def test_eximia_nxe():
"""Test reading Eximia NXE files"""
fname = op.join(data_path(), 'eximia', 'test_eximia.nxe')
raw = read_raw_eximia(fname, preload=True)
assert_true('RawEximia' in repr(raw))
_test_raw_reader(read_raw_eximia, fname=fname)
fname_mat = op.join(data_path(), 'eximia', 'test_eximia.mat')
mc = sio.loadmat(fname_mat)
m_data = mc['data']
m_header = mc['header']
assert_equal(raw._data.shape, m_data.shape)
assert_equal(m_header['Fs'][0, 0][0, 0], raw.info['sfreq'])
m_names = [x[0][0] for x in m_header['label'][0, 0]]
m_names = list(
map(lambda x: x.replace('GATE', 'GateIn').replace('TRIG', 'Trig'),
m_names))
assert_equal(raw.ch_names, m_names)
m_ch_types = [x[0][0] for x in m_header['chantype'][0, 0]]
m_ch_types = list(
map(lambda x: x.replace('unknown', 'stim').replace('trigger', 'stim'),
m_ch_types))
types_dict = {2: 'eeg', 3: 'stim', 202: 'eog'}
ch_types = [
types_dict[raw.info['chs'][x]['kind']]
for x in range(len(raw.ch_names))
]
assert_equal(ch_types, m_ch_types)
assert_array_equal(m_data, raw._data)
def test_brainvision_data():
"""Test reading raw Brain Vision files."""
pytest.raises(IOError, read_raw_brainvision, vmrk_path)
pytest.raises(ValueError,
read_raw_brainvision,
vhdr_path,
preload=True,
scale="foo")
raw_py = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_path,
eog=eog,
misc='auto')
assert ('RawBrainVision' in repr(raw_py))
assert_equal(raw_py.info['highpass'], 0.)
assert_equal(raw_py.info['lowpass'], 250.)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, times_py = raw_py[picks]
# compare with a file that was generated using MNE-C
raw_bin = read_raw_fif(eeg_bin, preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_bin, times_bin = raw_bin[picks]
assert_array_almost_equal(data_py, data_bin)
assert_array_almost_equal(times_py, times_bin)
# Make sure EOG channels are marked correctly
for ch in raw_py.info['chs']:
if ch['ch_name'] in eog:
assert_equal(ch['kind'], FIFF.FIFFV_EOG_CH)
elif ch['ch_name'] == 'STI 014':
assert_equal(ch['kind'], FIFF.FIFFV_STIM_CH)
elif ch['ch_name'] in ('CP5', 'CP6'):
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_NONE)
elif ch['ch_name'] == 'ReRef':
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_CEL)
elif ch['ch_name'] in raw_py.info['ch_names']:
assert_equal(ch['kind'], FIFF.FIFFV_EEG_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_V)
else:
raise RuntimeError("Unknown Channel: %s" % ch['ch_name'])
# test loading v2
read_raw_brainvision(vhdr_v2_path, eog=eog, preload=True, verbose='error')
# For the nanovolt unit test we use the same data file with a different
# header file.
raw_nV = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_nV_path,
eog=eog,
misc='auto')
assert_equal(raw_nV.info['chs'][0]['ch_name'], 'FP1')
assert_equal(raw_nV.info['chs'][0]['kind'], FIFF.FIFFV_EEG_CH)
data_nanovolt, _ = raw_nV[0]
assert_array_almost_equal(data_py[0, :], data_nanovolt[0, :])
def test_gdf2_data():
"""Test reading raw GDF 2.x files."""
raw = read_raw_edf(gdf2_path + '.gdf', eog=None, misc=None, preload=True,
stim_channel='STATUS')
nchan = raw.info['nchan']
ch_names = raw.ch_names # Renamed STATUS -> STI 014.
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
data, _ = raw[picks]
# This .mat was generated using the official biosig matlab package
mat = sio.loadmat(gdf2_path + '_biosig.mat')
data_biosig = mat['dat'] * 1e-6 # data are stored in microvolts
data_biosig = data_biosig[picks]
# Assert data are almost equal
assert_array_almost_equal(data, data_biosig, 8)
# Find events
events = find_events(raw, verbose=1)
events[:, 2] >>= 8 # last 8 bits are system events in biosemi files
assert_equal(events.shape[0], 2) # 2 events in file
assert_array_equal(events[:, 2], [20, 28])
with pytest.warns(RuntimeWarning, match='No events found'):
# header contains no events
raw = read_raw_edf(gdf2_path + '.gdf', stim_channel='auto')
assert_equal(nchan, raw.info['nchan']) # stim channel not constructed
assert_array_equal(ch_names[1:], raw.ch_names[1:])
# gh-5604
assert raw.info['meas_date'] == DATE_NONE
_test_raw_reader(read_raw_edf, input_fname=gdf2_path + '.gdf',
eog=None, misc=None, stim_channel='STATUS')
def test_gdf_data():
"""Test reading raw GDF 1.x files."""
with pytest.warns(RuntimeWarning, match='Overlapping events'):
raw = read_raw_edf(gdf1_path + '.gdf', eog=None,
misc=None, preload=True, stim_channel='auto')
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
data, _ = raw[picks]
# this .npy was generated using the official biosig python package
raw_biosig = np.load(gdf1_path + '_biosig.npy')
raw_biosig = raw_biosig * 1e-6 # data are stored in microvolts
data_biosig = raw_biosig[picks]
# Assert data are almost equal
assert_array_almost_equal(data, data_biosig, 8)
# Test for stim channel
events = find_events(raw, shortest_event=1)
# The events are overlapping.
assert_array_equal(events[:, 0], raw._raw_extras[0]['events'][1][::2])
# Test events are encoded to stim channel.
events = find_events(raw)
evs = raw.find_edf_events()
assert (all([event in evs[1] for event in events[:, 0]]))
# gh-5604
assert raw.info['meas_date'] == DATE_NONE
with pytest.warns(RuntimeWarning, match='Overlapping events'):
_test_raw_reader(read_raw_edf, input_fname=gdf1_path + '.gdf',
eog=None, misc=None, stim_channel='auto')
def test_io_set_raw_2021():
"""Test reading new default file format (no EEG struct)."""
assert "EEG" not in io.loadmat(raw_fname_2021)
_test_raw_reader(reader=read_raw_eeglab,
input_fname=raw_fname_2021,
test_preloading=False,
preload=True)
def test_gdf2_data():
"""Test reading raw GDF 2.x files."""
raw = read_raw_edf(gdf2_path + '.gdf', eog=None, misc=None, preload=True)
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
data, _ = raw[picks]
# This .mat was generated using the official biosig matlab package
mat = sio.loadmat(gdf2_path + '_biosig.mat')
data_biosig = mat['dat'] * 1e-6 # data are stored in microvolts
data_biosig = data_biosig[picks]
# Assert data are almost equal
assert_array_almost_equal(data, data_biosig, 8)
# Find events
events = find_events(raw, verbose=1)
events[:, 2] >>= 8 # last 8 bits are system events in biosemi files
assert_equal(events.shape[0], 2) # 2 events in file
assert_array_equal(events[:, 2], [20, 28])
# gh-5604
assert raw.info['meas_date'] == DATE_NONE
_test_raw_reader(read_raw_edf, input_fname=gdf2_path + '.gdf',
eog=None, misc=None)
def test_eximia_nxe():
"""Test reading Eximia NXE files"""
fname = op.join(data_path(), 'eximia', 'test_eximia.nxe')
raw = read_raw_eximia(fname, preload=True)
assert_true('RawEximia' in repr(raw))
_test_raw_reader(read_raw_eximia, fname=fname)
fname_mat = op.join(data_path(), 'eximia', 'test_eximia.mat')
mc = sio.loadmat(fname_mat)
m_data = mc['data']
m_header = mc['header']
assert_equal(raw._data.shape, m_data.shape)
assert_equal(m_header['Fs'][0, 0][0, 0], raw.info['sfreq'])
m_names = [x[0][0] for x in m_header['label'][0, 0]]
m_names = list(
map(lambda x: x.replace('GATE', 'GateIn').replace('TRIG', 'Trig'),
m_names))
assert_equal(raw.ch_names, m_names)
m_ch_types = [x[0][0] for x in m_header['chantype'][0, 0]]
m_ch_types = list(
map(lambda x: x.replace('unknown', 'stim').replace('trigger', 'stim'),
m_ch_types))
types_dict = {2: 'eeg', 3: 'stim', 202: 'eog'}
ch_types = [types_dict[raw.info['chs'][x]['kind']]
for x in range(len(raw.ch_names))]
assert_equal(ch_types, m_ch_types)
assert_array_equal(m_data, raw._data)
def test_edf_data():
"""Test edf files"""
_test_raw_reader(read_raw_edf, input_fname=edf_path, stim_channel=None)
raw_py = read_raw_edf(edf_path, preload=True)
# Test saving and loading when annotations were parsed.
tempdir = _TempDir()
raw_file = op.join(tempdir, 'test-raw.fif')
raw_py.save(raw_file, overwrite=True, buffer_size_sec=1)
Raw(raw_file, preload=True)
edf_events = find_events(raw_py, output='step', shortest_event=0,
stim_channel='STI 014')
# onset, duration, id
events = [[0.1344, 0.2560, 2],
[0.3904, 1.0000, 2],
[2.0000, 0.0000, 3],
[2.5000, 2.5000, 2]]
events = np.array(events)
events[:, :2] *= 512 # convert time to samples
events = np.array(events, dtype=int)
events[:, 1] -= 1
events[events[:, 1] <= 0, 1] = 1
events[:, 1] += events[:, 0]
onsets = events[:, [0, 2]]
offsets = events[:, [1, 2]]
events = np.zeros((2 * events.shape[0], 3), dtype=int)
events[0::2, [0, 2]] = onsets
events[1::2, [0, 1]] = offsets
assert_array_equal(edf_events, events)
def test_bdf_data():
"""Test reading raw bdf files."""
