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)
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
assert_raises(RuntimeError, fit_matched_points, tgt_pts, src_pts, tol=10)
def test_fit_point_cloud():
"""Test fit_point_cloud: fitting a set of points to a point cloud"""
# evenly spaced target points on a sphere
u = np.linspace(0, np.pi, 150)
v = np.linspace(0, np.pi, 150)
x = np.outer(np.cos(u), np.sin(v)).reshape((-1, 1))
y = np.outer(np.sin(u), np.sin(v)).reshape((-1, 1))
z = np.outer(np.ones(np.size(u)), np.cos(v)).reshape((-1, 1)) * 3
tgt_pts = np.hstack((x, y, z))
tgt_pts = _decimate_points(tgt_pts, .05)
# pick some points to fit
some_tgt_pts = tgt_pts[::362]
# rotation only
trans = rotation(1.5, .3, -0.4)
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=0, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation.")
# rotation and translation
trans = np.dot(rotation(0.5, .3, -0.4), translation(.3, .2, -.2))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=True,
scale=0, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and "
"translation.")
# rotation and 1 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.5, 1.5))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=1, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and 1 scaling "
"parameter.")
# rotation and 3 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.7, 1.1))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=3, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and 3 scaling "
"parameters.")
def test_fit_point_cloud():
"""Test fit_point_cloud: fitting a set of points to a point cloud"""
# evenly spaced target points on a sphere
u = np.linspace(0, np.pi, 150)
v = np.linspace(0, np.pi, 150)
x = np.outer(np.cos(u), np.sin(v)).reshape((-1, 1))
y = np.outer(np.sin(u), np.sin(v)).reshape((-1, 1))
z = np.outer(np.ones(np.size(u)), np.cos(v)).reshape((-1, 1)) * 3
tgt_pts = np.hstack((x, y, z))
tgt_pts = _decimate_points(tgt_pts, .05)
# pick some points to fit
some_tgt_pts = tgt_pts[::362]
# rotation only
trans = rotation(1.5, .3, -0.4)
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=0, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation.")
# rotation and translation
trans = np.dot(rotation(0.5, .3, -0.4), translation(.3, .2, -.2))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=True,
scale=0, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and "
"translation.")
# rotation and 1 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.5, 1.5))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=1, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and 1 scaling "
"parameter.")
# rotation and 3 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.7, 1.1))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=3, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and 3 scaling "
"parameters.")
def _deface(t1w, mri_landmarks, deface, trans, raw):
if not has_nibabel(): # pragma: no cover
raise ImportError('This function requires nibabel.')
import nibabel as nib
inset, theta = (20, 35.)
if isinstance(deface, dict):
if 'inset' in deface:
inset = deface['inset']
if 'theta' in deface:
theta = deface['theta']
if not _is_numeric(inset):
raise ValueError('inset must be numeric (float, int). '
'Got %s' % type(inset))
if not _is_numeric(theta):
raise ValueError('theta must be numeric (float, int). '
'Got %s' % type(theta))
if inset < 0:
raise ValueError('inset should be positive, ' 'Got %s' % inset)
if not 0 < theta < 90:
raise ValueError('theta should be between 0 and 90 '
'degrees. Got %s' % theta)
# x: L/R L+, y: S/I I+, z: A/P A+
t1w_data = t1w.get_data().copy()
idxs_vox = np.meshgrid(np.arange(t1w_data.shape[0]),
np.arange(t1w_data.shape[1]),
np.arange(t1w_data.shape[2]),
indexing='ij')
idxs_vox = np.array(idxs_vox) # (3, *t1w_data.shape)
idxs_vox = np.transpose(idxs_vox, [1, 2, 3, 0]) # (*t1w_data.shape, 3)
idxs_vox = idxs_vox.reshape(-1, 3) # (n_voxels, 3)
mri_landmarks_ras = apply_trans(t1w.affine, mri_landmarks)
ras_meg_t = \
get_ras_to_neuromag_trans(*mri_landmarks_ras[[1, 0, 2]])
idxs_ras = apply_trans(t1w.affine, idxs_vox)
idxs_meg = apply_trans(ras_meg_t, idxs_ras)
# now comes the actual defacing
# 1. move center of voxels to (nasion - inset)
# 2. rotate the head by theta from the normal to the plane passing
# through anatomical coordinates
trans_y = -mri_landmarks_ras[1, 1] + inset
idxs_meg = apply_trans(translation(y=trans_y), idxs_meg)
idxs_meg = apply_trans(rotation(x=-np.deg2rad(theta)), idxs_meg)
coords = idxs_meg.reshape(t1w.shape + (3, )) # (*t1w_data.shape, 3)
mask = (coords[..., 2] < 0) # z < 0
t1w_data[mask] = 0.
