def test_incorrect_input_1(self):
"""Different shapes."""
f_x = np.zeros((3, 4))
f_y = np.zeros((2, 4))
with pytest.raises(ValueError):
DisplacementField.from_transform(f_x, f_y)
def test_incorrect_input_2(self):
"""Additional dimensions."""
f_x = np.zeros((2, 2, 4))
f_y = np.zeros((2, 2, 4))
with pytest.raises(ValueError):
DisplacementField.from_transform(f_x, f_y)
def get_transform(p, dataset_id, ds_f=1):
"""Get transformation given a fixed coronal section.
Parameters
----------
p : int
Coronal slice coordinate in microns.
dataset_id : int
Id of the section dataset. Used to determine the 3D matrix.
ds_f : int, optional
Downsampling factor. If set to 1 no downsampling takes place. Note that if `ds_f` = 25, then
we obtain the shape (320, 456).
Returns
-------
df : DisplacementField
Displacement field of shape (8000 // `ds_f`, 11400 // `ds_f`, ?) representing reference -> moved transformation.
"""
output_shape = (8000 // ds_f, 11400 // ds_f)
y, x = np.indices(output_shape)
grid = np.stack((x.ravel(), y.ravel())).T
i_list = list(grid[:, 1] * ds_f)
r_list = list(grid[:, 0] * ds_f)
output_grid = np.empty_like(grid)
x_list, y_list, _, _ = pir_to_xy_local_coronal(p,
i_list,
r_list,
dataset_id=dataset_id)
output_grid[:, 1] = y_list
output_grid[:, 0] = x_list
df = DisplacementField.from_transform(
output_grid[:, 0].reshape(output_shape),
output_grid[:, 1].reshape(output_shape))
return df
def test_equivalent(self):
"""Make sure that transform and displacement constructors are equivalent."""
random_state = 10
shape = (3, 4)
np.random.seed(random_state)
delta_x = np.random.randint(10, size=shape)
delta_y = np.random.randint(10, size=shape)
df = DisplacementField(delta_x, delta_y)
df_new = DisplacementField.from_transform(*df.transformation)
atol = 1e-4
assert np.allclose(df.delta_x, df_new.delta_x, atol=atol)
assert np.allclose(df.delta_y, df_new.delta_y, atol=atol)
