def test_mean_predictor_estimate_coefficients_nans(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor, when one value from the input data is set to NaN.
The coefficients are estimated using a linear model.
"""
data = [1, 1, 2.66666667, 1]
cube = self.cube
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
truth = cube.collapsed("realization", iris.analysis.MAX)
distribution = "gaussian"
desired_units = "degreesC"
predictor_of_mean_flag = "mean"
estimate_coefficients_from_linear_model_flag = True
current_forecast_predictor.data[0][0][0] = np.nan
plugin = Plugin(distribution, desired_units)
result = plugin.compute_initial_guess(
truth, current_forecast_predictor, predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag)
self.assertArrayAlmostEqual(result, data)
def test_realizations_predictor_estimate_coefficients(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor. The coefficients are estimated using a linear model.
"""
import imp
try:
statsmodels_found = imp.find_module('statsmodels')
statsmodels_found = True
except ImportError:
statsmodels_found = False
if statsmodels_found:
data = [1., 1., 0.13559322, -0.11864407, 0.42372881, 0.69491525]
else:
data = [1, 1, 0, 1, 1, 1]
current_forecast_predictor = self.cube
truth = self.cube.collapsed("realization", iris.analysis.MAX)
distribution = "gaussian"
desired_units = "degreesC"
predictor_of_mean_flag = "realizations"
no_of_realizations = 3
estimate_coefficients_from_linear_model_flag = True
plugin = Plugin(distribution, desired_units)
result = plugin.compute_initial_guess(
truth,
current_forecast_predictor,
predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag,
no_of_realizations=no_of_realizations)
self.assertArrayAlmostEqual(result, data)
def test_basic_members_predictor_value_check(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the individual ensemble members
are used as predictors. As coefficients are not estimated using a
linear model, the default values for the initial guess are used.
"""
data = [1, 1, 0, 1, 1, 1]
cube = self.cube
current_forecast_predictor = cube.collapsed("realization",
iris.analysis.MEAN)
truth = cube.collapsed("realization", iris.analysis.MAX)
distribution = "gaussian"
desired_units = "degreesC"
predictor_of_mean_flag = "members"
no_of_members = 3
estimate_coefficients_from_linear_model_flag = False
plugin = Plugin(distribution, desired_units)
result = plugin.compute_initial_guess(
truth,
current_forecast_predictor,
predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag,
no_of_members=no_of_members)
self.assertArrayAlmostEqual(result, data)
def test_basic_realizations_predictor_value_check(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the individual ensemble
realizations are used as predictors. As coefficients are not estimated
using a linear model, the default values for the initial guess
are used.
"""
data = [
0, 1, 0,
np.sqrt(1. / self.no_of_realizations),
np.sqrt(1. / self.no_of_realizations),
np.sqrt(1. / self.no_of_realizations)
]
predictor_of_mean_flag = "realizations"
estimate_coefficients_from_linear_model_flag = False
plugin = Plugin(self.distribution, self.desired_units)
result = plugin.compute_initial_guess(
self.truth,
self.current_forecast_predictor_realizations,
predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag,
no_of_realizations=self.no_of_realizations)
self.assertArrayAlmostEqual(result, data)
def test_basic_mean_predictor(self):
"""
Test that the plugin returns a list containing the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor.
"""
estimate_coefficients_from_linear_model_flag = False
plugin = Plugin(self.distribution, self.desired_units)
result = plugin.compute_initial_guess(
self.truth, self.current_forecast_predictor_mean,
self.predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag)
self.assertIsInstance(result, np.ndarray)
def test_basic_mean_predictor_value_check(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor. As coefficients are not estimated using a
linear model, the default values for the initial guess are used.
"""
data = [0, 1, 0, 1]
estimate_coefficients_from_linear_model_flag = False
plugin = Plugin(self.distribution, self.desired_units)
result = plugin.compute_initial_guess(
self.truth, self.current_forecast_predictor_mean,
self.predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag)
self.assertArrayAlmostEqual(result, data)
def test_basic_realizations_predictor(self):
"""
Test that the plugin returns a list containing the initial guess
for the calibration coefficients, when the individual ensemble
realizations are used as predictors.
