def test_mask_cube(self):
"""Test that a NotImplementedError is raised if a mask cube is passed
in when generating percentiles from a circular neighbourhood, as this
option is not supported."""
self.cube.data[2, 2] = 0
radius = 4000.0
msg = "The use of a mask cube with a circular kernel is " "not yet implemented."
with self.assertRaisesRegex(NotImplementedError, msg):
GeneratePercentilesFromACircularNeighbourhood().run(
self.cube, radius, mask_cube=self.cube)
def test_single_point_lat_long(self):
"""Test behaviour for a single grid cell on lat long grid."""
data = np.ones((16, 16), dtype=np.float32)
data[7, 7] = 0
cube = set_up_variable_cube(data, spatial_grid="latlon",)
msg = "Unable to convert from"
radius = 6000.0
with self.assertRaisesRegex(ValueError, msg):
GeneratePercentilesFromACircularNeighbourhood().run(cube, radius)
def test_coord_is_dim_vector(self):
"""Test that the percentile coord is added as the zeroth dimension when
multiple percentiles are used."""
cube = set_up_cube(
zero_point_indices=((0, 0, 2, 2),), num_time_points=1,
num_grid_points=5)
result = (
GeneratePercentilesFromACircularNeighbourhood(
).make_percentile_cube(cube))
self.assertEqual(
result.coord_dims("percentiles_over_neighbourhood")[0], 0)
def test_mask_cube(self):
"""Test that a NotImplementedError is raised, if a mask cube is passed
in when generating percentiles from a circular neighbourhood, as this
option is not supported."""
cube = set_up_cube(zero_point_indices=((0, 0, 2, 2), ),
num_grid_points=5)[0, 0]
radius = 4000.0
msg = "The use of a mask cube with a circular kernel is " "not yet implemented."
with self.assertRaisesRegex(NotImplementedError, msg):
GeneratePercentilesFromACircularNeighbourhood().run(cube,
radius,
mask_cube=cube)
def test_coord_present(self):
"""Test that the percentile coord is added."""
cube = set_up_cube(zero_point_indices=((0, 0, 2, 2), ),
num_time_points=1,
num_grid_points=5)
result = (GeneratePercentilesFromACircularNeighbourhood().
make_percentile_cube(cube))
self.assertIsInstance(result.coord('percentile'), iris.coords.Coord)
self.assertArrayEqual(
result.coord('percentile').points, DEFAULT_PERCENTILES)
self.assertArrayEqual(result[0].data, cube.data)
self.assertDictEqual(cube.metadata._asdict(),
result.metadata._asdict())
def test_number_of_percentiles_equals_number_of_points(self):
"""Test when the number of percentiles is equal to the number of points
used to construct the percentiles."""
expected = np.array(
[[[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.2, 1., 1., 1.],
[1., 1., 0.2, 0.2, 0.2, 1., 1.],
[1., 1., 1., 0.2, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.4, 1., 1., 1.],
[1., 1., 0.4, 0.4, 0.4, 1., 1.],
[1., 1., 1., 0.4, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.6, 1., 1., 1.],
[1., 1., 0.6, 0.6, 0.6, 1., 1.],
[1., 1., 1., 0.6, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.8, 1., 1., 1.],
[1., 1., 0.8, 0.8, 0.8, 1., 1.],
[1., 1., 1., 0.8, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]]]])
cube = set_up_cube(
zero_point_indices=[(0, 0, 3, 3)],
num_grid_points=7)
percentiles = np.array([5, 10, 15, 20, 25])
radius = 2000.
result = (
GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(
cube, radius))
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_almost_edge(self):
"""Test behaviour for a non-zero grid cell quite near the edge."""
cube = set_up_cube(zero_point_indices=[(0, 0, 1, 1)],
num_grid_points=3) # Just within range of the edge.
