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from functools import reduce
from glob import glob
import os
import os.path as op
from shutil import copyfile, copytree
import pytest
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_allclose,
assert_array_equal)
import mne
from mne.datasets import testing
from mne.transforms import (Transform, apply_trans, rotation, translation,
scaling)
from mne.coreg import (fit_matched_points, create_default_subject, scale_mri,
_is_mri_subject, scale_labels, scale_source_space,
coregister_fiducials)
from mne.io.constants import FIFF
from mne.utils import _TempDir, run_tests_if_main, requires_nibabel
from mne.source_space import write_source_spaces
def test_coregister_fiducials():
"""Test coreg.coregister_fiducials()."""
# prepare head and MRI fiducials
trans = Transform('head', 'mri',
rotation(.4, .1, 0).dot(translation(.1, -.1, .1)))
coords_orig = np.array([[-0.08061612, -0.02908875, -0.04131077],
[0.00146763, 0.08506715, -0.03483611],
[0.08436285, -0.02850276, -0.04127743]])
coords_trans = apply_trans(trans, coords_orig)
def make_dig(coords, cf):
return ({'coord_frame': cf, 'ident': 1, 'kind': 1, 'r': coords[0]},
{'coord_frame': cf, 'ident': 2, 'kind': 1, 'r': coords[1]},
{'coord_frame': cf, 'ident': 3, 'kind': 1, 'r': coords[2]})
mri_fiducials = make_dig(coords_trans, FIFF.FIFFV_COORD_MRI)
info = {'dig': make_dig(coords_orig, FIFF.FIFFV_COORD_HEAD)}
# test coregister_fiducials()
trans_est = coregister_fiducials(info, mri_fiducials)
assert trans_est.from_str == trans.from_str
assert trans_est.to_str == trans.to_str
assert_array_almost_equal(trans_est['trans'], trans['trans'])
@testing.requires_testing_data
def test_scale_mri():
"""Test creating fsaverage and scaling it."""
# create fsaverage using the testing "fsaverage" instead of the FreeSurfer
# one
tempdir = _TempDir()
fake_home = testing.data_path()
create_default_subject(subjects_dir=tempdir, fs_home=fake_home,
verbose=True)
assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed"
fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif')
os.remove(fid_path)
create_default_subject(update=True, subjects_dir=tempdir,
fs_home=fake_home)
assert op.exists(fid_path), "Updating fsaverage"
# copy MRI file from sample data (shouldn't matter that it's incorrect,
# so here choose a small one)
path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri',
'T1.mgz')
path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
copyfile(path_from, path_to)
# remove redundant label files
label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label')
label_paths = glob(label_temp)
for label_path in label_paths[1:]:
os.remove(label_path)
# create source space
print('Creating surface source space')
path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif')
src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir,
add_dist=False)
mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
print('Creating volume source space')
vsrc = mne.setup_volume_source_space(
'fsaverage', pos=50, mri=mri, subjects_dir=tempdir,
add_interpolator=False)
write_source_spaces(path % 'vol-50', vsrc)
# scale fsaverage
for scale in (.9, [1, .2, .8]):
write_source_spaces(path % 'ico-0', src, overwrite=True)
os.environ['_MNE_FEW_SURFACES'] = 'true'
with pytest.warns(None): # sometimes missing nibabel
scale_mri('fsaverage', 'flachkopf', scale, True,
subjects_dir=tempdir, verbose='debug')
del os.environ['_MNE_FEW_SURFACES']
assert _is_mri_subject('flachkopf', tempdir), "Scaling failed"
spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif')
assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled"
assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf',
'lh.sphere.reg'))
vsrc_s = mne.read_source_spaces(spath % 'vol-50')
pt = np.array([0.12, 0.41, -0.22])
assert_array_almost_equal(
apply_trans(vsrc_s[0]['src_mri_t'], pt * np.array(scale)),
apply_trans(vsrc[0]['src_mri_t'], pt))
scale_labels('flachkopf', subjects_dir=tempdir)
# add distances to source space after hacking the properties to make
# it run *much* faster
src_dist = src.copy()
for s in src_dist:
s.update(rr=s['rr'][s['vertno']], nn=s['nn'][s['vertno']],
tris=s['use_tris'])
s.update(np=len(s['rr']), ntri=len(s['tris']),
vertno=np.arange(len(s['rr'])),
inuse=np.ones(len(s['rr']), int))
mne.add_source_space_distances(src_dist)
write_source_spaces(path % 'ico-0', src_dist, overwrite=True)
# scale with distances
os.remove(spath % 'ico-0')
scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
ssrc = mne.read_source_spaces(spath % 'ico-0')
assert ssrc[0]['dist'] is not None
@testing.requires_testing_data
@requires_nibabel()
def test_scale_mri_xfm():
"""Test scale_mri transforms and MRI scaling."""
