def test_missing_cube(self):
"""Test that an exception is raised if either of the historic
forecasts or truth were missing."""
self.historic_temperature_forecast_cube.convert_units("Fahrenheit")
plugin = Plugin(self.distribution)
msg = ".*cubes must be provided"
with self.assertRaisesRegex(ValueError, msg):
plugin.process(self.historic_temperature_forecast_cube, None)
def test_non_matching_units(self):
"""Test that an exception is raised if the historic forecasts and truth
have non matching units."""
self.historic_temperature_forecast_cube.convert_units("Fahrenheit")
plugin = Plugin(self.distribution)
msg = "The historic forecast units"
with self.assertRaisesRegex(ValueError, msg):
plugin.process(
self.historic_temperature_forecast_cube, self.temperature_truth_cube
)
def test_coefficient_values_for_truncnorm_distribution(self):
"""Ensure that the values for the optimised_coefficients match the
expected values, and the coefficient names also match
expected values for a truncated normal distribution. In this case,
a linear least-squares regression is used to construct the initial
guess."""
distribution = "truncnorm"
plugin = Plugin(distribution)
result = plugin.process(
self.historic_wind_speed_forecast_cube, self.wind_speed_truth_cube
)
self.assertEMOSCoefficientsAlmostEqual(
np.array([cube.data for cube in result]),
self.expected_mean_predictor_truncnorm,
)
self.assertArrayEqual(
[cube.name() for cube in result], self.expected_coeff_names
)
for cube in result:
self.assertArrayEqual(
cube.attributes["shape_parameters"],
np.array([0, np.inf], dtype=np.float32),
)
def test_basic(self):
"""Ensure that the optimised_coefficients are returned as a cube,
with the expected number of coefficients."""
plugin = Plugin(self.distribution)
result = plugin.process(
self.historic_temperature_forecast_cube, self.temperature_truth_cube
)
self.assertIsInstance(result, iris.cube.CubeList)
self.assertEqual(len(result), len(self.coeff_names))
def test_historic_forecast_unit_conversion(self):
"""Ensure the expected optimised coefficients are generated,
even if the input historic forecast cube has different units."""
self.historic_temperature_forecast_cube.convert_units("Fahrenheit")
desired_units = "Kelvin"
plugin = Plugin(self.distribution, desired_units=desired_units)
result = plugin.process(
self.historic_temperature_forecast_cube, self.temperature_truth_cube
)
self.assertEMOSCoefficientsAlmostEqual(
np.array([cube.data for cube in result]), self.expected_mean_predictor_norm,
)
def test_coefficient_values_for_norm_distribution(self):
"""Ensure that the values for the optimised_coefficients match the
expected values, and the coefficient names also match
expected values for a normal distribution. In this case,
a linear least-squares regression is used to construct the initial
guess."""
plugin = Plugin(self.distribution)
result = plugin.process(
self.historic_temperature_forecast_cube, self.temperature_truth_cube
)
self.assertEMOSCoefficientsAlmostEqual(
np.array([cube.data for cube in result]), self.expected_mean_predictor_norm,
)
self.assertArrayEqual(
[cube.name() for cube in result], self.expected_coeff_names
)
def test_coefficient_values_for_norm_distribution_max_iterations(self):
"""Ensure that the values for the optimised_coefficients match the
expected values, and the coefficient names also match
expected values for a normal distribution, when the max_iterations
argument is specified."""
max_iterations = 800
plugin = Plugin(self.distribution, max_iterations=max_iterations)
result = plugin.process(
self.historic_temperature_forecast_cube, self.temperature_truth_cube
)
self.assertEMOSCoefficientsAlmostEqual(
np.array([cube.data for cube in result]), self.expected_mean_predictor_norm,
)
self.assertArrayEqual(
[cube.name() for cube in result], self.expected_coeff_names
)
def test_coefficients_norm_realizations_no_statsmodels(self):
"""Ensure that the values for the optimised_coefficients match the
expected values, and the coefficient names also match
expected values for a normal distribution where the
realizations are used as the predictor.
"""
predictor = "realizations"
plugin = Plugin(self.distribution, predictor=predictor)
result = plugin.process(
self.historic_temperature_forecast_cube, self.temperature_truth_cube
)
self.assertEMOSCoefficientsAlmostEqual(
np.concatenate([np.atleast_1d(cube.data) for cube in result]),
self.expected_realizations_norm_no_statsmodels,
)
self.assertArrayEqual(
[cube.name() for cube in result], self.expected_coeff_names
)
def test_coefficient_values_for_norm_distribution_mismatching_inputs(self):
"""Test that the values for the optimised coefficients match the
expected values, and the coefficient names also match
expected values for a normal distribution for when the historic
forecasts and truths input having some mismatches in validity time.
"""
expected = [23.4593, 0.9128, 0.0041, 0.4885]
partial_historic_forecasts = (
self.historic_forecasts[:2] + self.historic_forecasts[3:]
).merge_cube()
partial_truth = self.truth[1:].merge_cube()
plugin = Plugin(self.distribution)
result = plugin.process(partial_historic_forecasts, partial_truth)
self.assertEMOSCoefficientsAlmostEqual(
np.array([cube.data for cube in result]), expected
)
self.assertArrayEqual(
[cube.name() for cube in result], self.expected_coeff_names
)
def test_coefficient_values_for_norm_distribution_landsea_mask(self):
"""Ensure that the values for the optimised_coefficients match the
expected values, and the coefficient names also match
expected values for a normal distribution. In this case,
a linear least-squares regression is used to construct the initial
guess. The original data is surrounded by a halo that is masked
out by the landsea_mask, giving the same results as the original data.
"""
plugin = Plugin(self.distribution)
result = plugin.process(
self.historic_temperature_forecast_cube_halo,
self.temperature_truth_cube_halo,
landsea_mask=self.landsea_cube,
)
self.assertEMOSCoefficientsAlmostEqual(
np.array([cube.data for cube in result]), self.expected_mean_predictor_norm,
)
self.assertArrayEqual(
[cube.name() for cube in result], self.expected_coeff_names
)
def test_coefficients_norm_distribution_default_initial_guess(self):
"""Ensure that the values for the optimised_coefficients match the
expected values, and the coefficient names also match
expected values for a normal distribution, where the
default values for the initial guess are used, rather than using a
linear least-squares regression to construct an initial guess.
Reducing the value for the tolerance would result in the coefficients
more closely matching the coefficients created when using a linear
least-squares regression to construct the initial guess."""
expected = [-0.0001, 0.9974, 0.0001, 1.0374]
plugin = Plugin(self.distribution)
plugin.ESTIMATE_COEFFICIENTS_FROM_LINEAR_MODEL_FLAG = False
result = plugin.process(
self.historic_temperature_forecast_cube, self.temperature_truth_cube
)
self.assertEMOSCoefficientsAlmostEqual(
np.array([cube.data for cube in result]), expected
)
self.assertArrayEqual(
[cube.name() for cube in result], self.expected_coeff_names
)