def _channels_tsv(raw, fname, verbose):
"""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.
verbose : bool
Set verbose output to true or false.
"""
map_chs = defaultdict(lambda: 'OTHER')
map_chs.update(grad='MEGGRAD', mag='MEGMAG', stim='TRIG', eeg='EEG',
ecog='ECOG', seeg='SEEG', eog='EOG', ecg='ECG', misc='MISC',
resp='RESPONSE', ref_meg='REFMEG')
map_desc = defaultdict(lambda: 'Other type of channel')
map_desc.update(grad='Gradiometer', mag='Magnetometer',
stim='Trigger',
eeg='ElectroEncephaloGram',
ecog='Electrocorticography',
seeg='StereoEEG',
ecg='ElectroCardioGram',
eog='ElectrOculoGram', misc='Miscellaneous',
ref_meg='Reference channel')
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')
ch_type.append(map_chs[channel_type(raw.info, idx)])
description.append(map_desc[channel_type(raw.info, idx)])
low_cutoff, high_cutoff = (raw.info['highpass'], raw.info['lowpass'])
units = [_unit2human.get(ch_i['unit'], 'n/a') for ch_i in raw.info['chs']]
n_channels = raw.info['nchan']
sfreq = raw.info['sfreq']
df = pd.DataFrame(OrderedDict([
('name', raw.info['ch_names']),
('type', ch_type),
('units', units),
('description', description),
('sampling_frequency', ['%.2f' % sfreq] * n_channels),
('low_cutoff', ['%.2f' % low_cutoff] * n_channels),
('high_cutoff', ['%.2f' % high_cutoff] * n_channels),
('status', status)]))
df.to_csv(fname, sep='\t', index=False)
if verbose:
print(os.linesep + "Writing '%s'..." % fname + os.linesep)
print(df.head())
return fname
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.
"""
map_chs = defaultdict(lambda: 'OTHER')
map_chs.update(meggradaxial='MEGGRADAXIAL',
megrefgradaxial='MEGREFGRADAXIAL',
meggradplanar='MEGGRADPLANAR',
megmag='MEGMAG',
megrefmag='MEGREFMAG',
eeg='EEG',
misc='MISC',
stim='TRIG',
emg='EMG',
ecog='ECOG',
seeg='SEEG',
eog='EOG',
ecg='ECG')
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
if hasattr(raw, 'filenames'):
# XXX: Hack for EEGLAB bug in MNE-Python 0.16; fixed in MNE-Python
# 0.17, ... remove the hack after upgrading dependencies in MNE-BIDS
if raw.filenames[0] is None: # hack
ext = '.set' # hack
else:
_, ext = _parse_ext(raw.filenames[0], verbose=verbose)
manufacturer = MANUFACTURERS[ext]
ignored_indexes = [
raw.ch_names.index(ch_name) for ch_name in raw.ch_names
if ch_name in 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)
ch_type.append(map_chs[_channel_type])
description.append(map_desc[_channel_type])
low_cutoff, high_cutoff = (raw.info['highpass'], raw.info['lowpass'])
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']
df = pd.DataFrame(
OrderedDict([('name', raw.info['ch_names']), ('type', ch_type),
('units', units), ('description', description),
('sampling_frequency', np.full((n_channels), sfreq)),
('low_cutoff', np.full((n_channels), low_cutoff)),
('high_cutoff', np.full((n_channels), high_cutoff)),
('status', status)]))
df.drop(ignored_indexes, inplace=True)
_write_tsv(fname, df, overwrite, verbose)
return fname
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
def _export_mne_raw(*, raw, fname, events=None, overwrite=False):
"""Export raw data from MNE-Python.
Parameters
----------
raw : mne.io.Raw
The raw data to export.
fname : str | pathlib.Path
The name of the file where raw data will be exported to. Must end with
``".vhdr"``, and accompanying *.vmrk* and *.eeg* files will be written
inside the same directory.
events : np.ndarray | None
Events to be written to the marker file (*.vmrk*). When array, must be in
`MNE-Python format <https://mne.tools/stable/glossary.html#term-events>`_.
When ``None`` (default), events will be written based on ``raw.annotations``.
overwrite : bool
Whether or not to overwrite existing data. Defaults to ``False``.
"""
# Prepare file location
if not str(fname).endswith(".vhdr"):
raise ValueError(
"`fname` must have the '.vhdr' extension for BrainVision.")
fname = Path(fname)
folder_out = fname.parents[0]
fname_base = fname.stem
# Prepare data from raw
data = raw.get_data() # gets data starting from raw.first_samp
sfreq = raw.info["sfreq"] # in Hz
meas_date = raw.info["meas_date"] # datetime.datetime
ch_names = raw.ch_names
# write voltage units as micro-volts and all other units without
# scaling
# We write units that we don't know as n/a
unit = []
for ch in raw.info["chs"]:
if ch["unit"] == FIFF.FIFF_UNIT_V:
unit.append("µV")
elif ch["unit"] == FIFF.FIFF_UNIT_CEL:
unit.append("°C")
else:
unit.append(_unit2human.get(ch["unit"], "n/a"))
unit = [u if u != "NA" else "n/a" for u in unit]
# We enforce conversion to float32 format
# XXX: Could add a feature that checks data and optimizes `unit`, `resolution`,
# and `format` so that raw.orig_format could be retained if reasonable.
if raw.orig_format != "single":
warn(
f"Encountered data in '{raw.orig_format}' format. "
"Converting to float32.",
RuntimeWarning,
)
fmt = "binary_float32"
resolution = 0.1
# Handle events
# if we got an ndarray, this is in MNE-Python format
msg = "`events` must be None or array in MNE-Python format."
if events is not None:
# Subtract raw.first_samp because brainvision marks events starting from
# the first available data point and ignores the raw.first_samp
assert isinstance(events, np.ndarray), msg
assert events.ndim == 2, msg
assert events.shape[-1] == 3, msg
events[:, 0] -= raw.first_samp
events = events[:, [0, 2]] # reorder for pybv required order
# else, prepare pybv style events from raw.annotations
else:
events = _mne_annots2pybv_events(raw)
# no information about reference channels in mne currently
ref_ch_names = None
# write to BrainVision
write_brainvision(
data=data,
sfreq=sfreq,
ch_names=ch_names,
ref_ch_names=ref_ch_names,
fname_base=fname_base,
folder_out=folder_out,
overwrite=overwrite,
events=events,
resolution=resolution,
unit=unit,
fmt=fmt,
meas_date=meas_date,
)