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tests/unit/image/test_spatial_smoothing_sparse_aware.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,118 +1,115 @@ | ||
| import unittest | ||
| import warnings | ||
| from datetime import timedelta | ||
| from functools import cached_property | ||
|
|
||
| import hypothesis | ||
| import numpy as np | ||
| from hypothesis import given, strategies, settings | ||
| import pytest | ||
| from hypothesis import given, strategies | ||
| from sparse import GCXS | ||
| from xarray import DataArray | ||
|
|
||
| from depiction.image.spatial_smoothing_sparse_aware import SpatialSmoothingSparseAware | ||
|
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|
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| def _treat_warnings_as_error(test_case) -> None: | ||
| """To be called from setUp.""" | ||
| # TODO reuse or refactor (and move out of integration_test_utils and rather a more general test_utils?) | ||
| warnings_ctx = warnings.catch_warnings() | ||
| warnings_ctx.__enter__() | ||
| warnings.simplefilter("error") | ||
| test_case.addCleanup(warnings_ctx.__exit__, None, None, None) | ||
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|
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| class TestSpatialSmoothingSparseAware(unittest.TestCase): | ||
| def setUp(self) -> None: | ||
| self.mock_kernel_size = 5 | ||
| self.mock_kernel_std = 1.0 | ||
| self.mock_use_interpolation = False | ||
| _treat_warnings_as_error(self) | ||
|
|
||
| @cached_property | ||
| def mock_smooth(self) -> SpatialSmoothingSparseAware: | ||
| return SpatialSmoothingSparseAware( | ||
| kernel_size=self.mock_kernel_size, | ||
| kernel_std=self.mock_kernel_std, | ||
| use_interpolation=self.mock_use_interpolation, | ||
| ) | ||
|
|
||
| def _convert_array(self, arr: DataArray, variant: str) -> DataArray: | ||
| # TODO duplicated with SpatialSmoothing test | ||
| if variant == "dense": | ||
| return arr | ||
| elif variant == "sparse": | ||
| values = GCXS.from_numpy(arr) | ||
| return DataArray(values, dims=arr.dims, coords=arr.coords, attrs=arr.attrs, name=arr.name) | ||
|
|
||
| def _test_variants(self, *variants, subtest=True): | ||
| # TODO duplicated with SpatialSmoothing test (but this one is extended) | ||
| for variant in variants: | ||
| if subtest: | ||
| with self.subTest(variant=variant): | ||
| yield variant | ||
| else: | ||
| yield variant | ||
|
|
||
| def test_smooth_when_unchanged(self) -> None: | ||
| for variant in self._test_variants("dense", "sparse"): | ||
| image = DataArray(np.concatenate([np.ones((5, 5, 1)), np.zeros((5, 5, 1))], axis=0), dims=("y", "x", "c")) | ||
| image = self._convert_array(image, variant) | ||
| smoothed = self.mock_smooth.smooth(image, bg_value=0) | ||
| np.testing.assert_array_almost_equal(1.0, smoothed.values[:5, :, 0], decimal=8) | ||
| np.testing.assert_array_almost_equal(0.0, smoothed.values[-5:, :, 0], decimal=8) | ||
| self.assertEqual(("y", "x", "c"), smoothed.dims) | ||
|
|
||
| @given(strategies.floats(min_value=0, max_value=1e12, allow_subnormal=False)) | ||
| @settings(deadline=timedelta(seconds=1)) | ||
| def test_smooth_preserves_values(self, fill_value) -> None: | ||
| for variant in self._test_variants("dense", "sparse", subtest=False): | ||
| dense_image = DataArray(np.full((2, 5, 1), fill_value=fill_value), dims=("y", "x", "c")) | ||
| image = self._convert_array(dense_image, variant) | ||
| smoothed = self.mock_smooth.smooth(image, bg_value=0) | ||
| np.testing.assert_allclose(dense_image.values, smoothed.values, rtol=1e-8) | ||
|
|
||
| def test_smooth_when_bg_nan(self) -> None: | ||
| for variant in self._test_variants("dense", "sparse"): | ||
| dense_image = DataArray( | ||
