def test_basic(self):
"""Test that a datetime object is returned of the expected value."""
dt = datetime(2017, 11, 22, 1, 0)
cycletime = "20171122T0100Z"
result = datetime_to_cycletime(dt)
self.assertIsInstance(result, str)
self.assertEqual(result, cycletime)
def test_define_cycletime_format_with_seconds(self):
"""Test when a cycletime is defined with seconds."""
dt = datetime(2017, 11, 22, 1, 0)
cycletime = "20171122010000"
result = datetime_to_cycletime(dt, cycletime_format="%Y%m%d%H%M%S")
self.assertEqual(result, cycletime)
def process(self, current_forecast, historic_forecast, truth):
"""
Performs ensemble calibration through the following steps:
1. Estimate optimised coefficients from training period.
2. Apply optimised coefficients to current forecast.
Args:
current_forecast (iris.cube.Cube):
The cube that provides the input forecast for
the current cycle.
historic_forecast (iris.cube.Cube):
The cube that provides the input historic forecasts
for calibration.
truth (iris.cube.Cube):
The cube that provides the input truth for
calibration with dates matching the historic forecasts.
Returns:
(tuple): tuple containing:
**calibrated_forecast_predictor** (iris.cube.Cube):
Cube containing the calibrated forecast predictor.
**calibrated_forecast_variance** (iris.cube.Cube):
Cube containing the calibrated forecast variance.
"""
def format_calibration_method(calibration_method):
"""Lowercase input string, and replace underscores with spaces."""
return calibration_method.lower().replace("_", " ")
# Ensure predictor_of_mean_flag is valid.
check_predictor_of_mean_flag(self.predictor_of_mean_flag)
if (format_calibration_method(self.calibration_method) in
["ensemble model output statistics",
"nonhomogeneous gaussian regression"]):
if (format_calibration_method(self.distribution) in
["gaussian", "truncated gaussian"]):
current_cycle = datetime_to_cycletime(
iris_time_to_datetime(
current_forecast.coord("forecast_reference_time"))[0])
ec = EstimateCoefficientsForEnsembleCalibration(
self.distribution, current_cycle=current_cycle,
desired_units=self.desired_units,
predictor_of_mean_flag=self.predictor_of_mean_flag)
coefficient_cube = (
ec.estimate_coefficients_for_ngr(
historic_forecast, truth))
else:
msg = ("Other calibration methods are not available. "
"{} is not available".format(
format_calibration_method(self.calibration_method)))
raise ValueError(msg)
ac = ApplyCoefficientsFromEnsembleCalibration(
current_forecast, coefficient_cube,
predictor_of_mean_flag=self.predictor_of_mean_flag)
(calibrated_forecast_predictor,
calibrated_forecast_variance) = ac.apply_params_entry()
# TODO: track down where np.float64 promotion takes place.
calibrated_forecast_predictor.data = (
calibrated_forecast_predictor.data.astype(np.float32))
calibrated_forecast_variance.data = (
calibrated_forecast_variance.data.astype(np.float32))
return calibrated_forecast_predictor, calibrated_forecast_variance
def test_define_cycletime_format(self):
"""Test when a cycletime is defined."""
dt = datetime.datetime(2017, 11, 22, 1, 0)
cycletime = "201711220100"
result = datetime_to_cycletime(dt, cycletime_format="%Y%m%d%H%M")
self.assertEqual(result, cycletime)
