def test_add_batch_dim():
add_batch = make_preprocessing([ADD_BATCH_DIM])
data = xr.DataArray(np.arange(18).reshape(2, 3, 3), dims=("c", "x", "y"))
result = add_batch(data)
assert result.shape == (1, 2, 3, 3)
assert result.dims == ("b", "c", "x", "y")
def test_binarize():
binarize_spec = Preprocessing(name="binarize", kwargs={"threshold": 14})
data = xr.DataArray(np.arange(30).reshape(2, 3, 5), dims=("x", "y", "c"))
expected = xr.zeros_like(data)
expected[{"x": slice(1, None)}] = 1
preprocessing = make_preprocessing([binarize_spec])
result = preprocessing(data)
xr.testing.assert_allclose(expected, result)
def test_scale_linear_no_channel():
spec = Preprocessing(name="scale_linear",
kwargs={
"offset": 1,
"gain": 2,
"axes": "yx"
})
data = xr.DataArray(np.arange(6).reshape(2, 3), dims=("x", "y"))
expected = xr.DataArray(np.array([[1, 3, 5], [7, 9, 11]]), dims=("x", "y"))
preprocessing = make_preprocessing([spec])
result = preprocessing(data)
xr.testing.assert_allclose(expected, result)
def test_scale_linear():
spec = Preprocessing(name="scale_linear",
kwargs={
"offset": [1, 2, 42],
"gain": [1, 2, 3],
"axes": "yx"
})
data = xr.DataArray(np.arange(6).reshape(1, 2, 3), dims=("x", "y", "c"))
expected = xr.DataArray(np.array([[[1, 4, 48], [4, 10, 57]]]),
dims=("x", "y", "c"))
preprocessing = make_preprocessing([spec])
result = preprocessing(data)
xr.testing.assert_allclose(expected, result)
def test_zero_mean_unit_variance_preprocessing():
zero_mean_spec = Preprocessing(name="zero_mean_unit_variance", kwargs={})
data = xr.DataArray(np.arange(9).reshape(3, 3), dims=("x", "y"))
expected = xr.DataArray(
np.array([
[-1.54919274, -1.16189455, -0.77459637],
[-0.38729818, 0.0, 0.38729818],
[0.77459637, 1.16189455, 1.54919274],
]),
dims=("x", "y"),
)
preprocessing = make_preprocessing([zero_mean_spec])
result = preprocessing(data)
xr.testing.assert_allclose(expected, result)
def test_combination_of_preprocessing_steps_with_dims_specified():
zero_mean_spec = Preprocessing(name="zero_mean_unit_variance",
kwargs={"axes": ("x", "y")})
data = xr.DataArray(np.arange(18).reshape(2, 3, 3), dims=("c", "x", "y"))
expected = xr.DataArray(
np.array([
[-1.54919274, -1.16189455, -0.77459637],
[-0.38729818, 0.0, 0.38729818],
[0.77459637, 1.16189455, 1.54919274],
]),
dims=("x", "y"),
)
preprocessing = make_preprocessing([ADD_BATCH_DIM, zero_mean_spec])
result = preprocessing(data)
xr.testing.assert_allclose(expected, result[0][0])
def test_unknown_preprocessing_should_raise():
mypreprocessing = Preprocessing(name="mycoolpreprocessing",
kwargs={"axes": ("x", "y")})
with pytest.raises(NotImplementedError):
make_preprocessing([mypreprocessing])