def test_get_array(self):
p = CombinationHyperparameters([
CovariateHyperparameters(23.6),
LocalHyperparameters(log_sigma=0.1, log_rho=1.2)
])
numpy.testing.assert_equal([23.6, 0.1, 1.2], p.get_array())
def test_get_element_ranges(self):
p = CombinationHyperparameters([
CovariateHyperparameters(23.6),
LocalHyperparameters(log_sigma=0.1, log_rho=1.2),
CovariateHyperparameters(24.7)
])
self.assertEqual([[0], [1, 2], [3]], p.get_element_ranges())
def test_set_array(self):
p = CombinationHyperparameters([
CovariateHyperparameters(23.6),
LocalHyperparameters(log_sigma=0.1, log_rho=1.2),
CovariateHyperparameters(24.7)
])
p.set_array(numpy.array([1.4, 1.5, 1.6, 1.7]))
self.assertEqual(3, len(p.elementparameters))
self.assertEqual(1.4, p.elementparameters[0].value)
self.assertEqual(1.5, p.elementparameters[1].log_sigma)
self.assertEqual(1.6, p.elementparameters[1].log_rho)
self.assertEqual(1.7, p.elementparameters[2].value)
numpy.testing.assert_equal([1.4, 1.5, 1.6, 1.7], p.get_array())
def __init__(self):
# 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))
super(LargeScaleDefinition, self).__init__(
CombinationElement(factors),
CombinationHyperparameters(factor_hyperparameters))
def __init__(self,
bias_terms=False,
global_biases_group_list=[],
local_hyperparameter_file=None):
setup = ShortScaleSetup(local_hyperparameter_file)
bias_elements, bias_hyperparameters = [], []
if bias_terms:
for groupname in global_biases_group_list:
if (groupname == 'surfaceairmodel_land_global') or (
groupname == 'surfaceairmodel_ocean_global'):
bias_elements.append(BiasElement(groupname, 1))
bias_hyperparameters.append(
CovariateHyperparameters(
numpy.log(setup.bias_settings.bias_amplitude)))
elif (groupname == 'surfaceairmodel_ice_global'):
bias_elements.append(BiasElement(groupname, 2))
bias_hyperparameters.append(
CovariateHyperparameters(
numpy.log(setup.bias_settings.bias_amplitude)))
local_hyperparameters = ExpandedLocalHyperparameters(log_sigma=None,
log_rho=None)
local_hyperparameters.values_from_npy_savefile(
local_hyperparameter_file)
super(NonStationaryLocalDefinition, self).__init__(
CombinationElement([
NonStationaryLocal(
setup.local_settings.n_triangulation_divisions)
] + bias_elements),
CombinationHyperparameters([local_hyperparameters] +
bias_hyperparameters))
def test_init(self):
p = CombinationHyperparameters(
[CovariateHyperparameters(23.6),
CovariateHyperparameters(22.9)])
self.assertEqual(2, len(p.elementparameters))
self.assertEqual(23.6, p.elementparameters[0].value)
self.assertEqual(22.9, p.elementparameters[1].value)
def test_prior_number_of_state_parameters(self):
h = CombinationHyperparameters(
[CovariateHyperparameters(0.1),
CovariateHyperparameters(0.2)])
priorlist = [
CovariatePrior(h.elementparameters[0],
number_of_state_parameters=4),
CovariatePrior(h.elementparameters[1],
number_of_state_parameters=5)
]
prior = CombinationPrior(h, priorlist)
self.assertEqual(9, prior.prior_number_of_state_parameters())
def test_element_prior(self):
hyperparameters = CombinationHyperparameters([
CovariateHyperparameters(23.6),
LocalHyperparameters(log_sigma=0.1, log_rho=1.2)
])
prior = CombinationElement([GrandMeanElement(),
LocalElement(0)
]).element_prior(hyperparameters)
self.assertTrue(isinstance(prior, CombinationPrior))
