def test_process(self):
"""Test that the RecursiveFilter plugin returns the correct data"""
plugin = RecursiveFilter(alpha_x=self.alpha_x, alpha_y=self.alpha_y,
iterations=self.iterations)
result = plugin.process(self.cube, alphas_x=None, alphas_y=None)
expected = 0.13382206
self.assertAlmostEqual(result.data[0][2][2], expected)
def test_return_type(self):
"""Test that the RecursiveFilter plugin returns an iris.cube.Cube."""
plugin = RecursiveFilter(alpha_x=self.alpha_x,
alpha_y=self.alpha_y,
iterations=self.iterations)
result = plugin.process(self.cube, alphas_x=None, alphas_y=None)
self.assertIsInstance(result, Cube)
def test_alpha_floats_nan_in_data(self):
"""Test that the RecursiveFilter plugin returns the correct data
when using float alpha values and the data contains nans."""
plugin = RecursiveFilter(alpha_x=self.alpha_x, alpha_y=self.alpha_y,
iterations=self.iterations)
self.cube.data[0][3][2] = np.nan
result = plugin.process(self.cube, alphas_x=None, alphas_y=None)
expected = 0.11979733
self.assertAlmostEqual(result.data[0][2][2], expected)
def test_dimensions_of_output_array_is_as_expected(self):
"""Test that the RecursiveFilter plugin returns a data array with
the correct dimensions"""
plugin = RecursiveFilter(alpha_x=self.alpha_x, alpha_y=self.alpha_y,
iterations=self.iterations)
result = plugin.process(self.cube, alphas_x=None, alphas_y=None)
# Output data array should have same dimensions as input data array
expected_shape = (1, 5, 5)
self.assertEqual(result.data.shape, expected_shape)
self.assertEqual(result.data.shape, expected_shape)
def test_alpha_cubes_masked_data(self):
"""Test that the RecursiveFilter plugin returns the correct data
when using alpha cubes and a masked data cube."""
plugin = RecursiveFilter(alpha_x=None, alpha_y=None,
iterations=self.iterations)
mask = np.zeros((self.cube.data.shape))
mask[0][3][2] = 1
self.cube.data = np.ma.MaskedArray(self.cube.data, mask=mask)
result = plugin.process(self.cube, alphas_x=self.alphas_cube,
alphas_y=self.alphas_cube)
expected = 0.11979733
self.assertAlmostEqual(result.data[0][2][2], expected)
def test_alpha_floats_nan_in_masked_data(self):
"""Test that the RecursiveFilter plugin returns the correct data
when using float alpha values, the data contains nans and the data
is masked (but not the nan value)."""
plugin = RecursiveFilter(alpha_x=self.alpha_x, alpha_y=self.alpha_y,
iterations=self.iterations)
self.cube.data[0][3][2] = np.nan
mask = np.zeros((self.cube.data.shape))
mask[0][1][2] = 1
self.cube.data = np.ma.MaskedArray(self.cube.data, mask=mask)
result = plugin.process(self.cube, alphas_x=None, alphas_y=None)
expected = 0.105854129
self.assertAlmostEqual(result.data[0][2][2], expected)
def test_coordinate_reordering_with_different_alphas(self):
"""Test that x and y alphas still apply to the right coordinate when
the input cube spatial dimensions are (x, y) not (y, x)"""
alpha_y = 0.5*self.alpha_x
cube = enforce_coordinate_ordering(self.cube,
["time", "longitude", "latitude"])
plugin = RecursiveFilter(alpha_x=self.alpha_x, alpha_y=alpha_y,
iterations=self.iterations)
result = plugin.process(cube)
expected_result = np.array(
[[0.01620921, 0.03978802, 0.10592333, 0.03978982, 0.01621686],
[0.02866841, 0.06457599, 0.15184643, 0.06457873, 0.02868005],
[0.05077430, 0.10290188, 0.19869247, 0.10290585, 0.05079120],
[0.02881413, 0.06486591, 0.15238355, 0.06486866, 0.02882582],
[0.01657352, 0.04051282, 0.10726611, 0.04051464, 0.01658128]])
self.assertSequenceEqual([x.name() for x in result.coords()],
["time", "longitude", "latitude"])
self.assertArrayAlmostEqual(result.data[0], expected_result)
