def test_missing_coordinate(self):
"""
Basic test that the result is a numpy array with the expected contents.
The array contents is also checked.
"""
cube = self.current_temperature_forecast_cube
plen = len(cube.coord("percentile").points)
msg = "coordinate is not available"
with self.assertRaisesRegex(CoordinateNotFoundError, msg):
restore_non_probabilistic_dimensions(
cube.data, cube, "nonsense", plen)
def test_if_percentile_is_not_first_dimension_coordinate(self):
"""
Test that the result have the expected size for the
probabilistic dimension and is generally of the expected size.
The array contents is also checked.
"""
cube = self.current_temperature_forecast_cube
cube.transpose([3, 2, 1, 0])
plen = len(cube.coord("percentile").points)
msg = "coordinate is a dimension coordinate but is not"
with self.assertRaisesRegex(ValueError, msg):
restore_non_probabilistic_dimensions(
cube.data, cube, "percentile", plen)
def test_percentile_is_dimension_coordinate_multiple_timesteps(self):
"""
Test that the data has been reshaped correctly when multiple timesteps
are in the cube.
The array contents is also checked.
"""
expected = np.array([[[[4., 4.71428571],
[5.42857143, 6.14285714]],
[[6.85714286, 7.57142857],
[8.28571429, 9.]]]])
data = np.tile(np.linspace(5, 10, 8), 3).reshape(3, 2, 2, 2)
data[0] -= 1
data[1] += 1
data[2] += 3
cube = set_up_cube(data, "air_temperature", "degreesC",
timesteps=2, x_dimension_length=2,
y_dimension_length=2)
cube.coord("realization").rename("percentile")
cube.coord("percentile").points = (
np.array([10, 50, 90]))
plen = 1
percentile_cube = (
add_forecast_reference_time_and_forecast_period(
cube, time_point=np.array([402295.0, 402296.0]),
fp_point=[2.0, 3.0]))
reshaped_array = (
restore_non_probabilistic_dimensions(
percentile_cube[0].data, percentile_cube,
"percentile", plen))
self.assertArrayAlmostEqual(reshaped_array, expected)
def test_percentile_is_dimension_coordinate_multiple_timesteps(self):
"""
Test that the data has been reshaped correctly when multiple timesteps
are in the cube. The array contents is also checked.
"""
expected = np.array([[
[[4.0, 4.71428571], [5.42857143, 6.14285714]],
[[6.85714286, 7.57142857], [8.28571429, 9.0]],
]])
data = np.tile(np.linspace(5, 10, 8), 3).reshape(3, 2, 2, 2)
data[0] -= 1
data[1] += 1
data[2] += 3
cubelist = CubeList([])
for i, hour in enumerate([7, 8]):
cubelist.append(
set_up_percentile_cube(
data[:, i, :, :].astype(np.float32),
np.array([10, 50, 90], dtype=np.float32),
units="degC",
time=datetime(2015, 11, 23, hour),
frt=datetime(2015, 11, 23, 6),
))
percentile_cube = cubelist.merge_cube()
percentile_cube.transpose([1, 0, 2, 3])
plen = 1
reshaped_array = restore_non_probabilistic_dimensions(
percentile_cube[0].data, percentile_cube, "percentile", plen)
self.assertArrayAlmostEqual(reshaped_array, expected)
def test_basic(self):
"""
Basic test that the result is a numpy array with the expected contents.
"""
cube = self.current_temperature_forecast_cube
plen = len(cube.coord("percentile").points)
reshaped_array = (restore_non_probabilistic_dimensions(
cube.data, cube, "percentile", plen))
self.assertIsInstance(reshaped_array, np.ndarray)
def test_percentile_is_dimension_coordinate_flattened_data(self):
"""
Test the array size, if a flattened input array is used as the input.
The array contents is also checked.
"""
cube = self.current_temperature_forecast_cube
flattened_data = cube.data.flatten()
plen = len(cube.coord("percentile").points)
reshaped_array = (restore_non_probabilistic_dimensions(
flattened_data, cube, "percentile", plen))
self.assertEqual(reshaped_array.shape[0], plen)
self.assertEqual(reshaped_array.shape, (3, 1, 3, 3))
self.assertArrayAlmostEqual(reshaped_array, cube.data)
def test_percentile_is_dimension_coordinate(self):
"""
Test that the result have the expected size for the
probabilistic dimension and is generally of the expected size.
The array contents is also checked.
"""
cube = self.current_temperature_forecast_cube
plen = len(cube.coord("percentile").points)
reshaped_array = (restore_non_probabilistic_dimensions(
cube.data, cube, "percentile", plen))
self.assertEqual(reshaped_array.shape[0], plen)
self.assertEqual(reshaped_array.shape, cube.data.shape)
self.assertArrayAlmostEqual(reshaped_array, cube.data)
def test_percentile_is_not_dimension_coordinate(self):
"""
Test the array size, if the percentile coordinate is not a dimension
coordinate on the cube.
The array contents is also checked.
"""
expected = np.array([[[[226.15, 237.4, 248.65], [259.9, 271.15, 282.4],
[293.65, 304.9, 316.15]]]],
dtype=np.float32)
cube = self.current_temperature_forecast_cube
for cube_slice in cube.slices_over("percentile"):
break
plen = len(cube_slice.coord("percentile").points)
reshaped_array = (restore_non_probabilistic_dimensions(
cube_slice.data, cube_slice, "percentile", plen))
self.assertEqual(reshaped_array.shape[0], plen)
self.assertEqual(reshaped_array.shape, (1, 1, 3, 3))
self.assertArrayAlmostEqual(reshaped_array, expected)