# smooth decided against for potential lack of anonymizaton
# https://gist.github.com/alexrockhill/15043928b716a432db3a84a050b241ae
t1w = nib.Nifti1Image(t1w_data, t1w.affine, t1w.header)
return t1w
def test_euler(quats):
"""Test euler transformations."""
euler = _quat_to_euler(quats)
quats_2 = _euler_to_quat(euler)
assert_allclose(quats, quats_2, atol=1e-14)
quat_rot = quat_to_rot(quats)
euler_rot = np.array([rotation(*e)[:3, :3] for e in euler])
assert_allclose(quat_rot, euler_rot, atol=1e-14)
def test_rotation():
"""Test conversion between rotation angles and transformation matrix."""
tests = [(0, 0, 1), (.5, .5, .5), (np.pi, 0, -1.5)]
for rot in tests:
x, y, z = rot
m = rotation3d(x, y, z)
m4 = rotation(x, y, z)
assert_array_equal(m, m4[:3, :3])
back = rotation_angles(m)
assert_equal(back, rot)
back4 = rotation_angles(m4)
assert_equal(back4, rot)
def test_rotation():
"""Test conversion between rotation angles and transformation matrix."""
tests = [(0, 0, 1), (.5, .5, .5), (np.pi, 0, -1.5)]
for rot in tests:
x, y, z = rot
m = rotation3d(x, y, z)
m4 = rotation(x, y, z)
assert_array_equal(m, m4[:3, :3])
back = rotation_angles(m)
assert_equal(back, rot)
back4 = rotation_angles(m4)
assert_equal(back4, rot)
def test_get_ras_to_neuromag_trans():
"""Test the coordinate transformation from ras to neuromag"""
# create model points in neuromag-like space
anterior = [0, 1, 0]
left = [-1, 0, 0]
right = [0.8, 0, 0]
up = [0, 0, 1]
rand_pts = np.random.uniform(-1, 1, (3, 3))
pts = np.vstack((anterior, left, right, up, rand_pts))
# change coord system
rx, ry, rz, tx, ty, tz = np.random.uniform(-2 * np.pi, 2 * np.pi, 6)
trans = np.dot(translation(tx, ty, tz), rotation(rx, ry, rz))
pts_changed = apply_trans(trans, pts)
# transform back into original space
nas, lpa, rpa = pts_changed[:3]
hsp_trans = get_ras_to_neuromag_trans(nas, lpa, rpa)
pts_restored = apply_trans(hsp_trans, pts_changed)
err = "Neuromag transformation failed"
assert_array_almost_equal(pts_restored, pts, 6, err)
def test_get_ras_to_neuromag_trans():
"""Test the coordinate transformation from ras to neuromag"""
# create model points in neuromag-like space
anterior = [0, 1, 0]
left = [-1, 0, 0]
right = [.8, 0, 0]
up = [0, 0, 1]
rand_pts = np.random.uniform(-1, 1, (3, 3))
pts = np.vstack((anterior, left, right, up, rand_pts))
# change coord system
rx, ry, rz, tx, ty, tz = np.random.uniform(-2 * np.pi, 2 * np.pi, 6)
trans = np.dot(translation(tx, ty, tz), rotation(rx, ry, rz))
pts_changed = apply_trans(trans, pts)
# transform back into original space
nas, lpa, rpa = pts_changed[:3]
hsp_trans = get_ras_to_neuromag_trans(nas, lpa, rpa)