self.assertEqual(2, len(prior.priorlist))
self.assertTrue(isinstance(prior.priorlist[0], CovariatePrior))
self.assertTrue(isinstance(prior.priorlist[1], LocalPrior))
def __init__(self, bias_terms = False, global_biases_group_list = []):
setup = LocalSetup()
if bias_terms:
bias_elements = [BiasElement(groupname, 1) for groupname in global_biases_group_list]
bias_hyperparameters = [CovariateHyperparameters(numpy.log(setup.bias_amplitude)) for index in range(len(global_biases_group_list))]
else:
bias_elements, bias_hyperparameters = [], []
super(LocalDefinition, self).__init__(
CombinationElement([LocalElement(setup.n_triangulation_divisions)] + bias_elements),
CombinationHyperparameters([LocalHyperparameters(numpy.log(setup.amplitude),
numpy.log(numpy.radians(setup.space_length_scale)))] + bias_hyperparameters))
def __init__(self, bias_terms = False, global_biases_group_list = [], local_hyperparameter_file = None):
setup = NonStationaryLocalSetup()
if bias_terms:
bias_elements = [BiasElement(groupname, 1) for groupname in global_biases_group_list]
bias_hyperparameters = [CovariateHyperparameters(numpy.log(setup.bias_amplitude)) for index in range(len(global_biases_group_list))]
else:
bias_elements, bias_hyperparameters = [], []
local_hyperparameters = ExpandedLocalHyperparameters(log_sigma = None, log_rho = None)
local_hyperparameters.values_from_npy_savefile(local_hyperparameter_file)
super(NonStationaryLocalDefinition, self).__init__(
CombinationElement([NonStationaryLocal(setup.n_triangulation_divisions)] + bias_elements),
CombinationHyperparameters([local_hyperparameters]+bias_hyperparameters))
def test_element_prior(self):
element = LatitudeHarmonicsElement()
prior = element.element_prior(
CombinationHyperparameters(
[CovariateHyperparameters(c) for c in [1.0, 1.1, 1.2, 1.3]]))
self.assertIsInstance(prior, LatitudeHarmonicsPrior)
# As an example check precision - should be diagonals with exp(-2 x hyperparameter)
precision = prior.prior_precision()
self.assertEqual(SPARSEFORMAT, precision.getformat())
self.assertEqual(4, precision.nnz)
self.assertAlmostEqual(numpy.exp(-2.0), precision[0, 0])
self.assertAlmostEqual(numpy.exp(-2.2), precision[1, 1])
self.assertAlmostEqual(numpy.exp(-2.4), precision[2, 2])
self.assertAlmostEqual(numpy.exp(-2.6), precision[3, 3])
def __init__(self):
setup = SlowSetupT2M1()
model_elements = CombinationElement( [AnnualKroneckerElement( setup.n_triangulation_divisions,
setup.alpha,
setup.starttime,
setup.endtime,
setup.n_nodes,
setup.overlap_factor,
setup.H ),] )
model_hyperparameters = CombinationHyperparameters( [SpaceTimeSPDEHyperparameters(numpy.log(setup.amplitude),
numpy.log(numpy.radians(setup.space_length_scale)),
numpy.log(setup.time_length_scale)),] )
super(TestComponentDefinition, self).__init__(model_elements, model_hyperparameters)
def __init__(self, bias_terms=False, breakpoints_file=None):
setup = MidScaleSetup()
if bias_terms:
bias_element = [
InsituLandBiasElement(breakpoints_file,
apply_policy=True,
cut_value=3)
]
bias_hyperparameters = [
CovariateHyperparameters(
numpy.log(setup.bias_settings.bias_amplitude))
]
else:
bias_element, bias_hyperparameters = [], []
super(LargeScaleDefinition, self).__init__(
CombinationElement([
SpaceTimeKroneckerElement(
setup.midscale_settings.n_triangulation_divisions, setup.
