def _electrodes_tsv(raw, fname, datatype, overwrite=False, verbose=True):
"""Create an electrodes.tsv file and save it.
Parameters
----------
raw : instance of Raw
The data as MNE-Python Raw object.
fname : str
Filename to save the electrodes.tsv to.
datatype : str
Type of the data recording. Can be ``meg``, ``eeg``,
or ``ieeg``.
overwrite : bool
Defaults to False.
Whether to overwrite the existing data in the file.
If there is already data for the given `fname` and overwrite is False,
an error will be raised.
verbose : bool
Set verbose output to true or false.
"""
# create list of channel coordinates and names
x, y, z, names = list(), list(), list(), list()
for ch in raw.info['chs']:
if _check_ch_locs([ch]):
x.append(ch['loc'][0])
y.append(ch['loc'][1])
z.append(ch['loc'][2])
else:
x.append('n/a')
y.append('n/a')
z.append('n/a')
names.append(ch['ch_name'])
# create OrderedDict to write to tsv file
if datatype == "ieeg":
# XXX: size should be included in the future
sizes = ['n/a'] * len(names)
data = OrderedDict([
('name', names),
('x', x),
('y', y),
('z', z),
('size', sizes),
])
elif datatype == 'eeg':
data = OrderedDict([
('name', names),
('x', x),
('y', y),
('z', z),
])
else: # pragma: no cover
raise RuntimeError("datatype {} not supported.".format(datatype))
# Add impedance values if available, currently only BrainVision:
# https://github.com/mne-tools/mne-python/pull/7974
if hasattr(raw, 'impedances'):
data['impedance'] = _get_impedances(raw, names)
_write_tsv(fname, data, overwrite=overwrite, verbose=verbose)
def _electrodes_tsv(raw, fname, kind, overwrite=False, verbose=True):
"""Create an electrodes.tsv file and save it.
Parameters
----------
raw : instance of Raw
The data as MNE-Python Raw object.
fname : str
Filename to save the electrodes.tsv to.
kind : str
Type of the data as in ALLOWED_KINDS. For iEEG, requires size.
overwrite : bool
Defaults to False.
Whether to overwrite the existing data in the file.
If there is already data for the given `fname` and overwrite is False,
an error will be raised.
verbose : bool
Set verbose output to true or false.
"""
# create list of channel coordinates and names
x, y, z, names = list(), list(), list(), list()
for ch in raw.info['chs']:
if _check_ch_locs([ch]):
x.append(ch['loc'][0])
y.append(ch['loc'][1])
z.append(ch['loc'][2])
else:
x.append('n/a')
y.append('n/a')
z.append('n/a')
names.append(ch['ch_name'])
# create OrderedDict to write to tsv file
if kind == "ieeg":
# XXX: size should be included in the future
sizes = ['n/a'] * len(names)
data = OrderedDict([
('name', names),
('x', x),
('y', y),
('z', z),
('size', sizes),
])
elif kind == 'eeg':
data = OrderedDict([
('name', names),
('x', x),
('y', y),
('z', z),
])
else: # pragma: no cover
raise RuntimeError("kind {} not supported.".format(kind))
_write_tsv(fname, data, overwrite=overwrite, verbose=verbose)
def test_check_ch_locs():
"""Test _check_ch_locs behavior."""
info = mne.io.read_info(fname_raw)
assert _check_ch_locs(info=info)
for picks in ([0], [0, 1], None):
assert _check_ch_locs(info=info, picks=picks)
for ch_type in ('meg', 'mag', 'grad', 'eeg'):
assert _check_ch_locs(info=info, ch_type=ch_type)
# drop locations for EEG
picks_eeg = _picks_to_idx(info=info, picks='eeg')
for idx in picks_eeg:
info['chs'][idx]['loc'][:3] = np.nan
# EEG tests should fail now
assert _check_ch_locs(info=info, picks=picks_eeg) is False
assert _check_ch_locs(info=info, ch_type='eeg') is False
# tests for other (and "all") channels should still pass
assert _check_ch_locs(info=info)
assert _check_ch_locs(info=info, ch_type='mag')
def _write_electrodes_tsv(raw, fname, datatype, overwrite=False, verbose=True):
"""Create an electrodes.tsv file and save it.
Parameters
----------
raw : mne.io.Raw
The data as MNE-Python Raw object.
fname : str
Filename to save the electrodes.tsv to.
datatype : str
Type of the data recording. Can be ``meg``, ``eeg``,
or ``ieeg``.
overwrite : bool
Defaults to False.
Whether to overwrite the existing data in the file.
If there is already data for the given `fname` and overwrite is False,
an error will be raised.
verbose : bool
Set verbose output to true or false.
"""
# create list of channel coordinates and names
x, y, z, names = list(), list(), list(), list()
for ch in raw.info['chs']:
if _check_ch_locs([ch]):
x.append(ch['loc'][0])
y.append(ch['loc'][1])
z.append(ch['loc'][2])
else:
x.append('n/a')
y.append('n/a')
z.append('n/a')
names.append(ch['ch_name'])
# create OrderedDict to write to tsv file
if datatype == "ieeg":
# XXX: size should be included in the future
sizes = ['n/a'] * len(names)
data = OrderedDict([
('name', names),
('x', x),
('y', y),
('z', z),
('size', sizes),
])
elif datatype == 'eeg':
data = OrderedDict([
('name', names),
('x', x),
('y', y),
('z', z),
])
else: # pragma: no cover
raise RuntimeError("datatype {} not supported.".format(datatype))
# Add impedance values if available, currently only BrainVision:
# https://github.com/mne-tools/mne-python/pull/7974
if hasattr(raw, 'impedances'):
data['impedance'] = _get_impedances(raw, names)
# note that any coordsystem.json file shared within sessions
# will be the same across all runs (currently). So
# overwrite is set to True always
# XXX: improve later when BIDS is updated
# check that there already exists a coordsystem.json
if Path(fname).exists() and not overwrite:
electrodes_tsv = _from_tsv(fname)
# cast values to str to make equality check work
if any([
list(map(str, vals1)) != list(vals2)
for vals1, vals2 in zip(data.values(), electrodes_tsv.values())
]):
raise RuntimeError(
f'Trying to write electrodes.tsv, but it already '
f'exists at {fname} and the contents do not match. '
f'You must differentiate this electrodes.tsv file '
f'from the existing one, or set "overwrite" to True.')
_write_tsv(fname, data, overwrite=True, verbose=verbose)