def test_crop():
# Testing that padding of arrays and cropping of images work symmetrically
shape = (4, 6, 2)
data = np.ones(shape)
padded = _pad_array(data, [3, 2, 4, 4, 5, 7])
padd_nii = Nifti1Image(padded, np.eye(4))
cropped = crop_img(padd_nii, pad=False)
np.testing.assert_equal(cropped.get_data(), data)
def test_resampling_result_axis_permutation():
# Transform real data using easily checkable transformations
# For now: axis permutations
# create a cuboid full of deterministic data, padded with one
# voxel thickness of zeros
core_shape = (3, 5, 4)
core_data = np.arange(np.prod(core_shape)).reshape(core_shape)
full_data_shape = np.array(core_shape) + 2
full_data = np.zeros(full_data_shape)
full_data[[slice(1, 1 + s) for s in core_shape]] = core_data
source_img = Nifti1Image(full_data, np.eye(4))
axis_permutations = [[0, 1, 2],
[1, 0, 2],
[2, 1, 0],
[0, 2, 1]]
# check 3x3 transformation matrix
for ap in axis_permutations:
target_affine = np.eye(3)[ap]
resampled_img = resample_img(source_img,
target_affine=target_affine)
resampled_data = get_data(resampled_img)
what_resampled_data_should_be = full_data.transpose(ap)
assert_array_almost_equal(resampled_data,
what_resampled_data_should_be)
# check 4x4 transformation matrix
offset = np.array([-2, 1, -3])
for ap in axis_permutations:
target_affine = np.eye(4)
target_affine[:3, :3] = np.eye(3)[ap]
target_affine[:3, 3] = offset
resampled_img = resample_img(source_img,
target_affine=target_affine)
resampled_data = get_data(resampled_img)
offset_cropping = np.vstack([-offset[ap][np.newaxis, :],
np.zeros([1, 3])]
).T.ravel().astype(int)
what_resampled_data_should_be = _pad_array(full_data.transpose(ap),
list(offset_cropping))
assert_array_almost_equal(resampled_data,
what_resampled_data_should_be)