raw_py = _test_raw_reader(read_raw_edf, input_fname=bdf_path,
eog=eog, misc=misc,
exclude=['M2', 'IEOG'])
assert len(raw_py.ch_names) == 71
raw_py = _test_raw_reader(read_raw_edf, input_fname=bdf_path,
montage=montage_path, eog=eog, misc=misc,
exclude=['M2', 'IEOG'])
assert len(raw_py.ch_names) == 71
assert 'RawEDF' in repr(raw_py)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, _ = raw_py[picks]
# this .mat was generated using the EEG Lab Biosemi Reader
raw_eeglab = loadmat(bdf_eeglab_path)
raw_eeglab = raw_eeglab['data'] * 1e-6 # data are stored in microvolts
data_eeglab = raw_eeglab[picks]
# bdf saved as a single, resolution to seven decimal points in matlab
assert_array_almost_equal(data_py, data_eeglab, 8)
# Manually checking that float coordinates are imported
assert (raw_py.info['chs'][0]['loc']).any()
assert (raw_py.info['chs'][25]['loc']).any()
assert (raw_py.info['chs'][63]['loc']).any()
def test_edf_data():
"""Test edf files"""
_test_raw_reader(read_raw_edf, input_fname=edf_path, stim_channel=None)
raw_py = read_raw_edf(edf_path, preload=True)
# Test saving and loading when annotations were parsed.
tempdir = _TempDir()
raw_file = op.join(tempdir, 'test-raw.fif')
raw_py.save(raw_file, overwrite=True, buffer_size_sec=1)
Raw(raw_file, preload=True)
edf_events = find_events(raw_py,
output='step',
shortest_event=0,
stim_channel='STI 014')
# onset, duration, id
events = [[0.1344, 0.2560, 2], [0.3904, 1.0000, 2], [2.0000, 0.0000, 3],
[2.5000, 2.5000, 2]]
events = np.array(events)
events[:, :2] *= 512 # convert time to samples
events = np.array(events, dtype=int)
events[:, 1] -= 1
events[events[:, 1] <= 0, 1] = 1
events[:, 1] += events[:, 0]
onsets = events[:, [0, 2]]
offsets = events[:, [1, 2]]
events = np.zeros((2 * events.shape[0], 3), dtype=int)
events[0::2, [0, 2]] = onsets
events[1::2, [0, 1]] = offsets
assert_array_equal(edf_events, events)
def test_gdf2_data():
"""Test reading raw GDF 2.x files."""
raw = read_raw_gdf(gdf2_path + '.gdf', eog=None, misc=None, preload=True)
assert raw._raw_extras[0]['subject_info']['age'] is None
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
data, _ = raw[picks]
# This .mat was generated using the official biosig matlab package
mat = sio.loadmat(gdf2_path + '_biosig.mat')
data_biosig = mat['dat'] * 1e-6 # data are stored in microvolts
data_biosig = data_biosig[picks]
# Assert data are almost equal
assert_array_almost_equal(data, data_biosig, 8)
# Find events
events = find_events(raw, verbose=1)
events[:, 2] >>= 8 # last 8 bits are system events in biosemi files
assert_equal(events.shape[0], 2) # 2 events in file
assert_array_equal(events[:, 2], [20, 28])
# gh-5604
assert raw.info['meas_date'] is None
_test_raw_reader(read_raw_gdf, input_fname=gdf2_path + '.gdf',
eog=None, misc=None,
test_scaling=False, # XXX this should be True
)
def test_snirf_standard(fname, boundary_decimal, test_scaling, test_rank):
"""Test standard operations."""
_test_raw_reader(read_raw_snirf,
fname=fname,
boundary_decimal=boundary_decimal,
test_scaling=test_scaling,
test_rank=test_rank) # low fs
def test_io_egi_pns_mff():
"""Test importing EGI MFF with PNS data"""
egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi_pns.mff')
raw = read_raw_egi(egi_fname_mff,
include=None,
preload=True,
verbose='error')
assert_true('RawMff' in repr(raw))
pns_chans = pick_types(raw.info, ecg=True, bio=True, emg=True)
assert_equal(len(pns_chans), 7)
names = [raw.ch_names[x] for x in pns_chans]
pns_names = [
'Resp. Temperature'[:15], 'Resp. Pressure', 'ECG', 'Body Position',
'Resp. Effort Chest'[:15], 'Resp. Effort Abdomen'[:15], 'EMG-Leg'
]
_test_raw_reader(read_raw_egi,
input_fname=egi_fname_mff,
channel_naming='EEG %03d',
verbose='error')
assert_equal(names, pns_names)
mat_names = [
'Resp_Temperature'[:15], 'Resp_Pressure', 'ECG', 'Body_Position',
'Resp_Effort_Chest'[:15], 'Resp_Effort_Abdomen'[:15], 'EMGLeg'
]
egi_fname_mat = op.join(data_path(), 'EGI', 'test_egi_pns.mat')
mc = sio.loadmat(egi_fname_mat)
for ch_name, ch_idx, mat_name in zip(pns_names, pns_chans, mat_names):
print('Testing {}'.format(ch_name))
mc_key = [x for x in mc.keys() if mat_name in x][0]
cal = raw.info['chs'][ch_idx]['cal']
mat_data = mc[mc_key] * cal
raw_data = raw[ch_idx][0]
assert_array_equal(mat_data, raw_data)
def test_bdf_data():
"""Test reading raw bdf files."""
raw_py = _test_raw_reader(read_raw_bdf,
input_fname=bdf_path,
eog=eog,
misc=misc,
exclude=['M2', 'IEOG'])
assert len(raw_py.ch_names) == 71
raw_py = _test_raw_reader(read_raw_bdf,
input_fname=bdf_path,
montage='biosemi64',
eog=eog,
misc=misc,
exclude=['M2', 'IEOG'])
assert len(raw_py.ch_names) == 71
assert 'RawEDF' in repr(raw_py)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, _ = raw_py[picks]
# this .mat was generated using the EEG Lab Biosemi Reader
raw_eeglab = loadmat(bdf_eeglab_path)
raw_eeglab = raw_eeglab['data'] * 1e-6 # data are stored in microvolts
data_eeglab = raw_eeglab[picks]
# bdf saved as a single, resolution to seven decimal points in matlab
assert_array_almost_equal(data_py, data_eeglab, 8)
# Manually checking that float coordinates are imported
assert (raw_py.info['chs'][0]['loc']).any()
assert (raw_py.info['chs'][25]['loc']).any()
assert (raw_py.info['chs'][63]['loc']).any()
def test_brainvision_data_highpass_filters():
"""Test reading raw Brain Vision files with amplifier filter settings."""
# Homogeneous highpass in seconds (default measurement unit)
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_highpass_path,
eog=eog)
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in seconds (default measurement unit)
with pytest.warns(RuntimeWarning, match='different .*pass filters') as w:
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_mixed_highpass_path,
eog=eog)
lowpass_warning = [
'different lowpass filters' in str(ww.message) for ww in w
]
highpass_warning = [
'different highpass filters' in str(ww.message) for ww in w
]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert (all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Homogeneous highpass in Hertz
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_highpass_hz_path,
eog=eog)
assert_equal(raw.info['highpass'], 10.)
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in Hertz
with pytest.warns(RuntimeWarning, match='different .*pass filters') as w:
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_mixed_highpass_hz_path,
eog=eog)
trigger_warning = ['will be dropped' in str(ww.message) for ww in w]
lowpass_warning = [
'different lowpass filters' in str(ww.message) for ww in w
]
highpass_warning = [
'different highpass filters' in str(ww.message) for ww in w
]
expected_warnings = zip(trigger_warning, lowpass_warning, highpass_warning)
assert (all(any([trg, lp, hp]) for trg, lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 5.)
assert_equal(raw.info['lowpass'], 250.)
def test_brainvision_data_highpass_filters():
"""Test reading raw Brain Vision files with amplifier filter settings."""
