def test_difftodect_com_flip_rot_scale(dim):
lt_ctx = lt.Context(InlineJobExecutor())
data_shape = (2, 2, dim, dim)
data = np.zeros(data_shape)
data[0, 0, 7, 7] = 1
data[0, 1, 7, 8] = 1
data[1, 1, 8, 8] = 1
data[1, 0, 8, 7] = 1
source_shape = data_shape[2:]
target_shape = data_shape[2:]
f = diffraction_to_detector(lamb=1,
diffraction_shape=target_shape,
pixel_size_real=1,
pixel_size_detector=1 /
(np.array(target_shape)) * 4,
cy=source_shape[0] / 2,
cx=source_shape[1] / 2,
flip_y=True,
scan_rotation=-90.)
m = image_transformation_matrix(
source_shape=source_shape,
target_shape=target_shape,
affine_transformation=f,
)
transformed_data = apply_matrix(data, m, target_shape)
ds = lt_ctx.load('memory', data=data, sig_dims=2)
transformed_ds = lt_ctx.load('memory', data=transformed_data, sig_dims=2)
com_a = lt_ctx.create_com_analysis(dataset=ds,
mask_radius=np.inf,
flip_y=True,
scan_rotation=-90.,
cy=target_shape[0] / 2,
cx=target_shape[1] / 2)
com_res = lt_ctx.run(com_a)
trans_com_a = lt_ctx.create_com_analysis(dataset=transformed_ds,
mask_radius=np.inf,
flip_y=False,
scan_rotation=0.,
cy=target_shape[0] / 2,
cx=target_shape[1] / 2)
trans_com_res = lt_ctx.run(trans_com_a)
print(com_res.field.raw_data)
print(trans_com_res.field.raw_data)
assert np.allclose(com_res.field.raw_data,
np.array(trans_com_res.field.raw_data) / 4)
def test_difftodect_identity():
data_shape = np.random.randint(1, 77, 3, dtype=int)
data = np.random.random(data_shape)
source_shape = data_shape[1:]
target_shape = data_shape[1:]
f = diffraction_to_detector(lamb=1,
diffraction_shape=target_shape,
pixel_size_real=1,
pixel_size_detector=1 /
(np.array(target_shape)),
cy=source_shape[0] / 2,
cx=source_shape[1] / 2,
flip_y=False,
scan_rotation=0.)
m = image_transformation_matrix(
source_shape=source_shape,
target_shape=target_shape,
affine_transformation=f,
)
assert np.allclose(data, apply_matrix(data, m, target_shape))