pts_restored = apply_trans(hsp_trans, pts_changed)
err = "Neuromag transformation failed"
assert_array_almost_equal(pts_restored, pts, 6, err)
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_equal(trans_est.from_str, trans.from_str)
assert_equal(trans_est.to_str, trans.to_str)
assert_array_almost_equal(trans_est['trans'], trans['trans'])
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'])
def test_fit_sphere_to_headshape():
"""Test fitting a sphere to digitization points"""
# Create points of various kinds
dig = [
# Left auricular
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'ident': FIFF.FIFFV_POINT_LPA,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'r': np.array([-1.0, 0.0, 0.0])
},
# Nasion
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'ident': FIFF.FIFFV_POINT_NASION,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'r': np.array([0.0, 1.0, 0.0])
},
# Right auricular
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'ident': FIFF.FIFFV_POINT_RPA,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'r': np.array([1.0, 0.0, 0.0])
},
# Top of the head (extra point)
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EXTRA,
'r': np.array([0.0, 0.0, 1.0])
},
# EEG points
# Fz
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([0, .72, .69])
},
# F3
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([-.55, .67, .50])
},
# F4
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([.55, .67, .50])
},
# Cz
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([0.0, 0.0, 1.0])
},
# Pz
{
'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([0, -.72, .69])
},
]
# Device to head transformation (rotate .2 rad over X-axis)
dev_head_t = {
'from': FIFF.FIFFV_COORD_DEVICE,
'to': FIFF.FIFFV_COORD_HEAD,
'trans': rotation(x=0.2),
}
info = {'dig': dig, 'dev_head_t': dev_head_t}
# # Test with 4 points that match a perfect sphere
dig_kinds = (FIFF.FIFFV_POINT_CARDINAL, FIFF.FIFFV_POINT_EXTRA)
r, oh, od = fit_sphere_to_headshape(info, dig_kinds=dig_kinds)
assert_almost_equal(r / 1000, 1.0, decimal=10)
assert_almost_equal(oh / 1000, [0.0, 0.0, 0.0], decimal=10)
assert_almost_equal(od / 1000, [0.0, 0.0, 0.0], decimal=10)
# Test with all points. Digitization points are no longer perfect, so
# allow for a wider margin of error.
dig_kinds = (FIFF.FIFFV_POINT_CARDINAL, FIFF.FIFFV_POINT_EXTRA,
FIFF.FIFFV_POINT_EXTRA)
r, oh, od = fit_sphere_to_headshape(info, dig_kinds=dig_kinds)
assert_almost_equal(r / 1000, 1.0, decimal=3)
assert_almost_equal(oh / 1000, [0.0, 0.0, 0.0], decimal=3)
assert_almost_equal(od / 1000, [0.0, 0.0, 0.0], decimal=3)