# Homogeneous highpass in seconds (default measurement unit)
with warnings.catch_warnings(record=True) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_highpass_path,
montage=montage, eog=eog)
assert_true(all('parse triggers that' in str(ww.message) for ww in w))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in seconds (default measurement unit)
with warnings.catch_warnings(record=True) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_highpass_path,
montage=montage, eog=eog, event_id=event_id)
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert_true(all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Homogeneous highpass in Hertz
with warnings.catch_warnings(record=True): # filter settings
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_highpass_hz_path,
montage=montage, eog=eog, event_id=event_id)
assert_equal(raw.info['highpass'], 10.)
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in Hertz
with warnings.catch_warnings(record=True): # filter settings
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_highpass_hz_path,
montage=montage, eog=eog, event_id=event_id)
trigger_warning = ['parse triggers that' in str(ww.message)
for ww in w]
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(trigger_warning, lowpass_warning, highpass_warning)
assert_true(all(any([trg, lp, hp]) for trg, lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 5.)
assert_equal(raw.info['lowpass'], 250.)
def test_brainvision_data_highpass_filters():
"""Test reading raw Brain Vision files with amplifier filter settings."""
# Homogeneous highpass in seconds (default measurement unit)
with warnings.catch_warnings(record=True) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_highpass_path,
montage=montage, eog=eog)
assert_true(all('parse triggers that' in str(ww.message) for ww in w))
assert_equal(raw.info['highpass'], 0.1)
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in seconds (default measurement unit)
with warnings.catch_warnings(record=True) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_highpass_path,
montage=montage, eog=eog, event_id=event_id)
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert_true(all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 0.1)
assert_equal(raw.info['lowpass'], 250.)
# Homogeneous highpass in Hertz
with warnings.catch_warnings(record=True): # filter settings
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_highpass_hz_path,
montage=montage, eog=eog, event_id=event_id)
assert_equal(raw.info['highpass'], 10.)
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in Hertz
with warnings.catch_warnings(record=True): # filter settings
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_highpass_hz_path,
montage=montage, eog=eog, event_id=event_id)
trigger_warning = ['parse triggers that' in str(ww.message)
for ww in w]
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(trigger_warning, lowpass_warning, highpass_warning)
assert_true(all(any([trg, lp, hp]) for trg, lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 5.)
assert_equal(raw.info['lowpass'], 250.)
def test_brainvision_data():
"""Test reading raw Brain Vision files."""
pytest.raises(IOError, read_raw_brainvision, vmrk_path)
pytest.raises(ValueError, read_raw_brainvision, vhdr_path, montage,
preload=True, scale="foo")
raw_py = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_path, montage=montage,
eog=eog, misc='auto', event_id=event_id)
assert ('RawBrainVision' in repr(raw_py))
assert_equal(raw_py.info['highpass'], 0.)
assert_equal(raw_py.info['lowpass'], 250.)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, times_py = raw_py[picks]
# compare with a file that was generated using MNE-C
raw_bin = read_raw_fif(eeg_bin, preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_bin, times_bin = raw_bin[picks]
assert_array_almost_equal(data_py, data_bin)
assert_array_almost_equal(times_py, times_bin)
# Make sure EOG channels are marked correctly
for ch in raw_py.info['chs']:
if ch['ch_name'] in eog:
assert_equal(ch['kind'], FIFF.FIFFV_EOG_CH)
elif ch['ch_name'] == 'STI 014':
assert_equal(ch['kind'], FIFF.FIFFV_STIM_CH)
elif ch['ch_name'] in ('CP5', 'CP6'):
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_NONE)
elif ch['ch_name'] == 'ReRef':
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_CEL)
elif ch['ch_name'] in raw_py.info['ch_names']:
assert_equal(ch['kind'], FIFF.FIFFV_EEG_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_V)
else:
raise RuntimeError("Unknown Channel: %s" % ch['ch_name'])
# test loading v2
read_raw_brainvision(vhdr_v2_path, eog=eog, preload=True,
event_id=event_id,
trig_shift_by_type={'response': 1000},
verbose='error')
# For the nanovolt unit test we use the same data file with a different
# header file.
raw_nV = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_nV_path, montage=montage,
eog=eog, misc='auto', event_id=event_id)
assert_equal(raw_nV.info['chs'][0]['ch_name'], 'FP1')
assert_equal(raw_nV.info['chs'][0]['kind'], FIFF.FIFFV_EEG_CH)
data_nanovolt, _ = raw_nV[0]
assert_array_almost_equal(data_py[0, :], data_nanovolt[0, :])
def test_data():
"""Test reading raw Artemis123 files."""
_test_raw_reader(read_raw_artemis123, input_fname=short_no_HPI_fname)
# test a random selected point
raw = read_raw_artemis123(short_no_HPI_fname, preload=True)
meg_picks = pick_types(raw.info, meg=True, eeg=False)
# checked against matlab reader.
assert_allclose(raw[meg_picks[12]][0][0][123], 3.072510659694672e-11)
def test_brainvision_data_lowpass_filters():
"""Test files with amplifier LP filter settings."""
# Homogeneous lowpass in Hertz (default measurement unit)
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_lowpass_path,
eog=eog)
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous lowpass in Hertz (default measurement unit)
with pytest.warns(RuntimeWarning) as w: # event parsing
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_mixed_lowpass_path,
eog=eog)
lowpass_warning = [
'different lowpass filters' in str(ww.message) for ww in w
]
highpass_warning = [
'different highpass filters' in str(ww.message) for ww in w
]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert (all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Homogeneous lowpass in seconds
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_lowpass_s_path,
eog=eog)
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 1. / (2 * np.pi * 0.004))
# Heterogeneous lowpass in seconds
with pytest.warns(RuntimeWarning) as w: # filter settings
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_mixed_lowpass_s_path,
eog=eog)
lowpass_warning = [
'different lowpass filters' in str(ww.message) for ww in w
]
highpass_warning = [
'different highpass filters' in str(ww.message) for ww in w
]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert (all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 1. / (2 * np.pi * 0.004))
def test_data():
"""Test reading raw nicolet files."""
_test_raw_reader(read_raw_nicolet,
input_fname=fname,
ch_type='eeg',
ecg='auto',
eog='auto',
emg='auto',
misc=['PHO'])
def test_data():
"""Test reading raw kit files
"""
assert_raises(TypeError, read_raw_kit, epochs_path)
assert_raises(TypeError, read_epochs_kit, sqd_path)
assert_raises(ValueError, read_raw_kit, sqd_path, mrk_path, elp_path)
assert_raises(ValueError, read_raw_kit, sqd_path, None, None, None,
list(range(200, 190, -1)))
assert_raises(ValueError, read_raw_kit, sqd_path, None, None, None,
list(range(167, 159, -1)), '*', 1, True)
# check functionality
raw_mrk = read_raw_kit(sqd_path, [mrk2_path, mrk3_path], elp_path,
hsp_path)
raw_py = _test_raw_reader(read_raw_kit,
input_fname=sqd_path, mrk=mrk_path, elp=elp_path,
hsp=hsp_path, stim=list(range(167, 159, -1)),
slope='+', stimthresh=1)
assert_true('RawKIT' in repr(raw_py))
assert_equal(raw_mrk.info['kit_system_id'], KIT.SYSTEM_NYU_2010)
assert_true(KIT_CONSTANTS[raw_mrk.info['kit_system_id']] is KIT_NY)
# Test stim channel
raw_stim = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path, stim='<',
preload=False)
for raw in [raw_py, raw_stim, raw_mrk]:
stim_pick = pick_types(raw.info, meg=False, ref_meg=False,
stim=True, exclude='bads')
stim1, _ = raw[stim_pick]
stim2 = np.array(raw.read_stim_ch(), ndmin=2)
assert_array_equal(stim1, stim2)
# Binary file only stores the sensor channels
py_picks = pick_types(raw_py.info, exclude='bads')
raw_bin = op.join(data_dir, 'test_bin_raw.fif')
raw_bin = Raw(raw_bin, preload=True)
bin_picks = pick_types(raw_bin.info, stim=True, exclude='bads')
data_bin, _ = raw_bin[bin_picks]
data_py, _ = raw_py[py_picks]
# this .mat was generated using the Yokogawa MEG Reader
data_Ykgw = op.join(data_dir, 'test_Ykgw.mat')
data_Ykgw = scipy.io.loadmat(data_Ykgw)['data']
data_Ykgw = data_Ykgw[py_picks]
assert_array_almost_equal(data_py, data_Ykgw)
py_picks = pick_types(raw_py.info, stim=True, ref_meg=False,
exclude='bads')
data_py, _ = raw_py[py_picks]
assert_array_almost_equal(data_py, data_bin)
# KIT-UMD data
_test_raw_reader(read_raw_kit, input_fname=sqd_umd_path)
raw = read_raw_kit(sqd_umd_path)
assert_equal(raw.info['kit_system_id'], KIT.SYSTEM_UMD_2014_12)
assert_true(KIT_CONSTANTS[raw.info['kit_system_id']] is KIT_UMD_2014)
def test_brainvision_data_highpass_filters():
"""Test reading raw Brain Vision files with amplifier filter settings."""
