def test_read_freesurfer_lut(fname, tmpdir):
"""Test reading volume label names."""
atlas_ids, colors = read_freesurfer_lut(fname)
assert list(atlas_ids).count('Brain-Stem') == 1
assert len(colors) == len(atlas_ids) == 1266
label_names, label_colors = get_volume_labels_from_aseg(aseg_fname,
return_colors=True)
assert isinstance(label_names, list)
assert isinstance(label_colors, list)
assert label_names.count('Brain-Stem') == 1
for c in label_colors:
assert isinstance(c, np.ndarray)
assert c.shape == (4, )
assert len(label_names) == len(label_colors) == 46
with pytest.raises(ValueError, match='must be False'):
get_volume_labels_from_aseg(aseg_fname,
return_colors=True,
atlas_ids=atlas_ids)
label_names_2 = get_volume_labels_from_aseg(aseg_fname,
atlas_ids=atlas_ids)
assert label_names == label_names_2
# long name (only test on one run)
if fname is not None:
return
fname = str(tmpdir.join('long.txt'))
names = [
'Anterior_Cingulate_and_Medial_Prefrontal_Cortex-' + hemi
for hemi in ('lh', 'rh')
]
ids = np.arange(1, len(names) + 1)
colors = [(id_, ) * 4 for id_ in ids]
with open(fname, 'w') as fid:
for name, id_, color in zip(names, ids, colors):
out_color = ' '.join('%3d' % x for x in color)
line = '%d %s %s\n' % (id_, name, out_color)
fid.write(line)
lut, got_colors = read_freesurfer_lut(fname)
assert len(lut) == len(got_colors) == len(names) == len(ids)
for name, id_, color in zip(names, ids, colors):
assert name in lut
assert name in got_colors
assert_array_equal(got_colors[name][:3], color[:3])
assert lut[name] == id_
with open(fname, 'w') as fid:
for name, id_, color in zip(names, ids, colors):
out_color = ' '.join('%3d' % x for x in color[:3]) # wrong length!
line = '%d %s %s\n' % (id_, name, out_color)
fid.write(line)
with pytest.raises(RuntimeError, match='formatted'):
read_freesurfer_lut(fname)
def test_suggest():
"""Test suggestions."""
names = mne.get_volume_labels_from_aseg(fname_mgz)
sug = _suggest('', names)
assert sug == '' # nothing
sug = _suggest('Left-cerebellum', names)
assert sug == " Did you mean 'Left-Cerebellum-Cortex'?"
sug = _suggest('Cerebellum-Cortex', names)
assert sug == " Did you mean one of ['Left-Cerebellum-Cortex', 'Right-Cerebellum-Cortex', 'Left-Cerebral-Cortex']?" # noqa: E501
def test_volume_labels_morph(tmpdir, sl, n_real, n_mri, n_orig):
"""Test generating a source space from volume label."""
import nibabel as nib
n_use = (sl.stop - sl.start) // (sl.step or 1)
# see gh-5224
evoked = mne.read_evokeds(fname_evoked)[0].crop(0, 0)
evoked.pick_channels(evoked.ch_names[:306:8])
evoked.info.normalize_proj()
n_ch = len(evoked.ch_names)
lut, _ = read_freesurfer_lut()
label_names = sorted(get_volume_labels_from_aseg(fname_aseg))
use_label_names = label_names[sl]
src = setup_volume_source_space('sample',
subjects_dir=subjects_dir,
volume_label=use_label_names,
mri=fname_aseg)
assert len(src) == n_use
assert src.kind == 'volume'
n_src = sum(s['nuse'] for s in src)
sphere = make_sphere_model('auto', 'auto', evoked.info)
fwd = make_forward_solution(evoked.info, fname_trans, src, sphere)
assert fwd['sol']['data'].shape == (n_ch, n_src * 3)
inv = make_inverse_operator(evoked.info,
fwd,
make_ad_hoc_cov(evoked.info),
loose=1.)