slice_2d = cube[0, 0, :, :]
expected = np.array([[[0.5, 0.5, 0.5], [0.5, 0.5, 0.5],
[0.5, 0.5, 0.5]],
[[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]],
[[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]])
percentiles = np.array([10, 50, 90])
kernel = np.array([[0., 0., 1., 0., 0.], [0., 1., 1., 1., 0.],
[1., 1., 1., 1., 1.], [0., 1., 1., 1., 0.],
[0., 0., 1., 0., 0.]])
result = (GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).pad_and_unpad_cube(slice_2d, kernel))
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point(self):
"""Test behaviour for a single non-zero grid cell."""
expected = np.array([[[[[1., 1., 1., 1., 1.], [1., 1., 0.4, 1., 1.],
[1., 0.4, 0.4, 0.4, 1.], [1., 1., 0.4, 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]]]])
percentiles = np.array([10, 50, 90])
radius = 2000.
result = (GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(self.cube, radius))
self.assertArrayAlmostEqual(result.data, expected)
def test_point_pair(self):
"""Test behaviour for two nearby non-zero grid cells."""
expected = np.array([[[[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 0., 0., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]]]])
cube = set_up_cube(zero_point_indices=((0, 0, 2, 2), (0, 0, 2, 1)),
num_grid_points=5)
percentiles = np.array([25, 50, 75])
radius = 2000.
result = (GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(cube, radius))
self.assertArrayAlmostEqual(result.data, expected)
def test_multi_point_multitimes(self):
"""Test behaviour for points over multiple times."""
cube = set_up_cube(
zero_point_indices=((0, 0, 2, 2), (0, 1, 2, 1)), num_time_points=2,
num_grid_points=5)
expected = np.array(
[[[[[1., 1., 1., 1., 1.],
[1., 1., 0.4, 1., 1.],
[1., 0.4, 0.4, 0.4, 1.],
[1., 1., 0.4, 1., 1.],
[1., 1., 1., 1., 1.]],
[[1., 1., 1., 1., 1.],
[1., 0.4, 1., 1., 1.],
[0.4, 0.4, 0.4, 1., 1.],
[1., 0.4, 1., 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]],
[[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]],
[[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]]]])
percentiles = np.array([10, 50, 90])
radius = 2000.
result = (
GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(
cube, radius))
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_low_percentiles(self):
"""Test behaviour with low percentiles."""
expected = np.array([[[[[1., 1., 1., 1., 1.], [1., 1., 0.2, 1., 1.],
[1., 0.2, 0.2, 0.2, 1.], [1., 1., 0.2, 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 0.4, 1., 1.],
[1., 0.4, 0.4, 0.4, 1.], [1., 1., 0.4, 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 0.8, 1., 1.],
[1., 0.8, 0.8, 0.8, 1.], [1., 1., 0.8, 1., 1.],
[1., 1., 1., 1., 1.]]]]])
cube = set_up_cube(zero_point_indices=((0, 0, 2, 2), ),
num_time_points=1,
num_grid_points=5)
percentiles = np.array([5, 10, 20])
radius = 2000.
result = (GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(cube, radius))
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_on_corner(self):
"""Test behaviour for a single non-zero grid cell on the corner."""
expected = np.array([[[0., 0.4, 1., 1., 1.], [0.4, 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]],
[[0., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]],
[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]])
cube = set_up_cube(zero_point_indices=[(0, 0, 0, 0)],
num_grid_points=5) # Point is right on the corner.
slice_2d = cube[0, 0, :, :]
percentiles = np.array([10, 50, 90])
kernel = np.array([[0., 1., 0.], [1., 1., 1.], [0., 1., 0.]])
result = (GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).pad_and_unpad_cube(slice_2d, kernel))
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_masked_to_null(self):
"""Test behaviour with a masked non-zero point.
The behaviour here is not right, as the mask is ignored.
