def test_get_array(self):
h = SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0)
numpy.testing.assert_equal(h.get_array(),
[0.8, 1.0, 0.8, 1.0, 0.8, 1.0])
def test_harmonic_index_for_spatial_component(self):
prior_a = SeasonalElementPrior(
SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0),
spde=SphereMeshSPDE(level=1, sparse_format=SPARSEFORMAT),
n_harmonics=2,
include_local_mean=True,
alpha=2)
self.assertEqual(0, prior_a.harmonic_index_for_spatial_component(0))
self.assertEqual(1, prior_a.harmonic_index_for_spatial_component(1))
self.assertEqual(1, prior_a.harmonic_index_for_spatial_component(2))
self.assertEqual(2, prior_a.harmonic_index_for_spatial_component(3))
self.assertEqual(2, prior_a.harmonic_index_for_spatial_component(4))
prior_b = SeasonalElementPrior(
SeasonalHyperparameters(n_spatial_components=2,
common_log_sigma=0.8,
common_log_rho=1.0),
spde=SphereMeshSPDE(level=1, sparse_format=SPARSEFORMAT),
n_harmonics=2,
include_local_mean=False,
alpha=2)
self.assertEqual(0, prior_b.harmonic_index_for_spatial_component(0))
self.assertEqual(0, prior_b.harmonic_index_for_spatial_component(1))
self.assertEqual(1, prior_b.harmonic_index_for_spatial_component(2))
self.assertEqual(1, prior_b.harmonic_index_for_spatial_component(3))
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 __init__(self):
super(ClimatologyDefinition, self).__init__(
SeasonalElement(n_triangulation_divisions=5,
n_harmonics=5,
include_local_mean=True),
SeasonalHyperparameters(n_spatial_components=6,
common_log_sigma=1.0,
common_log_rho=0.0))
def test_n_spatial_components(self):
# With local mean 2 harmonics plus mean = (1 + 2*2) = 5 parameters
self.assertEqual(
5,
SeasonalElementPrior(
SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0),
spde=SphereMeshSPDE(level=1, sparse_format=SPARSEFORMAT),
n_harmonics=2,
include_local_mean=True,
alpha=2).n_spatial_components())
# Without local mean 2 harmonics = 2*2 = 4 parameters
self.assertEqual(
4,
SeasonalElementPrior(
SeasonalHyperparameters(n_spatial_components=2,
common_log_sigma=0.8,
common_log_rho=1.0),
spde=SphereMeshSPDE(level=1, sparse_format=SPARSEFORMAT),
n_harmonics=2,
include_local_mean=False,
alpha=2).n_spatial_components())
# Also check state parameters
self.assertEqual(
210,
SeasonalElementPrior(
SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0),
spde=SphereMeshSPDE(level=1, sparse_format=SPARSEFORMAT),
n_harmonics=2,
include_local_mean=True,
alpha=2).prior_number_of_state_parameters())
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 test_element_prior(self):
element = SeasonalElement(n_triangulation_divisions=1,
n_harmonics=3,
include_local_mean=False)
prior = element.element_prior(
SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.5,
common_log_rho=0.7))
self.assertTrue(isinstance(prior, SeasonalElementPrior))
numpy.testing.assert_equal([0.5, 0.7, 0.5, 0.7, 0.5, 0.7],
prior.hyperparameters.get_array())
self.assertEqual(3, prior.n_harmonics)
self.assertEqual(False, prior.include_local_mean)
self.assertEqual(2, prior.alpha)
def test_prior_precision_derivative(self):
prior = SeasonalElementPrior(
SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0),
spde=SphereMeshSPDE(level=1, sparse_format=SPARSEFORMAT),
n_harmonics=2,
include_local_mean=True,
alpha=2)
dQ = prior.prior_precision_derivative(1)
# For now just check size and format of result
self.assertEqual(SPARSEFORMAT, dQ.getformat())
self.assertEqual((210, 210), dQ.shape)
def test_init(self):
prior = SeasonalElementPrior(
SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=0.8,
common_log_rho=1.0),
spde=SphereMeshSPDE(level=1, sparse_format=SPARSEFORMAT),
n_harmonics=2,
include_local_mean=True,
alpha=3)
numpy.testing.assert_equal(prior.hyperparameters.get_array(),
