#!/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 = 1, 'year'
#nwindow, dimlp = 9, 'year'
#lowpass = lambda x: x.filter.lowpass(1/nwindow, dim=dimlp, padtype='odd')
lowpass = lambda x: x.rolling(year=nwindow, center=True, min_periods=1).mean() if x.year.size>nwindow else x
import geoxarray
#
if __name__ == '__main__':
    tt.check('end import')
#
#start from here
daname = 't_surf'
func = lambda x: x.load().geo.fldmean()
funcname = 'glbmean'

das = []
labels = []

model = 'CM2.1p1'
#HistRCP45_FA
#for ens in range(1, 3+1):
for ens in list(range(1, 3+1)) + list(range(11,13+1)):
    label = f'CM2.1_HistRCP45_ens{ens:02d}'
    #expname = f'HistRCP45_tigercpu_intelmpi_18_576PE_e{ens}'
    expname = f'HistRCP45_e{ens}_tigercpu_intelmpi_18_80PE'
    da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)#, years=range(100,201))
    labels.append(label)
    das.append(da)

da_ref = ( 
    das[0].groupby('time.year').mean('time').isel(year=slice(0,20)).mean('year') 
    + das[1].groupby('time.year').mean('time').isel(year=slice(0,20)).mean('year')
    + das[2].groupby('time.year').mean('time').isel(year=slice(0,20)).mean('year')
    + das[3].groupby('time.year').mean('time').isel(year=slice(0,20)).mean('year') 
    + das[4].groupby('time.year').mean('time').isel(year=slice(0,20)).mean('year')
    + das[5].groupby('time.year').mean('time').isel(year=slice(0,20)).mean('year')
    )/6 #ens mean of the first 30 years mean
def wyplot(da, label,**kws):
    da = da.groupby('time.year').mean('time')
    #da = da - 273.15 #K -> degC
    da = da - da_ref # remove the mean of the first 30 years
    da = da.pipe(lowpass)
    da.plot(label=label, **kws)
    
 
if __name__ == '__main__':
    from wyconfig import * #my plot settings
    #ctl
    fig,ax = plt.subplots(figsize=(8,4))
    #FLOR HistRCP45
    ifile = '/tigress/wenchang/analysis/FLOR/ens/t_surf_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_10ens_1860-2100_glbmean.nc'
    da = xr.open_dataarray(ifile) \
        .groupby('time.year').mean('time') \
        .pipe(lambda x: x - x.isel(year=slice(0,20)).mean('year').mean('ens')) 
    #    .pipe(lowpass)
    for ii in range(1,10+1):
        da.sel(ens=ii).plot(ax=ax, lw=1, color='gray', alpha=0.2, ls='-')
    da.mean('ens').plot(ax=ax, color='gray', ls='-', label='FLOR HistRCP45')

    #CM2.1 HistRCP45
    ii = 0
    for da,label in zip(das, labels):
        if 'ens01' in label or 'ens02' in label or 'ens03' in label:
            color = 'C0'
        else:
            color = 'C1'
        wyplot(da, label, ax=ax, ls='-', alpha=0.5, color=color, lw=1)
        ii += 1
    ax.legend(ncol=1)
    ax.set_ylabel(f'GMST [degC], {nwindow}-{dimlp}-RunAvg')
    ax.set_xlim(1860, 2100)

    #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:
        pass
    else:
        plt.show()