def test_signed_fit_transform_plot():
SignedDistanceFiltration().fit_transform_plot(images_2D, sample=0)
def test_signed_transform(n_iterations, images, expected):
signed = SignedDistanceFiltration(n_iterations=n_iterations)
assert_almost_equal(signed.fit_transform(images), expected)
def test_signed_not_fitted():
signed = SignedDistanceFiltration()
with pytest.raises(NotFittedError):
signed.transform(images_2D)
def test_signed_errors():
n_iterations = 'a'
signed = SignedDistanceFiltration(n_iterations=n_iterations)
with pytest.raises(TypeError):
signed.fit(images_2D)
images_2D = np.stack([np.ones((3, 4)),
np.concatenate([np.ones((3, 2)), np.zeros((3, 2))],
axis=1),
np.zeros((3, 4))], axis=0)
images_3D = np.stack([np.ones((3, 4, 2)),
np.concatenate([np.ones((3, 2, 2)),
np.zeros((3, 2, 2))], axis=1),
np.zeros((3, 4, 2))], axis=0)
@pytest.mark.parametrize("transformer",
[HeightFiltration(), RadialFiltration(),
DilationFiltration(), ErosionFiltration(),
SignedDistanceFiltration(), DensityFiltration()])
def test_invalid_input_shape(transformer):
X = np.ones((1, 1, 1, 1, 1))
with pytest.raises(ValueError, match="Input of `fit`"):
transformer.fit(X)
def test_height_not_fitted():
height = HeightFiltration()
with pytest.raises(NotFittedError):
height.transform(images_2D)
def test_height_errors():
direction = 'a'
height = HeightFiltration(direction=direction)