class Test_process(IrisTest):
"""Test process method"""
def setUp(self):
"""
Set up a basic cube and linear weights cube for the process
method. Input cube has 2 thresholds on and 3
forecast_reference_times
"""
thresholds = [10, 20]
data = np.ones((2, 2, 3), dtype=np.float32)
cycle1 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 0, 0),
)
cycle2 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 1, 0),
)
cycle3 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 2, 0),
)
self.cube_to_collapse = CubeList([cycle1, cycle2, cycle3]).merge_cube()
self.cube_to_collapse = squeeze(self.cube_to_collapse)
self.cube_to_collapse.rename("weights")
# This input array has 3 forecast reference times and 2 thresholds.
# The two thresholds have the same weights.
self.cube_to_collapse.data = np.array(
[[[[1, 0, 1], [1, 1, 1]], [[1, 0, 1], [1, 1, 1]]],
[[[0, 0, 1], [0, 1, 1]], [[0, 0, 1], [0, 1, 1]]],
[[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1]]]],
dtype=np.float32)
self.cube_to_collapse.data = np.ma.masked_equal(
self.cube_to_collapse.data, 0)
# Create a one_dimensional weights cube by slicing the larger
# weights cube.
# The resulting cube only has a forecast_reference_time coordinate.
self.one_dimensional_weights_cube = self.cube_to_collapse[:, 0, 0, 0]
self.one_dimensional_weights_cube.remove_coord(
"projection_x_coordinate")
self.one_dimensional_weights_cube.remove_coord(
"projection_y_coordinate")
self.one_dimensional_weights_cube.remove_coord(
find_threshold_coordinate(self.one_dimensional_weights_cube))
self.one_dimensional_weights_cube.data = np.array([0.2, 0.5, 0.3],
dtype=np.float32)
self.plugin = SpatiallyVaryingWeightsFromMask(fuzzy_length=4)
@ManageWarnings(record=True)
def test_none_masked(self, warning_list=None):
"""Test when we have no masked data in the input cube."""
self.cube_to_collapse.data = np.ones(self.cube_to_collapse.data.shape)
self.cube_to_collapse.data = np.ma.masked_equal(
self.cube_to_collapse.data, 0)
expected_data = np.array([[[0.2, 0.2, 0.2], [0.2, 0.2, 0.2]],
[[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]],
[[0.3, 0.3, 0.3], [0.3, 0.3, 0.3]]],
dtype=np.float32)
message = ("Input cube to SpatiallyVaryingWeightsFromMask "
"must be masked")
result = self.plugin.process(self.cube_to_collapse,
self.one_dimensional_weights_cube,
"forecast_reference_time")
self.assertTrue(any(message in str(item) for item in warning_list))
self.assertArrayEqual(result.data, expected_data)
self.assertEqual(result.dtype, np.float32)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_all_masked(self):
"""Test when we have all masked data in the input cube."""
self.cube_to_collapse.data = np.ones(self.cube_to_collapse.data.shape)
self.cube_to_collapse.data = np.ma.masked_equal(
self.cube_to_collapse.data, 1)
result = self.plugin.process(self.cube_to_collapse,
self.one_dimensional_weights_cube,
"forecast_reference_time")
expected_data = np.zeros((3, 2, 3))
self.assertArrayAlmostEqual(expected_data, result.data)
self.assertTrue(result.metadata, self.cube_to_collapse.data)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_no_fuzziness_no_one_dimensional_weights(self):
"""Test a simple case where we have no fuzziness in the spatial
weights and no adjustment from the one_dimensional weights."""
plugin = SpatiallyVaryingWeightsFromMask(fuzzy_length=1)
self.one_dimensional_weights_cube.data = np.ones((3))
expected_result = np.array(
[[[0.5, 0., 0.33333333], [0.5, 0.33333333, 0.33333333]],
[[0., 0., 0.33333333], [0., 0.33333333, 0.33333333]],
[[0.5, 1., 0.33333333], [0.5, 0.33333333, 0.33333333]]],
dtype=np.float32)
result = plugin.process(self.cube_to_collapse,
self.one_dimensional_weights_cube,
"forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_no_fuzziness_no_one_dimensional_weights_transpose(self):
"""Test a simple case where we have no fuzziness in the spatial
weights and no adjustment from the one_dimensional weights and
transpose the input cube."""
