#!/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 modelout import get_modelout_data, update_modelout_data
from misc import get_kws_from_argv
import xfilter
nwindow, dimlp = 12*1, 'time'
lowpass = lambda x: x.filter.lowpass(1/nwindow, dim=dimlp, padtype=None)
#lowpass = lambda x: x.filter.lowpass(1/nwindow, dim=dimlp, method='gust')
#lowpass = lambda x: x.rolling(year=nwindow, center=True, min_periods=1).mean() if x.year.size>9 else x
import geoxarray
#
if __name__ == '__main__':
    tt.check('end import')
#
#start from here
#daname = 't_surf'
from modelout.getdata import funcs
daname = None #get_kws_from_argv('daname', 'blk_crb_col') #qo3_col
clearsky = True if 'clearsky' in sys.argv else False
funcname = 'netrad_toa_clr' if clearsky else 'netrad_toa' #get_kws_from_argv('funcname', 'glbmean')
#func = lambda x: x.load().geo.fldmean()
def netrad_toa(ds):
    if clearsky:
        da = ds.swdn_toa_clr - ds.swup_toa_clr - ds.olr_clr
    else:
        da = ds.swdn_toa - ds.swup_toa - ds.olr
    da = da.rename(grid_xt='lon', grid_yt='lat').load()
    da = da.geo.fldmean()
    da.attrs['units'] = 'W/m^2'
    if clearsky:
        da.attrs['long_name'] = 'glbmean netrad_toa_clr'
    else:
        da.attrs['long_name'] = 'glbmean netrad_toa'
    return da
func = netrad_toa #funcs[funcname]
dsname = 'atmos_month'


model = 'AM4.1'
"""
expname = 'CTL1850_tiger3_intel24ifort_openmpi_1536PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, dsname=dsname, func=func, funcname=funcname, odir='../SRM/CTL')#, years=range(100,201))
da_ctl1850 = da
"""
expname = 'CTL1990v202604v2_tiger3_intel24ifort_openmpi_1536PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, dsname=dsname, func=func, funcname=funcname, odir='CTL')#, years=range(100,201))
da_ctl1990 = da
units = da.attrs['units']

das = []; labels = []
#
for yy in range(11, 20, 2):
    ifile = f'/scratch/gpfs/GEOCLIM/wenchang/tiger3/AM4.1/work/CTL1990v202604v2_BC_L2_Y{yy:04d}_tiger3_intel24ifort_openmpi_1536PE/POSTP/{yy+1:04d}0101.atmos_month.nc'
    if yy == 11 or os.path.exists(ifile): #experiment available
        expname = f'CTL1990v202604v2_BC_L2_Y{yy:04d}_tiger3_intel24ifort_openmpi_1536PE'
        da = update_modelout_data(daname=daname, model=model, expname=expname, dsname=dsname, func=func, funcname=funcname)#, odir='BC_lat0_L6_Y0011plus')
        das.append(da.sel(time=slice(f'{yy:04d}', f'{yy+1:04d}')).drop('time')) #sel two years
        labels.append(expname.split("_tiger3")[0])
    else:
        continue
N = len(das)
da_ens = xr.concat(das, dim=pd.Index(range(1, N+1), name='ens'))
da_ens['time'] = da_ctl1990.sel(time=slice('0001', '0002')).time
#print(da_ens); sys.exit()

#ctl ens
das = []
for yy in range(11, 11+N*2, 2):
    da = da_ctl1990.sel(time=slice(f'{yy:04d}', f'{yy+1:04d}')).drop('time')
    das.append(da)
da_ctl1990_ens = xr.concat(das, dim=pd.Index(range(1, N+1), name='ens'))
da_ctl1990_ens['time'] = da_ctl1990.sel(time=slice('0001', '0002')).time
#print(da_ctl1990_ens); sys.exit()

#
daa_ens = da_ens - da_ctl1990_ens
daa_ens.attrs['units'] = units
da_ref = da_ctl1990_ens.groupby('time.month').mean(['time', 'ens'])
da_ref.attrs['units'] = units
    
 
if __name__ == '__main__':
    from wyconfig import * #my plot settings
    pct = True if 'pct' in sys.argv else False
    #ctl
    fig,ax = plt.subplots(figsize=(8,4))
    with xr.set_options(keep_attrs=True):
        colors = plt.colormaps['turbo'](np.linspace(0.1,0.9,len(das)))
        for da_bc,label,color in zip(daa_ens, labels, colors):
            if pct:
                daa = ( da_bc.groupby('time.month')/da_ref ) *100
                daa.attrs['units'] = '%'
            else:
                daa = da_bc
            daa.plot(color=color, lw=1, alpha=0.2, ls='-')
            daa.pipe(lowpass).plot(color=color, lw=1, ls='-')
        daa_ens.mean('ens').plot(color='gray', label=f'{N}-ens mean', ls='-')
        daa_ens.mean('ens').pipe(lowpass).plot(color='k', label=f'12-mon lowpass', ls='-')

    #ens mean and standard error of the mean for year 0002
    mean = daa_ens.sel(time='0002').mean('time').mean('ens').item()
    err = daa_ens.sel(time='0002').mean('time').std('ens').item()/np.sqrt(daa_ens.ens.size)
    title = f'{model} {funcname} anom, ${mean:.3g}\pm{err:.3g}$'
    ax.set_title(title)
    ax.axhline(0, color='gray', ls='--')
    #ax.legend(loc='upper left', bbox_to_anchor=(1,1))
    ax.legend()
    ax.axvline(daa_ens.sel(time='0002-01').time.item(), color='gray', ls='--')

    #savefig
    if 'savefig' in sys.argv or 's' in sys.argv:
        figname = __file__.replace('.py', f'__{funcname}_anom.png')
        if pct: figname = figname.replace('_anom.png', '_pct.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()