[0.8, 1.0, 0.8, 1.0, 0.8, 1.0])
self.assertEqual(2, prior.n_harmonics)
self.assertTrue(prior.include_local_mean)
self.assertEqual(3, prior.alpha)
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 = ClimatologySetup()
super(ClimatologyDefinition, self).__init__(
CombinationElement( [SeasonalElement(setup.n_triangulation_divisions,
setup.n_harmonics,
include_local_mean=True), GrandMeanElement()] + covariate_elements),
CombinationHyperparameters( [SeasonalHyperparameters(setup.n_spatial_components,
numpy.log(setup.amplitude),
numpy.log(numpy.radians(setup.space_length_scale))),
CovariateHyperparameters(numpy.log(setup.grandmean_amplitude))] + covariate_hyperparameters))
def main():
print 'Advanced standard example using a few days of EUSTACE data'
parser = argparse.ArgumentParser(description='Advanced standard example using a few days of EUSTACE data')
parser.add_argument('outpath', help='directory where the output should be redirected')
parser.add_argument('--json_descriptor', default = None, help='a json descriptor containing the covariates to include in the climatology model')
parser.add_argument('--land_biases', action='store_true', help='include insitu land homogenization bias terms')
parser.add_argument('--global_biases', action='store_true', help='include global satellite bias terms')
parser.add_argument('--n_iterations', type=int, default=5, help='number of solving iterations')
args = parser.parse_args()
# Input data path
basepath = os.path.join('/work/scratch/eustace/rawbinary3')
# Days to process
time_indices = range(int(days_since_epoch(datetime(2006, 2, 1))), int(days_since_epoch(datetime(2006, 2, 2))))
# Sources to use
sources = [ 'surfaceairmodel_land', 'surfaceairmodel_ocean', 'surfaceairmodel_ice', 'insitu_land', 'insitu_ocean' ]
#SETUP
# setup for the seasonal core: climatology covariates setup read from file
seasonal_setup = {'n_triangulation_divisions':5,
'n_harmonics':4,
'n_spatial_components':6,
'amplitude':2.,
'space_length_scale':5., # length scale in units of degrees
}
grandmean_amplitude = 15.0
# setup for the large scale component
spacetime_setup = {'n_triangulation_divisions':2,
'alpha':2,
'starttime':0,
'endtime':10.,
'n_nodes':2,
'overlap_factor':2.5,
'H':1,
'amplitude':1.,
'space_lenght_scale':15.0, # length scale in units of degrees
'time_length_scale':15.0 # length scal in units of days
}
bias_amplitude = .9
# setup for the local component
local_setup = {'n_triangulation_divisions':6,
'amplitude':2.,
'space_length_scale':2. # length scale in units of degrees
}
globalbias_amplitude = 15.0
# CLIMATOLOGY COMPONENT: combining the seasonal core along with latitude harmonics, altitude and coastal effects
if args.json_descriptor is not None:
loader = LoadCovariateElement(args.json_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 = [], []
climatology_element = CombinationElement( [SeasonalElement(n_triangulation_divisions=seasonal_setup['n_triangulation_divisions'],
n_harmonics=seasonal_setup['n_harmonics'],
include_local_mean=True),
GrandMeanElement()]+covariate_elements)
climatology_hyperparameters = CombinationHyperparameters( [SeasonalHyperparameters(n_spatial_components=seasonal_setup['n_spatial_components'],
common_log_sigma=numpy.log(seasonal_setup['amplitude']),
common_log_rho=numpy.log(numpy.radians(seasonal_setup['space_length_scale']))),
CovariateHyperparameters(numpy.log(grandmean_amplitude))] + covariate_hyperparameters )
climatology_component = SpaceTimeComponent(ComponentStorage_InMemory(climatology_element, climatology_hyperparameters), SpaceTimeComponentSolutionStorage_InMemory(),
compute_uncertainties=True, method='APPROXIMATED',
compute_sample=True, sample_size=definitions.GLOBAL_SAMPLE_SHAPE[3])
