def test_basic(self):
"""
Test that the plugin returns an iris.cube.Cube, the cube has a
realization coordinate and is correctly re-ordered to match the source
realizations.
"""
expected_data = self.raw_cube.data.copy()
result = Plugin().process(self.post_processed_percentiles, self.raw_cube)
self.assertIsInstance(result, Cube)
self.assertTrue(result.coords("realization"))
self.assertArrayEqual(result.coord("realization").points, [0, 1, 2])
self.assertArrayAlmostEqual(result.data, expected_data)
def test_realization_for_equal(self):
"""
Test to check the behaviour when the number of percentiles equals
the number of realizations.
"""
expected_data = np.array([
[[4.0, 4.625, 5.25], [5.875, 6.5, 7.125], [7.75, 8.375, 9.0]],
[[6.0, 6.625, 7.25], [7.875, 8.5, 9.125], [9.75, 10.375, 11.0]],
[[8.0, 8.625, 9.25], [9.875, 10.5, 11.125], [11.75, 12.375, 13.0]],
])
result = Plugin()._recycle_raw_ensemble_realizations(
self.percentile_cube,
self.realization_cube,
self.perc_coord,
)
self.assertIsInstance(result, Cube)
self.assertArrayEqual(result.coord("realization").points, [0, 1, 2])
self.assertArrayAlmostEqual(result.data, expected_data)
def test_realization_for_less_than(self):
"""
Test to check the behaviour when the number of percentiles is
less than the number of realizations.
"""
expected_data = np.array([
[[4.0, 4.625, 5.25], [5.875, 6.5, 7.125], [7.75, 8.375, 9.0]],
[[6.0, 6.625, 7.25], [7.875, 8.5, 9.125], [9.75, 10.375, 11.0]],
])
post_processed_forecast_percentiles = self.percentile_cube[:2, :, :]
result = Plugin()._recycle_raw_ensemble_realizations(
post_processed_forecast_percentiles,
self.realization_cube,
self.perc_coord,
)
self.assertIsInstance(result, Cube)
self.assertArrayEqual(result.coord("realization").points, [0, 1])
self.assertArrayAlmostEqual(result.data, expected_data)
def test_basic_masked_input_data_not_nans(self):
"""
Test that the plugin returns an iris.cube.Cube, the cube has a
realization coordinate with specific realization numbers and is
correctly re-ordered to match the source realizations, when the
input data is masked and the masked data is not a nan.
"""
# Assuming input data and raw ensemble are masked in the same way.
self.raw_cube.data[:, 0, 0] = 1000
self.raw_cube.data = np.ma.masked_equal(self.raw_cube.data, 1000)
self.post_processed_percentiles.data[:, 0, 0] = 1000
self.post_processed_percentiles.data = np.ma.masked_equal(
self.post_processed_percentiles.data, 1000)
expected_data = self.raw_cube.data.copy()
result = Plugin().process(self.post_processed_percentiles,
self.raw_cube)
self.assertIsInstance(result, Cube)
self.assertTrue(result.coords("realization"))
self.assertEqual(result.coord("realization"),
self.raw_cube.coord("realization"))
self.assertArrayAlmostEqual(result.data, expected_data)
self.assertArrayEqual(result.data.mask, expected_data.mask)
