def test_simple_data(self):
"""
Test that the plugin returns the expected values for the generated
percentiles when an idealised set of data values between 1 and 3
is used to create the mean and the variance.
"""
data = np.array([[[[1, 1, 1], [1, 1, 1], [1, 1, 1]]],
[[[2, 2, 2], [2, 2, 2], [2, 2, 2]]],
[[[3, 3, 3], [3, 3, 3], [3, 3, 3]]]])
result_data = np.array([[[[0.71844843, 0.71844843, 0.71844843],
[0.71844843, 0.71844843, 0.71844843],
[0.71844843, 0.71844843, 0.71844843]]],
[[[2., 2., 2.], [2., 2., 2.], [2., 2., 2.]]],
[[[3.28155157, 3.28155157, 3.28155157],
[3.28155157, 3.28155157, 3.28155157],
[3.28155157, 3.28155157, 3.28155157]]]])
cube = self.current_temperature_forecast_cube
cube.data = data
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
current_forecast_variance = cube.collapsed("realization",
iris.analysis.VARIANCE)
percentiles = [10, 50, 90]
plugin = Plugin()
result = plugin._mean_and_variance_to_percentiles(
current_forecast_predictor, current_forecast_variance, percentiles)
self.assertArrayAlmostEqual(result.data, result_data)
def test_if_nearly_identical_data(self):
"""
Test that the plugin returns the expected values, if every
percentile has an identical value. This causes an issue because
the default for the underlying scipy function is to yield a NaN for
tied values. For this application, any NaN values are overwritten with
the predicted mean value for all probability thresholds.
"""
data = np.array([[[[1., 1., 1.], [4., 2., 2.], [3., 3., 3.]]],
[[[1., 1., 1.], [2., 2., 2.], [3., 3., 3.]]],
[[[1., 1., 1.], [2., 2., 2.], [3., 3., 3.]]]])
result_data = np.array([[[[1., 1., 1.], [1.18685838, 2., 2.],
[3., 3., 3.]]],
[[[1., 1., 1.], [2.66666667, 2., 2.],
[3., 3., 3.]]],
[[[1., 1., 1.], [4.14647495, 2., 2.],
[3., 3., 3.]]]])
cube = self.current_temperature_forecast_cube
cube.data = data
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
current_forecast_variance = cube.collapsed("realization",
iris.analysis.VARIANCE)
percentiles = [10, 50, 90]
plugin = Plugin()
result = plugin._mean_and_variance_to_percentiles(
current_forecast_predictor, current_forecast_variance, percentiles)
self.assertArrayAlmostEqual(result.data, result_data)
def test_check_data(self):
"""
Test that the plugin returns an Iris.cube.Cube matching the expected
data values when a cube containing mean and variance is passed in.
The resulting data values are the percentiles, which have been
generated.
"""
data = np.array([[[[225.568115, 236.818115, 248.068115],
[259.318115, 270.568115, 281.818115],
[293.068115, 304.318115, 315.568115]]],
[[[229.483322, 240.733322, 251.983322],
[263.233307, 274.483307, 285.733307],
[296.983307, 308.233307, 319.483307]]],
[[[233.398529, 244.648529, 255.898529],
[267.148499, 278.398499, 289.648499],
[300.898499, 312.148499, 323.398499]]]],
dtype=np.float32)
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]
plugin = Plugin()
result = plugin._mean_and_variance_to_percentiles(
current_forecast_predictor, current_forecast_variance, percentiles)
self.assertIsInstance(result, Cube)
self.assertArrayAlmostEqual(result.data, data)
def test_negative_percentiles(self):
"""
Test that the plugin returns the expected values for the
percentiles if negative probabilities are requested.
"""
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, 10]
plugin = Plugin()
msg = "NaNs are present within the result for the"
with self.assertRaisesRegex(ValueError, msg):
plugin._mean_and_variance_to_percentiles(
current_forecast_predictor, current_forecast_variance, cube,
percentiles)
def test_many_percentiles(self):
"""
Test that the plugin returns an iris.cube.Cube if many percentiles
are requested.
"""
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 = np.linspace(1, 99, num=1000, endpoint=True)
plugin = Plugin()
result = plugin._mean_and_variance_to_percentiles(
current_forecast_predictor, current_forecast_variance, percentiles)
self.assertIsInstance(result, Cube)