| np.concatenate([np.full((5, 5, 1), np.nan), np.ones((5, 5, 1)), np.zeros((5, 5, 1))]), | ||
| dims=("y", "x", "c"), | ||
| ) | ||
| image = self._convert_array(dense_image, variant) | ||
| smoothed = self.mock_smooth.smooth(image, bg_value=np.nan) | ||
| np.testing.assert_array_equal(np.nan, smoothed.values[:5, 0]) | ||
| np.testing.assert_array_almost_equal(1.0, smoothed.values[5:8, :, 0], decimal=8) | ||
| np.testing.assert_array_almost_equal(0.0, smoothed.values[-3:, :, 0], decimal=8) | ||
| smoothed_part = smoothed.values[8:12, :, 0] | ||
| for i_value, value in enumerate([0.94551132, 0.70130997, 0.29869003, 0.05448868]): | ||
| np.testing.assert_array_almost_equal(value, smoothed_part[i_value, :], decimal=8) | ||
|
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||
| def test_smooth_casts_when_integer(self) -> None: | ||
| for variant in self._test_variants("dense", "sparse"): | ||
| image_dense = DataArray(np.full((2, 5, 1), fill_value=10, dtype=int), dims=("y", "x", "c")) | ||
| image = self._convert_array(image_dense, variant) | ||
| res_values = self.mock_smooth.smooth(image=image) | ||
| self.assertEqual(np.float64, res_values.dtype) | ||
| np.testing.assert_allclose(image_dense.values, res_values.values, rtol=1e-8) | ||
|
|
||
| def test_smooth_dense_when_use_interpolation(self) -> None: | ||
| self.mock_use_interpolation = True | ||
| mock_image = np.full((9, 5), fill_value=3.0) | ||
| mock_image[4, 2] = np.nan | ||
| smoothed = self.mock_smooth._smooth_dense(image_2d=mock_image, bg_value=np.nan) | ||
| self.assertEqual(0, np.sum(np.isnan(smoothed))) | ||
| self.assertAlmostEqual(3, smoothed[4, 2], places=6) | ||
|
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||
| def test_gaussian_kernel(self) -> None: | ||
| self.mock_kernel_size = 3 | ||
| self.mock_kernel_std = 1.0 | ||
| expected_arr = np.array( | ||
| [ | ||
| [0.075114, 0.123841, 0.075114], | ||
| [0.123841, 0.20418, 0.123841], | ||
| [0.075114, 0.123841, 0.075114], | ||
| ] | ||
| ) | ||
| np.testing.assert_allclose(expected_arr, self.mock_smooth.gaussian_kernel, atol=1e-6) | ||
|
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|
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| if __name__ == "__main__": | ||
| unittest.main() | ||
| @pytest.fixture(autouse=True) | ||
| def _setup_test(treat_warnings_as_error): | ||
| pass | ||
|
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|
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| @pytest.fixture | ||
| def mock_kernel_size(): | ||
| return 5 | ||
|
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|
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| @pytest.fixture | ||
| def mock_kernel_std(): | ||
| return 1.0 | ||
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|
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| @pytest.fixture | ||
| def mock_use_interpolation(): | ||
| return False | ||
|
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|
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| @pytest.fixture | ||
| def mock_smooth(mock_kernel_size, mock_kernel_std, mock_use_interpolation): | ||
| return SpatialSmoothingSparseAware( | ||
| kernel_size=mock_kernel_size, | ||
| kernel_std=mock_kernel_std, | ||
| use_interpolation=mock_use_interpolation, | ||
| ) | ||
|
|
||
|
|
||
| def _convert_array(arr: DataArray, variant: str) -> DataArray: | ||
| if variant == "dense": | ||
| return arr | ||
| elif variant == "sparse": | ||
| values = GCXS.from_numpy(arr) | ||
| return DataArray(values, dims=arr.dims, coords=arr.coords, attrs=arr.attrs, name=arr.name) | ||
|
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|
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| @pytest.mark.parametrize("variant", ["dense", "sparse"]) | ||
| def test_smooth_when_unchanged(mock_smooth, variant): | ||
