def apply_seasons(data_set_dict):
"""Extract seaons and apply a time mean per season."""
data_file = data_set_dict['filename']
logger.info("Loading %s for seasonal extraction", data_file)
data_cube = iris.load_cube(data_file)
seasons = ['DJF', 'MAM', 'JJA', 'SON']
season_cubes = [extract_season(data_cube, season) for season in seasons]
season_meaned_cubes = [
season_cube.collapsed('time', iris.analysis.MEAN)
for season_cube in season_cubes
]
return season_meaned_cubes
logger.info("Opening dataset.")
data = xr.open_mfdataset(args.pattern, combine='by_coords')
data = data[args.varname]
logger.info("Selecting time period.")
data = data.loc[dict(time=slice(args.startdate, args.enddate))]
if args.downsample:
logger.info(f"Downsampling spatial resolution (1 out of 10 gridpoints).")
data = data[:, ::10, ::10]
# Now convert to Iris for using esmvalcore preprocessor functions
logger.debug("Converting to Iris cube")
cube = data.to_iris()
if args.period in ['DJF', 'MAM', 'JJA', 'SON']:
logger.info(f"Statistics over season {args.period}")
cube = pp.extract_season(cube, args.period)
else:
logger.info(f"Statistics over full year")
logger.info(f"Calculating statistics '{args.statistics}' over period")
cube = pp.annual_statistics(cube, operator=args.statistics)
# Back to xarray
data = xr.DataArray.from_iris(cube)
logger.info('Calculating theil-sen trend')
theilsen = theilsen_trend(data)
logger.info('Calculating Mann Kendall test')
mk = mannkendall(data, alpha=args.alpha)
# Mask theilsen with mk test results
theilsen_masked = theilsen.where(mk!=0, np.nan)