#!/usr/bin/env python
# Wenchang Yang (wenchang@princeton.edu)
# Thu Mar  9 16:56:46 EST 2023
if __name__ == '__main__':
    import sys,os
    from misc.timer import Timer
    tt = Timer(f'[{os.getcwd()}] 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
wython = '/tigress/wenchang/wython'
if wython not in sys.path: sys.path.append(wython); print('added to python path:', wython)
#from misc import get_kws_from_argv
#
if __name__ == '__main__':
    tt.check('end import')
#
#start from here
model = 'AM2.1'
expname = 'amip_rmWarming'
#years_ref = slice(1981,2010)
#years_ref = slice(850,1850)
years_ref = slice(1871,1900)
#ifile = 'rrvco2_AM2.1p1_amipHadISSTchancorr_ens_tigercpu_intelmpi_18_30PE_5ens_1871-2020_atmos_scalar.nc'
#ifile = 'precip_AM2.1p1_amipHadISSTchancorr_ens_tigercpu_intelmpi_18_30PE_5ens_1871-2020_glbmean.nc'
#ifile = 'precip_AM2.1p1_amipLMR2019SST0850_ens_tigercpu_intelmpi_18_30PE_5ens_0850-1999_glbmean.nc'
ifile = 'precip_AM2.1p1_amipHadISSTchancorr_rmWarming_ens_tigercpu_intelmpi_18_30PE_5ens_1871-2020_glbmean.nc'
da = xr.open_dataarray(ifile)
da = da.mean('ens').groupby('time.year').mean('time')
da = da/da.sel(year=years_ref).mean('year')*100 -100 
da.attrs['units'] = '%'
precip = da
#da.plot() 

#gmst
#ifile = 't_surf_AM2.1p1_amipHadISSTchancorr_ens_tigercpu_intelmpi_18_30PE_5ens_1871-2020_glbmean.nc'
#ifile = 't_surf_AM2.1p1_amipLMR2019SST0850_ens_tigercpu_intelmpi_18_30PE_5ens_0850-1999_glbmean.nc'
ifile = 't_surf_AM2.1p1_amipHadISSTchancorr_rmWarming_ens_tigercpu_intelmpi_18_30PE_5ens_1871-2020_glbmean.nc'
da = xr.open_dataarray(ifile)
da = da.mean('ens').groupby('time.year').mean('time')
da = da - da.sel(year=years_ref).mean('year')
da.attrs['units'] = 'K'
gmsta = da
b_gmst = 2.63 #% per K
#plt.figure(); da.plot()

"""
#co2
ifile = 'rrvco2_AM2.1p1_amipHadISSTchancorr_ens_tigercpu_intelmpi_18_30PE_5ens_1871-2020_atmos_scalar.nc'
da = xr.open_dataarray(ifile).squeeze(drop=True).pipe(np.log2)
da = da.mean('ens').groupby('time.year').mean('time')
da = da - da.sel(year=years_ref).mean('year')
da.attrs['units'] = '2xCO2 forcing'
fco2 = da
b_co2 = -2.40 #% per 2xCO2
#plt.figure(); da.plot()
"""


 
if __name__ == '__main__':
    from wyconfig import * #my plot settings
    func_lp = lambda da: da.rolling(year=31, center=True, min_periods=1).mean()
    fig,ax = plt.subplots()
    alpha = 0.3
    #precip
    color = 'k'
    da = precip
    da.plot(color=color, alpha=alpha)
    da.pipe(func_lp).plot(label=f'{model} {expname} 5ens glbmean precip', color=color)
    y = da

    #modeled precip
    ii = 0; color = f'C{ii}'
    da = b_gmst * gmsta #+ b_co2 * fco2
    da.plot(color=color, alpha=alpha)
    da.pipe(func_lp).plot(label='precip from gmsta', color=color)
    y_hat = da

    """
    ls = '--'
    #gmsta only
    ii += 1; color = f'C{ii}'
    da = b_gmst * gmsta 
    da.plot(color=color, alpha=alpha)
    da.pipe(func_lp).plot(label='gmsta only', color=color, ls=ls)

    #co2 only
    ii += 1; color = f'C{ii}'
    da = b_co2 * fco2
    #da.plot(color=color, alpha=alpha)
    da.pipe(func_lp).plot(label='co2 only', color=color, ls=ls)

    #residual
    ii += 1; color = f'C{ii}'
    #da = precip - (b_gmst * gmsta + b_co2 * fco2)
    da = precip - (b_gmst * gmsta)
    da.plot(color=color, alpha=alpha)
    da.pipe(func_lp).plot(label='residual', color=color, ls=ls)
    """


    ax.legend()
    ax.set_ylabel(f'% of {years_ref.start}-{years_ref.stop} annual clim')

    import xlinregress
    reg = y.linregress.on(gmsta, dim='year')
    print(reg)
    
    #savefig
    if 'savefig' in sys.argv or 's' in sys.argv:
        figname = __file__.replace('.py', f'.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 or 'n' in sys.argv:
        pass
    else:
        if 'plt' in globals(): plt.show()