#!/usr/bin/env python
# Wenchang Yang (wenchang@princeton.edu)
# Wed Oct  5 11:09:59 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.modelout import get_modelout_data, update_modelout_data
import xfilter
nwindow, dimlp = 9, 'year'
#lowpass = lambda x: x.filter.lowpass(1/nwindow, dim=dimlp, padtype='odd')
lowpass = lambda x: x.rolling(year=nwindow, center=False, min_periods=1).mean()
import geoxarray
#
if __name__ == '__main__':
    tt.check('end import')
#
#start from here
model = 'FLOR' # 'CM2.1p1'
daname = 't_surf'
func = lambda x: x.load().geo.fldmean()
funcname = 'glbmean'

cleanup = True #False #remove cache or not

das = dict()
expname = 'CTL1860_newdiag_tigercpu_intelmpi_18_576PE'
#da = get_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)
das['CTL'] = da#.sel(time=slice('0100', '1000'))

expname = 'co2x4_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['4xCO2'] = da
 
expname = 'co2x2_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['2xCO2'] = da
 
expname = 'co2xp5_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['0.5xCO2'] = da
 
expname = 'co2xp25_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['0.25xCO2'] = da
 
expname = 'p6p0sol_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['+6% Solar'] = da
 
expname = 'p2p0sol_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['+2% Solar'] = da
 
expname = 'p1p0sol_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['+1% Solar'] = da
 
expname = 'm1p0sol_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['-1% Solar'] = da
 
expname = 'm2p0sol_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['-2% Solar'] = da
 
expname = 'm6p0sol_CTL1860_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['-6% Solar'] = da
 
expname = 'm6p0sol_CTL1860_recover0251_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['-6% Solar back251'] = da
 
expname = 'm6p0sol_CTL1860_recover0501_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['-6% Solar back501'] = da
 
expname = 'm6p0sol_CTL1860_recover0591_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['-6% Solar back591'] = da
 
expname = 'CTL1860_newdiag_1pct4xCO2_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['1%to4xCO2'] = da
 
expname = 'CTL1860_newdiag_1pct4xCO2back_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
das['1%to4xCO2back'] = da
 
#expname = 'CTL1860_1pct4xCO2back_tigercpu_intelmpi_18_80PE'
#da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, cleanup=cleanup)
#das['1%to4xCO2back'] = da
 
 
def wyplot(da, flip=False, **kws):
    ax = kws.pop('ax', plt.gca())
    da = da.groupby('time.year').mean('time') - das['CTL'].groupby('time.year').mean('time').sel(year=slice(101,200)).mean('year') #anom
    #if da.year[0] == 101: #fill zero to year 100
    #    da = xr.concat([das['CTL'].sel(time='0100').groupby('time.year').mean('time')*0, da], dim='year')
    #da = da.groupby('time.month') - da_ctl.sel(time=slice('0101','0200')).groupby('time.month').mean('time') #anom
    if flip: da = -da
    da = da.pipe(lowpass)
    #da = da.assign_coords(year=da.year-100) #shift the year axis to start with 0 (instead of 100)
    da.plot(**kws)
    #ax.plot(da.isel(year=-1).year, da.isel(year=-1), marker='o', fillstyle='none', color='gray')
    #ax.text(da.year.values[-1], da.values[-1], f'{da.year.values[-1]}', color='gray')
    
 
if __name__ == '__main__':
    from wyconfig import * #my plot settings
    fig,ax = plt.subplots(figsize=(8,4))

    for label,da in das.items():
        if label=='CTL':
            da.pipe(wyplot, label=label, color='k')
            ax.set_prop_cycle(None)
        else:
            da.pipe(wyplot, label=label)

    ax.legend(ncol=3)
    ax.set_ylabel(f'{model} GMST anom [K], {nwindow}-{dimlp}-lp')
    ax.set_xlim(None, das['CTL'].time.dt.year.values[-1]+500*0)
    
    #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)

    #mirror the negative forcing
    fig,ax = plt.subplots(figsize=(8,4))

    label = 'CTL'
    da = das[label]
    da.pipe(wyplot, label=label, color='k')

    label = '2xCO2'
    ii = 0
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')
    label = '0.5xCO2'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='--', flip=True)

    label = '4xCO2'
    ii += 1
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')
    label = '0.25xCO2'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='--', flip=True)


    label = '+1% Solar'
    ii += 1
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')
    label = '-1% Solar'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='--', flip=True)

    label = '+2% Solar'
    ii += 1
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')
    label = '-2% Solar'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='--', flip=True)

    label = '+6% Solar'
    ii += 1
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')
    label = '-6% Solar'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='--', flip=True)
    label = '-6% Solar back251'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-.', flip=True)
    label = '-6% Solar back501'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-.', flip=True)
    label = '-6% Solar back591'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-.', flip=True)

    label = '1%to4xCO2'
    ii += 1
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')

    label = '1%to4xCO2back'
    ii += 1
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')

    """
    label = '1%to4xCO2new'
    ii += 1
    color = f'C{ii}'
    da = das[label]
    da.pipe(wyplot, label=label, color=color, ls='-')
    """

    ax.legend(ncol=3)
    ax.set_ylabel(f'{model} GMST anom [K], {nwindow}-{dimlp}-lp')
    ax.set_xlim(None, das['CTL'].time.dt.year.values[-1]+500*0)
    #ax.set_xlim(100, None)
    #ax.set_xscale('log')
    
    #savefig
    if 'savefig' in sys.argv or 's' in sys.argv:
        figname = __file__.replace('.py', f'_mirror.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()