# LARGE SCALE (kronecker product) COMPONENT: combining large scale trends with bias terms accounting for homogeneization effects
if args.land_biases:
bias_element, bias_hyperparameters = [InsituLandBiasElement(BREAKPOINTS_FILE)], [CovariateHyperparameters(numpy.log(bias_amplitude))]
print('Adding bias terms for insitu land homogenization')
else:
bias_element, bias_hyperparameters = [], []
large_scale_element = CombinationElement( [SpaceTimeKroneckerElement(n_triangulation_divisions=spacetime_setup['n_triangulation_divisions'],
alpha=spacetime_setup['alpha'],
starttime=spacetime_setup['starttime'],
endtime=spacetime_setup['endtime'],
n_nodes=spacetime_setup['n_nodes'],
overlap_factor=spacetime_setup['overlap_factor'],
H=spacetime_setup['H'])] + bias_element)
large_scale_hyperparameters = CombinationHyperparameters( [SpaceTimeSPDEHyperparameters(space_log_sigma=numpy.log(spacetime_setup['amplitude']),
space_log_rho=numpy.log(numpy.radians(spacetime_setup['space_lenght_scale'])),
time_log_rho=numpy.log(spacetime_setup['time_length_scale']))] + bias_hyperparameters)
large_scale_component = SpaceTimeComponent(ComponentStorage_InMemory(large_scale_element, large_scale_hyperparameters), SpaceTimeComponentSolutionStorage_InMemory(),
compute_uncertainties=True, method='APPROXIMATED',
compute_sample=True, sample_size=definitions.GLOBAL_SAMPLE_SHAPE[3])
# LOCAL COMPONENT: combining local scale variations with global satellite bias terms
if args.global_biases:
bias_elements = [BiasElement(groupname, 1) for groupname in GLOBAL_BIASES_GROUP_LIST]
bias_hyperparameters = [CovariateHyperparameters(numpy.log(globalbias_amplitude)) for index in range(len(GLOBAL_BIASES_GROUP_LIST))]
print('Adding global bias terms for all the surfaces')
else:
bias_elements, bias_hyperparameters = [], []
local_scale_element = CombinationElement([LocalElement(n_triangulation_divisions=local_setup['n_triangulation_divisions'])] + bias_elements)
local_scale_hyperparameters = CombinationHyperparameters([LocalHyperparameters(log_sigma=numpy.log(local_setup['amplitude']),
log_rho=numpy.log(numpy.radians(local_setup['space_length_scale'])))] + bias_hyperparameters)
local_component = SpatialComponent(ComponentStorage_InMemory(local_scale_element, local_scale_hyperparameters), SpatialComponentSolutionStorage_InMemory(),
compute_uncertainties=True, method='APPROXIMATED',
compute_sample=True, sample_size=definitions.GLOBAL_SAMPLE_SHAPE[3])
# Analysis system using the specified components, for the Tmean observable
print 'Analysing inputs'
analysis_system = AnalysisSystem(
[ climatology_component, large_scale_component, local_component ],
ObservationSource.TMEAN)
# Object to load raw binary inputs at time indices
inputloaders = [ AnalysisSystemInputLoaderRawBinary_Sources(basepath, source, time_indices) for source in sources ]
for iteration in range(args.n_iterations):
message = 'Iteration {}'.format(iteration)
print(message)
# Update with data
analysis_system.update(inputloaders, time_indices)
print 'Computing outputs'
# Produce an output for each time index
for time_index in time_indices:
# Get date for output
outputdate = inputloaders[0].datetime_at_time_index(time_index)
print 'Evaluating output grid: ', outputdate
#Configure output grid
outputstructure = OutputRectilinearGridStructure(
time_index, outputdate,
latitudes=numpy.linspace(-90.+definitions.GLOBAL_FIELD_RESOLUTION/2., 90.-definitions.GLOBAL_FIELD_RESOLUTION/2., num=definitions.GLOBAL_FIELD_SHAPE[1]),
longitudes=numpy.linspace(-180.+definitions.GLOBAL_FIELD_RESOLUTION/2., 180.-definitions.GLOBAL_FIELD_RESOLUTION/2., num=definitions.GLOBAL_FIELD_SHAPE[2]))
# Evaluate expected value at these locations
for field in ['MAP', 'post_STD']:
print 'Evaluating: ',field
result_expected_value = analysis_system.evaluate_expected_value('MAP', outputstructure, 'GRID_CELL_AREA_AVERAGE', [1,1], 1000)
