#!/usr/bin/env python # Wenchang Yang (wenchang@princeton.edu) # Wed Dec 14 09:50:06 EST 2022 if __name__ == '__main__': import sys from misc.timer import Timer tt = Timer('start ' + ' '.join(sys.argv)) import sys, os.path, os, glob, datetime import xarray as xr, numpy as np, pandas as pd, matplotlib.pyplot as plt #more imports import geoxarray from misc.seasons import sel_season # if __name__ == '__main__': tt.check('end import') # #start from here #ifile = 't_ref_max_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_10ens_1860-2100_Argentina_masked.nc' #ifile = sys.argv[1] if len(sys.argv)>1 and sys.argv[1].endswith('.nc') \ # else 'precip_AM2.5C360_amipHadISSTrcp45_tigercpu_intelmpi_18_1080PE_3ens_1871-2050_NewZealand_masked.nc' season = """ OND MAM 2year """.split()[-1] ifile = glob.glob('precip_*_masked.nc')[0] ofile = ifile.replace('masked.nc', f'{season}index.nc') daname = f'precip' units = 'mm' long_name = f'{season} precip' if os.path.exists(ofile): print('[exists]:', ofile) da = xr.open_dataarray(ofile) else: #make index print('loading...') da = xr.open_dataarray(ifile).load() #daname = da.name print('making index...') if season == '2year': #2-year accumulation da = da.geo.fldmean() \ .groupby('time.year').sum('time') \ .rolling(year=2, center=False, min_periods=2).sum() \ .assign_attrs(units=units, long_name=long_name) else: #season sum da = da.geo.fldmean() \ .pipe(sel_season, season).groupby('time.year').sum('time') \ .assign_attrs(units=units, long_name=long_name) #print(da); sys.exit() #save print('saving...') da.to_dataset(name=daname).to_netcdf(ofile) print('[saved]:', ofile) if __name__ == '__main__': from wyconfig import * #my plot settings #da.mean('ens', keep_attrs=True).plot() da.plot(hue='ens') #savefig if 'savefig' in sys.argv or 's' in sys.argv: figname = __file__.replace('.py', f'.{season}.png') if 'overwritefig' in sys.argv or 'o' in sys.argv: wysavefig(figname, overwritefig=True) else: wysavefig(figname) tt.check(f'**Done**') print() if 'notshowfig' in sys.argv: pass else: plt.show()