# Homogeneous highpass in seconds (default measurement unit)
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_highpass_path,
montage=montage, eog=eog)
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in seconds (default measurement unit)
with pytest.warns(RuntimeWarning, match='different .*pass filters') as w:
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_highpass_path,
montage=montage, eog=eog)
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert (all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Homogeneous highpass in Hertz
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_highpass_hz_path,
montage=montage, eog=eog)
assert_equal(raw.info['highpass'], 10.)
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous highpass in Hertz
with pytest.warns(RuntimeWarning, match='different .*pass filters') as w:
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_highpass_hz_path,
montage=montage, eog=eog)
trigger_warning = ['will be dropped' in str(ww.message)
for ww in w]
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(trigger_warning, lowpass_warning, highpass_warning)
assert (all(any([trg, lp, hp]) for trg, lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 5.)
assert_equal(raw.info['lowpass'], 250.)
def test_nihon_eeg():
"""Test reading Nihon Kohden EEG files."""
fname = data_path / 'NihonKohden' / 'MB0400FU.EEG'
raw = read_raw_nihon(fname.as_posix(), preload=True)
assert 'RawNihon' in repr(raw)
_test_raw_reader(read_raw_nihon, fname=fname, test_scaling=False)
fname_edf = data_path / 'NihonKohden' / 'MB0400FU.EDF'
raw_edf = read_raw_edf(fname_edf, preload=True)
assert raw._data.shape == raw_edf._data.shape
assert raw.info['sfreq'] == raw.info['sfreq']
# ch names and order are switched in the EDF
edf_ch_names = {
x: x.split(' ')[1].replace('-Ref', '')
for x in raw_edf.ch_names
}
raw_edf.rename_channels(edf_ch_names)
assert raw.ch_names == raw_edf.ch_names
for i, an1 in enumerate(raw.annotations):
# EDF has some weird annotations, which are not in the LOG file
an2 = raw_edf.annotations[i * 2 + 1]
assert an1['onset'] == an2['onset']
assert an1['duration'] == an2['duration']
# Also, it prepends 'Segment: ' to some annotations
t_desc = an2['description'].replace('Segment: ', '')
assert an1['description'] == t_desc
assert_array_almost_equal(raw._data, raw_edf._data)
with pytest.raises(ValueError, match='Not a valid Nihon Kohden EEG file'):
raw = read_raw_nihon(fname_edf, preload=True)
with pytest.raises(ValueError, match='Not a valid Nihon Kohden EEG file'):
raw = _read_nihon_header(fname_edf)
bad_fname = data_path / 'eximia' / 'text_eximia.nxe'
msg = 'No PNT file exists. Metadata will be blank'
with pytest.warns(RuntimeWarning, match=msg):
meta = _read_nihon_metadata(bad_fname)
assert len(meta) == 0
msg = 'No LOG file exists. Annotations will not be read'
with pytest.warns(RuntimeWarning, match=msg):
annot = _read_nihon_annotations(bad_fname)
assert all(len(x) == 0 for x in annot.values())
# the nihon test file has $A1 and $A2 in it, which are not EEG
assert '$A1' in raw.ch_names
# assert that channels with $ are 'misc'
picks = [ch for ch in raw.ch_names if ch.startswith('$')]
ch_types = raw.get_channel_types(picks=picks)
assert all(ch == 'misc' for ch in ch_types)
def test_io_set():
"""Test importing EEGLAB .set files"""
from scipy import io
_test_raw_reader(read_raw_eeglab, input_fname=raw_fname, montage=montage)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
_test_raw_reader(read_raw_eeglab, input_fname=raw_fname_onefile,
montage=montage)
raw = read_raw_eeglab(input_fname=raw_fname_onefile, montage=montage)
raw2 = read_raw_eeglab(input_fname=raw_fname, montage=montage)
assert_array_equal(raw[:][0], raw2[:][0])
# one warning per each preload=False or str with raw_fname_onefile
assert_equal(len(w), 3)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
epochs = read_epochs_eeglab(epochs_fname)
epochs2 = read_epochs_eeglab(epochs_fname_onefile)
# 3 warnings for each read_epochs_eeglab because there are 3 epochs
# associated with multiple events
assert_equal(len(w), 6)
assert_array_equal(epochs.get_data(), epochs2.get_data())
# test different combinations of events and event_ids
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test-eve.fif')
write_events(out_fname, epochs.events)
event_id = {'S255/S8': 1, 'S8': 2, 'S255/S9': 3}
epochs = read_epochs_eeglab(epochs_fname, epochs.events, event_id)
epochs = read_epochs_eeglab(epochs_fname, out_fname, event_id)
assert_raises(ValueError, read_epochs_eeglab, epochs_fname,
None, event_id)
assert_raises(ValueError, read_epochs_eeglab, epochs_fname,
epochs.events, None)
# test if .dat file raises an error
eeg = io.loadmat(epochs_fname, struct_as_record=False,
squeeze_me=True)['EEG']
eeg.data = 'epochs_fname.dat'
bad_epochs_fname = op.join(temp_dir, 'test_epochs.set')
io.savemat(bad_epochs_fname, {'EEG':
{'trials': eeg.trials, 'srate': eeg.srate,
'nbchan': eeg.nbchan, 'data': eeg.data,
'epoch': eeg.epoch, 'event': eeg.event,
'chanlocs': eeg.chanlocs}})
shutil.copyfile(op.join(base_dir, 'test_epochs.fdt'),
op.join(temp_dir, 'test_epochs.dat'))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_raises(NotImplementedError, read_epochs_eeglab,
bad_epochs_fname)
assert_equal(len(w), 3)
def test_data():
"""Test reading raw Artemis123 files."""
_test_raw_reader(read_raw_artemis123,
input_fname=short_hpi_1kz_fname,
pos_fname=dig_fname,
verbose='error')
# test a random selected point
raw = read_raw_artemis123(short_hpi_1kz_fname,
preload=True,
add_head_trans=False)
meg_picks = pick_types(raw.info, meg=True, eeg=False)
# checked against matlab reader.
assert_allclose(raw[meg_picks[12]][0][0][123], 1.08239606023e-11)
dev_head_t_1 = np.array([[9.713e-01, 2.340e-01, -4.164e-02, 1.302e-04],
[-2.371e-01, 9.664e-01, -9.890e-02, 1.977e-03],
[1.710e-02, 1.059e-01, 9.942e-01, -8.159e-03],
[0.0, 0.0, 0.0, 1.0]])
dev_head_t_2 = np.array([[9.890e-01, 1.475e-01, -8.090e-03, 4.997e-04],
[-1.476e-01, 9.846e-01, -9.389e-02, 1.962e-03],
[-5.888e-03, 9.406e-02, 9.955e-01, -1.610e-02],
[0.0, 0.0, 0.0, 1.0]])
expected_dev_hpi_rr = np.array([[-0.01579644, 0.06527367, 0.00152648],
[0.06666813, 0.0148956, 0.00545488],
[-0.06699212, -0.01732376, 0.0112027]])
# test with head loc no digitization
raw = read_raw_artemis123(short_HPI_dip_fname, add_head_trans=True)
_assert_trans(raw.info['dev_head_t']['trans'], dev_head_t_1)
assert_equal(raw.info['sfreq'], 5000.0)
# test with head loc and digitization
with pytest.warns(RuntimeWarning, match='Large difference'):
raw = read_raw_artemis123(short_HPI_dip_fname,
add_head_trans=True,
pos_fname=dig_fname)
_assert_trans(raw.info['dev_head_t']['trans'], dev_head_t_1)
# test cHPI localization..
dev_hpi_rr = np.array([
p['r'] for p in raw.info['dig']
if p['coord_frame'] == FIFF.FIFFV_COORD_DEVICE
])
# points should be within 0.1 mm (1e-4m) and within 1%
assert_allclose(dev_hpi_rr, expected_dev_hpi_rr, atol=1e-4, rtol=0.01)
# test 1kz hpi head loc (different freq)
raw = read_raw_artemis123(short_hpi_1kz_fname, add_head_trans=True)
_assert_trans(raw.info['dev_head_t']['trans'], dev_head_t_2)
assert_equal(raw.info['sfreq'], 1000.0)
def test_brainvision_data_lowpass_filters():
"""Test files with amplifier LP filter settings."""
# Homogeneous lowpass in Hertz (default measurement unit)
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_lowpass_path,
montage=montage, eog=eog)
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Heterogeneous lowpass in Hertz (default measurement unit)
with pytest.warns(RuntimeWarning) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_lowpass_path,
montage=montage, eog=eog)
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert (all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 250.)
