def test_fnirs_channel_naming_and_order_custom_optical_density():
"""Ensure fNIRS channel checking on manually created data."""
data = np.random.normal(size=(6, 10))
# Start with a correctly named raw intensity dataset
# These are the steps required to build an fNIRS Raw object from scratch
ch_names = [
'S1_D1 760', 'S1_D1 850', 'S2_D1 760', 'S2_D1 850', 'S3_D1 760',
'S3_D1 850'
]
ch_types = np.repeat("fnirs_od", 6)
info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0)
raw = RawArray(data, info, verbose=True)
freqs = np.tile([760, 850], 3)
for idx, f in enumerate(freqs):
raw.info["chs"][idx]["loc"][9] = f
freqs = np.unique(_channel_frequencies(raw.info))
picks = _check_channels_ordered(raw.info, freqs)
assert len(picks) == len(raw.ch_names)
assert len(picks) == 6
# Check block naming for optical density
ch_names = [
'S1_D1 760', 'S2_D1 760', 'S3_D1 760', 'S1_D1 850', 'S2_D1 850',
'S3_D1 850'
]
ch_types = np.repeat("fnirs_od", 6)
info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0)
raw = RawArray(data, info, verbose=True)
freqs = np.repeat([760, 850], 3)
for idx, f in enumerate(freqs):
raw.info["chs"][idx]["loc"][9] = f
with pytest.raises(ValueError, match='channels not ordered correctly'):
_check_channels_ordered(raw.info, [760, 850])
# and this is how you would fix the ordering, then it should pass
raw.pick(picks=[0, 3, 1, 4, 2, 5])
_check_channels_ordered(raw.info, [760, 850])
# Check that if you mix types you get an error
ch_names = [
'S1_D1 hbo', 'S1_D1 hbr', 'S2_D1 hbo', 'S2_D1 hbr', 'S3_D1 hbo',
'S3_D1 hbr'
]
ch_types = np.tile(["hbo", "hbr"], 3)
info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=1.0)
raw2 = RawArray(data, info, verbose=True)
raw.add_channels([raw2])
with pytest.raises(ValueError, match='does not support a combination'):
_check_channels_ordered(raw.info, [760, 850])
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)