result_expected_uncertainties = analysis_system.evaluate_expected_value('post_STD', outputstructure, 'GRID_CELL_AREA_AVERAGE', [1,1], 1000)
print 'Evaluating: climatology fraction'
climatology_fraction = analysis_system.evaluate_climatology_fraction(outputstructure, [1,1], 1000)
print 'Evaluating: the sample'
sample = analysis_system.evaluate_projected_sample(outputstructure)
# Make output filename
pathname = 'eustace_example_output_{0:04d}{1:02d}{2:02d}.nc'.format(outputdate.year, outputdate.month, outputdate.day)
pathname = os.path.join(args.outpath, pathname)
print 'Saving: ', pathname
# Save results
filebuilder = FileBuilderGlobalField(
pathname,
time_index,
'Infilling Example',
'UNVERSIONED',
definitions.TAS.name,
'',
'Example data only',
'eustace.analysis.advanced_standard.examples.example_eustace_few_days',
'')
filebuilder.add_global_field(definitions.TAS, result_expected_value.reshape(definitions.GLOBAL_FIELD_SHAPE))
filebuilder.add_global_field(definitions.TASUNCERTAINTY, result_expected_uncertainties.reshape(definitions.GLOBAL_FIELD_SHAPE))
filebuilder.add_global_field(definitions.TAS_CLIMATOLOGY_FRACTION, climatology_fraction.reshape(definitions.GLOBAL_FIELD_SHAPE))
for index in range(definitions.GLOBAL_SAMPLE_SHAPE[3]):
variable = copy.deepcopy(definitions.TASENSEMBLE)
variable.name = variable.name + '_' + str(index)
selected_sample = sample[:,index].ravel()+result_expected_value
filebuilder.add_global_field(variable, selected_sample.reshape(definitions.GLOBAL_FIELD_SHAPE))
filebuilder.save_and_close()
print 'Complete'
def test_mini_world_noiseless(self):
number_of_simulated_time_steps = 1
# Build system
element = SeasonalElement(n_triangulation_divisions=3,
n_harmonics=5,
include_local_mean=True)
hyperparameters = SeasonalHyperparameters(n_spatial_components=6,
common_log_sigma=0.0,
common_log_rho=0.0)
component = SpaceTimeComponent(
ComponentStorage_InMemory(element, hyperparameters),
SpaceTimeComponentSolutionStorage_InMemory())
analysis_system = AnalysisSystem([component],
ObservationSource.TMEAN,
log=StringIO())
# use fixed locations from icosahedron
fixed_locations = cartesian_to_polar2d(
MeshIcosahedronSubdivision.build(3).points)
# random measurement at each location
numpy.random.seed(8976)
field_basis = numpy.random.randn(fixed_locations.shape[0])
#print(field_basis.shape)
#time_basis = numpy.array(harmonics_list)
# some time function that varies over a year
#decimal_years = numpy.array([datetime_to_decimal_year(epoch_plus_days(step)) for step in range(number_of_simulated_time_steps)])
time_basis = numpy.cos(
numpy.linspace(0.1, 1.75 * numpy.pi,
number_of_simulated_time_steps))
# kronecker product of the two
#print(numpy.expand_dims(time_basis, 1))
measurement = numpy.kron(field_basis, numpy.expand_dims(
time_basis, 1)) #numpy.expand_dims(time_basis, 1))
#print(measurement.shape)
# Simulated inputs
simulated_input_loader = SimulatedInputLoader(fixed_locations,
measurement, 0.0001)
# Simulate evaluation of this time index
simulated_time_indices = range(number_of_simulated_time_steps)
# Iterate
for iteration in range(5):
analysis_system.update([simulated_input_loader],
simulated_time_indices)
# Get all results
result = numpy.zeros(measurement.shape)
for t in range(number_of_simulated_time_steps):
result[t, :] = analysis_system.evaluate_expected_value(
'MAP',
SimulatedObservationStructure(t, fixed_locations, None, None),
flag='POINTWISE')
# Should be very close to original because specified noise is low
numpy.testing.assert_almost_equal(result, measurement)
max_disparity = (numpy.abs(result - measurement)).ravel().max()
self.assertTrue(max_disparity < 1E-5)
# test output gridding, pointwise limit
outputstructure = OutputRectilinearGridStructure(