midscale_settings.alpha, setup.midscale_settings.starttime,
setup.midscale_settings.endtime, setup.midscale_settings.
n_nodes, setup.midscale_settings.overlap_factor,
setup.midscale_settings.H),
AnnualKroneckerElement(
setup.slow_settings.n_triangulation_divisions,
setup.slow_settings.alpha, setup.slow_settings.starttime,
setup.slow_settings.endtime, setup.slow_settings.n_nodes,
setup.slow_settings.overlap_factor, setup.slow_settings.H),
] + bias_element),
CombinationHyperparameters([
SpaceTimeSPDEHyperparameters(
numpy.log(setup.midscale_settings.amplitude),
numpy.log(
numpy.radians(
setup.midscale_settings.space_length_scale)),
numpy.log(setup.midscale_settings.time_length_scale)),
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)),
] + bias_hyperparameters))
def test_prior_precision_derivative(self):
h = CombinationHyperparameters([
CovariateHyperparameters(-0.5 * numpy.log(23.6)),
CovariateHyperparameters(-0.5 * numpy.log(88.9))
])
priorlist = [
CovariatePrior(h.elementparameters[0],
number_of_state_parameters=2),
CovariatePrior(h.elementparameters[1],
number_of_state_parameters=3)
]
prior = CombinationPrior(h, priorlist)
dQ = prior.prior_precision_derivative(1)
self.assertEqual(SPARSEFORMAT, dQ.getformat())
self.assertEqual((5, 5), dQ.shape)
self.assertEqual(3, dQ.nnz)
self.assertAlmostEqual(-2.0 * 88.9, dQ[2, 2])
self.assertAlmostEqual(-2.0 * 88.9, dQ[3, 3])
self.assertAlmostEqual(-2.0 * 88.9, dQ[4, 4])
def test_prior_precision(self):
h = CombinationHyperparameters([
CovariateHyperparameters(-0.5 * numpy.log(23.6)),
CovariateHyperparameters(-0.5 * numpy.log(88.9))
])
priorlist = [
CovariatePrior(h.elementparameters[0],
number_of_state_parameters=2),
CovariatePrior(h.elementparameters[1],
number_of_state_parameters=3)
]
prior = CombinationPrior(h, priorlist)
Q = prior.prior_precision()
self.assertEqual(SPARSEFORMAT, Q.getformat())
numpy.testing.assert_almost_equal(
Q.todense(),
[[23.6, 0.0, 0.0, 0.0, 0.0], [0.0, 23.6, 0.0, 0.0, 0.0],
[0.0, 0.0, 88.9, 0.0, 0.0], [0.0, 0.0, 0.0, 88.9, 0.0],
[0.0, 0.0, 0.0, 0.0, 88.9]])
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 __init__(self, bias_terms=False, global_biases_group_list=[]):
setup = ShortScaleSetup()
if bias_terms:
bias_elements = [
BiasElement(groupname, 1)
for groupname in global_biases_group_list
]
bias_hyperparameters = [
CovariateHyperparameters(
numpy.log(setup.bias_settings.bias_amplitude))
for index in range(len(global_biases_group_list))
]
else:
bias_elements, bias_hyperparameters = [], []
super(PureBiasComponentDefinition,
self).__init__(CombinationElement(bias_elements),
CombinationHyperparameters(bias_hyperparameters))
def test_mini_world_latitude_harmonics(self):
"""Testing on a simple mock data file using latitude harmonics"""
# GENERATING OBSERVATIONS
# Simulated locations: they will exactly sits on the grid points of the covariate datafile
locations = numpy.array([[0.0, 0.0], [0.25, 0.5], [0.5, 0.0]])
# Simulated model is y = a*cos(2x) + c*cos(4*x) + b*sin(2x) + d*sin(4*x) with x = latitude, so we expect a=c=1, c=d=0
measurement = LatitudeFunction(numpy.cos, 2.0).compute(