# Homogeneous lowpass in seconds
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_lowpass_s_path,
montage=montage, eog=eog)
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 1. / (2 * np.pi * 0.004))
# Heterogeneous lowpass in seconds
with pytest.warns(RuntimeWarning) as w: # filter settings
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_mixed_lowpass_s_path,
montage=montage, eog=eog)
lowpass_warning = ['different lowpass filters' in str(ww.message)
for ww in w]
highpass_warning = ['different highpass filters' in str(ww.message)
for ww in w]
expected_warnings = zip(lowpass_warning, highpass_warning)
assert (all(any([lp, hp]) for lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 10))
assert_equal(raw.info['lowpass'], 1. / (2 * np.pi * 0.004))
def test_data():
"""Test reading raw nicolet files."""
_test_raw_reader(read_raw_nicolet,
input_fname=fname_data,
ch_type='eeg',
ecg='auto',
eog='auto',
emg='auto',
misc=['PHO'])
with pytest.raises(ValueError,
match='File name should end with .data not ".head".'):
read_raw_nicolet(fname_head, 'eeg')
def test_io_set_raw(fname):
"""Test importing EEGLAB .set files."""
_test_raw_reader(read_raw_eeglab, input_fname=fname,
montage=montage)
# test that preloading works
raw0 = read_raw_eeglab(input_fname=fname, montage=montage,
preload=True)
raw0.filter(1, None, l_trans_bandwidth='auto', filter_length='auto',
phase='zero')
# test that using uint16_codec does not break stuff
raw0 = read_raw_eeglab(input_fname=fname, montage=montage,
preload=False, uint16_codec='ascii')
def test_io_egi_pns_mff(tmpdir):
"""Test importing EGI MFF with PNS data."""
raw = read_raw_egi(egi_mff_pns_fname, include=None, preload=True,
verbose='error')
assert ('RawMff' in repr(raw))
pns_chans = pick_types(raw.info, ecg=True, bio=True, emg=True)
assert_equal(len(pns_chans), 7)
names = [raw.ch_names[x] for x in pns_chans]
pns_names = ['Resp. Temperature'[:15],
'Resp. Pressure',
'ECG',
'Body Position',
'Resp. Effort Chest'[:15],
'Resp. Effort Abdomen'[:15],
'EMG-Leg']
_test_raw_reader(read_raw_egi, input_fname=egi_mff_pns_fname,
channel_naming='EEG %03d', verbose='error',
test_rank='less',
test_scaling=False, # XXX probably some bug
)
assert_equal(names, pns_names)
mat_names = [
'Resp_Temperature'[:15],
'Resp_Pressure',
'ECG',
'Body_Position',
'Resp_Effort_Chest'[:15],
'Resp_Effort_Abdomen'[:15],
'EMGLeg'
]
egi_fname_mat = op.join(data_path(), 'EGI', 'test_egi_pns.mat')
mc = sio.loadmat(egi_fname_mat)
for ch_name, ch_idx, mat_name in zip(pns_names, pns_chans, mat_names):
print('Testing {}'.format(ch_name))
mc_key = [x for x in mc.keys() if mat_name in x][0]
cal = raw.info['chs'][ch_idx]['cal']
mat_data = mc[mc_key] * cal
raw_data = raw[ch_idx][0]
assert_array_equal(mat_data, raw_data)
# EEG missing
new_mff = str(tmpdir.join('temp.mff'))
shutil.copytree(egi_mff_pns_fname, new_mff)
read_raw_egi(new_mff, verbose='error')
os.remove(op.join(new_mff, 'info1.xml'))
os.remove(op.join(new_mff, 'signal1.bin'))
with pytest.raises(FileNotFoundError, match='Could not find any EEG'):
read_raw_egi(new_mff, verbose='error')
def test_edf_data():
"""Test edf files."""
raw = _test_raw_reader(read_raw_edf,
input_fname=edf_path,
stim_channel=None,
exclude=['Ergo-Left', 'H10'])
raw_py = read_raw_edf(edf_path, preload=True)
assert_equal(len(raw.ch_names) + 2, len(raw_py.ch_names))
# Test saving and loading when annotations were parsed.
edf_events = find_events(raw_py,
output='step',
shortest_event=0,
stim_channel='STI 014')
# onset, duration, id
events = [[0.1344, 0.2560, 2], [0.3904, 1.0000, 2], [2.0000, 0.0000, 3],
[2.5000, 2.5000, 2]]
events = np.array(events)
events[:, :2] *= 512 # convert time to samples
events = np.array(events, dtype=int)
events[:, 1] -= 1
events[events[:, 1] <= 0, 1] = 1
events[:, 1] += events[:, 0]
onsets = events[:, [0, 2]]
offsets = events[:, [1, 2]]
events = np.zeros((2 * events.shape[0], 3), dtype=int)
events[0::2, [0, 2]] = onsets
events[1::2, [0, 1]] = offsets
assert_array_equal(edf_events, events)
def test_data():
"""Test reading raw cnt files."""
raw = _test_raw_reader(read_raw_cnt, montage=None, input_fname=fname,
eog='auto', misc=['NA1', 'LEFT_EAR'])
eog_chs = mne.pick_types(raw.info, eog=True, exclude=[])
assert_equal(len(eog_chs), 2) # test eog='auto'
assert_equal(raw.info['bads'], ['LEFT_EAR', 'VEOGR']) # test bads
def test_io_egi_mff():
"""Test importing EGI MFF simple binary files."""
egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi.mff')
raw = read_raw_egi(egi_fname_mff, include=None)
assert ('RawMff' in repr(raw))
include = ['DIN1', 'DIN2', 'DIN3', 'DIN4', 'DIN5', 'DIN7']
raw = _test_raw_reader(read_raw_egi, input_fname=egi_fname_mff,
include=include, channel_naming='EEG %03d')
assert_equal('eeg' in raw, True)
eeg_chan = [c for c in raw.ch_names if 'EEG' in c]
assert_equal(len(eeg_chan), 129)
picks = pick_types(raw.info, eeg=True)
assert_equal(len(picks), 129)
assert_equal('STI 014' in raw.ch_names, True)
events = find_events(raw, stim_channel='STI 014')
assert_equal(len(events), 8)
assert_equal(np.unique(events[:, 1])[0], 0)
assert (np.unique(events[:, 0])[0] != 0)
assert (np.unique(events[:, 2])[0] != 0)
pytest.raises(ValueError, read_raw_egi, egi_fname_mff, include=['Foo'],
preload=False)
pytest.raises(ValueError, read_raw_egi, egi_fname_mff, exclude=['Bar'],
preload=False)
for ii, k in enumerate(include, 1):
assert (k in raw.event_id)
assert (raw.event_id[k] == ii)
def test_brainvision_data():
"""Test reading raw Brain Vision files
"""
assert_raises(IOError, read_raw_brainvision, vmrk_path)
assert_raises(ValueError, read_raw_brainvision, vhdr_path, montage,
preload=True, scale="foo")
raw_py = _test_raw_reader(read_raw_brainvision, test_preloading=True,
vhdr_fname=vhdr_path, montage=montage, eog=eog)
assert_true('RawBrainVision' in repr(raw_py))
assert_equal(raw_py.info['highpass'], 0.)
assert_equal(raw_py.info['lowpass'], 250.)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, times_py = raw_py[picks]
# compare with a file that was generated using MNE-C
raw_bin = Raw(eeg_bin, preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_bin, times_bin = raw_bin[picks]
assert_array_almost_equal(data_py, data_bin)
assert_array_almost_equal(times_py, times_bin)
# Make sure EOG channels are marked correctly
for ch in raw_py.info['chs']:
if ch['ch_name'] in eog:
assert_equal(ch['kind'], FIFF.FIFFV_EOG_CH)
elif ch['ch_name'] == 'STI 014':
assert_equal(ch['kind'], FIFF.FIFFV_STIM_CH)
elif ch['ch_name'] in raw_py.info['ch_names']:
assert_equal(ch['kind'], FIFF.FIFFV_EEG_CH)
else:
raise RuntimeError("Unknown Channel: %s" % ch['ch_name'])
def test_brainvision_data_partially_disabled_hw_filters():
"""Test reading raw Brain Vision files with heterogeneous amplifier
filter settings including non-numeric values
"""
with warnings.catch_warnings(record=True) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision,
vhdr_fname=vhdr_partially_disabled_hw_filter_path,
montage=montage,
eog=eog)
trigger_warning = ['parse triggers that' in str(ww.message) for ww in w]
lowpass_warning = [
'different lowpass filters' in str(ww.message) for ww in w
]
highpass_warning = [
'different highpass filters' in str(ww.message) for ww in w
]
expected_warnings = zip(trigger_warning, lowpass_warning, highpass_warning)
assert_true(all(any([trg, lp, hp]) for trg, lp, hp in expected_warnings))
assert_equal(raw.info['highpass'], 0.)
