def test_get_bids_path_from_fname(fname):
bids_path = get_bids_path_from_fname(fname)
assert bids_path.basename == Path(fname).name
if '/bids_root/' in fname:
assert Path(bids_path.root) == Path('/bids_root')
else:
if 'meg' in fname:
# directory should match
assert Path(bids_path.directory) == Path('sub-01/ses-02/meg')
# root should be default '.'
assert str(bids_path.root) == '.'
def _summarize_channels_tsv(root, scans_fpaths):
"""Summarize channels.tsv data in BIDS root directory.
Currently, summarizes all REQUIRED components of channels
data, and some RECOMMENDED and OPTIONAL components.
Parameters
----------
root : str | pathlib.Path
The path of the root of the BIDS compatible folder.
scans_fpaths : list
A list of all *_scans.tsv files in ``root``. The summary
will occur for all scans listed in the *_scans.tsv files.
Returns
-------
template_dict : dict
A dictionary of values for various template strings.
"""
root = Path(root)
# keep track of channel type, status
ch_status_count = {'bad': [], 'good': []}
ch_count = []
# loop through each scan
for scan_fpath in scans_fpaths:
# load in the scans.tsv file
# and read metadata for each scan
scans_tsv = _from_tsv(scan_fpath)
scans = scans_tsv['filename']
for scan in scans:
# summarize metadata of recordings
bids_path, _ = _parse_ext(scan)
datatype = op.dirname(scan)
if datatype not in ['meg', 'eeg', 'ieeg']:
continue
# convert to BIDS Path
if not isinstance(bids_path, BIDSPath):
bids_path = get_bids_path_from_fname(bids_path)
bids_path.root = root
# XXX: improve to allow emptyroom
if bids_path.subject == 'emptyroom':
continue
channels_fname = _find_matching_sidecar(bids_path=bids_path,
suffix='channels',
extension='.tsv')
# summarize channels.tsv
channels_tsv = _from_tsv(channels_fname)
for status in ch_status_count.keys():
ch_status = [
ch for ch in channels_tsv['status'] if ch == status
]
ch_status_count[status].append(len(ch_status))
ch_count.append(len(channels_tsv['name']))
# create summary template strings for status
template_dict = {
'mean_chs': np.mean(ch_count),
'std_chs': np.std(ch_count),
'mean_good_chs': np.mean(ch_status_count['good']),
'std_good_chs': np.std(ch_status_count['good']),
'mean_bad_chs': np.mean(ch_status_count['bad']),
'std_bad_chs': np.std(ch_status_count['bad']),
}
for key, val in template_dict.items():
template_dict[key] = round(val, 2)
return template_dict
def _summarize_sidecar_json(root, scans_fpaths):
"""Summarize scans in BIDS root directory.
Parameters
----------
root : str | pathlib.Path
The path of the root of the BIDS compatible folder.
scans_fpaths : list
A list of all *_scans.tsv files in ``root``. The summary
will occur for all scans listed in the *_scans.tsv files.
Returns
-------
template_dict : dict
A dictionary of values for various template strings.
"""
n_scans = 0
powerlinefreqs, sfreqs = set(), set()
manufacturers = set()
length_recordings = []
# loop through each scan
for scan_fpath in scans_fpaths:
# load in the scans.tsv file
# and read metadata for each scan
scans_tsv = _from_tsv(scan_fpath)
scans = scans_tsv['filename']
for scan in scans:
# summarize metadata of recordings
bids_path, ext = _parse_ext(scan)
datatype = op.dirname(scan)
if datatype not in ALLOWED_DATATYPES:
continue
n_scans += 1
# convert to BIDS Path
if not isinstance(bids_path, BIDSPath):
bids_path = get_bids_path_from_fname(bids_path)
bids_path.root = root
# XXX: improve to allow emptyroom
if bids_path.subject == 'emptyroom':
continue
sidecar_fname = _find_matching_sidecar(bids_path=bids_path,
suffix=datatype,
extension='.json')
with open(sidecar_fname, 'r', encoding='utf-8-sig') as fin:
sidecar_json = json.load(fin)
# aggregate metadata from each scan
# REQUIRED kwargs
sfreq = sidecar_json['SamplingFrequency']
powerlinefreq = str(sidecar_json['PowerLineFrequency'])
software_filters = sidecar_json.get('SoftwareFilters')
if not software_filters:
software_filters = 'n/a'
# RECOMMENDED kwargs
manufacturer = sidecar_json.get('Manufacturer', 'n/a')
record_duration = sidecar_json.get('RecordingDuration', 'n/a')
sfreqs.add(str(np.round(sfreq, 2)))
powerlinefreqs.add(str(powerlinefreq))
if manufacturer != 'n/a':
manufacturers.add(manufacturer)
length_recordings.append(record_duration)
# XXX: length summary is only allowed, if no 'n/a' was found
if any([dur == 'n/a' for dur in length_recordings]):
length_recordings = None
template_dict = {
'n_scans': n_scans,
'manufacturer': list(manufacturers),
'sfreq': sfreqs,
'powerlinefreq': powerlinefreqs,
'software_filters': software_filters,
'length_recordings': length_recordings,
}
return template_dict
def get_head_mri_trans(bids_path,
extra_params=None,
t1_bids_path=None,
fs_subject=None,
fs_subjects_dir=None,
*,
kind=None,
verbose=None):
"""Produce transformation matrix from MEG and MRI landmark points.