locations[:, 0]).ravel() + LatitudeFunction(
numpy.cos, 4.0).compute(locations[:, 0]).ravel()
# Simulated errors
uncorrelatederror = 0.1 * numpy.ones(measurement.shape)
# Simulated inputs
simulated_input_loader = SimulatedInputLoader(locations, measurement,
uncorrelatederror)
# Simulate evaluation of this time index
simulated_time_indices = [0]
latitude_harmonics_component = SpatialComponent(
ComponentStorage_InMemory(
LatitudeHarmonicsElement(),
CombinationHyperparameters([
CovariateHyperparameters(-0.5 * numpy.log(p))
for p in [10.0, 10.0, 10.0, 10.0]
])), SpatialComponentSolutionStorage_InMemory())
# Analysis system using the specified components, for the Tmean observable
analysis_system = AnalysisSystem([latitude_harmonics_component],
ObservationSource.TMEAN,
log=StringIO())
# GENERATING THE ANALYSIS
# Update with data
analysis_system.update([simulated_input_loader],
simulated_time_indices)
# Check state vector directly
statevector = analysis_system.components[
0].solutionstorage.partial_state_read(0).ravel()
# These are the nodes where observations were put (see SimulatedObservationSource above)
# - check they correspond to within 3 times the stated noise level
self.assertAlmostEqual(1., statevector[0], delta=0.3)
self.assertAlmostEqual(1., statevector[2], delta=0.3)
self.assertAlmostEqual(0., statevector[1], delta=0.3)
self.assertAlmostEqual(0., statevector[3], delta=0.3)
# Also check entire state vector within outer bounds set by obs
self.assertTrue(all(statevector < 1.0))
# And check output corresponds too
# (evaluate result on output structure same as input)
simulated_output_structure = SimulatedObservationStructure(
0, locations, None, None)
result = analysis_system.evaluate_expected_value(
'MAP', simulated_output_structure, flag='POINTWISE')
expected = statevector[0]*LatitudeFunction(numpy.cos, 2.0).compute(locations[:,0]).ravel() + statevector[1]*LatitudeFunction(numpy.sin, 2.0).compute(locations[:,0]).ravel()\
+ statevector[2] *LatitudeFunction(numpy.cos, 4.0).compute(locations[:,0]).ravel()+ statevector[3]*LatitudeFunction(numpy.sin, 4.0).compute(locations[:,0]).ravel()
numpy.testing.assert_almost_equal(expected, result)
def __init__(self, bias_terms=False, global_biases_group_list=[]):
setup = ShortScaleSetup()
bias_elements, bias_hyperparameters = [], []
if bias_terms:
for groupname in global_biases_group_list:
#if (groupname == 'surfaceairmodel_land_global') or (groupname == 'surfaceairmodel_ocean_global'):
#bias_elements.append( BiasElement(groupname, 1) )
#bias_hyperparameters.append( CovariateHyperparameters(numpy.log(setup.bias_settings.bias_amplitude)) )
if (groupname == 'surfaceairmodel_land_global'):
# global mean term
bias_elements.append(BiasElement(groupname, 1))
bias_hyperparameters.append(
CovariateHyperparameters(
numpy.log(setup.bias_settings.bias_amplitude)))
# spatial bias term
bias_elements.append(
SpatialBiasElement(
groupname, setup.spatial_bias_settings.
n_triangulation_divisions))
bias_hyperparameters.append(
LocalHyperparameters(
numpy.log(setup.spatial_bias_settings.
spatial_bias_amplitutde),
numpy.log(
numpy.radians(setup.spatial_bias_settings.
