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!