| image = DataArray(np.concatenate([np.ones((5, 5, 1)), np.zeros((5, 5, 1))], axis=0), dims=("y", "x", "c")) | ||
| image = _convert_array(image, variant) | ||
| smoothed = mock_smooth.smooth(image, bg_value=0) | ||
| np.testing.assert_array_almost_equal(1.0, smoothed.values[:5, :, 0], decimal=8) | ||
| np.testing.assert_array_almost_equal(0.0, smoothed.values[-5:, :, 0], decimal=8) | ||
| assert smoothed.dims == ("y", "x", "c") | ||
|
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|
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| @pytest.mark.parametrize("variant", ["dense", "sparse"]) | ||
| @given(fill_value=strategies.floats(min_value=0, max_value=1e12, allow_subnormal=False)) | ||
| @hypothesis.settings( | ||
| deadline=timedelta(seconds=1), suppress_health_check=[hypothesis.HealthCheck.function_scoped_fixture] | ||
| ) | ||
| def test_smooth_preserves_values(mock_smooth, fill_value, variant): | ||
| dense_image = DataArray(np.full((2, 5, 1), fill_value=fill_value), dims=("y", "x", "c")) | ||
| image = _convert_array(dense_image, variant) | ||
| smoothed = mock_smooth.smooth(image, bg_value=0) | ||
| np.testing.assert_allclose(dense_image.values, smoothed.values, rtol=1e-8) | ||
|
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|
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| @pytest.mark.parametrize("variant", ["dense", "sparse"]) | ||
| def test_smooth_when_bg_nan(mock_smooth, variant): | ||
| dense_image = DataArray( | ||
| np.concatenate([np.full((5, 5, 1), np.nan), np.ones((5, 5, 1)), np.zeros((5, 5, 1))]), | ||
| dims=("y", "x", "c"), | ||
| ) | ||
| image = _convert_array(dense_image, variant) | ||
| smoothed = mock_smooth.smooth(image, bg_value=np.nan) | ||
| np.testing.assert_array_equal(np.nan, smoothed.values[:5, 0]) | ||
| np.testing.assert_array_almost_equal(1.0, smoothed.values[5:8, :, 0], decimal=8) | ||
| np.testing.assert_array_almost_equal(0.0, smoothed.values[-3:, :, 0], decimal=8) | ||
| smoothed_part = smoothed.values[8:12, :, 0] | ||
| for i_value, value in enumerate([0.94551132, 0.70130997, 0.29869003, 0.05448868]): | ||
| np.testing.assert_array_almost_equal(value, smoothed_part[i_value, :], decimal=8) | ||
|
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|
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| @pytest.mark.parametrize("variant", ["dense", "sparse"]) | ||
| def test_smooth_casts_when_integer(mock_smooth, variant): | ||
| image_dense = DataArray(np.full((2, 5, 1), fill_value=10, dtype=int), dims=("y", "x", "c")) | ||
| image = _convert_array(image_dense, variant) | ||
| res_values = mock_smooth.smooth(image=image) | ||
| assert res_values.dtype == np.float64 | ||
| np.testing.assert_allclose(image_dense.values, res_values.values, rtol=1e-8) | ||
|
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|
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| @pytest.mark.parametrize("mock_use_interpolation", [True]) | ||
| def test_smooth_dense_when_use_interpolation(mock_smooth): | ||
| mock_image = np.full((9, 5), fill_value=3.0) | ||
| mock_image[4, 2] = np.nan | ||
| smoothed = mock_smooth._smooth_dense(image_2d=mock_image, bg_value=np.nan) | ||
| assert np.sum(np.isnan(smoothed)) == 0 | ||
| np.testing.assert_almost_equal(smoothed[4, 2], 3, decimal=6) | ||
|
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|
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| @pytest.mark.parametrize("mock_kernel_size, mock_kernel_std", [(3, 1.0)]) | ||
| def test_gaussian_kernel(mock_smooth): | ||
| expected_arr = np.array( | ||
| [ | ||
| [0.075114, 0.123841, 0.075114], | ||
| [0.123841, 0.20418, 0.123841], | ||
| [0.075114, 0.123841, 0.075114], | ||
| ] | ||
| ) | ||
| np.testing.assert_allclose(expected_arr, mock_smooth.gaussian_kernel, atol=1e-6) |