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_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_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_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 test_openephys_markers():
d = Dataset(filename, session=1)
markers = d.read_markers()
assert len(markers) == 10
assert markers[0]['name'] == 'START RECORDING #0'
assert markers[7]['name'] == 'Network Event'
assert markers[7]['end'] == 95.402
assert markers[-1]['name'] == 'END RECORDING #1'
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_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_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_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_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_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 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_blackrock_markers_00():
d = Dataset(ns2_file)
markers = d.read_markers()
assert len(markers) == 176
def test_xltek_marker():
d = Dataset(ktlx_file)
markers = d.read_markers()
assert markers[0]['name'] == 'Gain/Filter change (-unknown-)'
assert markers[-1]['end'] == 1052.1
def test_xltek_marker():
d = Dataset(ktlx_file)
markers = d.read_markers()
assert markers[0]['name'] == 'Start Recording (varkha)'
assert int(markers[-1]['end']) == 1314
def test_edf_annot():
remove_datetime(generated_file)
generated = Dataset(generated_file)
markers = generated.read_markers()
assert len(markers) == 2
