def test_mismatching_number_of_coefficients(self):
"""Test that an exception is raised if the number of coefficients
provided for creating the coefficients cube is not equal to the
number of coefficient names."""
distribution = "truncated_gaussian"
desired_units = "Fahrenheit"
predictor_of_mean_flag = "realizations"
optimised_coeffs = [1, 2, 3, 4, 5]
plugin = Plugin(distribution=distribution,
current_cycle=self.current_cycle,
desired_units=desired_units,
predictor_of_mean_flag=predictor_of_mean_flag)
msg = "The number of coefficients in"
with self.assertRaisesRegex(ValueError, msg):
plugin.create_coefficients_cube(
optimised_coeffs, self.historic_forecast_with_realizations)
def test_coefficients_from_realizations(self):
"""Test that the expected coefficient cube is returned when the
ensemble realizations are used as the predictor."""
expected_coeff_names = ([
"gamma", "delta", "alpha", "beta0", "beta1", "beta2"
])
predictor_of_mean_flag = "realizations"
optimised_coeffs = [0, 1, 2, 3, 4, 5]
# Set up an expected cube.
coefficient_index = iris.coords.DimCoord(optimised_coeffs,
long_name="coefficient_index",
units="1")
dim_coords_and_dims = [(coefficient_index, 0)]
coefficient_name = iris.coords.AuxCoord(expected_coeff_names,
long_name="coefficient_name",
units="no_unit")
time_point = (np.max(self.historic_forecast.coord("time").points) +
60 * 60 * 24)
time_coord = self.historic_forecast.coord("time").copy(time_point)
frt_orig_coord = (
self.historic_forecast.coord("forecast_reference_time"))
frt_point = np.max(frt_orig_coord.points) + 60 * 60 * 24
frt_coord = frt_orig_coord.copy(frt_point)
aux_coords_and_dims = [
(coefficient_name, 0), (time_coord, None), (frt_coord, None),
(self.historic_forecast[-1].coord("forecast_period"), None),
(self.x_coord, None), (self.y_coord, None)
]
attributes = {
"mosg__model_configuration": "uk_det",
"diagnostic_standard_name": "air_temperature"
}
expected = iris.cube.Cube(optimised_coeffs,
long_name="emos_coefficients",
units="1",
dim_coords_and_dims=dim_coords_and_dims,
aux_coords_and_dims=aux_coords_and_dims,
attributes=attributes)
plugin = Plugin(distribution=self.distribution,
current_cycle=self.current_cycle,
desired_units=self.desired_units,
predictor_of_mean_flag=predictor_of_mean_flag)
result = plugin.create_coefficients_cube(
optimised_coeffs, self.historic_forecast_with_realizations)
self.assertEqual(result, expected)
self.assertArrayEqual(
result.coord("coefficient_name").points, expected_coeff_names)