test_subj')
d = Dataset(wonambi_file)
data = d.read_data()
assert_array_equal(data(trial=0), gen_data(trial=0))
def test_ioeeg_eeglab_begsam():
d1 = Dataset(eeglab_1_file)
data = d1.read_data(begsam=-10, endsam=1)
assert isnan(data.data[0][0, 0])
def test_write_read_fieldtrip_hdf5():
d = Dataset(hdf5_file)
d.read_data()
from pytest import approx, raises
from wonambi import Dataset
from wonambi.detect.spindle import DetectSpindle
from .paths import psg_file
d = Dataset(psg_file)
data = d.read_data(chan=('EEG Fpz-Cz', 'EEG Pz-Oz'), begtime=35790, endtime=35820)
def test_detect_spindle_Moelle2011():
detsp = DetectSpindle()
assert repr(detsp) == 'detsp_Moelle2011_12-15Hz_00.5-03.0s'
sp = detsp(data)
assert len(sp.events) == 4
def test_detect_spindle_Nir2011():
detsp = DetectSpindle(method='Nir2011')
sp = detsp(data)
assert len(sp.events) == 4
def test_detect_spindle_Wamsley2012():
detsp = DetectSpindle(method='Wamsley2012')
sp = detsp(data)
assert len(sp.events) == 4
def extract_band_power(
edf_filepath: str,
bands: dict = pysleep_defaults.default_freq_bands,
chans_to_consider: List[str] = None,
epochoffset_secs: float = None,
end_time: float = None,
epoch_len: int = pysleep_defaults.epoch_len) -> pd.DataFrame:
"""
:param edf_filepath: The edf to extract bandpower for
:param bands: bands to extract power in, if None, then defaults will be used i.e.
bands = {
'delta': (1, 4),
'theta': (4, 7),
'alpha': (8, 12),
'sigma': (11, 16),
'slow_sigma': (11, 13),
'fast_sigma': (13, 16),
'beta': (13, 30)
}
:param chans_to_consider: which channels to consider
:param epochoffset_secs: start time of the recording to extract band power for (when do epochs start), onset is measured from this
:param end_time: end time of the recording to extract band power for
:param epoch_len: how long a time bin you want your power to be averaged over
:return: chan_epoch_band as a numpy array, and times, bands, chans
"""
d = Dataset(edf_filepath)
if not (epochoffset_secs is None or epochoffset_secs >= 0):
raise error_handling.EEGError('Epochoffset is negative!' +
str(epochoffset_secs))
if not ((end_time is None) or
(end_time <= d.header['n_samples'] / d.header['s_freq'])):
raise error_handling.EEGError("end time (" + str(end_time) +
") larger than record end!" +
str(d.header['n_samples'] /
d.header['s_freq']))
data = d.read_data(begtime=epochoffset_secs,
endtime=end_time,
chan=chans_to_consider)
power = timefrequency(data, method='spectrogram')
abs_power = math(power, operator_name='abs')
chan_time_freq = abs_power.data[0]
all_chans = np.ones((chan_time_freq.shape[0], ), dtype=bool)
epochoffset_secs = 0 if epochoffset_secs is None else epochoffset_secs
time_axis = np.round(abs_power.axis['time'][0], 2) - epochoffset_secs
freq_axis = np.round(abs_power.axis['freq'][0], 2)
chan_axis = abs_power.axis['chan'][0]
freq_binsize = freq_axis[1] - freq_axis[0]
assert epoch_len > 0, "epoch len must be greater than zero"
times = np.arange(0, time_axis[-1], epoch_len)
cont = []
for band, freqs in bands.items():
freq_mask = (freqs[0] <= freq_axis) & (freqs[1] >= freq_axis)
for win_start in times:
time_mask = (win_start < time_axis) & (time_axis <
win_start + epoch_len)
idx = np.ix_(all_chans, time_mask, freq_mask)
if idx:
chan_epoch_per_band = chan_time_freq[idx].mean(axis=1).mean(
axis=1) / freq_binsize
else:
chan_epoch_per_band = np.zeros((len(chans_to_consider), ))
for chan, power in zip(chan_axis, chan_epoch_per_band):
cont.append(
pd.Series({
'onset': win_start,
'duration': epoch_len,
'band': band.split('_')[0],
'chan': chan,
'power': power
}))
band_power = pd.concat(
cont, axis=1).T.apply(lambda x: pd.to_numeric(x, errors='ignore'))
return band_power
def test_xltek_data():
d = Dataset(ktlx_file)
data = d.read_data(begsam=1000, endsam=1001, chan=('FZ', ))
assert_array_almost_equal(data.data[0][0, 0], -90.119315)
from pytest import approx
from wonambi import Dataset
from wonambi.detect.slowwave import DetectSlowWave
from .paths import psg_file
d = Dataset(psg_file)
data = d.read_data(chan=('EEG Fpz-Cz', 'EEG Pz-Oz'),
begtime=27930,
endtime=27960)
def test_detect_slowwave_Massimini2004():
detsw = DetectSlowWave()
detsw.invert = True
assert repr(detsw) == 'detsw_Massimini2004_0.10-4.00Hz'
sw = detsw(data)
assert len(sw.events) == 1
def test_detect_slowwave_AASM_Massimini2004():
detsw = DetectSlowWave(method='AASM/Massimini2004')
detsw.invert = True
assert repr(detsw) == 'detsw_AASM/Massimini2004_0.10-4.00Hz'
sw = detsw(data)
assert len(sw.events) == 15
def test_blackrock_ns4_00():
d = Dataset(ns4_file)
data = d.read_data(begsam=10, endsam=11)
assert data.data[0][0, 0] == 3463.0878627887814
def test_blackrock_nev():
d = Dataset(nev_file)
with raises(TypeError):
d.read_data()
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_blackrock_ns4_01():
d = Dataset(ns4_file)
data = d.read_data(begsam=-10, endsam=1)
assert isnan(data.data[0][0, :10]).all()
def test_ioeeg_mobert_begin():
d = Dataset(moberg_file)
data = d.read_data(begsam=-10, endsam=10)
assert isnan(data(trial=0, chan='Fp1')[0])
assert data(trial=0, chan='Fp1')[-1] == -1678.8678197860718
def test_xltek_data():
d = Dataset(ktlx_file)
data = d.read_data(begsam=223380, endsam=223381, chan=('Fz', ))
assert_array_almost_equal(data.data[0][0, 0], -2021.171532)