spatial_bias_length_scale))))
elif (groupname == 'surfaceairmodel_ocean_global'):
# global mean term
bias_elements.append(BiasElement(groupname, 1))
bias_hyperparameters.append(
CovariateHyperparameters(
numpy.log(setup.bias_settings.bias_amplitude)))
elif (groupname == 'surfaceairmodel_ice_global'):
# hemispheric term
bias_elements.append(BiasElement(groupname, 2))
bias_hyperparameters.append(
CovariateHyperparameters(
numpy.log(setup.bias_settings.bias_amplitude)))
# spatial bias term
bias_elements.append(
SpatialBiasElement(
groupname, setup.spatial_bias_settings.
n_triangulation_divisions))
bias_hyperparameters.append(
LocalHyperparameters(
numpy.log(setup.spatial_bias_settings.
spatial_bias_amplitutde),
numpy.log(
numpy.radians(setup.spatial_bias_settings.
spatial_bias_length_scale))))
super(LocalDefinition, self).__init__(
CombinationElement(
[LocalElement(setup.local_settings.n_triangulation_divisions)
] + bias_elements),
CombinationHyperparameters([
LocalHyperparameters(
numpy.log(setup.local_settings.amplitude),
numpy.log(
numpy.radians(
setup.local_settings.space_length_scale)))
] + bias_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))))
#time_indices = range(int(days_since_epoch(datetime(1906, 2, 1))), int(days_since_epoch(datetime(1906, 2, 2))))
date_list = [
datetime(2006, 1, 1) + relativedelta(days=k) for k in range(3)
]
#backwards_list = [date_list[i] for i in range(11, -1, -1)]
#date_list = backwards_list
time_indices = [int(days_since_epoch(date)) for date in date_list]
# Sources to use
sources = [
'surfaceairmodel_land', 'surfaceairmodel_ocean', 'surfaceairmodel_ice',
'insitu_land', 'insitu_ocean'
]
sources = ['insitu_land', 'insitu_ocean']
#sources = [ 'surfaceairmodel_land' ]
# 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=2, n_harmonics=2, include_local_mean=False), GrandMeanElement()]+covariate_elements)
#climatology_hyperparameters = CombinationHyperparameters( [SeasonalHyperparameters(n_spatial_components=2, common_log_sigma=0.0, common_log_rho=0.0), CovariateHyperparameters(numpy.log(15.0))] + covariate_hyperparameters )
climatology_element = CombinationElement([
GrandMeanElement(),
] + covariate_elements)
climatology_hyperparameters = CombinationHyperparameters([
CovariateHyperparameters(numpy.log(15.0)),
] + covariate_hyperparameters)
#climatology_element =SeasonalElement(n_triangulation_divisions=2, n_harmonics=2, include_local_mean=False)
#climatology_hyperparameters = SeasonalHyperparameters(n_spatial_components=2, common_log_sigma=0.0, common_log_rho=0.0)
climatology_component = SpaceTimeComponent(
ComponentStorage_InMemory(climatology_element,
climatology_hyperparameters),
SpaceTimeComponentSolutionStorage_InMemory(),
compute_uncertainties=True,
method='APPROXIMATED')
# 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(.9))]
print('Adding bias terms for insitu land homogenization')
else:
bias_element, bias_hyperparameters = [], []
large_scale_element = CombinationElement([
SpaceTimeKroneckerElement(n_triangulation_divisions=2,
alpha=2,
starttime=-30,
endtime=365 * 1 + 30,
n_nodes=12 * 1 + 2,
overlap_factor=2.5,
H=1)
] + bias_element)
large_scale_hyperparameters = CombinationHyperparameters([
SpaceTimeSPDEHyperparameters(space_log_sigma=0.0,
space_log_rho=numpy.log(
numpy.radians(15.0)),
time_log_rho=numpy.log(15.0))
] + bias_hyperparameters)
large_scale_component = SpaceTimeComponent(
ComponentStorage_InMemory(large_scale_element,
large_scale_hyperparameters),
SpaceTimeComponentSolutionStorage_InMemory(),
compute_uncertainties=True,
method='APPROXIMATED')
# 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(15.0)) for index in range(3)
]
print('Adding global bias terms for all the surfaces')
else:
bias_elements, bias_hyperparameters = [], []
