def test_input_not_masked(self):
"""Test case where input cube is not masked."""
self.percentiles_land.data = np.ones((2, 3, 4), dtype=np.float32)
expected_cube = self.percentiles_land.copy()
merge_land_and_sea(self.percentiles_land, self.percentiles_sea)
self.assertArrayEqual(self.percentiles_land.data, expected_cube.data)
self.assertEqual(expected_cube.xml(checksum=True),
self.percentiles_land.xml(checksum=True))
self.assertEqual(self.percentiles_land.data.dtype, np.float32)
def test_nothing_to_merge(self):
"""Test case where there is no missing data to fill in."""
input_mask = np.ones((2, 3, 4)) * False
self.percentiles_land.data.mask = input_mask
expected_cube = self.percentiles_land.copy()
merge_land_and_sea(self.percentiles_land, self.percentiles_sea)
self.assertArrayEqual(self.percentiles_land.data, expected_cube.data)
self.assertEqual(expected_cube.xml(checksum=True),
self.percentiles_land.xml(checksum=True))
self.assertFalse(np.ma.is_masked(self.percentiles_land.data))
self.assertEqual(self.percentiles_land.data.dtype, np.float32)
def test_merge(self):
"""Test merged data."""
expected_merged_data = np.array(
[
[[3.0, 1.0, 1.0, 1.0], [3.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 3.0]],
[[3.0, 1.0, 1.0, 1.0], [3.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 3.0]],
],
dtype=np.float32,
)
expected_cube = self.percentiles_land.copy()
expected_cube.data = expected_merged_data
merge_land_and_sea(self.percentiles_land, self.percentiles_sea)
self.assertArrayEqual(self.percentiles_land.data, expected_merged_data)
self.assertEqual(
expected_cube.xml(checksum=True), self.percentiles_land.xml(checksum=True)
)
self.assertFalse(np.ma.is_masked(self.percentiles_land.data))
self.assertEqual(self.percentiles_land.data.dtype, np.float32)
def test_mismatch_dim_length(self):
"""Check an error is raised if a dim coord has a different length"""
land_slice = self.percentiles_land[:, 1:, :]
message = "Input cubes do not have the same dimension coordinates"
with self.assertRaisesRegex(ValueError, message):
merge_land_and_sea(land_slice, self.percentiles_sea)
def test_missing_dim(self):
"""Check that an error is raised if missing dimensional coordinate"""
single_percentile = squeeze(self.percentiles_land[0])
message = "Input cubes do not have the same dimension coordinates"
with self.assertRaisesRegex(ValueError, message):
merge_land_and_sea(single_percentile, self.percentiles_sea)
def process(cube: cli.inputcube,
coefficients: cli.inputcube = None,
land_sea_mask: cli.inputcube = None,
*,
distribution,
realizations_count: int = None,
randomise=False,
random_seed: int = None,
ignore_ecc_bounds=False,
predictor='mean',
shape_parameters: cli.comma_separated_list = None):
"""Applying coefficients for Ensemble Model Output Statistics.
Load in arguments for applying coefficients for Ensemble Model Output
Statistics (EMOS), otherwise known as Non-homogeneous Gaussian
Regression (NGR). The coefficients are applied to the forecast
that is supplied, so as to calibrate the forecast. The calibrated
forecast is written to a cube. If no coefficients are provided the input
forecast is returned unchanged.
Args:
cube (iris.cube.Cube):
A Cube containing the forecast to be calibrated. The input format
could be either realizations, probabilities or percentiles.
coefficients (iris.cube.Cube):
A cube containing the coefficients used for calibration or None.
If none then then input is returned unchanged.
land_sea_mask (iris.cube.Cube):
A cube containing the land-sea mask on the same domain as the
forecast that is to be calibrated. Land points are "
"specified by ones and sea points are specified by zeros. "
"If not None this argument will enable land-only calibration, in "
"which sea points are returned without the application of "
"calibration."
distribution (str):
The distribution for constructing realizations, percentiles or
probabilities. This should typically match the distribution used
for minimising the Continuous Ranked Probability Score when
estimating the EMOS coefficients. The distributions available are
those supported by :data:`scipy.stats`.
realizations_count (int):
Option to specify the number of ensemble realizations that will be
created from probabilities or percentiles for input into EMOS.
randomise (bool):
Option to reorder the post-processed forecasts randomly. If not
set, the ordering of the raw ensemble is used. This option is
only valid when the input format is realizations.
random_seed (int):
Option to specify a value for the random seed for testing
purposes, otherwise the default random seen behaviour is utilised.
The random seed is used in the generation of the random numbers
used for either the randomise option to order the input
percentiles randomly, rather than use the ordering from the raw
ensemble, or for splitting tied values within the raw ensemble,
so that the values from the input percentiles can be ordered to
match the raw ensemble.
ignore_ecc_bounds (bool):
If True, where the percentiles exceed the ECC bounds range,
raises a warning rather than an exception. This occurs when the
current forecasts is in the form of probabilities and is
converted to percentiles, as part of converting the input
probabilities into realizations.
predictor (str):
String to specify the form of the predictor used to calculate
the location parameter when estimating the EMOS coefficients.