plugin = SpatiallyVaryingWeightsFromMask(fuzzy_length=1)
self.one_dimensional_weights_cube.data = np.ones((3))
expected_result = np.array(
[[[0.5, 0., 0.33333333], [0.5, 0.33333333, 0.33333333]],
[[0., 0., 0.33333333], [0., 0.33333333, 0.33333333]],
[[0.5, 1., 0.33333333], [0.5, 0.33333333, 0.33333333]]],
dtype=np.float32)
self.cube_to_collapse.transpose([2, 0, 1, 3])
result = plugin.process(self.cube_to_collapse,
self.one_dimensional_weights_cube,
"forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_no_fuzziness_with_one_dimensional_weights(self):
"""Test a simple case where we have no fuzziness in the spatial
weights and an adjustment from the one_dimensional weights."""
plugin = SpatiallyVaryingWeightsFromMask(fuzzy_length=1)
expected_result = np.array([[[0.4, 0., 0.2], [0.4, 0.2, 0.2]],
[[0., 0., 0.5], [0., 0.5, 0.5]],
[[0.6, 1., 0.3], [0.6, 0.3, 0.3]]],
dtype=np.float32)
result = plugin.process(self.cube_to_collapse,
self.one_dimensional_weights_cube,
"forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_fuzziness_no_one_dimensional_weights(self):
"""Test a simple case where we have some fuzziness in the spatial
weights and no adjustment from the one_dimensional weights."""
plugin = SpatiallyVaryingWeightsFromMask(fuzzy_length=2)
self.one_dimensional_weights_cube.data = np.ones((3))
expected_result = np.array(
[[[0.33333334, 0., 0.25], [0.41421354, 0.25, 0.2928932]],
[[0., 0., 0.25], [0., 0.25, 0.2928932]],
[[0.6666667, 1., 0.5], [0.5857864, 0.5, 0.41421354]]],
dtype=np.float32)
result = plugin.process(self.cube_to_collapse,
self.one_dimensional_weights_cube,
"forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_fuzziness_with_one_dimensional_weights(self):
"""Test a simple case where we have some fuzziness in the spatial
sweights and with adjustment from the one_dimensional weights."""
plugin = SpatiallyVaryingWeightsFromMask(fuzzy_length=2)
expected_result = np.array(
[[[0.25, 0., 0.15384616], [0.32037723, 0.15384616, 0.17789416]],
[[0., 0., 0.3846154], [0., 0.3846154, 0.44473538]],
[[0.75, 1., 0.4615385], [0.6796227, 0.4615385, 0.3773705]]],
dtype=np.float32)
result = plugin.process(self.cube_to_collapse,
self.one_dimensional_weights_cube,
"forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
class Test_process(IrisTest):
"""Test process method"""
def setUp(self):
"""
Set up a basic cube and linear weights cube for the process
method. Input cube has 2 thresholds and 3 forecast_reference_times
"""
thresholds = [10, 20]
data = np.ones((2, 2, 3), dtype=np.float32)
cycle1 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 0, 0),
)
cycle2 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 1, 0),
)
cycle3 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 2, 0),
)
self.cube_to_collapse = CubeList([cycle1, cycle2, cycle3]).merge_cube()
self.cube_to_collapse = squeeze(self.cube_to_collapse)
self.cube_to_collapse.rename("weights")
