def test_preserve_dimensions_input(self):
"""Test that the dimensions on the output cube are the same as the
input cube, apart from the additional topographic zone coordinate.
"""
self.cube.remove_coord("realization")
cube = add_dimensions_to_cube(
self.cube, OrderedDict([("threshold", 3), ("realization", 4)]))
coord_for_masking = "topographic_zone"
radii = 2000
result = ApplyNeighbourhoodProcessingWithAMask(
coord_for_masking, radii).process(cube, self.mask_cube)
expected_dims = list(cube.dim_coords)
expected_dims.insert(2, self.mask_cube.coord("topographic_zone"))
self.assertEqual(result.dim_coords, tuple(expected_dims))
self.assertEqual(result.coord_dims("realization"), (0,))
self.assertEqual(result.coord_dims("threshold"), (1,))
self.assertEqual(result.coord_dims("topographic_zone"), (2,))
self.assertEqual(result.coord_dims("projection_y_coordinate"), (3,))
self.assertEqual(result.coord_dims("projection_x_coordinate"), (4,))
def test_preserve_dimensions_with_single_point(self):
"""Test that the dimensions on the output cube are the same as the
input cube, apart from the collapsed dimension.
Check that a dimension coordinate with a single point is preserved
and not demoted to a scalar coordinate."""
self.cube.remove_coord("realization")
cube = add_dimensions_to_cube(self.cube,
{"threshold": 4, "realization": 1})
coord_for_masking = "topographic_zone"
radii = 2000
result = ApplyNeighbourhoodProcessingWithAMask(
coord_for_masking, radii).process(cube, self.mask_cube)
expected_dims = list(cube.dim_coords)
expected_dims.insert(2, self.mask_cube.coord("topographic_zone"))
self.assertEqual(result.dim_coords, tuple(expected_dims))
self.assertEqual(result.coord_dims("realization"), (0,))
self.assertEqual(result.coord_dims("threshold"), (1,))
self.assertEqual(result.coord_dims("topographic_zone"), (2,))
self.assertEqual(result.coord_dims("projection_y_coordinate"), (3,))
self.assertEqual(result.coord_dims("projection_x_coordinate"), (4,))
def test_collapse_preserve_dimensions_input(self):
"""Test that the dimensions on the output cube are the same as the
input cube, apart from the additional topographic zone coordinate.
"""
self.cube.remove_coord("realization")
cube = add_dimensions_to_cube(
self.cube, OrderedDict([("threshold", 3), ("realization", 4)])
)
coord_for_masking = "topographic_zone"
radii = 2000
uncollapsed_result = None
mask_data = np.array(
[
[
[1, 1, 1, 1, 1],
[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
[
[0, 0, 0, 0, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[1, 1, 0, 1, 0],
[1, 1, 0, 0, 0],
],
[
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
],
]
)
mask_cube = self.mask_cube.copy(mask_data)
for collapse_weights in [None, self.weights_cube]:
coords_order = [
"realization",
"threshold",
"topographic_zone",
"projection_y_coordinate",
"projection_x_coordinate",
]
result = ApplyNeighbourhoodProcessingWithAMask(
coord_for_masking, radii, collapse_weights=collapse_weights,
)(cube, mask_cube)
expected_dims = list(cube.dim_coords)
if collapse_weights is None:
uncollapsed_result = result
expected_dims.insert(2, self.mask_cube.coord("topographic_zone"))
else:
coords_order.remove("topographic_zone")
collapsed_result = CollapseMaskedNeighbourhoodCoordinate(
coord_for_masking, collapse_weights
)(uncollapsed_result)
self.assertArrayAlmostEqual(result.data, collapsed_result.data)
self.assertEqual(result.dim_coords, tuple(expected_dims))
for dim, coord in enumerate(coords_order):
self.assertEqual(result.coord_dims(coord), (dim,))