Will attempt to read the landmarks of Nasion, LPA, and RPA from the sidecar
files of (i) the MEG and (ii) the T1-weighted MRI data. The two sets of
points will then be used to calculate a transformation matrix from head
coordinates to MRI coordinates.
.. note:: The MEG and MRI data need **not** necessarily be stored in the
same session or even in the same BIDS dataset. See the
``t1_bids_path`` parameter for details.
Parameters
----------
bids_path : BIDSPath
The path of the electrophysiology recording. If ``datatype`` and
``suffix`` are not present, they will be set to ``'meg'``, and a
warning will be raised.
.. versionchanged:: 0.10
A warning is raised it ``datatype`` or ``suffix`` are not set.
extra_params : None | dict
Extra parameters to be passed to :func:`mne.io.read_raw` when reading
the MEG file.
t1_bids_path : BIDSPath | None
If ``None`` (default), will try to discover the T1-weighted MRI file
based on the name and location of the MEG recording specified via the
``bids_path`` parameter. Alternatively, you explicitly specify which
T1-weighted MRI scan to use for extraction of MRI landmarks. To do
that, pass a :class:`mne_bids.BIDSPath` pointing to the scan.
Use this parameter e.g. if the T1 scan was recorded during a different
session than the MEG. It is even possible to point to a T1 image stored
in an entirely different BIDS dataset than the MEG data.
fs_subject : str
The subject identifier used for FreeSurfer.
.. versionchanged:: 0.10
Does not default anymore to ``bids_path.subject`` if ``None``.
fs_subjects_dir : path-like | None
The FreeSurfer subjects directory. If ``None``, defaults to the
``SUBJECTS_DIR`` environment variable.
.. versionadded:: 0.8
kind : str | None
The suffix of the anatomical landmark names in the JSON sidecar.
A suffix might be present e.g. to distinguish landmarks between
sessions. If provided, should not include a leading underscore ``_``.
For example, if the landmark names in the JSON sidecar file are
``LPA_ses-1``, ``RPA_ses-1``, ``NAS_ses-1``, you should pass
``'ses-1'`` here.
If ``None``, no suffix is appended, the landmarks named
``Nasion`` (or ``NAS``), ``LPA``, and ``RPA`` will be used.
.. versionadded:: 0.10
%(verbose)s
Returns
-------
trans : mne.transforms.Transform
The data transformation matrix from head to MRI coordinates.
"""
if not has_nibabel(): # pragma: no cover
raise ImportError('This function requires nibabel.')
import nibabel as nib
if not isinstance(bids_path, BIDSPath):
raise RuntimeError('"bids_path" must be a BIDSPath object. Please '
'instantiate using mne_bids.BIDSPath().')
# check root available
meg_bids_path = bids_path.copy()
del bids_path
if meg_bids_path.root is None:
raise ValueError('The root of the "bids_path" must be set. '
'Please use `bids_path.update(root="<root>")` '
'to set the root of the BIDS folder to read.')