n_triangulation_divisions_local = 7
local_log_sigma = numpy.log(5)
local_log_rho = numpy.log(numpy.radians(5.0))
local_element = NonStationaryLocal(
n_triangulation_divisions=n_triangulation_divisions_local)
n_local_nodes = local_element.spde.n_latent_variables()
local_scale_element = CombinationElement([local_element] + bias_elements)
local_hyperparameters = ExpandedLocalHyperparameters(
log_sigma=numpy.repeat(local_log_sigma, n_local_nodes),
log_rho=numpy.repeat(local_log_rho, n_local_nodes))
local_scale_hyperparameters = CombinationHyperparameters(
[local_hyperparameters] + bias_hyperparameters)
local_component = DelayedSpatialComponent(
ComponentStorage_InMemory(local_scale_element,
local_scale_hyperparameters),
SpatialComponentSolutionStorage_InMemory(),
compute_uncertainties=True,
method='APPROXIMATED')
print "hyperparameter storage:", local_component.storage.hyperparameters
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)
analysis_system = OptimizationSystem(
[climatology_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)
##################################################
# Optimize local model hyperparameters
# Loop over local regions, generate optimization systems, fit hyperparameters and save
# split spde and bias models for local component into two components
global_spde_sub_component_definition = ComponentStorage_InMemory(
CombinationElement([local_element]),
CombinationHyperparameters([local_hyperparameters]))
global_spde_sub_component_storage_solution = SpatialComponentSolutionStorage_InMemory(
)
global_spde_sub_component = DelayedSpatialComponent(
global_spde_sub_component_definition,
global_spde_sub_component_storage_solution)
bias_sub_component_definition = ComponentStorage_InMemory(
CombinationElement(bias_elements),
CombinationHyperparameters(bias_hyperparameters))
bias_sub_component_storage_solution = SpatialComponentSolutionStorage_InMemory(
)
bias_sub_component = DelayedSpatialComponent(
bias_sub_component_definition, bias_sub_component_storage_solution)
element_optimisation_flags = [True, False, False,
False] # one spde, three biases
for time_key in time_indices:
split_states_time(local_component, global_spde_sub_component,
bias_sub_component, element_optimisation_flags,
time_key)
# Define subregions and extract their states
neighbourhood_level = 1
n_subregions = global_spde_sub_component.storage.element_read(
).combination[0].spde.n_triangles_at_level(neighbourhood_level)
hyperparameter_file_template = "local_hyperparameters.%i.%i.%i.npy"
fit_hyperparameters = True
optimization_component_index = 2
if fit_hyperparameters:
for region_index in range(n_subregions):
# Setup model for local subregion of neighours with super triangle
view_flags = [
True,
]
region_element = CombinationElement([
LocalSubRegion(n_triangulation_divisions_local,
neighbourhood_level, region_index)
])
region_hyperparameters = ExtendedCombinationHyperparameters([
LocalHyperparameters(log_sigma=local_log_sigma,
log_rho=local_log_rho)
])
region_component_storage_solution = SpatialComponentSolutionStorage_InMemory(
)
region_sub_component = DelayedSpatialComponent(
ComponentStorage_InMemory(region_element,
region_hyperparameters),
region_component_storage_solution)
for time_key in time_indices:
print "region_index, time_key:", region_index, time_key
extract_local_view_states_time(global_spde_sub_component,
region_sub_component,
view_flags, time_key)
print "running optimization for region:", region_index
region_optimization_system = OptimizationSystem([
climatology_component, bias_sub_component, region_sub_component
], ObservationSource.TMEAN)
for time_key in time_indices:
region_optimization_system.update_component_time(
inputloaders, optimization_component_index, time_key)