stc = apply_inverse(evoked, inv)
assert stc.data.shape == (n_src, 1)
img = stc.as_volume(src, mri_resolution=True)
assert img.shape == (86, 86, 86, 1)
n_on = np.array(img.dataobj).astype(bool).sum()
aseg_img = _get_img_fdata(nib.load(fname_aseg))
n_got_real = np.in1d(aseg_img.ravel(),
[lut[name] for name in use_label_names]).sum()
assert n_got_real == n_real
# - This was 291 on `main` before gh-5590
# - Refactoring transforms it became 279 with a < 1e-8 change in vox_mri_t
# - Dropped to 123 once nearest-voxel was used in gh-7653
# - Jumped back up to 330 with morphing fixes actually correctly
# interpolating across all volumes
assert aseg_img.shape == img.shape[:3]
assert n_on == n_mri
for ii in range(2):
# should work with (ii=0) or without (ii=1) the interpolator
if ii:
src[0]['interpolator'] = None
img = stc.as_volume(src, mri_resolution=False)
n_on = np.array(img.dataobj).astype(bool).sum()
# was 20 on `main` before gh-5590
# then 44 before gh-7653, which took it back to 20
assert n_on == n_orig
# without the interpolator, this should fail
assert src[0]['interpolator'] is None
with pytest.raises(RuntimeError, match=r'.*src\[0\], .* mri_resolution'):
stc.as_volume(src, mri_resolution=True)
def src_volume_labels():
"""Create a 7mm source space with labels."""
volume_labels = mne.get_volume_labels_from_aseg(fname_aseg)
src = mne.setup_volume_source_space(
'sample', 7., mri='aseg.mgz', volume_label=volume_labels,
add_interpolator=False, bem=fname_bem,
subjects_dir=subjects_dir)
lut, _ = mne.read_freesurfer_lut()
assert len(volume_labels) == 46
assert volume_labels[0] == 'Unknown'
assert lut['Unknown'] == 0 # it will be excluded during label gen
return src, tuple(volume_labels), lut
def src_volume_labels():
"""Create a 7mm source space with labels."""
pytest.importorskip('nibabel')
volume_labels = mne.get_volume_labels_from_aseg(fname_aseg)
with pytest.warns(RuntimeWarning, match='Found no usable.*Left-vessel.*'):
src = mne.setup_volume_source_space(
'sample', 7., mri='aseg.mgz', volume_label=volume_labels,
add_interpolator=False, bem=fname_bem,
subjects_dir=subjects_dir)
lut, _ = mne.read_freesurfer_lut()
assert len(volume_labels) == 46
assert volume_labels[0] == 'Unknown'
assert lut['Unknown'] == 0 # it will be excluded during label gen
return src, tuple(volume_labels), lut
def test_forward_mixed_source_space(tmp_path):
"""Test making the forward solution for a mixed source space."""
# get the surface source space
rng = np.random.RandomState(0)
surf = read_source_spaces(fname_src)
# setup two volume source spaces
label_names = get_volume_labels_from_aseg(fname_aseg)
vol_labels = rng.choice(label_names, 2)
with pytest.warns(RuntimeWarning, match='Found no usable.*CC_Mid_Ant.*'):
vol1 = setup_volume_source_space('sample',
pos=20.,
mri=fname_aseg,
volume_label=vol_labels[0],
add_interpolator=False)
vol2 = setup_volume_source_space('sample',
pos=20.,
mri=fname_aseg,
volume_label=vol_labels[1],
add_interpolator=False)
# merge surfaces and volume
src = surf + vol1 + vol2
# calculate forward solution
fwd = make_forward_solution(fname_raw, fname_trans, src, fname_bem)
assert (repr(fwd))
# extract source spaces
src_from_fwd = fwd['src']
# get the coordinate frame of each source space
coord_frames = np.array([s['coord_frame'] for s in src_from_fwd])
# assert that all source spaces are in head coordinates
assert ((coord_frames == FIFF.FIFFV_COORD_HEAD).all())
# run tests for SourceSpaces.export_volume
fname_img = tmp_path / 'temp-image.mgz'
# head coordinates and mri_resolution, but trans file
with pytest.raises(ValueError, match='trans containing mri to head'):
src_from_fwd.export_volume(fname_img, mri_resolution=True, trans=None)
# head coordinates and mri_resolution, but wrong trans file
vox_mri_t = vol1[0]['vox_mri_t']
with pytest.raises(ValueError, match='head<->mri, got mri_voxel->mri'):
src_from_fwd.export_volume(fname_img,
mri_resolution=True,
trans=vox_mri_t)
