def test_bdf(_bids_validate):
"""Test write_raw_bids conversion for Biosemi data."""
output_path = _TempDir()
data_path = op.join(base_path, 'edf', 'tests', 'data')
raw_fname = op.join(data_path, 'test.bdf')
raw = mne.io.read_raw_bdf(raw_fname)
with pytest.warns(UserWarning, match='No line frequency found'):
write_raw_bids(raw, bids_basename, output_path, overwrite=False)
_bids_validate(output_path)
# Test also the reading of channel types from channels.tsv
# the first channel in the raw data is not MISC right now
test_ch_idx = 0
assert coil_type(raw.info, test_ch_idx) != 'misc'
# we will change the channel type to MISC and overwrite the channels file
bids_fname = bids_basename + '_eeg.bdf'
channels_fname = _find_matching_sidecar(bids_fname, output_path,
'channels.tsv')
channels_dict = _from_tsv(channels_fname)
channels_dict['type'][test_ch_idx] = 'MISC'
_to_tsv(channels_dict, channels_fname)
# Now read the raw data back from BIDS, with the tampered TSV, to show
# that the channels.tsv truly influences how read_raw_bids sets ch_types
# in the raw data object
raw = read_raw_bids(bids_fname, output_path)
assert coil_type(raw.info, test_ch_idx) == 'misc'
# Test cropped assertion error
raw = mne.io.read_raw_bdf(raw_fname)
raw.crop(0, raw.times[-2])
with pytest.raises(AssertionError, match='cropped'):
write_raw_bids(raw, bids_basename, output_path)
def test_coil_type():
"""Test the correct coil type is retrieved."""
data_path = testing.data_path()
raw_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
raw = read_raw_fif(raw_fname)
assert coil_type(raw.info, 0) == 'meggradplanar'
assert coil_type(raw.info, 2) == 'megmag'
assert coil_type(raw.info, 306) == 'misc'
assert coil_type(raw.info, 315) == 'eeg'
raw.info['chs'][0]['coil_type'] = 1234
assert coil_type(raw.info, 0) == 'n/a'
def _channels_tsv(raw, fname, overwrite=False, verbose=True):
"""Create a channels.tsv file and save it.
Parameters
----------
raw : instance of Raw
The data as MNE-Python Raw object.
fname : str
Filename to save the channels.tsv to.
overwrite : bool
Whether to overwrite the existing file.
Defaults to False.
verbose : bool
Set verbose output to true or false.
"""
# Get channel type mappings between BIDS and MNE nomenclatures
map_chs = _get_ch_type_mapping(fro='mne', to='bids')
# Prepare the descriptions for each channel type
map_desc = defaultdict(lambda: 'Other type of channel')
map_desc.update(meggradaxial='Axial Gradiometer',
megrefgradaxial='Axial Gradiometer Reference',
meggradplanar='Planar Gradiometer',
megmag='Magnetometer',
megrefmag='Magnetometer Reference',
stim='Trigger',
eeg='ElectroEncephaloGram',
ecog='Electrocorticography',
seeg='StereoEEG',
ecg='ElectroCardioGram',
eog='ElectroOculoGram',
emg='ElectroMyoGram',
misc='Miscellaneous')
get_specific = ('mag', 'ref_meg', 'grad')
# get the manufacturer from the file in the Raw object
manufacturer = None
_, ext = _parse_ext(raw.filenames[0], verbose=verbose)
manufacturer = MANUFACTURERS[ext]
ignored_channels = IGNORED_CHANNELS.get(manufacturer, list())
status, ch_type, description = list(), list(), list()
for idx, ch in enumerate(raw.info['ch_names']):
status.append('bad' if ch in raw.info['bads'] else 'good')
_channel_type = channel_type(raw.info, idx)
if _channel_type in get_specific:
_channel_type = coil_type(raw.info, idx, _channel_type)
ch_type.append(map_chs[_channel_type])
description.append(map_desc[_channel_type])
low_cutoff, high_cutoff = (raw.info['highpass'], raw.info['lowpass'])
if raw._orig_units:
units = [raw._orig_units.get(ch, 'n/a') for ch in raw.ch_names]
else:
units = [_unit2human.get(ch_i['unit'], 'n/a')
for ch_i in raw.info['chs']]
units = [u if u not in ['NA'] else 'n/a' for u in units]
n_channels = raw.info['nchan']
sfreq = raw.info['sfreq']
ch_data = OrderedDict([
('name', raw.info['ch_names']),
('type', ch_type),
('units', units),
('low_cutoff', np.full((n_channels), low_cutoff)),
('high_cutoff', np.full((n_channels), high_cutoff)),
('description', description),
('sampling_frequency', np.full((n_channels), sfreq)),
('status', status)])
ch_data = _drop(ch_data, ignored_channels, 'name')
_write_tsv(fname, ch_data, overwrite, verbose)
return fname
