def test_number_of_percentiles(self):
"""
Test that the plugin returns a cube with the expected number of
percentiles.
"""
expected = np.array([
[
[227.42273, 238.67273, 249.92273],
[261.1727, 272.4227, 283.6727],
[294.9227, 306.1727, 317.4227],
],
[
[229.48332, 240.73332, 251.98332],
[263.2333, 274.4833, 285.7333],
[296.9833, 308.2333, 319.4833],
],
[
[231.54391, 242.79391, 254.04391],
[265.2939, 276.5439, 287.7939],
[299.0439, 310.2939, 321.5439],
],
])
result = Plugin().process(
self.forecast_predictor,
self.forecast_variance,
self.cube,
no_of_percentiles=self.no_of_percentiles,
)
self.assertEqual(len(result.coord("percentile").points),
self.no_of_percentiles)
self.assertArrayAlmostEqual(expected, result.data, decimal=4)
def test_list_of_percentiles(self):
"""
Test that the plugin returns a cube with the expected percentiles
when a specific list of percentiles is provided.
"""
percentiles = [10, 50, 90]
expected = np.array([
[
[225.56812, 236.81812, 248.06812],
[259.3181, 270.5681, 281.8181],
[293.0681, 304.3181, 315.5681],
],
[
[229.48332, 240.73332, 251.98332],
[263.2333, 274.4833, 285.7333],
[296.9833, 308.2333, 319.4833],
],
[
[233.39853, 244.64853, 255.89853],
[267.1485, 278.3985, 289.6485],
[300.8985, 312.1485, 323.3985],
],
])
result = Plugin().process(
self.forecast_predictor,
self.forecast_variance,
self.cube,
percentiles=percentiles,
)
self.assertEqual(len(percentiles),
len(result.coord("percentile").points))
self.assertArrayAlmostEqual(percentiles,
result.coord("percentile").points)
self.assertArrayAlmostEqual(expected, result.data, decimal=4)