# For this visualization, ``nilearn`` must be installed.
# This visualization is interactive. Click on any of the anatomical slices
# to explore the time series. Clicking on any time point will bring up the
# corresponding anatomical map.
#
# We could visualize the source estimate on a glass brain. Unlike the previous
# visualization, a glass brain does not show us one slice but what we would
# see if the brain was transparent like glass.
stc.plot(src, subject='sample', subjects_dir=subjects_dir, mode='glass_brain')
###############################################################################
# You can also extract label time courses using volumetric atlases. Here we'll
# use the built-in ``aparc.a2009s+aseg.mgz``:
fname_aseg = op.join(subjects_dir, 'sample', 'mri', 'aparc.a2009s+aseg.mgz')
label_names = mne.get_volume_labels_from_aseg(fname_aseg)
label_tc = stc.extract_label_time_course(fname_aseg,
src=src,
trans=inv['mri_head_t'])
lidx, tidx = np.unravel_index(np.argmax(label_tc), label_tc.shape)
fig, ax = plt.subplots(1)
ax.plot(stc.times, label_tc.T, 'k', lw=1., alpha=0.5)
xy = np.array([stc.times[tidx], label_tc[lidx, tidx]])
xytext = xy + [0.01, 1]
ax.annotate(label_names[lidx],
xy,
xytext,
arrowprops=dict(arrowstyle='->'),
color='r')
ax.set(xlim=stc.times[[0, -1]], xlabel='Time (s)', ylabel='Activation')
for key in ('right', 'top'):
def test_volume_source_morph_round_trip(tmpdir, subject_from, subject_to,
lower, upper, dtype, morph_mat,
monkeypatch):
"""Test volume source estimate morph round-trips well."""
import nibabel as nib
from nibabel.processing import resample_from_to
src = dict()
if morph_mat:
# ~1.5 minutes with pos=7. (4157 morphs!) for sample, so only test
# morph_mat computation mode with a few labels
label_names = sorted(get_volume_labels_from_aseg(fname_aseg))[1:2]
if 'sample' in (subject_from, subject_to):
src['sample'] = setup_volume_source_space(
'sample',
subjects_dir=subjects_dir,
volume_label=label_names,
mri=fname_aseg)
assert sum(s['nuse'] for s in src['sample']) == 12
if 'fsaverage' in (subject_from, subject_to):
src['fsaverage'] = setup_volume_source_space(
'fsaverage',
subjects_dir=subjects_dir,
volume_label=label_names[:3],
mri=fname_aseg_fs)
assert sum(s['nuse'] for s in src['fsaverage']) == 16
else:
assert not morph_mat
if 'sample' in (subject_from, subject_to):
src['sample'] = mne.read_source_spaces(fname_vol)
src['sample'][0]['subject_his_id'] = 'sample'
assert src['sample'][0]['nuse'] == 4157
if 'fsaverage' in (subject_from, subject_to):
# Created to save space with:
#
# bem = op.join(op.dirname(mne.__file__), 'data', 'fsaverage',
# 'fsaverage-inner_skull-bem.fif')
# src_fsaverage = mne.setup_volume_source_space(
# 'fsaverage', pos=7., bem=bem, mindist=0,
# subjects_dir=subjects_dir, add_interpolator=False)
# mne.write_source_spaces(fname_fs_vol, src_fsaverage,
# overwrite=True)
#
# For speed we do it without the interpolator because it's huge.
src['fsaverage'] = mne.read_source_spaces(fname_fs_vol)
src['fsaverage'][0].update(vol_dims=np.array([23, 29, 25]),
seg_name='brain')
_add_interpolator(src['fsaverage'])
assert src['fsaverage'][0]['nuse'] == 6379
src_to, src_from = src[subject_to], src[subject_from]
del src
# No SDR just for speed once everything works
kwargs = dict(niter_sdr=(),
niter_affine=(1, ),
subjects_dir=subjects_dir,
verbose=True)
morph_from_to = compute_source_morph(src=src_from,
src_to=src_to,
subject_to=subject_to,
**kwargs)
morph_to_from = compute_source_morph(src=src_to,
src_to=src_from,
subject_to=subject_from,
**kwargs)
nuse = sum(s['nuse'] for s in src_from)
assert nuse > 10
use = np.linspace(0, nuse - 1, 10).round().astype(int)
data = np.eye(nuse)[:, use]
if dtype is complex:
data = data * 1j
vertices = [s['vertno'] for s in src_from]
stc_from = VolSourceEstimate(data, vertices, 0, 1)
with catch_logging() as log:
stc_from_rt = morph_to_from.apply(
morph_from_to.apply(stc_from, verbose='debug'))
log = log.getvalue()
assert 'individual volume morph' in log
maxs = np.argmax(stc_from_rt.data, axis=0)
src_rr = np.concatenate([s['rr'][s['vertno']] for s in src_from])
dists = 1000 * np.linalg.norm(src_rr[use] - src_rr[maxs], axis=1)
mu = np.mean(dists)
# fsaverage=5.99; 7.97 without additional src_ras_t fix
# fsaverage=7.97; 25.4 without src_ras_t fix
assert lower <= mu < upper, f'round-trip distance {mu}'
# check that pre_affine is close to identity when subject_to==subject_from
if subject_to == subject_from:
for morph in (morph_to_from, morph_from_to):
assert_allclose(morph.pre_affine.affine, np.eye(4), atol=1e-2)
# check that power is more or less preserved (labelizing messes with this)
if morph_mat:
if subject_to == 'fsaverage':
limits = (14.0, 14.2)
else:
limits = (7, 7.5)
else:
limits = (1, 1.2)
stc_from_unit = stc_from.copy().crop(0, 0)
stc_from_unit._data.fill(1.)
