#!/usr/bin/env python
# Wenchang Yang (wenchang@princeton.edu)
# Sat May  7 12:43:43 EDT 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
#from misc.landmask import flagland
import geoxarray
#
if __name__ == '__main__':
    tt.check('end import')
#
#start from here
#to_degC = lambda x: (x-273.15).assign_attrs(units='degC')
#mask = lambda x: x.where(flagland(x)>0.5)
#ifile = 't_ref_max_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_5ens_1860-2100_SouthAsia.nc'
#ifile = 'precip_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_5ens_1860-2100_Pernambuco_masked.nc'
#ifile = 'precip_AM2.5C360_amipHadISSTlong_chancorr_tigercpu_intelmpi_18_1080PE_10ens_1871-2021_Pernambuco_masked.nc'
#daname = 'precip'
#units = 'mm/day'
sel_season = lambda da: da.isel(time=(da.time.dt.month>=6)&(da.time.dt.month<=8))
#daname = 't_ref' if 't_ref' in sys.argv else 't_ref_max'
daname = 'soil_liq'
model = 'AM2.5C360' if 'AM2.5C360' in sys.argv else 'FLOR'
#nens = 5 if model == 'FLOR' else 10
nens = 10
#units = '$^\circ$C'
#func_units = lambda x: (x-273.15).assign_attrs(units=units)
#ifiles = 'soil_liq_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_ens??_1860-2100_NHE_surface.nc'
ifiles = [
    sorted(glob.glob('soil_liq_AM2.5C360_amipHadISSTlong_chancorr_tigercpu_intelmpi_18_1080PE_ens??_1871-2019_NHE_surface.nc')),
    sorted(glob.glob('soil_liq_AM2.5C360_amipHadISSTlong_chancorr_tigercpu_intelmpi_18_1080PE_extend2020_ens??_2020-2020_NHE_surface.nc')),
    sorted(glob.glob('soil_liq_AM2.5C360_amipHadISSTlong_chancorr_tigercpu_intelmpi_18_1080PE_extend2021_ens??_2021-2021_NHE_surface.nc')),
    ]
#print(ifiles); sys.exit()
def func(da):
    """JJA season; kg/m^3->m^3/m^3; scale by std;"""
    return da.pipe(sel_season).load().geo.fldmean() \
        .pipe(lambda x: x/1000) \
        .pipe(lambda x: x.groupby('time.month')/x.sel(time=slice('1950','2022')).groupby('time.month').std(['time', 'ens']) ) \
        .groupby('time.year').mean('time')
    
#ofile = ifile.replace('.nc', '_index.nc') 
ofile = ifiles[0][0].replace('ens01', '10ens').replace('1871-2019', '1871-2021').replace('.nc', f'_NHEindex.nc') 
if os.path.exists(ofile) and not 'o' in sys.argv:
    da = xr.open_dataarray(ofile)
    print('[loaded]:', ofile)
else:
    da = xr.open_mfdataset(ifiles, combine='nested', concat_dim=['time', 'ens'])[daname].pipe(func)
    da.to_dataset(name=daname).to_netcdf(ofile)
    print('[saved]:', ofile)
 
if __name__ == '__main__':
    #from wyconfig import * #my plot settings
    
    #savefig
    if len(sys.argv)>1 and 'savefig' in sys.argv[1:]:
        figname = __file__.replace('.py', f'.png')
        if 'overwritefig' in sys.argv[1:]:
            wysavefig(figname, overwritefig=True)
        else:
            wysavefig(figname)
    tt.check(f'**Done**')
    print()
    #plt.show()