assert_equal(raw.info['lowpass'], 500.)
def test_edf_data_broken(tmp_path):
"""Test edf files."""
raw = _test_raw_reader(read_raw_edf, input_fname=edf_path,
exclude=['Ergo-Left', 'H10'], verbose='error')
raw_py = read_raw_edf(edf_path)
data = raw_py.get_data()
assert_equal(len(raw.ch_names) + 2, len(raw_py.ch_names))
# Test with number of records not in header (-1).
broken_fname = op.join(tmp_path, 'broken.edf')
with open(edf_path, 'rb') as fid_in:
fid_in.seek(0, 2)
n_bytes = fid_in.tell()
fid_in.seek(0, 0)
rbytes = fid_in.read()
with open(broken_fname, 'wb') as fid_out:
fid_out.write(rbytes[:236])
fid_out.write(b'-1 ')
fid_out.write(rbytes[244:244 + int(n_bytes * 0.4)])
with pytest.warns(RuntimeWarning,
match='records .* not match the file size'):
raw = read_raw_edf(broken_fname, preload=True)
read_raw_edf(broken_fname, exclude=raw.ch_names[:132], preload=True)
# Test with \x00's in the data
with open(broken_fname, 'wb') as fid_out:
fid_out.write(rbytes[:184])
assert rbytes[184:192] == b'36096 '
fid_out.write(rbytes[184:192].replace(b' ', b'\x00'))
fid_out.write(rbytes[192:])
raw_py = read_raw_edf(broken_fname)
data_new = raw_py.get_data()
assert_allclose(data, data_new)
def test_bdf_stim_channel():
"""Test BDF stim channel."""
# test if last channel is detected as STIM by default
raw_py = _test_raw_reader(read_raw_edf, input_fname=bdf_path,
stim_channel='auto')
assert channel_type(raw_py.info, raw_py.info["nchan"] - 1) == 'stim'
# test BDF file with wrong scaling info in header - this should be ignored
# for BDF stim channels
events = [[242, 0, 4],
[310, 0, 2],
[952, 0, 1],
[1606, 0, 1],
[2249, 0, 1],
[2900, 0, 1],
[3537, 0, 1],
[4162, 0, 1],
[4790, 0, 1]]
with pytest.deprecated_call(match='stim_channel'):
raw = read_raw_edf(bdf_stim_channel_path, preload=True)
bdf_events = find_events(raw)
assert_array_equal(events, bdf_events)
raw = read_raw_edf(bdf_stim_channel_path, preload=False,
stim_channel='auto')
bdf_events = find_events(raw)
assert_array_equal(events, bdf_events)
def test_edf_data():
"""Test edf files."""
raw = _test_raw_reader(read_raw_edf, input_fname=edf_path,
stim_channel=None, exclude=['Ergo-Left', 'H10'])
raw_py = read_raw_edf(edf_path, preload=True)
assert_equal(len(raw.ch_names) + 2, len(raw_py.ch_names))
# Test saving and loading when annotations were parsed.
edf_events = find_events(raw_py, output='step', shortest_event=0,
stim_channel='STI 014')
# onset, duration, id
events = [[0.1344, 0.2560, 2],
[0.3904, 1.0000, 2],
[2.0000, 0.0000, 3],
[2.5000, 2.5000, 2]]
events = np.array(events)
events[:, :2] *= 512 # convert time to samples
events = np.array(events, dtype=int)
events[:, 1] -= 1
events[events[:, 1] <= 0, 1] = 1
events[:, 1] += events[:, 0]
onsets = events[:, [0, 2]]
offsets = events[:, [1, 2]]
events = np.zeros((2 * events.shape[0], 3), dtype=int)
events[0::2, [0, 2]] = onsets
events[1::2, [0, 1]] = offsets
assert_array_equal(edf_events, events)
def test_io_egi_mff():
"""Test importing EGI MFF simple binary files."""
raw = read_raw_egi(egi_mff_fname, include=None)
assert ('RawMff' in repr(raw))
include = ['DIN1', 'DIN2', 'DIN3', 'DIN4', 'DIN5', 'DIN7']
raw = _test_raw_reader(read_raw_egi, input_fname=egi_mff_fname,
include=include, channel_naming='EEG %03d',
test_scaling=False, # XXX probably some bug
)
assert raw.info['sfreq'] == 1000.
assert_equal('eeg' in raw, True)
eeg_chan = [c for c in raw.ch_names if 'EEG' in c]
assert_equal(len(eeg_chan), 129)
picks = pick_types(raw.info, eeg=True)
assert_equal(len(picks), 129)
assert_equal('STI 014' in raw.ch_names, True)
events = find_events(raw, stim_channel='STI 014')
assert_equal(len(events), 8)
assert_equal(np.unique(events[:, 1])[0], 0)
assert (np.unique(events[:, 0])[0] != 0)
assert (np.unique(events[:, 2])[0] != 0)
pytest.raises(ValueError, read_raw_egi, egi_mff_fname, include=['Foo'],
preload=False)
pytest.raises(ValueError, read_raw_egi, egi_mff_fname, exclude=['Bar'],
preload=False)
for ii, k in enumerate(include, 1):
assert (k in raw.event_id)
assert (raw.event_id[k] == ii)
def test_edf_data():
"""Test edf files."""
raw = _test_raw_reader(read_raw_edf,
input_fname=edf_path,
exclude=['Ergo-Left', 'H10'],
verbose='error')
raw_py = read_raw_edf(edf_path, preload=True)
assert_equal(len(raw.ch_names) + 2, len(raw_py.ch_names))
# Test with number of records not in header (-1).
tempdir = _TempDir()
broken_fname = op.join(tempdir, 'broken.edf')
with open(edf_path, 'rb') as fid_in:
fid_in.seek(0, 2)
n_bytes = fid_in.tell()
fid_in.seek(0, 0)
rbytes = fid_in.read(int(n_bytes * 0.4))
with open(broken_fname, 'wb') as fid_out:
fid_out.write(rbytes[:236])
fid_out.write(b'-1 ')
fid_out.write(rbytes[244:])
with pytest.warns(RuntimeWarning,
match='records .* not match the file size'):
raw = read_raw_edf(broken_fname, preload=True)
read_raw_edf(broken_fname, exclude=raw.ch_names[:132], preload=True)
def test_io_set_raw(fname):
"""Test importing EEGLAB .set files."""
montage = _read_eeglab_montage(montage_path)
montage.ch_names = [
'EEG {0:03d}'.format(ii) for ii in range(len(montage.ch_names))
]
kws = dict(reader=read_raw_eeglab, input_fname=fname)
if fname.endswith('test_raw_chanloc.set'):
with pytest.warns(RuntimeWarning,
match="The data contains 'boundary' events"):
raw0 = _test_raw_reader(**kws)
elif '_h5' in fname: # should be safe enough, and much faster
raw0 = read_raw_eeglab(fname, preload=True)
else:
raw0 = _test_raw_reader(**kws)
# test that preloading works
if fname.endswith('test_raw_chanloc.set'):
raw0.set_montage(montage, on_missing='ignore')
# crop to check if the data has been properly preloaded; we cannot
# filter as the snippet of raw data is very short
raw0.crop(0, 1)
else:
raw0.set_montage(montage)
raw0.filter(1,
None,
l_trans_bandwidth='auto',
filter_length='auto',
phase='zero')
# test that using uint16_codec does not break stuff
read_raw_kws = dict(input_fname=fname, preload=False, uint16_codec='ascii')
if fname.endswith('test_raw_chanloc.set'):
with pytest.warns(RuntimeWarning,
match="The data contains 'boundary' events"):
raw0 = read_raw_eeglab(**read_raw_kws)
raw0.set_montage(montage, on_missing='ignore')
else:
raw0 = read_raw_eeglab(**read_raw_kws)
raw0.set_montage(montage)
# Annotations
if fname != raw_fname_chanloc:
assert len(raw0.annotations) == 154
assert set(raw0.annotations.description) == {'rt', 'square'}
assert_array_equal(raw0.annotations.duration, 0.)
def test_io_egi():
"""Test importing EGI simple binary files."""