# This input array has 3 forecast reference times and 2 thresholds.
# The two thresholds have the same weights.
self.cube_to_collapse.data = np.array(
[
[[[1, 0, 1], [1, 1, 1]], [[1, 0, 1], [1, 1, 1]]],
[[[0, 0, 1], [0, 1, 1]], [[0, 0, 1], [0, 1, 1]]],
[[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1]]],
],
dtype=np.float32,
)
self.cube_to_collapse.data = np.ma.masked_equal(
self.cube_to_collapse.data, 0)
# Create a one_dimensional weights cube by slicing the larger
# weights cube.
# The resulting cube only has a forecast_reference_time coordinate.
self.one_dimensional_weights_cube = self.cube_to_collapse[:, 0, 0, 0]
self.one_dimensional_weights_cube.remove_coord(
"projection_x_coordinate")
self.one_dimensional_weights_cube.remove_coord(
"projection_y_coordinate")
self.one_dimensional_weights_cube.remove_coord(
find_threshold_coordinate(self.one_dimensional_weights_cube))
self.one_dimensional_weights_cube.data = np.array([0.2, 0.5, 0.3],
dtype=np.float32)
self.plugin = SpatiallyVaryingWeightsFromMask(
"forecast_reference_time", fuzzy_length=2)
self.plugin_no_fuzzy = SpatiallyVaryingWeightsFromMask(
"forecast_reference_time", fuzzy_length=1)
@ManageWarnings(record=True)
def test_none_masked(self, warning_list=None):
"""Test when we have no masked data in the input cube."""
self.cube_to_collapse.data = np.ones(self.cube_to_collapse.data.shape)
self.cube_to_collapse.data = np.ma.masked_equal(
self.cube_to_collapse.data, 0)
expected_data = np.array(
[
[[0.2, 0.2, 0.2], [0.2, 0.2, 0.2]],
[[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]],
[[0.3, 0.3, 0.3], [0.3, 0.3, 0.3]],
],
dtype=np.float32,
)
message = "Expected masked input"
result = self.plugin.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
self.assertTrue(any(message in str(item) for item in warning_list))
self.assertArrayEqual(result.data, expected_data)
self.assertEqual(result.dtype, np.float32)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_all_masked(self):
"""Test when we have all masked data in the input cube."""
self.cube_to_collapse.data = np.ones(self.cube_to_collapse.data.shape)
self.cube_to_collapse.data = np.ma.masked_equal(
self.cube_to_collapse.data, 1)
result = self.plugin.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
expected_data = np.zeros((3, 2, 3))
self.assertArrayAlmostEqual(expected_data, result.data)
self.assertTrue(result.metadata, self.cube_to_collapse.data)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_no_fuzziness_no_one_dimensional_weights(self):
"""Test a simple case where we have no fuzziness in the spatial
weights and no adjustment from the one_dimensional weights."""
self.one_dimensional_weights_cube.data = np.ones((3))
expected_result = np.array(
[
[[0.5, 0.0, 0.333333], [0.5, 0.333333, 0.333333]],
[[0.0, 0.0, 0.333333], [0.0, 0.333333, 0.333333]],
[[0.5, 1.0, 0.333333], [0.5, 0.333333, 0.333333]],
],
dtype=np.float32,
)
result = self.plugin_no_fuzzy.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_no_fuzziness_no_one_dimensional_weights_transpose(self):
"""Test a simple case where we have no fuzziness in the spatial
weights and no adjustment from the one_dimensional weights and
transpose the input cube."""
self.one_dimensional_weights_cube.data = np.ones((3))
expected_result = np.array(
[
[[0.5, 0.0, 0.333333], [0.5, 0.333333, 0.333333]],
[[0.0, 0.0, 0.333333], [0.0, 0.333333, 0.333333]],
[[0.5, 1.0, 0.333333], [0.5, 0.333333, 0.333333]],
],
dtype=np.float32,
)
self.cube_to_collapse.transpose([2, 0, 1, 3])
result = self.plugin_no_fuzzy.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_no_fuzziness_with_one_dimensional_weights(self):
"""Test a simple case where we have no fuzziness in the spatial
weights and an adjustment from the one_dimensional weights."""
