def process(
self,
cubes_in: Union[List[Cube], Cube, CubeList],
cycletime: Optional[str] = None,
) -> Cube:
"""
Prepares merged input cube for cycle and grid blending. Makes updates to
metadata (attributes and time-type coordinates) ONLY in so far as these are
needed to ensure inputs can be merged into a single cube.
Args:
cubes_in:
Cubes to be merged.
cycletime:
The cycletime in a YYYYMMDDTHHMMZ format e.g. 20171122T0100Z.
Can be used in rationalise_blend_time_coordinates.
Returns:
Merged cube.
Raises:
ValueError:
If self.blend_coord is not present on all cubes (unless
blending over models)
"""
cubelist = ([cubes_in.copy()] if isinstance(cubes_in, iris.cube.Cube)
else [cube.copy() for cube in cubes_in])
if self.record_run_attr is not None and self.model_id_attr is not None:
set_record_run_attr(cubelist, self.record_run_attr,
self.model_id_attr)
if "model" in self.blend_coord:
cubelist = [self._remove_blend_time(cube) for cube in cubelist]
cubelist = [
self._remove_deprecation_warnings(cube) for cube in cubelist
]
if (self.weighting_coord is not None
and "forecast_period" in self.weighting_coord):
# if blending models using weights by forecast period, unify
# forecast periods (assuming validity times are identical);
# method returns a new cubelist with copies of input cubes
cubelist = rebadge_forecasts_as_latest_cycle(
cubelist, cycletime=cycletime)
# check all input cubes have the blend coordinate
for cube in cubelist:
if "model" not in self.blend_coord and not cube.coords(
self.blend_coord):
raise ValueError("{} coordinate is not present on all input "
"cubes".format(self.blend_coord))
# create model ID and model configuration coordinates if blending
# different models
if "model" in self.blend_coord and self.model_id_attr is not None:
self._create_model_coordinates(cubelist)
# merge resulting cubelist
result = MergeCubes()(cubelist, check_time_bounds_ranges=True)
return result
def test_cubelist(self):
"""Test a list of cubes is returned with the latest frt"""
expected = self.cube_late.copy()
result = rebadge_forecasts_as_latest_cycle(
[self.cube_early, self.cube_late])
self.assertIsInstance(result, iris.cube.CubeList)
self.assertEqual(len(result), 2)
for cube in result:
for coord in ["forecast_reference_time", "forecast_period"]:
self.assertEqual(cube.coord(coord), expected.coord(coord))
def test_single_cube_with_cycletime(self):
"""Test a single cube has its forecast reference time and period
updated if cycletime is specified"""
expected_frt_point = (
self.cube_late.coord("forecast_reference_time").points[0] + 2*3600)
expected_fp_point = (
self.cube_late.coord("forecast_period").points[0] - 2*3600)
result, = rebadge_forecasts_as_latest_cycle(
[self.cube_late], cycletime=self.cycletime)
self.assertEqual(result.coord("forecast_reference_time").points[0],
expected_frt_point)
self.assertEqual(result.coord("forecast_period").points[0],
expected_fp_point)
def test_cycletime(self):
"""Test a list of cubes using the cycletime argument"""
expected_frt_point = (
self.cube_late.coord("forecast_reference_time").points[0] + 2*3600)
expected_fp_point = (
self.cube_late.coord("forecast_period").points[0] - 2*3600)
result = rebadge_forecasts_as_latest_cycle(
[self.cube_early, self.cube_late], cycletime=self.cycletime)
for cube in result:
self.assertEqual(cube.coord("forecast_reference_time").points[0],
expected_frt_point)
self.assertEqual(cube.coord("forecast_period").points[0],
expected_fp_point)
