def test_double_export_edf(tmp_path):
"""Test exporting an EDF file multiple times."""
rng = np.random.RandomState(123456)
format = 'edf'
ch_types = [
'eeg', 'eeg', 'stim', 'ecog', 'ecog', 'seeg', 'eog', 'ecg', 'emg',
'dbs', 'bio'
]
ch_names = np.arange(len(ch_types)).astype(str).tolist()
info = create_info(ch_names, sfreq=1000, ch_types=ch_types)
data = rng.random(size=(len(ch_names), 1000)) * 1.e-5
# include subject info and measurement date
subject_info = dict(first_name='mne',
last_name='python',
birthday=(1992, 1, 20),
sex=1,
hand=3)
info['subject_info'] = subject_info
raw = RawArray(data, info)
# export once
temp_fname = op.join(str(tmp_path), f'test.{format}')
raw.export(temp_fname, add_ch_type=True)
raw_read = read_raw_edf(temp_fname, preload=True)
# export again
raw_read.load_data()
raw_read.export(temp_fname, add_ch_type=True)
raw_read = read_raw_edf(temp_fname, preload=True)
# stim channel should be dropped
raw.drop_channels('2')
assert raw.ch_names == raw_read.ch_names
# only compare the original length, since extra zeros are appended
orig_raw_len = len(raw)
assert_array_almost_equal(raw.get_data(),
raw_read.get_data()[:, :orig_raw_len],
decimal=4)
assert_allclose(raw.times,
raw_read.times[:orig_raw_len],
rtol=0,
atol=1e-5)
# check channel types except for 'bio', which loses its type
orig_ch_types = raw.get_channel_types()
read_ch_types = raw_read.get_channel_types()
assert_array_equal(orig_ch_types, read_ch_types)
def test_integer_sfreq_edf(tmp_path):
"""Test saving a Raw array with integer sfreq to EDF."""
np.random.RandomState(12345)
format = 'edf'
info = create_info(['1', '2', '3'], sfreq=1000,
ch_types=['eeg', 'eeg', 'stim'])
data = np.random.random(size=(3, 1000)) * 1e-6
# include subject info and measurement date
subject_info = dict(first_name='mne', last_name='python',
birthday=(1992, 1, 20), sex=1, hand=3)
info['subject_info'] = subject_info
raw = RawArray(data, info)
raw.set_meas_date(datetime.now(tz=timezone.utc))
temp_fname = op.join(str(tmp_path), f'test.{format}')
raw.export(temp_fname)
raw_read = read_raw_edf(temp_fname, preload=True)
# stim channel should be dropped
raw.drop_channels('3')
assert raw.ch_names == raw_read.ch_names
# only compare the original length, since extra zeros are appended
orig_raw_len = len(raw)
assert_array_almost_equal(
raw.get_data(), raw_read.get_data()[:, :orig_raw_len], decimal=4)
assert_allclose(
raw.times, raw_read.times[:orig_raw_len], rtol=0, atol=1e-5)
# export now by hard-coding physical range
raw.export(temp_fname, physical_range=(-3200, 3200))
# include bad birthday that is non-EDF compliant
bad_info = info.copy()
bad_info['subject_info']['birthday'] = (1700, 1, 20)
raw = RawArray(data, bad_info)
with pytest.raises(RuntimeError, match='Setting patient birth date'):
raw.export(temp_fname)
# include bad measurement date that is non-EDF compliant
raw = RawArray(data, info)
meas_date = datetime(year=1984, month=1, day=1, tzinfo=timezone.utc)
raw.set_meas_date(meas_date)
with pytest.raises(RuntimeError, match='Setting start date time'):
raw.export(temp_fname)
def test_rawarray_edf(tmp_path):
"""Test saving a Raw array with integer sfreq to EDF."""