# scale fsaverage
tempdir = _TempDir()
os.environ['_MNE_FEW_SURFACES'] = 'true'
fake_home = testing.data_path()
# add fsaverage
create_default_subject(subjects_dir=tempdir, fs_home=fake_home,
verbose=True)
# add sample (with few files)
sample_dir = op.join(tempdir, 'sample')
os.mkdir(sample_dir)
os.mkdir(op.join(sample_dir, 'bem'))
for dirname in ('mri', 'surf'):
copytree(op.join(fake_home, 'subjects', 'sample', dirname),
op.join(sample_dir, dirname))
subject_to = 'flachkopf'
spacing = 'oct2'
for subject_from in ('fsaverage', 'sample'):
if subject_from == 'fsaverage':
scale = 1. # single dim
else:
scale = [0.9, 2, .8] # separate
src_from_fname = op.join(tempdir, subject_from, 'bem',
'%s-%s-src.fif' % (subject_from, spacing))
src_from = mne.setup_source_space(
subject_from, spacing, subjects_dir=tempdir, add_dist=False)
write_source_spaces(src_from_fname, src_from)
print(src_from_fname)
vertices_from = np.concatenate([s['vertno'] for s in src_from])
assert len(vertices_from) == 36
hemis = ([0] * len(src_from[0]['vertno']) +
[1] * len(src_from[0]['vertno']))
mni_from = mne.vertex_to_mni(vertices_from, hemis, subject_from,
subjects_dir=tempdir)
if subject_from == 'fsaverage': # identity transform
source_rr = np.concatenate([s['rr'][s['vertno']]
for s in src_from]) * 1e3
assert_allclose(mni_from, source_rr)
if subject_from == 'fsaverage':
overwrite = skip_fiducials = False
else:
with pytest.raises(IOError, match='No fiducials file'):
scale_mri(subject_from, subject_to, scale,
subjects_dir=tempdir)
skip_fiducials = True
with pytest.raises(IOError, match='already exists'):
scale_mri(subject_from, subject_to, scale,
subjects_dir=tempdir, skip_fiducials=skip_fiducials)
overwrite = True
scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir,
verbose='debug', overwrite=overwrite,
skip_fiducials=skip_fiducials)
if subject_from == 'fsaverage':
assert _is_mri_subject(subject_to, tempdir), "Scaling failed"
src_to_fname = op.join(tempdir, subject_to, 'bem',
'%s-%s-src.fif' % (subject_to, spacing))
assert op.exists(src_to_fname), "Source space was not scaled"
# Check MRI scaling
fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz')
assert op.exists(fname_mri), "MRI was not scaled"
# Check MNI transform
src = mne.read_source_spaces(src_to_fname)
vertices = np.concatenate([s['vertno'] for s in src])
assert_array_equal(vertices, vertices_from)
mni = mne.vertex_to_mni(vertices, hemis, subject_to,
subjects_dir=tempdir)
assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm
del os.environ['_MNE_FEW_SURFACES']
def test_fit_matched_points():
"""Test fit_matched_points: fitting two matching sets of points."""
tgt_pts = np.random.RandomState(42).uniform(size=(6, 3))
# rotation only
trans = rotation(2, 6, 3)
src_pts = apply_trans(trans, tgt_pts)
trans_est = fit_matched_points(src_pts, tgt_pts, translate=False,
out='trans')
est_pts = apply_trans(trans_est, src_pts)
assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with "
"rotation")
# rotation & translation
trans = np.dot(translation(2, -6, 3), rotation(2, 6, 3))
src_pts = apply_trans(trans, tgt_pts)
trans_est = fit_matched_points(src_pts, tgt_pts, out='trans')
est_pts = apply_trans(trans_est, src_pts)
assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with "
"rotation and translation.")
# rotation & translation & scaling
trans = reduce(np.dot, (translation(2, -6, 3), rotation(1.5, .3, 1.4),
scaling(.5, .5, .5)))
src_pts = apply_trans(trans, tgt_pts)
trans_est = fit_matched_points(src_pts, tgt_pts, scale=1, out='trans')
est_pts = apply_trans(trans_est, src_pts)
assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with "
"rotation, translation and scaling.")
# test exceeding tolerance
tgt_pts[0, :] += 20
pytest.raises(RuntimeError, fit_matched_points, tgt_pts, src_pts, tol=10)
run_tests_if_main()
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