def test_end_to_end_point_by_point_sites_realizations(self):
"""An example end-to-end calculation when a separate set of
coefficients are computed for each site using the realizations as the
predictor. This repeats the test elements above but all grouped together."""
plugin = Plugin(predictor="realizations")
calibrated_forecast_predictor, calibrated_forecast_var = plugin.process(
self.current_forecast_spot_cube, self.coeffs_from_realizations_sites
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_predictor.data,
self.expected_loc_param_realizations_sites,
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_var.data, self.expected_scale_param_realizations_sites
)
self.assertEqual(calibrated_forecast_predictor.dtype, np.float32)
class Test_process(SetupCoefficientsCubes, EnsembleCalibrationAssertions):
"""Test the process plugin."""
def setUp(self):
"""Set-up the plugin for testing."""
super().setUp()
self.plugin = Plugin()
@ManageWarnings(ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_diagnostic_match(self):
"""Test that an error is raised if the diagnostic_standard_name does
not match when comparing a forecast cube and coefficients cubelist."""
msg = "The forecast diagnostic"
with self.assertRaisesRegex(ValueError, msg):
self.plugin.process(
self.current_wind_speed_forecast_cube, self.coeffs_from_mean
)
@ManageWarnings(ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_variable_setting(self):
"""Test that the cubes passed into the plugin are allocated to
plugin variables appropriately."""
_, _ = self.plugin.process(
self.current_temperature_forecast_cube, self.coeffs_from_mean
)
self.assertEqual(
self.current_temperature_forecast_cube, self.plugin.current_forecast
)
self.assertEqual(self.coeffs_from_mean, self.plugin.coefficients_cubelist)
@ManageWarnings(ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_end_to_end(self):
"""An example end-to-end calculation. This repeats the test elements
above but all grouped together."""
calibrated_forecast_predictor, calibrated_forecast_var = self.plugin.process(
self.current_temperature_forecast_cube, self.coeffs_from_mean
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_predictor.data, self.expected_loc_param_mean
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_var.data, self.expected_scale_param_mean
)
self.assertEqual(calibrated_forecast_predictor.dtype, np.float32)
@ManageWarnings(ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_end_to_end_point_by_point(self):
"""An example end-to-end calculation when a separate set of
coefficients are computed for each grid point. This repeats the test
elements above but all grouped together."""
calibrated_forecast_predictor, calibrated_forecast_var = self.plugin.process(
self.current_temperature_forecast_cube, self.coeffs_from_mean_point_by_point
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_predictor.data, self.expected_loc_param_mean
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_var.data, self.expected_scale_param_mean
)
self.assertEqual(calibrated_forecast_predictor.dtype, np.float32)
@ManageWarnings(ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_end_to_end_point_by_point_sites_realizations(self):
"""An example end-to-end calculation when a separate set of
coefficients are computed for each site using the realizations as the
predictor. This repeats the test elements above but all grouped together."""
plugin = Plugin(predictor="realizations")
calibrated_forecast_predictor, calibrated_forecast_var = plugin.process(
self.current_forecast_spot_cube, self.coeffs_from_realizations_sites
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_predictor.data,
self.expected_loc_param_realizations_sites,
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_var.data, self.expected_scale_param_realizations_sites
)
self.assertEqual(calibrated_forecast_predictor.dtype, np.float32)
@ManageWarnings(ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_end_to_end_with_mask(self):
"""An example end-to-end calculation, but making sure that the
areas that are masked within the landsea mask, are masked at the
end."""
# Construct a mask and encapsulate as a cube.
mask = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
mask_cube = self.current_temperature_forecast_cube[0].copy(data=mask)
# Convention for IMPROVER is that land points are ones and sea points
# are zeros in land-sea masks. In this case we want to mask sea points.
expected_mask = np.array(
[[False, True, True], [True, False, True], [True, True, False]]
)
calibrated_forecast_predictor, calibrated_forecast_var = self.plugin.process(
self.current_temperature_forecast_cube,
self.coeffs_from_mean,
landsea_mask=mask_cube,
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_predictor.data.data, self.expected_loc_param_mean
)
self.assertArrayEqual(calibrated_forecast_predictor.data.mask, expected_mask)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_var.data.data, self.expected_scale_param_mean
)
self.assertArrayEqual(calibrated_forecast_var.data.mask, expected_mask)
class Test_process(SetupCoefficientsCubes, EnsembleCalibrationAssertions):
"""Test the process plugin."""
def setUp(self):
"""Set-up the plugin for testing."""
super().setUp()
self.plugin = Plugin()
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_variable_setting(self):
"""Test that the cubes passed into the plugin are allocated to
plugin variables appropriately."""
_, _ = self.plugin.process(self.current_temperature_forecast_cube,
self.coeffs_from_mean)
self.assertEqual(self.current_temperature_forecast_cube,
self.plugin.current_forecast)
self.assertEqual(self.coeffs_from_mean, self.plugin.coefficients_cube)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_end_to_end(self):
"""An example end-to-end calculation. This repeats the test elements
above but all grouped together."""
calibrated_forecast_predictor, calibrated_forecast_var = self.plugin.process(
self.current_temperature_forecast_cube, self.coeffs_from_mean)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_predictor.data, self.expected_loc_param_mean)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_var.data, self.expected_scale_param_mean)
self.assertEqual(calibrated_forecast_predictor.dtype, np.float32)
@ManageWarnings(
ignored_messages=["Collapsing a non-contiguous coordinate."])
def test_end_to_end_with_mask(self):
"""An example end-to-end calculation, but making sure that the
areas that are masked within the landsea mask, are masked at the
end."""
# Construct a mask and encapsulate as a cube.
mask = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
mask_cube = self.current_temperature_forecast_cube[0].copy(data=mask)
# Convention for IMPROVER is that land points are ones and sea points
# are zeros in land-sea masks. In this case we want to mask sea points.
expected_mask = np.array([[False, True, True], [True, False, True],
[True, True, False]])
calibrated_forecast_predictor, calibrated_forecast_var = self.plugin.process(
self.current_temperature_forecast_cube,
self.coeffs_from_mean,
landsea_mask=mask_cube,
)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_predictor.data.data,
self.expected_loc_param_mean)
self.assertArrayEqual(calibrated_forecast_predictor.data.mask,
expected_mask)
self.assertCalibratedVariablesAlmostEqual(
calibrated_forecast_var.data.data, self.expected_scale_param_mean)
self.assertArrayEqual(calibrated_forecast_var.data.mask, expected_mask)