rng = np.random.RandomState(12345)
format = 'edf'
ch_types = [
'eeg', 'eeg', 'stim', 'ecog', 'seeg', 'eog', 'ecg', 'emg', 'dbs', 'bio'
]
ch_names = np.arange(len(ch_types)).astype(str).tolist()
info = create_info(ch_names, sfreq=1000, ch_types=ch_types)
data = rng.random(size=(len(ch_names), 1000)) * 1e-5
# include subject info and measurement date
subject_info = dict(first_name='mne',
last_name='python',
birthday=(1992, 1, 20),
sex=1,
hand=3)
info['subject_info'] = subject_info
raw = RawArray(data, info)
time_now = datetime.now()
meas_date = datetime(year=time_now.year,
month=time_now.month,
day=time_now.day,
hour=time_now.hour,
minute=time_now.minute,
second=time_now.second,
tzinfo=timezone.utc)
raw.set_meas_date(meas_date)
temp_fname = op.join(str(tmp_path), f'test.{format}')
raw.export(temp_fname, add_ch_type=True)
raw_read = read_raw_edf(temp_fname, preload=True)
# stim channel should be dropped
raw.drop_channels('2')
assert raw.ch_names == raw_read.ch_names
# only compare the original length, since extra zeros are appended
orig_raw_len = len(raw)
assert_array_almost_equal(raw.get_data(),
raw_read.get_data()[:, :orig_raw_len],
decimal=4)
assert_allclose(raw.times,
raw_read.times[:orig_raw_len],
rtol=0,
atol=1e-5)
# check channel types except for 'bio', which loses its type
orig_ch_types = raw.get_channel_types()
read_ch_types = raw_read.get_channel_types()
assert_array_equal(orig_ch_types, read_ch_types)
assert raw.info['meas_date'] == raw_read.info['meas_date']
# channel name can't be longer than 16 characters with the type added
raw_bad = raw.copy()
raw_bad.rename_channels({'1': 'abcdefghijklmnopqrstuvwxyz'})
with pytest.raises(RuntimeError, match='Signal label'), \
pytest.warns(RuntimeWarning, match='Data has a non-integer'):
raw_bad.export(temp_fname)
# include bad birthday that is non-EDF compliant
bad_info = info.copy()
bad_info['subject_info']['birthday'] = (1700, 1, 20)
raw = RawArray(data, bad_info)
with pytest.raises(RuntimeError, match='Setting patient birth date'):
raw.export(temp_fname)
# include bad measurement date that is non-EDF compliant
raw = RawArray(data, info)
meas_date = datetime(year=1984, month=1, day=1, tzinfo=timezone.utc)
raw.set_meas_date(meas_date)
with pytest.raises(RuntimeError, match='Setting start date time'):
raw.export(temp_fname)
# test that warning is raised if there are non-voltage based channels
raw = RawArray(data, info)
with pytest.warns(RuntimeWarning, match='The unit'):
raw.set_channel_types({'9': 'hbr'})
with pytest.warns(RuntimeWarning, match='Non-voltage channels'):
raw.export(temp_fname)
# data should match up to the non-accepted channel
raw_read = read_raw_edf(temp_fname, preload=True)
orig_raw_len = len(raw)
assert_array_almost_equal(raw.get_data()[:-1, :],
raw_read.get_data()[:, :orig_raw_len],
decimal=4)
assert_allclose(raw.times,
raw_read.times[:orig_raw_len],
rtol=0,
atol=1e-5)
# the data should still match though
raw_read = read_raw_edf(temp_fname, preload=True)
raw.drop_channels('2')
assert raw.ch_names == raw_read.ch_names
orig_raw_len = len(raw)
assert_array_almost_equal(raw.get_data(),
raw_read.get_data()[:, :orig_raw_len],
decimal=4)
assert_allclose(raw.times,
raw_read.times[:orig_raw_len],
rtol=0,
atol=1e-5)
def bv2fif(dataf,
corf,
ch_order=None,
eogs=('VEOG', 'HEOG'),
ecg='ECG',
emg='EMG',
preload='default',
ref_ch='Fp1',
dbs=False,
new_sfreq=1000.0):
montage = read_dig_montage(bvct=corf)
if preload == 'default':
preload = os.path.dirname(dataf) + '/workfile'
raw = read_raw_brainvision(dataf, preload=preload)
if dbs:
event_ch = get_events(raw)
# save downsampled raw for multitaper spectrogram
raw_data = np.zeros(
(raw._data.shape[0],
int(raw._data.shape[1] / raw.info['sfreq'] * new_sfreq)))
raw_info = raw.info.copy()
raw_info['sfreq'] = new_sfreq
for i in tqdm(range(len(raw._data))):
ch = raw._data[i, ::int(raw.info['sfreq'] / new_sfreq)]
raw_data[i] = ch
del ch
raw_resampled = RawArray(raw_data, raw_info)
raw_resampled.annotations = raw.annotations
if dbs:
old_event_ch = [ch for ch in raw.info['ch_names'] if 'STI' in ch]
if old_event_ch:
raw_resampled.drop_channels([old_event_ch[0]])
event_ch._data = event_ch._data[:, ::int(raw.info['sfreq'] /
new_sfreq)]
event_ch.info['sfreq'] = new_sfreq
event_ch.__len__ = len(event_ch._data[0])
event_ch.info['lowpass'] = raw_resampled.info['lowpass']
raw_resampled.add_channels([event_ch])
prepInst(raw_resampled, dataf, 'raw', montage, ref_ch, eogs, ecg, emg)
events, event_ids = events_from_annotations(raw)
if len(np.unique(events[:, 2])) > 1:
events = events[np.where(events[:,
2] == events[1,
2])[0]] #skip new segment
epochs = Epochs(raw,
events,
tmin=-2,
tmax=2,
proj=False,
preload=op.dirname(dataf) + '/workfile-epo',
baseline=(-0.5, -0.1),
verbose=False,
detrend=1)
events = events[
epochs.