stc_from_unit_rt = morph_to_from.apply(morph_from_to.apply(stc_from_unit))
assert_power_preserved(stc_from_unit, stc_from_unit_rt, limits=limits)
if morph_mat:
fname = tmpdir.join('temp-morph.h5')
morph_to_from.save(fname)
morph_to_from = read_source_morph(fname)
assert morph_to_from.vol_morph_mat is None
morph_to_from.compute_vol_morph_mat(verbose=True)
morph_to_from.save(fname, overwrite=True)
morph_to_from = read_source_morph(fname)
assert isinstance(morph_to_from.vol_morph_mat, csr_matrix), 'csr'
# equivalence (plus automatic calling)
assert morph_from_to.vol_morph_mat is None
monkeypatch.setattr(mne.morph, '_VOL_MAT_CHECK_RATIO', 0.)
with catch_logging() as log:
with pytest.warns(RuntimeWarning, match=r'calling morph\.compute'):
stc_from_rt_lin = morph_to_from.apply(
morph_from_to.apply(stc_from, verbose='debug'))
assert isinstance(morph_from_to.vol_morph_mat, csr_matrix), 'csr'
log = log.getvalue()
assert 'sparse volume morph matrix' in log
assert_allclose(stc_from_rt.data, stc_from_rt_lin.data)
del stc_from_rt_lin
stc_from_unit_rt_lin = morph_to_from.apply(
morph_from_to.apply(stc_from_unit))
assert_allclose(stc_from_unit_rt.data, stc_from_unit_rt_lin.data)
del stc_from_unit_rt_lin
del stc_from, stc_from_rt
# before and after morph, check the proportion of vertices
# that are inside and outside the brainmask.mgz
brain = nib.load(op.join(subjects_dir, subject_from, 'mri', 'brain.mgz'))
mask = _get_img_fdata(brain) > 0
if subject_from == subject_to == 'sample':
for stc in [stc_from_unit, stc_from_unit_rt]:
img = stc.as_volume(src_from, mri_resolution=True)
img = nib.Nifti1Image( # abs to convert complex
np.abs(_get_img_fdata(img)[:, :, :, 0]), img.affine)
img = _get_img_fdata(resample_from_to(img, brain, order=1))
assert img.shape == mask.shape
in_ = img[mask].astype(bool).mean()
out = img[~mask].astype(bool).mean()
if morph_mat:
out_max = 0.001
in_min, in_max = 0.005, 0.007
else:
out_max = 0.02
in_min, in_max = 0.97, 0.98
assert out < out_max, f'proportion out of volume {out}'
assert in_min < in_ < in_max, f'proportion inside volume {in_}'
eeg=False)
fwd_disc = mne.convert_forward_solution(fwd_disc,
surf_ori=True,
force_fixed=True)
mne.write_forward_solution(vfname.fwd_discrete, fwd_disc, overwrite=False)
else:
fwd_disc = mne.read_forward_solution(vfname.fwd_discrete)
###############################################################################
# Create trials of simulated data
###############################################################################
volume_labels = mne.get_volume_labels_from_aseg(vfname.aseg)
n_noise_dipoles = config.n_noise_dipoles_vol
# select n_noise_dipoles entries from rr and their corresponding entries from nn
poss_indices = np.arange(rr.shape[0])
raw_list = []
for i in range(config.n_trials):
###########################################################################
# Simulate random noise dipoles
###########################################################################
stc_noise = simulate_sparse_stc(src,
n_noise_dipoles,
times,
data_fun=generate_random,
