def test_multiple_timesteps(self):
"""
Test that the data has been reshaped correctly when there are multiple timesteps.
The array contents are also checked. The output cube has only a single percentile,
which is therefore demoted to a scalar coordinate.
"""
expected = np.array([
[[4.0, 4.71428571], [5.42857143, 6.14285714]],
[[6.85714286, 7.57142857], [8.28571429, 9.0]],
])
cubelist = CubeList([])
for i, hour in enumerate([7, 8]):
cubelist.append(
set_up_percentile_cube(
np.array([expected[i, :, :]], dtype=np.float32),
np.array([50], dtype=np.float32),
units="degC",
time=datetime(2015, 11, 23, hour),
frt=datetime(2015, 11, 23, 6),
))
percentile_cube = cubelist.merge_cube()
reshaped_array = restore_non_percentile_dimensions(
percentile_cube.data.flatten(),
next(percentile_cube.slices_over("percentile")),
1,
)
self.assertArrayAlmostEqual(reshaped_array, expected)
def test_multiple_percentiles(self):
"""
Test the result is an array with the expected shape and contents.
"""
reshaped_array = restore_non_percentile_dimensions(
self.input_data, self.cube, 3)
self.assertIsInstance(reshaped_array, np.ndarray)
self.assertArrayAlmostEqual(reshaped_array, self.expected_data)
def test_single_percentile(self):
"""
Test the array size and contents if the percentile coordinate is scalar.
"""
expected = np.array(
[[226.15, 237.4, 248.65], [259.9, 271.15, 282.4],
[293.65, 304.9, 316.15]],
dtype=np.float32,
)
reshaped_array = restore_non_percentile_dimensions(
self.input_data[0], self.cube, 1)
self.assertArrayAlmostEqual(reshaped_array, expected)