# commented version that works for few days inputs
#region_optimization_system.components[optimization_component_index].component_solution().optimize()
#region_optimization_system.components[optimization_component_index].storage.hyperparameters.get_array()
#hyperparameter_file = os.path.join(args.outpath, hyperparameter_file_template % (n_triangulation_divisions_local, neighbourhood_level, region_index) )
#region_sub_component.storage.hyperparameters.values_to_npy_savefile( hyperparameter_file )
# replaced with version for full processing based json dump of input files - need to generate the input_descriptor dict
hyperparameter_file = os.path.join(
args.outpath, hyperparameter_file_template %
(n_triangulation_divisions_local, neighbourhood_level,
region_index))
region_optimization_system.process_inputs(
input_descriptor, optimization_component_index, time_indices)
region_optimization_system.optimize_component(
optimization_component_index,
hyperparameter_storage_file=hyperparameter_file)
fitted_hyperparameters_converted = region_sub_component.storage.hyperparameters.get_array(
)
fitted_hyperparameters_converted[0] = numpy.exp(
fitted_hyperparameters_converted[0])
fitted_hyperparameters_converted[1] = numpy.exp(
fitted_hyperparameters_converted[1]) * 180.0 / numpy.pi
print 'fitted_hyperparameters_converted:', fitted_hyperparameters_converted
# Setup model for the super triangle without neighbours for hyperparameter merging
region_spdes = []
region_hyperparameter_values = []
for region_index in range(n_subregions):
# Redefine the region sub component as a supertriangle rather than a neighbourhood
region_element = CombinationElement([
LocalSuperTriangle(n_triangulation_divisions_local,
neighbourhood_level, region_index)
])
region_hyperparameters = ExtendedCombinationHyperparameters([
LocalHyperparameters(log_sigma=local_log_sigma,
log_rho=local_log_rho)
])
region_component_storage_solution = SpatialComponentSolutionStorage_InMemory(
)
region_sub_component = DelayedSpatialComponent(
ComponentStorage_InMemory(region_element, region_hyperparameters),
region_component_storage_solution)
# Read the optimized hyperparameters
hyperparameter_file = os.path.join(
args.outpath,
hyperparameter_file_template % (n_triangulation_divisions_local,
neighbourhood_level, region_index))
region_sub_component.storage.hyperparameters.values_from_npy_savefile(
hyperparameter_file)
# Append the spde model and hyperparameters to their lists for merging
region_spdes.append(region_element.combination[0].spde)
region_hyperparameter_values.append(
region_sub_component.storage.hyperparameters.get_array())
# merge and save hyperparameters
full_spde = local_element.spde
new_hyperparameter_values, global_sigma_design, global_rho_design = full_spde.merge_local_parameterisations(
region_spdes, region_hyperparameter_values, merge_method='exp_average')
local_hyperparameters.set_array(new_hyperparameter_values)
hyperparameter_file_merged = "merged_hyperparameters.%i.%i.npy" % (
n_triangulation_divisions_local, neighbourhood_level)
local_hyperparameters.values_to_npy_savefile(
os.path.join(args.outpath, hyperparameter_file_merged))
# Refit local model with the optimized hyperparameters
analysis_system.update_component(inputloaders, 1, 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(-89.875,
89.875,
num=definitions.GLOBAL_FIELD_SHAPE[1]),
longitudes=numpy.linspace(-179.875,
179.875,
num=definitions.GLOBAL_FIELD_SHAPE[2]))
# print 'Size of grid : ', outputstructure.number_of_observations()
# Evaluate expected value at these locations
result_expected_value = analysis_system.evaluate_expected_value(
'MAP', outputstructure, 'POINTWISE')
result_expected_uncertainties = analysis_system.evaluate_expected_value(
