def process(*cubes: cli.inputcube,
coordinates: cli.comma_separated_list = None):
"""Aggregate reliability tables.
Aggregate multiple reliability calibration tables and/or aggregate over
coordinates within the table(s) to produce a new reliability calibration
table.
Args:
cubes (list of iris.cube.Cube):
The cube or cubes containing the reliability calibration tables
to aggregate.
coordinates (list):
A list of coordinates over which to aggregate the reliability
calibration table using summation. If the list is empty
and a single cube is provided, this cube will be returned
unchanged.
Returns:
iris.cube.Cube:
Aggregated reliability table.
"""
from improver.calibration.reliability_calibration import (
AggregateReliabilityCalibrationTables, )
return AggregateReliabilityCalibrationTables()(cubes,
coordinates=coordinates)
def test_process_and_aggregate(create_rel_table_inputs):
"""Test that aggregation during construction produces the same result as
applying the two plugins sequentially."""
# use the spatial coordinates for aggregation - input is a parameterised fixture
if create_rel_table_inputs.forecast.coords("spot_index"):
agg_coords = ["spot_index"]
else:
agg_coords = ["longitude", "latitude"]
# construct and aggregate as two separate plugins
constructed = Plugin(
single_value_lower_limit=True, single_value_upper_limit=True
).process(create_rel_table_inputs.forecast, create_rel_table_inputs.truth)
aggregated = AggregateReliabilityCalibrationTables().process(
[constructed], agg_coords
)
# construct plugin with aggregate_coords option
constructed_with_agg = Plugin(
single_value_lower_limit=True, single_value_upper_limit=True
).process(
create_rel_table_inputs.forecast, create_rel_table_inputs.truth, agg_coords
)
# check that the two cubes are identical
assert constructed_with_agg == aggregated
