def test_multiple_keyword_arguments_error(self):
"""
Test that the plugin raises an error when both the no_of_percentiles
keyword argument and the percentiles keyword argument are provided.
"""
cube = self.current_temperature_forecast_cube
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
current_forecast_variance = cube.collapsed("realization",
iris.analysis.VARIANCE)
raw_forecast = cube.copy()
no_of_percentiles = len(raw_forecast.coord("realization").points)
percentiles = [10, 25, 50, 75, 90]
plugin = Plugin()
msg = "Please specify either the number of percentiles or"
with self.assertRaisesRegex(ValueError, msg):
plugin.process(
current_forecast_predictor,
current_forecast_variance,
cube,
no_of_percentiles=no_of_percentiles,
percentiles=percentiles,
)
def test_number_of_percentiles(self):
"""
Test that the plugin returns a cube with the expected number of
percentiles.
"""
cube = self.current_temperature_forecast_cube
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
current_forecast_variance = cube.collapsed("realization",
iris.analysis.VARIANCE)
raw_forecast = cube.copy()
no_of_percentiles = len(raw_forecast.coord("realization").points)
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]]]])
plugin = Plugin()
result = plugin.process(current_forecast_predictor,
current_forecast_variance,
cube,
no_of_percentiles=no_of_percentiles)
self.assertEqual(len(raw_forecast.coord("realization").points),
len(result.coord("percentile").points))
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.
"""
cube = self.current_temperature_forecast_cube
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
current_forecast_variance = cube.collapsed("realization",
iris.analysis.VARIANCE)
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]]]])
plugin = Plugin()
result = plugin.process(current_forecast_predictor,
current_forecast_variance,
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)
def test_basic(self):
"""Test that the plugin returns an Iris.cube.Cube."""
cube = self.current_temperature_forecast_cube
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
current_forecast_variance = cube.collapsed("realization",
iris.analysis.VARIANCE)
raw_forecast = cube.copy()
no_of_percentiles = len(raw_forecast.coord("realization").points)
plugin = Plugin()
result = plugin.process(current_forecast_predictor,
current_forecast_variance,
cube,
no_of_percentiles=no_of_percentiles)
self.assertIsInstance(result, Cube)