# test default
with open(egi_txt_fname) as fid:
data = np.loadtxt(fid)
t = data[0]
data = data[1:]
data *= 1e-6 # µV
with pytest.warns(RuntimeWarning, match='Did not find any event code'):
raw = read_raw_egi(egi_fname, include=None)
assert 'RawEGI' in repr(raw)
data_read, t_read = raw[:256]
assert_allclose(t_read, t)
assert_allclose(data_read, data, atol=1e-10)
include = ['TRSP', 'XXX1']
raw = _test_raw_reader(
read_raw_egi,
input_fname=egi_fname,
include=include,
test_rank='less',
test_scaling=False, # XXX probably some bug
)
assert_equal('eeg' in raw, True)
eeg_chan = [c for c in raw.ch_names if c.startswith('E')]
assert_equal(len(eeg_chan), 256)
picks = pick_types(raw.info, eeg=True)
assert_equal(len(picks), 256)
assert_equal('STI 014' in raw.ch_names, True)
events = find_events(raw, stim_channel='STI 014')
assert_equal(len(events), 2) # ground truth
assert_equal(np.unique(events[:, 1])[0], 0)
assert (np.unique(events[:, 0])[0] != 0)
assert (np.unique(events[:, 2])[0] != 0)
triggers = np.array([[0, 1, 1, 0], [0, 0, 1, 0]])
# test trigger functionality
triggers = np.array([[0, 1, 0, 0], [0, 0, 1, 0]])
events_ids = [12, 24]
new_trigger = _combine_triggers(triggers, events_ids)
assert_array_equal(np.unique(new_trigger), np.unique([0, 12, 24]))
pytest.raises(ValueError,
read_raw_egi,
egi_fname,
include=['Foo'],
preload=False)
pytest.raises(ValueError,
read_raw_egi,
egi_fname,
exclude=['Bar'],
preload=False)
for ii, k in enumerate(include, 1):
assert (k in raw.event_id)
assert (raw.event_id[k] == ii)
def test_data():
"""Test reading raw cnt files."""
with pytest.warns(RuntimeWarning, match='number of bytes'):
raw = _test_raw_reader(read_raw_cnt, montage=None, input_fname=fname,
eog='auto', misc=['NA1', 'LEFT_EAR'])
eog_chs = pick_types(raw.info, eog=True, exclude=[])
assert len(eog_chs) == 2 # test eog='auto'
assert raw.info['bads'] == ['LEFT_EAR', 'VEOGR'] # test bads
def test_brainvision_data_filters():
"""Test reading raw Brain Vision files
"""
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_highpass_path, montage=montage,
eog=eog)
assert_equal(raw.info['highpass'], 0.1)
assert_equal(raw.info['lowpass'], 250.)
def test_brainvision_data():
"""Test reading raw Brain Vision files."""
assert_raises(IOError, read_raw_brainvision, vmrk_path)
assert_raises(ValueError,
read_raw_brainvision,
vhdr_path,
montage,
preload=True,
scale="foo")
with warnings.catch_warnings(record=True) as w: # event parsing
raw_py = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_path,
montage=montage,
eog=eog,
misc='auto')
assert_true(all('parse triggers that' in str(ww.message) for ww in w))
assert_true('RawBrainVision' in repr(raw_py))
assert_equal(raw_py.info['highpass'], 0.)
assert_equal(raw_py.info['lowpass'], 250.)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, times_py = raw_py[picks]
# compare with a file that was generated using MNE-C
raw_bin = read_raw_fif(eeg_bin, preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_bin, times_bin = raw_bin[picks]
assert_array_almost_equal(data_py, data_bin)
assert_array_almost_equal(times_py, times_bin)
# Make sure EOG channels are marked correctly
for ch in raw_py.info['chs']:
if ch['ch_name'] in eog:
assert_equal(ch['kind'], FIFF.FIFFV_EOG_CH)
elif ch['ch_name'] == 'STI 014':
assert_equal(ch['kind'], FIFF.FIFFV_STIM_CH)
elif ch['ch_name'] in ('CP5', 'CP6'):
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_NONE)
elif ch['ch_name'] == 'ReRef':
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_CEL)
elif ch['ch_name'] in raw_py.info['ch_names']:
assert_equal(ch['kind'], FIFF.FIFFV_EEG_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_V)
else:
raise RuntimeError("Unknown Channel: %s" % ch['ch_name'])
# test loading v2
read_raw_brainvision(vhdr_v2_path,
eog=eog,
preload=True,
response_trig_shift=1000)
def test_brainvision_data_software_filters_latin1_global_units():
"""Test reading raw Brain Vision files."""
with pytest.warns(RuntimeWarning, match='software filter'):
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_old_path,
eog=("VEOGo", "VEOGu", "HEOGli", "HEOGre"), misc=("A2",))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 0.9))
assert_equal(raw.info['lowpass'], 50.)
def test_data():
"""Test reading raw cnt files."""
with warnings.catch_warnings(record=True) as w:
raw = _test_raw_reader(read_raw_cnt, montage=None, input_fname=fname,
eog='auto', misc=['NA1', 'LEFT_EAR'])
assert_true(all('meas date' in str(ww.message) for ww in w))
eog_chs = mne.pick_types(raw.info, eog=True, exclude=[])
assert_equal(len(eog_chs), 2) # test eog='auto'
assert_equal(raw.info['bads'], ['LEFT_EAR', 'VEOGR']) # test bads
def test_brainvision_data_software_filters_latin1_global_units():
"""Test reading raw Brain Vision files."""
with pytest.warns(RuntimeWarning, match='software filter'):
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_old_path,
eog=("VEOGo", "VEOGu", "HEOGli", "HEOGre"), misc=("A2",))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 0.9))
assert_equal(raw.info['lowpass'], 50.)
# test sensor name with spaces (#9299)
with pytest.warns(RuntimeWarning, match='software filter'):
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_old_longname_path,
eog=("VEOGo", "VEOGu", "HEOGli", "HEOGre"), misc=("A2",))
assert_equal(raw.info['highpass'], 1. / (2 * np.pi * 0.9))
assert_equal(raw.info['lowpass'], 50.)
def test_io_set_raw(fname):
"""Test importing EEGLAB .set files."""
montage = _read_eeglab_montage(montage_path)
montage.ch_names = [
'EEG {0:03d}'.format(ii) for ii in range(len(montage.ch_names))
]
_test_raw_reader(read_raw_eeglab, input_fname=fname)
# test that preloading works
raw0 = read_raw_eeglab(input_fname=fname, preload=True)
raw0.set_montage(montage)
raw0.filter(1, None, l_trans_bandwidth='auto', filter_length='auto',
phase='zero')
# test that using uint16_codec does not break stuff
raw0 = read_raw_eeglab(input_fname=fname,
preload=False, uint16_codec='ascii')
raw0.set_montage(montage)
def test_io_set_raw(fname):
"""Test importing EEGLAB .set files."""
montage = _read_eeglab_montage(montage_path)
montage.ch_names = [
'EEG {0:03d}'.format(ii) for ii in range(len(montage.ch_names))
]
kws = dict(reader=read_raw_eeglab, input_fname=fname)
if fname.endswith('test_raw_chanloc.set'):
with pytest.warns(RuntimeWarning,
match="The data contains 'boundary' events"):
_test_raw_reader(**kws)
else:
_test_raw_reader(**kws)
# test that preloading works
read_raw_kws = dict(input_fname=fname, preload=True)
if fname.endswith('test_raw_chanloc.set'):
with pytest.warns(RuntimeWarning,
match="The data contains 'boundary' events"):
raw0 = read_raw_eeglab(**read_raw_kws)
raw0.set_montage(montage, on_missing='ignore')
# crop to check if the data has been properly preloaded; we cannot
# filter as the snippet of raw data is very short
raw0.crop(0, 1)
else:
raw0 = read_raw_eeglab(**read_raw_kws)
raw0.set_montage(montage)
raw0.filter(1,
None,
l_trans_bandwidth='auto',
filter_length='auto',
phase='zero')
# test that using uint16_codec does not break stuff
read_raw_kws = dict(input_fname=fname, preload=False, uint16_codec='ascii')
if fname.endswith('test_raw_chanloc.set'):
with pytest.warns(RuntimeWarning,
match="The data contains 'boundary' events"):
raw0 = read_raw_eeglab(**read_raw_kws)
raw0.set_montage(montage, on_missing='ignore')
else:
raw0 = read_raw_eeglab(**read_raw_kws)
raw0.set_montage(montage)
def test_brainvision_data_software_filters_latin1_global_units():
"""Test reading raw Brain Vision files"""
with warnings.catch_warnings(record=True) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_old_path,
eog=("VEOGo", "VEOGu", "HEOGli", "HEOGre"), misc=("A2",))
assert_true(all('software filter detected' in str(ww.message) for ww in w))
assert_equal(raw.info['highpass'], 1. / 0.9)
assert_equal(raw.info['lowpass'], 50.)
def test_brainvision_data_filters():
"""Test reading raw Brain Vision files
"""
raw = _test_raw_reader(read_raw_brainvision,
vhdr_fname=vhdr_highpass_path,
montage=montage,
eog=eog)
assert_equal(raw.info['highpass'], 0.1)
assert_equal(raw.info['lowpass'], 250.)
def test_brainvision_data_filters():
"""Test reading raw Brain Vision files
"""
with warnings.catch_warnings(record=True) as w: # event parsing
raw = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_highpass_path,
montage=montage, eog=eog)
assert_true(all('parse triggers that' in str(ww.message) for ww in w))
assert_equal(raw.info['highpass'], 0.1)
assert_equal(raw.info['lowpass'], 250.)