Currently the ensemble mean ("mean") and the ensemble
realizations ("realizations") are supported as the predictors.
shape_parameters (float or str):
The shape parameters required for defining the distribution
specified by the distribution argument. The shape parameters
should either be a number or 'inf' or '-inf' to represent
infinity. Further details about appropriate shape parameters
are available in scipy.stats. For the truncated normal
distribution with a lower bound of zero, as available when
estimating EMOS coefficients, the appropriate shape parameters
are 0 and inf.
Returns:
iris.cube.Cube:
The calibrated forecast cube.
Raises:
ValueError:
If the current forecast is a coefficients cube.
ValueError:
If the coefficients cube does not have the right name of
"emos_coefficients".
ValueError:
If the forecast type is 'percentiles' or 'probabilities' and the
realizations_count argument is not provided.
"""
import warnings
import numpy as np
from iris.exceptions import CoordinateNotFoundError
from improver.calibration.ensemble_calibration import (
ApplyCoefficientsFromEnsembleCalibration)
from improver.ensemble_copula_coupling.ensemble_copula_coupling import (
EnsembleReordering, ConvertLocationAndScaleParametersToPercentiles,
ConvertLocationAndScaleParametersToProbabilities,
ConvertProbabilitiesToPercentiles, RebadgePercentilesAsRealizations,
ResamplePercentiles)
from improver.calibration.utilities import merge_land_and_sea
from improver.metadata.probabilistic import find_percentile_coordinate
current_forecast = cube
if current_forecast.name() in ['emos_coefficients', 'land_binary_mask']:
msg = "The current forecast cube has the name {}"
raise ValueError(msg.format(current_forecast.name()))
if coefficients is None:
msg = ("There are no coefficients provided for calibration. The "
"uncalibrated forecast will be returned.")
warnings.warn(msg)
return current_forecast
if coefficients.name() != 'emos_coefficients':
msg = ("The current coefficients cube does not have the "
"name 'emos_coefficients'")
raise ValueError(msg)
if land_sea_mask and land_sea_mask.name() != 'land_binary_mask':
msg = ("The land_sea_mask cube does not have the "
"name 'land_binary_mask'")
raise ValueError(msg)
original_current_forecast = current_forecast.copy()
try:
find_percentile_coordinate(current_forecast)
input_forecast_type = "percentiles"
except CoordinateNotFoundError:
input_forecast_type = "realizations"
if current_forecast.name().startswith("probability_of"):
input_forecast_type = "probabilities"
conversion_plugin = ConvertProbabilitiesToPercentiles(
ecc_bounds_warning=ignore_ecc_bounds)
elif input_forecast_type == "percentiles":
# Initialise plugin to resample percentiles so that the percentiles are
# evenly spaced.
conversion_plugin = ResamplePercentiles(
ecc_bounds_warning=ignore_ecc_bounds)
if input_forecast_type in ["percentiles", "probabilities"]:
if not realizations_count:
raise ValueError(
"The current forecast has been provided as {0}. "
"These {0} need to be converted to realizations "
"for ensemble calibration. The realizations_count "
"argument is used to define the number of realizations "
"to construct from the input {0}, so if the "
"current forecast is provided as {0} then "
"realizations_count must be defined.".format(
input_forecast_type))
current_forecast = conversion_plugin.process(
current_forecast, no_of_percentiles=realizations_count)
current_forecast = (
RebadgePercentilesAsRealizations().process(current_forecast))
# Apply coefficients as part of Ensemble Model Output Statistics (EMOS).
ac = ApplyCoefficientsFromEnsembleCalibration(predictor=predictor)
location_parameter, scale_parameter = ac.process(
current_forecast, coefficients, landsea_mask=land_sea_mask)
if shape_parameters:
shape_parameters = [np.float32(x) for x in shape_parameters]
# Convert the output forecast type (i.e. realizations, percentiles,
# probabilities) to match the input forecast type.
if input_forecast_type == "probabilities":
result = ConvertLocationAndScaleParametersToProbabilities(
distribution=distribution,
shape_parameters=shape_parameters).process(
location_parameter, scale_parameter, original_current_forecast)
elif input_forecast_type == "percentiles":
perc_coord = find_percentile_coordinate(original_current_forecast)
result = ConvertLocationAndScaleParametersToPercentiles(
distribution=distribution,
shape_parameters=shape_parameters).process(
location_parameter,
scale_parameter,
original_current_forecast,
percentiles=perc_coord.points)
elif input_forecast_type == "realizations":
# Ensemble Copula Coupling to generate realizations
# from the location and scale parameter.
no_of_percentiles = len(current_forecast.coord('realization').points)
percentiles = ConvertLocationAndScaleParametersToPercentiles(
distribution=distribution,
shape_parameters=shape_parameters).process(
location_parameter,
scale_parameter,
original_current_forecast,
no_of_percentiles=no_of_percentiles)
result = EnsembleReordering().process(percentiles,
current_forecast,
random_ordering=randomise,
random_seed=random_seed)
if land_sea_mask:
# Fill in masked sea points with uncalibrated data.
merge_land_and_sea(result, original_current_forecast)
return result
