def test_volume_source_morph_round_trip(tmpdir, subject_from, subject_to,
lower, upper):
"""Test volume source estimate morph round-trips well."""
import nibabel as nib
from nibabel.processing import resample_from_to
src = dict()
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'], True)
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)
use = np.linspace(0, src_from[0]['nuse'] - 1, 10).round().astype(int)
stc_from = VolSourceEstimate(
np.eye(src_from[0]['nuse'])[:, use], [src_from[0]['vertno']], 0, 1)
stc_from_rt = morph_to_from.apply(morph_from_to.apply(stc_from))
maxs = np.argmax(stc_from_rt.data, axis=0)
src_rr = src_from[0]['rr'][src_from[0]['vertno']]
dists = 1000 * np.linalg.norm(src_rr[use] - src_rr[maxs], axis=1)
mu = np.mean(dists)
assert lower <= mu < upper # fsaverage=7.97; 25.4 without src_ras_t fix
# 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
ratio = stc_from.data.size / stc_from_rt.data.size
limits = ratio * np.array([1, 1.2])
stc_from.crop(0, 0)._data.fill(1.)
stc_from_rt = morph_to_from.apply(morph_from_to.apply(stc_from))
assert_power_preserved(stc_from, stc_from_rt, limits=limits)
# 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, stc_from_rt]:
img = stc.as_volume(src_from, mri_resolution=True)
img = nib.Nifti1Image(_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()
assert 0.97 < in_ < 0.98
assert out < 0.02
