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"""Provide support utilities for time lagging ensembles"""
import numpy as np
from improver import BasePlugin
from improver.metadata.forecast_times import rebadge_forecasts_as_latest_cycle
from improver.utilities.cube_manipulation import concatenate_cubes
[docs]class GenerateTimeLaggedEnsemble(BasePlugin):
"""Combine realizations from different forecast cycles into one cube"""
[docs] def process(self, cubelist):
"""
Take an input cubelist containing forecasts from different cycles and
merges them into a single cube.
The steps taken are:
1. Update forecast reference time and period to match the latest
contributing cycle.
2. Check for duplicate realization numbers. If a duplicate is
found, renumber all of the realizations uniquely.
3. Concatenate into one cube along the realization axis.
Args:
cubelist (iris.cube.CubeList or list of iris.cube.Cube):
List of input forecasts
Returns:
iris.cube.Cube:
Concatenated forecasts
"""
cubelist = rebadge_forecasts_as_latest_cycle(cubelist)
# Take all the realizations from all the input cube and
# put in one array
all_realizations = [
cube.coord("realization").points for cube in cubelist]
all_realizations = np.concatenate(all_realizations)
# Find unique realizations
unique_realizations = np.unique(all_realizations)
# If we have fewer unique realizations than total realizations we have
# duplicate realizations so we rebadge all realizations in the cubelist
if len(unique_realizations) < len(all_realizations):
first_realization = 0
for cube in cubelist:
n_realization = len(cube.coord("realization").points)
cube.coord("realization").points = np.arange(
first_realization, first_realization + n_realization,
dtype=np.int32)
first_realization = first_realization + n_realization
# slice over realization to deal with cases where direct concatenation
# would result in a non-monotonic coordinate
lagged_ensemble = concatenate_cubes(
cubelist, master_coord="realization",
coords_to_slice_over=["realization"])
return lagged_ensemble