This comes directly from the numpy.percentile base
behaviour."""
expected = np.array(
[
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 0.4, 0.4, 0.4, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
]
)
self.cube.data[2, 2] = 0
mask = np.zeros_like(self.cube.data)
mask[2, 2] = 1
self.cube.data = np.ma.masked_array(self.cube.data, mask=mask)
percentiles = np.array([10, 50, 90])
radius = 2000.0
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles
).run(self.cube, radius)
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_masked_other_point(self):
"""Test behaviour with a non-zero point next to a masked point.
The behaviour here is not right, as the mask is ignored."""
expected = np.array([[[[[1., 1., 1., 1., 1.], [1., 1., 0.4, 1., 1.],
[1., 0.4, 0.4, 0.4, 1.], [1., 1., 0.4, 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]]]]])
cube = self.cube
mask = np.zeros_like(cube.data)
mask[0, 0, 2, 3] = 1
cube.data = np.ma.masked_array(cube.data, mask=mask)
percentiles = np.array([10, 50, 90])
radius = 2000.
result = (GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(cube, radius))
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_on_corner(self):
"""Test behaviour for a single non-zero grid cell on the corner."""
expected = np.array(
[
[
[0.0, 0.4, 1.0, 1.0, 1.0],
[0.4, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[0.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
]
)
self.cube.data[0, 0] = 0
# Point is right on the corner.
percentiles = np.array([10, 50, 90])
kernel = np.array([[0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0]])
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles
).pad_and_unpad_cube(self.cube, kernel)
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point(self):
"""Test behaviour for a single non-zero grid cell."""
expected = np.array([
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 0.4, 0.4, 0.4, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
])
data = np.ones((5, 5), dtype=np.float32)
data[2, 2] = 0
cube = set_up_variable_cube(
data,
spatial_grid="equalarea",
)
percentiles = np.array([10, 50, 90])
radius = 2000.0
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(cube, radius)
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_adjacent_edge(self):
"""Test behaviour for a single non-zero grid cell near the edge."""
self.cube.data[2, 1] = 0
# Range 3 goes over the edge
expected = np.array(
[
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 0.4, 1.0, 1.0, 1.0],
[0.4, 0.4, 0.4, 1.0, 1.0],
[1.0, 0.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
]
)
percentiles = np.array([10, 50, 90])
kernel = np.array([[0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0]])
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles
).pad_and_unpad_cube(self.cube, kernel)
self.assertArrayAlmostEqual(result.data, expected)
def test_point_pair(self):
"""Test behaviour for two nearby non-zero grid cells."""
expected = np.array(
[
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 0.0, 0.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
]
)
self.cube.data[2, 2] = 0
self.cube.data[2, 1] = 0
percentiles = np.array([25, 50, 75])
radius = 2000.0
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles
).run(self.cube, radius)
self.assertArrayAlmostEqual(result.data, expected)
def test_single_point_low_percentiles(self):
"""Test behaviour with low percentiles."""
expected = np.array(
[
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.2, 1.0, 1.0],
[1.0, 0.2, 0.2, 0.2, 1.0],
[1.0, 1.0, 0.2, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 0.4, 0.4, 0.4, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.8, 1.0, 1.0],
[1.0, 0.8, 0.8, 0.8, 1.0],
[1.0, 1.0, 0.8, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
]
)
self.cube.data[2, 2] = 0
percentiles = np.array([5, 10, 20])
radius = 2000.0
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles
).run(self.cube, radius)
self.assertArrayAlmostEqual(result.data, expected)
def test_number_of_points_half_of_number_of_percentiles(self):
"""Test when the number of points is half the number of percentiles."""