expected_result = np.array(
[
[[0.4, 0.0, 0.2], [0.4, 0.2, 0.2]],
[[0.0, 0.0, 0.5], [0.0, 0.5, 0.5]],
[[0.6, 1.0, 0.3], [0.6, 0.3, 0.3]],
],
dtype=np.float32,
)
result = self.plugin_no_fuzzy.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_fuzziness_no_one_dimensional_weights(self):
"""Test a simple case where we have some fuzziness in the spatial
weights and no adjustment from the one_dimensional weights."""
self.one_dimensional_weights_cube.data = np.ones((3))
expected_result = np.array(
[
[[0.25, 0.0, 0.166667], [0.353553, 0.166667, 0.235702]],
[[0.00, 0.0, 0.166667], [0.000000, 0.166667, 0.235702]],
[[0.75, 1.0, 0.666667], [0.646447, 0.666667, 0.528595]],
],
dtype=np.float32,
)
result = self.plugin.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_fuzziness_with_one_dimensional_weights(self):
"""Test a simple case where we have some fuzziness in the spatial
weights and with adjustment from the one_dimensional weights."""
expected_result = np.array(
[
[[0.2, 0.0, 0.10], [0.282843, 0.10, 0.141421]],
[[0.0, 0.0, 0.25], [0.000000, 0.25, 0.353553]],
[[0.8, 1.0, 0.65], [0.717157, 0.65, 0.505025]],
],
dtype=np.float32,
)
result = self.plugin.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.cube_to_collapse.metadata)
def test_fuzziness_with_unequal_weightings(self):
"""Simulate the case of two models and a nowcast at short lead times: two
unmasked slices with low weights, and one masked slice with high weights"""
self.cube_to_collapse.data[0].mask = np.full_like(
self.cube_to_collapse.data[0], False)
self.one_dimensional_weights_cube.data = np.array([0.025, 1.0, 0.075],
dtype=np.float32)
expected_data = np.array(
[
[[0.25, 0.25, 0.136364], [0.25, 0.136364, 0.0892939]],
[[0.0, 0.0, 0.45454544], [0.0, 0.454545, 0.642824]],
[[0.75, 0.75, 0.409091], [0.75, 0.409091, 0.267882]],
],
dtype=np.float32,
)
result = self.plugin.process(
self.cube_to_collapse,
self.one_dimensional_weights_cube,
)
self.assertArrayAlmostEqual(result.data, expected_data)
class Test_normalised_masked_weights(IrisTest):
"""Test normalised_masked_weights method"""
def setUp(self):
"""Set up a cube with 2 thresholds to test normalisation. We are
testing normalising along the leading dimension in this cube."""
thresholds = [10, 20]
data = np.ones((2, 2, 3), dtype=np.float32)
cycle1 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 0, 0),
)
cycle2 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 1, 0),
)
cycle3 = set_up_probability_cube(
data,
thresholds,
spatial_grid="equalarea",
time=datetime(2017, 11, 10, 4, 0),
frt=datetime(2017, 11, 10, 2, 0),
)
self.spatial_weights_cube = CubeList([cycle1, cycle2,
cycle3]).merge_cube()
self.spatial_weights_cube = squeeze(self.spatial_weights_cube)
self.spatial_weights_cube.rename("weights")