# if the bids_path is underspecified, only get info for MEG data
if meg_bids_path.datatype is None:
meg_bids_path.datatype = 'meg'
warn(
'bids_path did not have a datatype set. Assuming "meg". This '
'will raise an exception in the future.',
module='mne_bids',
category=DeprecationWarning)
if meg_bids_path.suffix is None:
meg_bids_path.suffix = 'meg'
warn(
'bids_path did not have a suffix set. Assuming "meg". This '
'will raise an exception in the future.',
module='mne_bids',
category=DeprecationWarning)
# Get the sidecar file for MRI landmarks
t1w_bids_path = ((meg_bids_path if t1_bids_path is None else
t1_bids_path).copy().update(datatype='anat',
suffix='T1w',
task=None))
t1w_json_path = _find_matching_sidecar(bids_path=t1w_bids_path,
extension='.json',
on_error='ignore')
del t1_bids_path
if t1w_json_path is not None:
t1w_json_path = Path(t1w_json_path)
if t1w_json_path is None or not t1w_json_path.exists():
raise FileNotFoundError(
f'Did not find T1w JSON sidecar file, tried location: '
f'{t1w_json_path}')
for extension in ('.nii', '.nii.gz'):
t1w_path_candidate = t1w_json_path.with_suffix(extension)
if t1w_path_candidate.exists():
t1w_bids_path = get_bids_path_from_fname(fname=t1w_path_candidate)
break
if not t1w_bids_path.fpath.exists():
raise FileNotFoundError(
f'Did not find T1w recording file, tried location: '
f'{t1w_path_candidate.name.replace(".nii.gz", "")}[.nii, .nii.gz]')
# Get MRI landmarks from the JSON sidecar
t1w_json = json.loads(t1w_json_path.read_text(encoding='utf-8'))
mri_coords_dict = t1w_json.get('AnatomicalLandmarkCoordinates', dict())
# landmarks array: rows: [LPA, NAS, RPA]; columns: [x, y, z]
suffix = f"_{kind}" if kind is not None else ""
mri_landmarks = np.full((3, 3), np.nan)
for landmark_name, coords in mri_coords_dict.items():
if landmark_name.upper() == ('LPA' + suffix).upper():
mri_landmarks[0, :] = coords
elif landmark_name.upper() == ('RPA' + suffix).upper():
mri_landmarks[2, :] = coords
elif (landmark_name.upper() == ('NAS' + suffix).upper()
or landmark_name.lower() == ('nasion' + suffix).lower()):
mri_landmarks[1, :] = coords
else:
continue
if np.isnan(mri_landmarks).any():
raise RuntimeError(
f'Could not extract fiducial points from T1w sidecar file: '
f'{t1w_json_path}\n\n'
f'The sidecar file SHOULD contain a key '
f'"AnatomicalLandmarkCoordinates" pointing to an '
f'object with the keys "LPA", "NAS", and "RPA". '
f'Yet, the following structure was found:\n\n'
f'{mri_coords_dict}')
# The MRI landmarks are in "voxels". We need to convert them to the
# Neuromag RAS coordinate system in order to compare them with MEG
# landmarks. See also: `mne_bids.write.write_anat`
if fs_subject is None:
warn(
'Passing "fs_subject=None" has been deprecated and will raise '
'an error in future versions. Please explicitly specify the '
'FreeSurfer subject name.', DeprecationWarning)
fs_subject = f'sub-{meg_bids_path.subject}'
fs_subjects_dir = get_subjects_dir(fs_subjects_dir, raise_error=False)
fs_t1_path = Path(fs_subjects_dir) / fs_subject / 'mri' / 'T1.mgz'
if not fs_t1_path.exists():
raise ValueError(
f"Could not find {fs_t1_path}. Consider running FreeSurfer's "
f"'recon-all` for subject {fs_subject}.")
fs_t1_mgh = nib.load(str(fs_t1_path))
t1_nifti = nib.load(str(t1w_bids_path.fpath))
# Convert to MGH format to access vox2ras method
t1_mgh = nib.MGHImage(t1_nifti.dataobj, t1_nifti.affine)
# convert to scanner RAS
mri_landmarks = apply_trans(t1_mgh.header.get_vox2ras(), mri_landmarks)
# convert to FreeSurfer T1 voxels (same scanner RAS as T1)
mri_landmarks = apply_trans(fs_t1_mgh.header.get_ras2vox(), mri_landmarks)
# now extract transformation matrix and put back to RAS coordinates of MRI
vox2ras_tkr = fs_t1_mgh.header.get_vox2ras_tkr()
mri_landmarks = apply_trans(vox2ras_tkr, mri_landmarks)
mri_landmarks = mri_landmarks * 1e-3
# Get MEG landmarks from the raw file
_, ext = _parse_ext(meg_bids_path)
if extra_params is None:
extra_params = dict()
if ext == '.fif':
extra_params['allow_maxshield'] = True
raw = read_raw_bids(bids_path=meg_bids_path, extra_params=extra_params)
if (raw.get_montage() is None or raw.get_montage().get_positions() is None
or any([
raw.get_montage().get_positions()[fid_key] is None
for fid_key in ('nasion', 'lpa', 'rpa')
])):
raise RuntimeError(
f'Could not extract fiducial points from ``raw`` file: '
f'{meg_bids_path}\n\n'
f'The ``raw`` file SHOULD contain digitization points '
'for the nasion and left and right pre-auricular points '
'but none were found')
pos = raw.get_montage().get_positions()
meg_landmarks = np.asarray((pos['lpa'], pos['nasion'], pos['rpa']))
# Given the two sets of points, fit the transform
trans_fitted = fit_matched_points(src_pts=meg_landmarks,
tgt_pts=mri_landmarks)
trans = mne.transforms.Transform(fro='head', to='mri', trans=trans_fitted)
return trans