"""
predictor_of_mean_flag = "realizations"
estimate_coefficients_from_linear_model_flag = False
plugin = Plugin(self.distribution, self.desired_units)
result = plugin.compute_initial_guess(
self.truth,
self.current_forecast_predictor_realizations,
predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag,
no_of_realizations=self.no_of_realizations)
self.assertIsInstance(result, np.ndarray)
def test_mean_predictor_estimate_coefficients(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor. The coefficients are estimated using a linear model,
where there is an offset of one between the truth and the forecast
during the training period. Therefore, in this case the result of the
linear regression is a gradient of 1 and an intercept of 1.
"""
data = np.array([0., 1., 1., 1.], dtype=np.float32)
estimate_coefficients_from_linear_model_flag = True
plugin = Plugin(self.distribution, self.desired_units)
result = plugin.compute_initial_guess(
self.truth, self.current_forecast_predictor_mean,
self.predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag)
self.assertArrayAlmostEqual(result, data)
def test_basic_mean_predictor(self):
"""
Test that the plugin returns a list containing the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor.
"""
current_forecast_predictor = self.cube.collapsed(
"realization", iris.analysis.MEAN)
truth = self.cube.collapsed("realization", iris.analysis.MAX)
distribution = "gaussian"
desired_units = "degreesC"
predictor_of_mean_flag = "mean"
estimate_coefficients_from_linear_model_flag = False
plugin = Plugin(distribution, desired_units)
result = plugin.compute_initial_guess(
truth, current_forecast_predictor, predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag)
self.assertIsInstance(result, np.ndarray)
def test_realizations_predictor_estimate_coefficients(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor. The coefficients are estimated using a linear model.
In this case, the result of the linear regression is for an intercept
of 0.333333 with different weights for the realizations because
some of the realizations are closer to the truth, in this instance.
"""
data = [0., 1., 0.333333, 0., 0.333333, 0.666667]
predictor_of_mean_flag = "realizations"
estimate_coefficients_from_linear_model_flag = True
plugin = Plugin(self.distribution, self.desired_units)
result = plugin.compute_initial_guess(
self.truth,
self.current_forecast_predictor_realizations,
predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag,
no_of_realizations=self.no_of_realizations)
self.assertArrayAlmostEqual(result, data)
def test_mean_predictor_estimate_coefficients(self):
"""
Test that the plugin returns the expected values for the initial guess
for the calibration coefficients, when the ensemble mean is used
as the predictor. The coefficients are estimated using a linear model.
"""
data = np.array([1, 1, 2.66663, 1], dtype=np.float32)
current_forecast_predictor = self.cube.collapsed(
"realization", iris.analysis.MEAN)
truth = self.cube.collapsed("realization", iris.analysis.MAX)
distribution = "gaussian"
desired_units = "degreesC"
predictor_of_mean_flag = "mean"
estimate_coefficients_from_linear_model_flag = True
plugin = Plugin(distribution, desired_units)
result = plugin.compute_initial_guess(
truth, current_forecast_predictor, predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag)
self.assertArrayAlmostEqual(result, data, decimal=5)
def test_basic_members_predictor(self):
"""
Test that the plugin returns a list containing the initial guess
for the calibration coefficients, when the individual ensemble members
are used as predictors.
"""
cube = self.cube
current_forecast_predictor = cube.copy()
truth = cube.collapsed("realization", iris.analysis.MAX)
distribution = "gaussian"
desired_units = "degreesC"
predictor_of_mean_flag = "members"
no_of_members = 3
estimate_coefficients_from_linear_model_flag = False
plugin = Plugin(distribution, desired_units)
result = plugin.compute_initial_guess(
truth,
current_forecast_predictor,
predictor_of_mean_flag,
estimate_coefficients_from_linear_model_flag,
no_of_members=no_of_members)
self.assertIsInstance(result, list)