gdp_da.coords["year_id"].values,
np.concatenate([china_years, usa_years]))
assert missing_years.size == 0
# find first year in future where china takes lead
china_year = china_years.min()
# find first year when US regains lead
usa_year = usa_years[usa_years > china_year].min()
print(f"China is expected to become the largest economy by {china_year} "
f"but in the reference scenario the USA would once again become in "
f"the largest economy in {usa_year}.\n")
if __name__ == "__main__":
tfr_2100()
pop_peak()
most_populated_2100()
age_pops()
tfr_below_replacement()
pop_declines()
alt_scenario_pops()
#wpp_witt_pops()
#wpp_fhs_diff()
largest_gdp()
great_job.congratulations()
migration = open_xr(mig_path).data
migration = migration.squeeze(drop=True)
# end up with migration counts with age and sex (loc, draw, year, age, sex)
split_data = migration * pattern
# Save it!
LOGGER.debug("Saving age-sex split migration data")
split_path = mig_dir / "migration_split.nc"
save_xr(split_data, split_path, metric="number", space="identity")
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
"--migration_version",
type=str,
required=True,
help="Which version of migrations to use in WPP directory")
parser.add_argument(
"--gbd_round_id",
type=int,
required=True,
help="Which gbd_round_id to use in file loading and saving")
args = parser.parse_args()
main(migration_version=args.migration_version,
gbd_round_id=args.gbd_round_id)
great_job.congratulations() # You did it!
