def test_forecast_period_and_model_configuration_dict(self):
"""Test blending models over forecast_period with a configuration
dictionary."""
# set up data cubes with forecast periods [ 6. 7. 8.] hours
time_points = [
dt(2017, 1, 10, 9),
dt(2017, 1, 10, 10),
dt(2017, 1, 10, 11)
]
cube1 = set_up_basic_model_config_cube(frt=dt(2017, 1, 10, 3),
time_points=time_points)
cube2 = cube1.copy()
cube2.coord("model_id").points = [2000]
cube2.coord("model_configuration").points = ["uk_ens"]
cubes = iris.cube.CubeList([cube1, cube2])
expected_weights = np.array([[1.0, 1.0, 0.8], [0.0, 0.0, 0.2]])
plugin = ChooseWeightsLinear(self.weighting_coord_name,
self.config_dict_fp)
result = plugin.process(cubes)
self.assertIsInstance(result, iris.cube.Cube)
self.assertArrayAlmostEqual(result.data, expected_weights)
self.assertAlmostEqual(result.name(), "weights")
result_coords = {coord.name() for coord in result.coords()}
self.assertSetEqual(result_coords,
self.expected_coords_model_blend_weights)
def test_forecast_period_and_model_configuration_three_models_dict(self):
"""Test blending three models over forecast period with a
configuration dictionary returns a sorted weights cube."""
time_points = [
dt(2017, 1, 10, 9), dt(2017, 1, 10, 10), dt(2017, 1, 10, 11)]
cube1 = set_up_basic_model_config_cube(
frt=dt(2017, 1, 10, 3), time_points=time_points)
cubes = iris.cube.CubeList([cube1])
for i, model in enumerate(["uk_ens", "gl_ens"]):
cube = cube1.copy()
cube.coord("model_id").points = [1000*(i+2)]
cube.coord("model_configuration").points = [model]
cubes.append(cube)
expected_weights = np.array([[1., 1., 0.72],
[0., 0., 0.18],
[0., 0., 0.1]])
self.config_dict_fp["gl_ens"] = {"forecast_period": [7, 16, 48, 54],
"weights": [0, 1, 1, 1],
"units": "hours"}
plugin = ChooseWeightsLinear(
self.weighting_coord_name, self.config_dict_fp)
result = plugin.process(cubes)
self.assertIsInstance(result, iris.cube.Cube)
self.assertArrayAlmostEqual(result.data, expected_weights)
self.assertAlmostEqual(result.name(), "weights")
self.assertArrayAlmostEqual(result.coord('model_id').points,
[1000, 2000, 3000])
self.assertArrayEqual(
result.coord('model_configuration').points,
["uk_det", "uk_ens", "gl_ens"])
def test_height_and_realization_dict(self):
"""Test blending members with a configuration dictionary."""
cube = set_up_variable_cube(274.0 * np.ones((2, 2, 2), dtype=np.float32))
cube = add_coordinate(cube, [10.0, 20.0], "height", coord_units="m")
cubes = iris.cube.CubeList([])
for cube_slice in cube.slices_over("realization"):
cubes.append(cube_slice)
expected_weights = np.array([[1.0, 0.5], [0.0, 0.5]])
config_dict = {
0: {"height": [15, 25], "weights": [1, 0], "units": "m"},
1: {"height": [15, 25], "weights": [0, 1], "units": "m"},
}
plugin = ChooseWeightsLinear(
"height", config_dict, config_coord_name="realization"
)
result = plugin.process(cubes)
self.assertIsInstance(result, iris.cube.Cube)
self.assertArrayAlmostEqual(result.data, expected_weights)
self.assertAlmostEqual(result.name(), "weights")
expected_coords = {
"time",
"forecast_reference_time",
"forecast_period",
"height",
"realization",
}
result_coords = {coord.name() for coord in result.coords()}
self.assertSetEqual(result_coords, expected_coords)
