def test_set_array(self):
h = SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0)
h.set_array([88.2, 103.4, 0.2, 0.3, 0.4, 0.5])
numpy.testing.assert_equal(h.harmonics,
[88.2, 103.4, 0.2, 0.3, 0.4, 0.5])
def test_harmonic_parameters(self):
h = SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0)
h.set_array([88.2, 103.4, 0.2, 0.3, 0.4, 0.5])
self.assertEqual(0.2, h.harmonic_parameters(1).log_sigma)
self.assertEqual(103.4, h.harmonic_parameters(0).log_rho)
self.assertEqual({
'log_sigma': 0.4,
'log_rho': 0.5
},
h.harmonic_parameters(2).get_dictionary())
def __init__(self, covariates_descriptor):
if covariates_descriptor is not None:
loader = LoadCovariateElement(covariates_descriptor)
loader.check_keys()
covariate_elements, covariate_hyperparameters = loader.load_covariates_and_hyperparameters(
)
print(
'The following fields have been added as covariates of the climatology model'
)
print(loader.data.keys())
else:
covariate_elements, covariate_hyperparameters = [], []
setup = MovingClimatologySetup()
model_elements = CombinationElement([
AnnualKroneckerElement(
setup.seasonal_spline_settings.n_triangulation_divisions, setup
.seasonal_spline_settings.alpha, setup.seasonal_spline_settings
.starttime, setup.seasonal_spline_settings.endtime,
setup.seasonal_spline_settings.n_nodes,
setup.seasonal_spline_settings.overlap_factor,
setup.seasonal_spline_settings.H,
setup.seasonal_spline_settings.wrap_dimensions),
#SeasonalElement(setup.seasonal_settings.n_triangulation_divisions,
#setup.seasonal_settings.n_harmonics,
#include_local_mean=setup.seasonal_settings.include_local_mean),
GrandMeanElement(),
LatitudeSplineElement(
setup.latitude_settings.alpha,
setup.latitude_settings.n_nodes,
setup.latitude_settings.overlap_factor,
setup.latitude_settings.H,
),
] + covariate_elements)
seasonal_hyperparameters = SeasonalHyperparameters(
setup.seasonal_settings.n_spatial_components,
numpy.log(setup.seasonal_settings.amplitude),
numpy.log(numpy.radians(
setup.seasonal_settings.space_length_scale)))
seasonal_spline_hyperparameters = SpaceTimeSPDEHyperparameters(
numpy.log(setup.seasonal_spline_settings.amplitude),
numpy.log(
numpy.radians(
setup.seasonal_spline_settings.space_length_scale)),
numpy.log(setup.seasonal_spline_settings.time_length_scale))
seasonal_params = zip(
numpy.log(setup.seasonal_settings.harmonic_amplitudes),
numpy.log(
numpy.radians(setup.seasonal_settings.harmonic_length_scales)))
seasonal_hyperparameters.set_array(
[val for pair in seasonal_params for val in pair])
model_hyperparameters = CombinationHyperparameters([
seasonal_spline_hyperparameters,
#seasonal_hyperparameters,
CovariateHyperparameters(
numpy.log(setup.covariate_settings.grandmean_amplitude)),
#SpaceTimeSPDEHyperparameters(numpy.log(setup.slow_settings.amplitude),
#numpy.log(numpy.radians(setup.slow_settings.space_length_scale)),
#numpy.log(setup.slow_settings.time_length_scale)),
LocalHyperparameters(
numpy.log(setup.latitude_settings.amplitude),
numpy.log(setup.latitude_settings.length_scale))
] + covariate_hyperparameters)
super(ClimatologyDefinition, self).__init__(model_elements,
model_hyperparameters)