selection] #in case any epochs don't have data and get thrown out (poorly placed at beginning or end)
epo_data = np.zeros(
(epochs._data.shape[0], epochs._data.shape[1],
int(np.ceil(epochs._data.shape[2] / epochs.info['sfreq'] *
new_sfreq))))
for i in tqdm(range(epochs._data.shape[0])):
for j in range(epochs._data.shape[1]):
epo_curr = epochs._data[i,
j, ::int(epochs.info['sfreq'] / new_sfreq)]
epo_data[i, j] = epo_curr
del epo_curr
events[:, 0] = np.array(events[:, 0] * new_sfreq / raw.info['sfreq'],
dtype=int)
epo_resampled = EpochsArray(epo_data, epochs.info.copy(), events, tmin=-2)
epo_resampled.info['sfreq'] = new_sfreq
epo_resampled.events[:, 2] = np.arange(len(events))
epo_resampled.event_id = {str(i): i for i in range(len(events))}
prepInst(epo_resampled, dataf, 'epo', montage, ref_ch, eogs, ecg, emg)
def test_plot_sensors(raw):
"""Test plotting of sensor array."""
plt.close('all')
fig = raw.plot_sensors('3d')
_fake_click(fig, fig.gca(), (-0.08, 0.67))
raw.plot_sensors('topomap', ch_type='mag',
show_names=['MEG 0111', 'MEG 0131'])
plt.close('all')
ax = plt.subplot(111)
raw.plot_sensors(ch_groups='position', axes=ax)
raw.plot_sensors(ch_groups='selection', to_sphere=False)
raw.plot_sensors(ch_groups=[[0, 1, 2], [3, 4]])
pytest.raises(ValueError, raw.plot_sensors, ch_groups='asd')
pytest.raises(TypeError, plot_sensors, raw) # needs to be info
pytest.raises(ValueError, plot_sensors, raw.info, kind='sasaasd')
plt.close('all')
fig, sels = raw.plot_sensors('select', show_names=True)
ax = fig.axes[0]
# Click with no sensors
_fake_click(fig, ax, (0., 0.), xform='data')
_fake_click(fig, ax, (0, 0.), xform='data', kind='release')
assert fig.lasso.selection == []
# Lasso with 1 sensor (upper left)
_fake_click(fig, ax, (0, 1), xform='ax')
fig.canvas.draw()
assert fig.lasso.selection == []
_fake_click(fig, ax, (0.65, 1), xform='ax', kind='motion')
_fake_click(fig, ax, (0.65, 0.7), xform='ax', kind='motion')
fig.canvas.key_press_event('control')
_fake_click(fig, ax, (0, 0.7), xform='ax', kind='release')
assert fig.lasso.selection == ['MEG 0121']
# check that point appearance changes
fc = fig.lasso.collection.get_facecolors()
ec = fig.lasso.collection.get_edgecolors()
assert (fc[:, -1] == [0.5, 1., 0.5]).all()
assert (ec[:, -1] == [0.25, 1., 0.25]).all()
_fake_click(fig, ax, (0.7, 1), xform='ax', kind='motion')
xy = ax.collections[0].get_offsets()
_fake_click(fig, ax, xy[2], xform='data') # single selection
assert fig.lasso.selection == ['MEG 0121', 'MEG 0131']
_fake_click(fig, ax, xy[2], xform='data') # deselect
assert fig.lasso.selection == ['MEG 0121']
plt.close('all')
raw.info['dev_head_t'] = None # like empty room
with pytest.warns(RuntimeWarning, match='identity'):
raw.plot_sensors()
# Test plotting with sphere='eeglab'
info = create_info(
ch_names=['Fpz', 'Oz', 'T7', 'T8'],
sfreq=100,
ch_types='eeg'
)
data = 1e-6 * np.random.rand(4, 100)
raw_eeg = RawArray(data=data, info=info)
raw_eeg.set_montage('biosemi64')
raw_eeg.plot_sensors(sphere='eeglab')
# Should work with "FPz" as well
raw_eeg.rename_channels({'Fpz': 'FPz'})
raw_eeg.plot_sensors(sphere='eeglab')
# Should still work without Fpz/FPz, as long as we still have Oz
raw_eeg.drop_channels('FPz')
raw_eeg.plot_sensors(sphere='eeglab')
# Should raise if Oz is missing too, as we cannot reconstruct Fpz anymore
raw_eeg.drop_channels('Oz')
with pytest.raises(ValueError, match='could not find: Fpz'):
raw_eeg.plot_sensors(sphere='eeglab')
# Should raise if we don't have a montage
chs = deepcopy(raw_eeg.info['chs'])
raw_eeg.set_montage(None)
with raw_eeg.info._unlock():
raw_eeg.info['chs'] = chs
with pytest.raises(ValueError, match='No montage was set'):
raw_eeg.plot_sensors(sphere='eeglab')
