def test_truncated_normal_members_predictor_max_iterations(self):
"""
Test that the plugin returns a list of coefficients
equal to specific values, when the ensemble members are the predictor
assuming a truncated normal distribution and the value specified
for the MAX_ITERATIONS is overriden. The coefficients are
calculated by minimising the CRPS and using a set default value for
the initial guess.
"""
initial_guess = [5, 1, 0, 1, 1, 1]
initial_guess = np.array(initial_guess, dtype=np.float32)
cube = set_up_temperature_cube()
forecast_predictor = cube.copy()
forecast_variance = cube.collapsed("realization",
iris.analysis.VARIANCE)
truth = cube.collapsed("realization", iris.analysis.MAX)
predictor_of_mean_flag = "members"
plugin = Plugin()
plugin.MAX_ITERATIONS = 400
distribution = "truncated gaussian"
result = plugin.crps_minimiser_wrapper(initial_guess,
forecast_predictor, truth,
forecast_variance,
predictor_of_mean_flag,
distribution)
self.assertArrayAlmostEqual(
result, [5.375955, 1.45785, 0.002567, 0.193423, 0.55406, 0.811599])
def test_truncated_normal_mean_predictor_max_iterations(self):
"""
Test that the plugin returns a list of coefficients
equal to specific values, when the ensemble mean is the predictor
assuming a truncated normal distribution and the value specified
for the MAX_ITERATIONS is overriden. The coefficients are
calculated by minimising the CRPS and using a set default value for
the initial guess.
"""
warnings.simplefilter("always")
initial_guess = [5, 1, 0, 1]
initial_guess = np.array(initial_guess, dtype=np.float32)
cube = set_up_temperature_cube()
forecast_predictor = cube.collapsed("realization", iris.analysis.MEAN)
forecast_variance = cube.collapsed(
"realization", iris.analysis.VARIANCE)
truth = cube.collapsed("realization", iris.analysis.MAX)
predictor_of_mean_flag = "mean"
plugin = Plugin()
plugin.MAX_ITERATIONS = 400
distribution = "truncated gaussian"
result = plugin.crps_minimiser_wrapper(
initial_guess, forecast_predictor, truth, forecast_variance,
predictor_of_mean_flag, distribution)
self.assertArrayAlmostEqual(
result, [-0.303343, -0.022553, 0.008502, 1.009565])