def _check_vol_to_surf_results(img, mesh):
mni_mask = datasets.load_mni152_brain_mask()
for kind, interpolation, mask_img in itertools.product(
['ball', 'line'], ['linear', 'nearest'], [mni_mask, None]):
proj_1 = vol_to_surf(img,
mesh,
kind=kind,
interpolation=interpolation,
mask_img=mask_img)
assert_true(proj_1.ndim == 1)
img_rot = image.resample_img(img,
target_affine=rotation(
np.pi / 3., np.pi / 4.))
proj_2 = vol_to_surf(img_rot,
mesh,
kind=kind,
interpolation=interpolation,
mask_img=mask_img)
# The projection values for the rotated image should be close
# to the projection for the original image
diff = np.abs(proj_1 - proj_2) / np.abs(proj_1)
assert_true(np.mean(diff[diff < np.inf]) < .03)
img_4d = image.concat_imgs([img, img])
proj_4d = vol_to_surf(img_4d,
mesh,
kind=kind,
interpolation=interpolation,
mask_img=mask_img)
nodes, _ = surface.load_surf_mesh(mesh)
assert_array_equal(proj_4d.shape, [nodes.shape[0], 2])
assert_array_almost_equal(proj_4d[:, 0], proj_1, 3)
def test_vol_to_surf(kind, n_scans, use_mask):
img, mask_img = data_gen.generate_mni_space_img(n_scans)
if not use_mask:
mask_img = None
if n_scans == 1:
img = image.new_img_like(img, image.get_data(img).squeeze())
fsaverage = datasets.fetch_surf_fsaverage()
mesh = surface.load_surf_mesh(fsaverage["pial_left"])
inner_mesh = surface.load_surf_mesh(fsaverage["white_left"])
center_mesh = np.mean([mesh[0], inner_mesh[0]], axis=0), mesh[1]
proj = surface.vol_to_surf(
img, mesh, kind="depth", inner_mesh=inner_mesh, mask_img=mask_img)
other_proj = surface.vol_to_surf(
img, center_mesh, kind=kind, mask_img=mask_img)
correlation = pearsonr(proj.ravel(), other_proj.ravel())[0]
assert correlation > .99
with pytest.raises(ValueError, match=".*interpolation.*"):
surface.vol_to_surf(img, mesh, interpolation="bad")
def test_check_mesh():
mesh = surface._check_mesh('fsaverage5')
assert mesh is surface._check_mesh(mesh)
with pytest.raises(ValueError):
surface._check_mesh('fsaverage2')
mesh.pop('pial_left')
with pytest.raises(ValueError):
surface._check_mesh(mesh)
with pytest.raises(TypeError):
surface._check_mesh(surface.load_surf_mesh(mesh['pial_right']))
def _check_vol_to_surf_results(img, mesh):
mni_mask = datasets.load_mni152_brain_mask()
for kind, interpolation, mask_img in itertools.product(
['ball', 'line'], ['linear', 'nearest'], [mni_mask, None]):
proj_1 = vol_to_surf(
img, mesh, kind=kind, interpolation=interpolation,
mask_img=mask_img)
assert_true(proj_1.ndim == 1)
img_rot = image.resample_img(
img, target_affine=rotation(np.pi / 3., np.pi / 4.))
proj_2 = vol_to_surf(
img_rot, mesh, kind=kind, interpolation=interpolation,
mask_img=mask_img)
# The projection values for the rotated image should be close
# to the projection for the original image
diff = np.abs(proj_1 - proj_2) / np.abs(proj_1)
assert_true(np.mean(diff[diff < np.inf]) < .03)
img_4d = image.concat_imgs([img, img])
proj_4d = vol_to_surf(
img_4d, mesh, kind=kind, interpolation=interpolation,
mask_img=mask_img)
nodes, _ = surface.load_surf_mesh(mesh)
assert_array_equal(proj_4d.shape, [nodes.shape[0], 2])
assert_array_almost_equal(proj_4d[:, 0], proj_1, 3)
def test_depth_ball_sampling():
img, *_ = data_gen.generate_mni_space_img()
mesh = surface.load_surf_mesh(datasets.fetch_surf_fsaverage()["pial_left"])
with pytest.raises(ValueError, match=".*does not support.*"):
surface.vol_to_surf(img, mesh, kind="ball", depth=[.5])