'post_STD', outputstructure, 'POINTWISE')
# 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.save_and_close()
print 'Complete'
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 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_altitude_with_latitude(self):
"""Testing using altitude as a covariate"""
# GENERATING OBSERVATIONS
# Simulated locations: they will exactly sits on the grid points of the covariate datafile
DEM = Dataset(self.altitude_datafile)
latitude = DEM.variables['lat'][:]
longitude = DEM.variables['lon'][:]
altitude = DEM.variables['dem'][:]
indices = numpy.stack(
(numpy.array([1, 3, 5, 7, 8, 9, 10, 11
]), numpy.array([0, 0, 0, 0, 0, 0, 0, 0])),
axis=1)
selected_location = []
altitude_observations = []
for couple in indices:
selected_location.append([
latitude[couple[0], couple[1]], longitude[couple[0], couple[1]]
])
altitude_observations.append(altitude[couple[0], couple[1]])
DEM.close()
locations = numpy.array(selected_location)
# Simulated model is y = z + a*cos(2x) + c*cos(4*x) + b*sin(2x) + d*sin(4*x), with z = altitude, x = latitude, a=b=c=d=0
slope = 1e-3
measurement = slope * numpy.array(altitude_observations)
# Simulated errors
uncorrelatederror = 0.1 * numpy.ones(measurement.shape)
# Simulated inputs
simulated_input_loader = SimulatedInputLoader(locations, measurement,
uncorrelatederror)
# Simulate evaluation of this time index
simulated_time_indices = [0]
# GENERATING THE MODEL
# Local component
geography_covariate_element = GeographyBasedElement(
self.altitude_datafile, 'lat', 'lon', 'dem', 1.0)
geography_covariate_element.load()
combined_element = CombinationElement(
[geography_covariate_element,
LatitudeHarmonicsElement()])
combined_hyperparamters = CombinationHyperparameters([
CovariateHyperparameters(-0.5 * numpy.log(10.)),
CombinationHyperparameters([
CovariateHyperparameters(-0.5 * numpy.log(p))
for p in [10.0, 10.0, 10.0, 10.0]
])
])
combined_component = SpatialComponent(
ComponentStorage_InMemory(combined_element,
combined_hyperparamters),
SpatialComponentSolutionStorage_InMemory())
# GENERATING THE ANALYSIS
# Analysis system using the specified components, for the Tmean observable
analysis_system = AnalysisSystem([combined_component],
ObservationSource.TMEAN,
log=StringIO())
# Update with data
analysis_system.update([simulated_input_loader],
simulated_time_indices)
# Check state vector directly
statevector = analysis_system.components[
0].solutionstorage.partial_state_read(0).ravel()
# These are the nodes where observations were put (see SimulatedObservationSource above)
# - check they correspond to within 3 times the stated noise level
self.assertAlmostEqual(slope, statevector[0], delta=0.3)
self.assertAlmostEqual(0., statevector[1], delta=0.3)
self.assertAlmostEqual(0., statevector[2], delta=0.3)
self.assertAlmostEqual(0., statevector[3], delta=0.3)
self.assertAlmostEqual(0., statevector[4], delta=0.3)
# And check output corresponds too
# (evaluate result on output structure same as input)
simulated_output_structure = SimulatedObservationStructure(
0, locations, None, None)
result = analysis_system.evaluate_expected_value(
'MAP', simulated_output_structure, flag='POINTWISE')
expected = statevector[0]*numpy.array(altitude_observations)\
+ statevector[1]*LatitudeFunction(numpy.cos, 2.0).compute(locations[:,0]).ravel()\
+ statevector[2]*LatitudeFunction(numpy.sin, 2.0).compute(locations[:,0]).ravel()\
+ statevector[3]*LatitudeFunction(numpy.cos, 4.0).compute(locations[:,0]).ravel()\
+ statevector[4]*LatitudeFunction(numpy.sin, 2.0).compute(locations[:,0]).ravel()
numpy.testing.assert_almost_equal(expected, result)
# 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 __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)