def test_io_egi():
"""Test importing EGI simple binary files"""
# test default
with open(egi_txt_fname) as fid:
data = np.loadtxt(fid)
t = data[0]
data = data[1:]
data *= 1e-6 # μV
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw = read_raw_egi(egi_fname, include=None)
assert_true('RawEGI' in repr(raw))
assert_equal(len(w), 1)
assert_true(w[0].category == RuntimeWarning)
msg = 'Did not find any event code with more than one event.'
assert_true(msg in '%s' % w[0].message)
data_read, t_read = raw[:256]
assert_allclose(t_read, t)
assert_allclose(data_read, data, atol=1e-10)
include = ['TRSP', 'XXX1']
with warnings.catch_warnings(record=True): # preload=None
raw = _test_raw_reader(read_raw_egi, input_fname=egi_fname,
include=include)
assert_equal('eeg' in raw, True)
eeg_chan = [c for c in raw.ch_names if 'EEG' in c]
assert_equal(len(eeg_chan), 256)
picks = pick_types(raw.info, eeg=True)
assert_equal(len(picks), 256)
assert_equal('STI 014' in raw.ch_names, True)
events = find_events(raw, stim_channel='STI 014')
assert_equal(len(events), 2) # ground truth
assert_equal(np.unique(events[:, 1])[0], 0)
assert_true(np.unique(events[:, 0])[0] != 0)
assert_true(np.unique(events[:, 2])[0] != 0)
triggers = np.array([[0, 1, 1, 0], [0, 0, 1, 0]])
# test trigger functionality
triggers = np.array([[0, 1, 0, 0], [0, 0, 1, 0]])
events_ids = [12, 24]
new_trigger = _combine_triggers(triggers, events_ids)
assert_array_equal(np.unique(new_trigger), np.unique([0, 12, 24]))
assert_raises(ValueError, read_raw_egi, egi_fname, include=['Foo'],
preload=False)
assert_raises(ValueError, read_raw_egi, egi_fname, exclude=['Bar'],
preload=False)
for ii, k in enumerate(include, 1):
assert_true(k in raw.event_id)
assert_true(raw.event_id[k] == ii)
def test_crop_append():
"""Test crop and append raw."""
raw = _test_raw_reader(
read_raw_bti, pdf_fname=pdf_fnames[0],
config_fname=config_fnames[0], head_shape_fname=hs_fnames[0])
y, t = raw[:]
t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
mask = (t0 <= t) * (t <= t1)
raw_ = raw.copy().crop(t0, t1)
y_, _ = raw_[:]
assert_true(y_.shape[1] == mask.sum())
assert_true(y_.shape[0] == y.shape[0])
def test_io_egi_pns_mff():
"""Test importing EGI MFF with PNS data."""
egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi_pns.mff')
raw = read_raw_egi(egi_fname_mff, include=None, preload=True,
verbose='error')
assert ('RawMff' in repr(raw))
pns_chans = pick_types(raw.info, ecg=True, bio=True, emg=True)
assert_equal(len(pns_chans), 7)
names = [raw.ch_names[x] for x in pns_chans]
pns_names = ['Resp. Temperature'[:15],
'Resp. Pressure',
'ECG',
'Body Position',
'Resp. Effort Chest'[:15],
'Resp. Effort Abdomen'[:15],
'EMG-Leg']
_test_raw_reader(read_raw_egi, input_fname=egi_fname_mff,
channel_naming='EEG %03d', verbose='error')
assert_equal(names, pns_names)
mat_names = [
'Resp_Temperature'[:15],
'Resp_Pressure',
'ECG',
'Body_Position',
'Resp_Effort_Chest'[:15],
'Resp_Effort_Abdomen'[:15],
'EMGLeg'
]
egi_fname_mat = op.join(data_path(), 'EGI', 'test_egi_pns.mat')
mc = sio.loadmat(egi_fname_mat)
for ch_name, ch_idx, mat_name in zip(pns_names, pns_chans, mat_names):
print('Testing {}'.format(ch_name))
mc_key = [x for x in mc.keys() if mat_name in x][0]
cal = raw.info['chs'][ch_idx]['cal']
mat_data = mc[mc_key] * cal
raw_data = raw[ch_idx][0]
assert_array_equal(mat_data, raw_data)
def test_data():
"""Test reading raw Artemis123 files."""
_test_raw_reader(read_raw_artemis123, input_fname=short_hpi_1kz_fname,
pos_fname=dig_fname, verbose='error')
# test a random selected point
raw = read_raw_artemis123(short_hpi_1kz_fname, preload=True,
add_head_trans=False)
meg_picks = pick_types(raw.info, meg=True, eeg=False)
# checked against matlab reader.
assert_allclose(raw[meg_picks[12]][0][0][123], 1.08239606023e-11)
dev_head_t_1 = np.array([[9.713e-01, 2.340e-01, -4.164e-02, 1.302e-04],
[-2.371e-01, 9.664e-01, -9.890e-02, 1.977e-03],
[1.710e-02, 1.059e-01, 9.942e-01, -8.159e-03],
[0.0, 0.0, 0.0, 1.0]])
dev_head_t_2 = np.array([[9.890e-01, 1.475e-01, -8.090e-03, 4.997e-04],
[-1.476e-01, 9.846e-01, -9.389e-02, 1.962e-03],
[-5.888e-03, 9.406e-02, 9.955e-01, -1.610e-02],
[0.0, 0.0, 0.0, 1.0]])
# test with head loc no digitization
raw = read_raw_artemis123(short_HPI_dip_fname, add_head_trans=True)
_assert_trans(raw.info['dev_head_t']['trans'], dev_head_t_1)
assert_equal(raw.info['sfreq'], 5000.0)
# test with head loc and digitization
with pytest.warns(RuntimeWarning, match='Large difference'):
raw = read_raw_artemis123(short_HPI_dip_fname, add_head_trans=True,
pos_fname=dig_fname)
_assert_trans(raw.info['dev_head_t']['trans'], dev_head_t_1)
# test 1kz hpi head loc (different freq)
raw = read_raw_artemis123(short_hpi_1kz_fname, add_head_trans=True)
_assert_trans(raw.info['dev_head_t']['trans'], dev_head_t_2)
assert_equal(raw.info['sfreq'], 1000.0)
def test_brainvision_data():
"""Test reading raw Brain Vision files
"""
assert_raises(IOError, read_raw_brainvision, vmrk_path)
assert_raises(ValueError, read_raw_brainvision, vhdr_path, montage,
preload=True, scale="foo")
with warnings.catch_warnings(record=True) as w: # event parsing
raw_py = _test_raw_reader(
read_raw_brainvision, vhdr_fname=vhdr_path, montage=montage,
eog=eog, misc='auto')
assert_true(all('parse triggers that' in str(ww.message) for ww in w))
assert_true('RawBrainVision' in repr(raw_py))
assert_equal(raw_py.info['highpass'], 0.)
assert_equal(raw_py.info['lowpass'], 250.)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, times_py = raw_py[picks]
# compare with a file that was generated using MNE-C
raw_bin = Raw(eeg_bin, preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_bin, times_bin = raw_bin[picks]
assert_array_almost_equal(data_py, data_bin)
assert_array_almost_equal(times_py, times_bin)
# Make sure EOG channels are marked correctly
for ch in raw_py.info['chs']:
if ch['ch_name'] in eog:
assert_equal(ch['kind'], FIFF.FIFFV_EOG_CH)
elif ch['ch_name'] == 'STI 014':
assert_equal(ch['kind'], FIFF.FIFFV_STIM_CH)
elif ch['ch_name'] == 'CP6':
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_NONE)
elif ch['ch_name'] == 'ReRef':
assert_equal(ch['kind'], FIFF.FIFFV_MISC_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_CEL)
elif ch['ch_name'] in raw_py.info['ch_names']:
assert_equal(ch['kind'], FIFF.FIFFV_EEG_CH)
assert_equal(ch['unit'], FIFF.FIFF_UNIT_V)
else:
raise RuntimeError("Unknown Channel: %s" % ch['ch_name'])
# test loading v2
read_raw_brainvision(vhdr_v2_path, eog=eog, preload=True,
response_trig_shift=1000)
def test_crop_append():
""" Test crop and append raw """
with warnings.catch_warnings(record=True): # preload warning
warnings.simplefilter('always')
raw = _test_raw_reader(
read_raw_bti, pdf_fname=pdf_fnames[0],
config_fname=config_fnames[0], head_shape_fname=hs_fnames[0])
y, t = raw[:]
t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
mask = (t0 <= t) * (t <= t1)
raw_ = raw.crop(t0, t1)
y_, _ = raw_[:]
assert_true(y_.shape[1] == mask.sum())
assert_true(y_.shape[0] == y.shape[0])