2,
epoch_plus_days(2),
latitudes=numpy.linspace(-60., 60., num=5),
longitudes=numpy.linspace(-90., 90, num=10))
pointwise_result = analysis_system.evaluate_expected_value(
'MAP', outputstructure, 'POINTWISE')
pointwise_limit_result = analysis_system.evaluate_expected_value(
'MAP', outputstructure, 'GRID_CELL_AREA_AVERAGE', [1, 1], 10)
numpy.testing.assert_array_almost_equal(pointwise_result,
pointwise_limit_result)
def main():
print 'EUSTACE example using HadCRUT4 monthly data'
# Input data path
input_basepath = os.path.join(WORKSPACE_PATH, 'data/incoming/HadCRUT4.5.0.0')
# Input filenames
input_filenames = [
'hadcrut4_median_netcdf.nc',
'hadcrut4_uncorrelated_supplementary.nc',
'hadcrut4_blended_uncorrelated.nc' ]
# Months to process
time_indices = range(2)
# Climatology component
climatology_component = SpaceTimeComponent(ComponentStorage_InMemory(SeasonalElement(n_triangulation_divisions=5, n_harmonics=5, include_local_mean=True),
SeasonalHyperparameters(n_spatial_components=6, common_log_sigma=1.0, common_log_rho=0.0)),
SpaceTimeComponentSolutionStorage_InMemory())
# Number of factors for large scale (factor analysis) component and initial hyperparameters
n_factors = 5
factors = [ ]
factor_hyperparameters = [ ]
for factor_index in range(n_factors):
factor_hyperparameters.append( SpaceTimeSPDEHyperparameters(
space_log_sigma=0.0,
space_log_rho=numpy.log(10.0 * numpy.pi/180 + 25.0 * numpy.pi/180 *(n_factors - factor_index) / n_factors),
time_log_rho=numpy.log(1/12.0 + 6/12.0*(n_factors - factor_index) / n_factors)) )
factors.append( SpaceTimeFactorElement(n_triangulation_divisions=5, alpha=2, starttime=0, endtime=36, overlap_factor=2.5, H=1) )
# Large scale (factor analysis) component
large_scale_component = SpaceTimeComponent(ComponentStorage_InMemory(CombinationElement(factors), CombinationHyperparameters(factor_hyperparameters)),
SpaceTimeComponentSolutionStorage_InMemory())
# Local component
local_component = SpatialComponent(ComponentStorage_InMemory(LocalElement(n_triangulation_divisions=4),
LocalHyperparameters(log_sigma=0.0, log_rho=numpy.log(10.0 * numpy.pi/180))),
SpatialComponentSolutionStorage_InMemory())
print 'Analysing inputs'
# Analysis system using the specified components, for the Tmean observable
analysis_system = AnalysisSystem(
[ climatology_component, large_scale_component, local_component ],
ObservationSource.TMEAN)
# Make filelist
input_filelist = [ os.path.join(input_basepath, filename) for filename in input_filenames ]
# Object to load HadCRUT4 inputs at time indices
inputloader = AnalysisSystemInputLoaderHadCRUT4(input_filelist)
# Update with data
analysis_system.update([ inputloader ], time_indices)
print 'Computing outputs'
# Produce an output for each time index
for time_index in time_indices:
# Make output filename
outputdate = inputloader.datetime_at_time_index(time_index)
pathname = 'example_output_{0:04d}{1:02d}.nc'.format(outputdate.year, outputdate.month)
print 'Saving: ', pathname
# Configure output grid
outputstructure = OutputRectilinearGridStructure(
time_index, outputdate,
latitudes=numpy.linspace(-87.5, 87.5, num=36),
longitudes=numpy.linspace(-177.5, 177.5, num=72))
# Evaluate expected value at these locations
result_expected_value = analysis_system.evaluate_expected_value(outputstructure)
# Save results
filebuilder = FileBuilderHadCRUT4ExampleOutput(pathname, outputstructure)
filebuilder.add_global_field(TAS_ANOMALY, result_expected_value.reshape(1,36,72))
filebuilder.save_and_close()
print 'Complete'
def test_init(self):
h = SeasonalHyperparameters(n_spatial_components=3,
common_log_sigma=1.1,
common_log_rho=1.2)
numpy.testing.assert_equal(h.harmonics, [1.1, 1.2, 1.1, 1.2, 1.1, 1.2])
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)
