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 _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)