# This input array has 3 forecast reference times and 2 thresholds.
# The two thresholds have the same weights.
self.spatial_weights_cube.data = np.array(
[[[[0.2, 0, 0.2], [0.2, 0, 0.2]], [[0.2, 0, 0.2], [0.2, 0, 0.2]]],
[[[0, 0, 0.5], [0, 0, 0.5]], [[0, 0, 0.5], [0, 0, 0.5]]],
[[[0.3, 0.3, 0.3], [0.3, 0.3, 0.3]],
[[0.3, 0.3, 0.3], [0.3, 0.3, 0.3]]]],
dtype=np.float32)
self.plugin = SpatiallyVaryingWeightsFromMask()
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_basic(self):
"""Test a basic example normalising along forecast_reference_time"""
expected_result = np.array(
[[[[0.4, 0, 0.2], [0.4, 0, 0.2]], [[0.4, 0, 0.2], [0.4, 0, 0.2]]],
[[[0, 0, 0.5], [0, 0, 0.5]], [[0, 0, 0.5], [0, 0, 0.5]]],
[[[0.6, 1.0, 0.3], [0.6, 1.0, 0.3]],
[[0.6, 1.0, 0.3], [0.6, 1.0, 0.3]]]],
dtype=np.float32)
result = self.plugin.normalised_masked_weights(
self.spatial_weights_cube, "forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.spatial_weights_cube.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_less_input_dims(self):
"""Test a smaller input cube"""
expected_result = np.array(
[[[0.4, 0, 0.2], [0.4, 0, 0.2]], [[0, 0, 0.5], [0, 0, 0.5]],
[[0.6, 1.0, 0.3], [0.6, 1.0, 0.3]]],
dtype=np.float32)
result = self.plugin.normalised_masked_weights(
self.spatial_weights_cube[:, 0, :, :], "forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result[:, 0, :, :].metadata,
self.spatial_weights_cube.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_transpose_cube(self):
"""Test the function still works when we transpose the input cube.
Same as test_basic except for transpose."""
expected_result = np.array(
[[[[0.4, 0, 0.2], [0.4, 0, 0.2]], [[0.4, 0, 0.2], [0.4, 0, 0.2]]],
[[[0, 0, 0.5], [0, 0, 0.5]], [[0, 0, 0.5], [0, 0, 0.5]]],
[[[0.6, 1.0, 0.3], [0.6, 1.0, 0.3]],
[[0.6, 1.0, 0.3], [0.6, 1.0, 0.3]]]],
dtype=np.float32)
# The function always puts the blend_coord as a leading dimension.
# The process method will ensure the order of the output dimensions
# matches those in the input.
expected_result = np.transpose(expected_result, axes=[0, 3, 2, 1])
self.spatial_weights_cube.transpose(new_order=[3, 2, 0, 1])
result = self.plugin.normalised_masked_weights(
self.spatial_weights_cube, "forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.spatial_weights_cube.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_already_normalised(self):
"""Test nothing happens if the input data is already normalised."""
self.spatial_weights_cube.data = np.array(
[[[[0.4, 0, 0.2], [0.4, 0, 0.2]], [[0.4, 0, 0.2], [0.4, 0, 0.2]]],
[[[0, 0, 0.5], [0, 0, 0.5]], [[0, 0, 0.5], [0, 0, 0.5]]],
[[[0.6, 1.0, 0.3], [0.6, 1.0, 0.3]],
[[0.6, 1.0, 0.3], [0.6, 1.0, 0.3]]]],
dtype=np.float32)
result = self.plugin.normalised_masked_weights(
self.spatial_weights_cube, "forecast_reference_time")
self.assertArrayAlmostEqual(result.data,
self.spatial_weights_cube.data)
self.assertEqual(result.metadata, self.spatial_weights_cube.metadata)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_weights_sum_to_zero(self):
"""Test all x-y slices have zero weight in the same index.
This case corresponds to the case when all the input fields are
all masked in the same place."""
self.spatial_weights_cube.data[:, :, :, 0] = 0
expected_result = np.array(
[[[[0, 0, 0.2], [0, 0, 0.2]], [[0, 0, 0.2], [0, 0, 0.2]]],
[[[0, 0, 0.5], [0, 0, 0.5]], [[0, 0, 0.5], [0, 0, 0.5]]],
[[[0, 1.0, 0.3], [0, 1.0, 0.3]], [[0, 1.0, 0.3], [0, 1.0, 0.3]]]],
dtype=np.float32)
result = self.plugin.normalised_masked_weights(
self.spatial_weights_cube, "forecast_reference_time")
self.assertArrayAlmostEqual(result.data, expected_result)
self.assertEqual(result.metadata, self.spatial_weights_cube.metadata)