# Test with some noisy EEG points only.
dig_kinds = (FIFF.FIFFV_POINT_EEG, )
r, oh, od = fit_sphere_to_headshape(info, dig_kinds=dig_kinds)
assert_almost_equal(r / 1000, 1.0, decimal=2)
assert_almost_equal(oh / 1000, [0.0, 0.0, 0.0], decimal=2)
assert_almost_equal(od / 1000, [0.0, 0.0, 0.0], decimal=2)
def test_fit_sphere_to_headshape():
""" Test fitting a sphere to digitization points. """
# Create points of various kinds
dig = [
# Left auricular
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'ident': FIFF.FIFFV_POINT_LPA,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'r': np.array([-1.0, 0.0, 0.0])},
# Nasion
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'ident': FIFF.FIFFV_POINT_NASION,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'r': np.array([0.0, 1.0, 0.0])},
# Right auricular
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'ident': FIFF.FIFFV_POINT_RPA,
'kind': FIFF.FIFFV_POINT_CARDINAL,
'r': np.array([1.0, 0.0, 0.0])},
# Top of the head (extra point)
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EXTRA,
'r': np.array([0.0, 0.0, 1.0])},
# EEG points
# Fz
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([0, .72, .69])},
# F3
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([-.55, .67, .50])},
# F4
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([.55, .67, .50])},
# Cz
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([0.0, 0.0, 1.0])},
# Pz
{'coord_frame': FIFF.FIFFV_COORD_DEVICE,
'kind': FIFF.FIFFV_POINT_EEG,
'r': np.array([0, -.72, .69])},
]
# Device to head transformation (rotate .2 rad over X-axis)
dev_head_t = {
'from': FIFF.FIFFV_COORD_DEVICE,
'to': FIFF.FIFFV_COORD_HEAD,
'trans': rotation(x=0.2),
}
info = {'dig': dig, 'dev_head_t': dev_head_t}
# # Test with 4 points that match a perfect sphere
dig_kinds = (FIFF.FIFFV_POINT_CARDINAL, FIFF.FIFFV_POINT_EXTRA)
r, oh, od = fit_sphere_to_headshape(info, dig_kinds=dig_kinds,
verbose=logging.ERROR)
assert_almost_equal(r / 1000, 1.0, decimal=10)
assert_almost_equal(oh / 1000, [0.0, 0.0, 0.0], decimal=10)
assert_almost_equal(od / 1000, [0.0, 0.0, 0.0], decimal=10)
# Test with all points. Digitization points are no longer perfect, so
# allow for a wider margin of error.
dig_kinds = (FIFF.FIFFV_POINT_CARDINAL, FIFF.FIFFV_POINT_EXTRA,
FIFF.FIFFV_POINT_EXTRA)
r, oh, od = fit_sphere_to_headshape(info, dig_kinds=dig_kinds,
verbose=logging.ERROR)
assert_almost_equal(r / 1000, 1.0, decimal=3)
assert_almost_equal(oh / 1000, [0.0, 0.0, 0.0], decimal=3)
assert_almost_equal(od / 1000, [0.0, 0.0, 0.0], decimal=3)
# Test with some noisy EEG points only.
dig_kinds = (FIFF.FIFFV_POINT_EEG,)
r, oh, od = fit_sphere_to_headshape(info, dig_kinds=dig_kinds,
verbose=logging.ERROR)
assert_almost_equal(r / 1000, 1.0, decimal=2)
assert_almost_equal(oh / 1000, [0.0, 0.0, 0.0], decimal=2)
assert_almost_equal(od / 1000, [0.0, 0.0, 0.0], decimal=2)
def test_scale_mri_xfm(tmp_path, few_surfaces, subjects_dir_tmp_few):
"""Test scale_mri transforms and MRI scaling."""
# scale fsaverage
tempdir = str(subjects_dir_tmp_few)
sample_dir = subjects_dir_tmp_few / 'sample'
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)
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
if subject_from == 'sample': # support for not needing all surf files
os.remove(op.join(sample_dir, 'surf', 'lh.curv'))
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
# Check head_to_mni (the `trans` here does not really matter)
trans = rotation(0.001, 0.002, 0.003) @ translation(0.01, 0.02, 0.03)
trans = Transform('head', 'mri', trans)
pos_head_from = np.random.RandomState(0).randn(4, 3)
pos_mni_from = mne.head_to_mni(pos_head_from, subject_from, trans,
tempdir)
pos_mri_from = apply_trans(trans, pos_head_from)
pos_mri = pos_mri_from * scale
pos_head = apply_trans(invert_transform(trans), pos_mri)
pos_mni = mne.head_to_mni(pos_head, subject_to, trans, tempdir)
assert_allclose(pos_mni, pos_mni_from, atol=1e-3)