expected = np.array([
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.1, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.1, 0.1, 0.1, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.1, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.2, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.2, 0.2, 0.2, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.2, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.3, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.3, 0.3, 0.3, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.3, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.4, 0.4, 0.4, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.5, 0.5, 0.5, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.6, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.6, 0.6, 0.6, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.6, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.7, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.7, 0.7, 0.7, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.7, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.8, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.8, 0.8, 0.8, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.8, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.9, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.9, 0.9, 0.9, 1.0, 1.0],
[1.0, 1.0, 1.0, 0.9, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
],
])
data = np.ones((7, 7), dtype=np.float32)
data[3, 3] = 0
cube = set_up_variable_cube(
data,
spatial_grid="equalarea",
)
percentiles = np.array([2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25])
radius = 2000.0
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(cube, radius)
self.assertArrayAlmostEqual(result.data, expected)
def test_basic(self):
"""Test that the __repr__ returns the expected string."""
result = str(GeneratePercentilesFromACircularNeighbourhood())
msg = ('<GeneratePercentilesFromACircularNeighbourhood: '
'percentiles: {}>'.format(DEFAULT_PERCENTILES))
self.assertEqual(str(result), msg)
def test_number_of_points_half_of_number_of_percentiles(self):
"""Test when the number of points is half the number of percentiles."""
expected = np.array([[[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.1, 1., 1., 1.],
[1., 1., 0.1, 0.1, 0.1, 1., 1.],
[1., 1., 1., 0.1, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.2, 1., 1., 1.],
[1., 1., 0.2, 0.2, 0.2, 1., 1.],
[1., 1., 1., 0.2, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.3, 1., 1., 1.],
[1., 1., 0.3, 0.3, 0.3, 1., 1.],
[1., 1., 1., 0.3, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.4, 1., 1., 1.],
[1., 1., 0.4, 0.4, 0.4, 1., 1.],
[1., 1., 1., 0.4, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.5, 1., 1., 1.],
[1., 1., 0.5, 0.5, 0.5, 1., 1.],
[1., 1., 1., 0.5, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.6, 1., 1., 1.],
[1., 1., 0.6, 0.6, 0.6, 1., 1.],
[1., 1., 1., 0.6, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.7, 1., 1., 1.],
[1., 1., 0.7, 0.7, 0.7, 1., 1.],
[1., 1., 1., 0.7, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.8, 1., 1., 1.],
[1., 1., 0.8, 0.8, 0.8, 1., 1.],
[1., 1., 1., 0.8, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 0.9, 1., 1., 1.],
[1., 1., 0.9, 0.9, 0.9, 1., 1.],
[1., 1., 1., 0.9, 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]],
[[[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.]]]]])
cube = set_up_cube(zero_point_indices=[(0, 0, 3, 3)],
num_grid_points=7)
percentiles = np.array([2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25])
radius = 2000.
result = (GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(cube, radius))
self.assertArrayAlmostEqual(result.data, expected)
def test_multi_point_multitimes(self):
"""Test behaviour for points over multiple times."""
data = np.ones((5, 5), dtype=np.float32)
cube = set_up_variable_cube(
data,
spatial_grid="equalarea",
)
time_points = [
datetime(2017, 11, 10, 2),
datetime(2017, 11, 10, 3),
]
cube = add_coordinate(
cube,
coord_points=time_points,
coord_name="time",
is_datetime="true",
)
cube.data[0, 2, 2] = 0
cube.data[1, 2, 1] = 0
expected = np.array([
[
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 0.4, 0.4, 0.4, 1.0],
[1.0, 1.0, 0.4, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 0.4, 1.0, 1.0, 1.0],
[0.4, 0.4, 0.4, 1.0, 1.0],
[1.0, 0.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
],
[
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
],
[
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
],
])
percentiles = np.array([10, 50, 90])
radius = 2000.0
result = GeneratePercentilesFromACircularNeighbourhood(
percentiles=percentiles).run(cube, radius)
self.assertArrayAlmostEqual(result.data, expected)
def test_basic(self):
"""Test that the plugin returns an iris.cube.Cube."""
result = GeneratePercentilesFromACircularNeighbourhood(
).make_percentile_cube(self.cube)
self.assertIsInstance(result, Cube)
