#!/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 modelout.getdata import funcs
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=True, min_periods=1).mean() if x.year.size>9 else x
import geoxarray
from misc import get_kws_from_argv
#
if __name__ == '__main__':
    tt.check('end import')
#
#start from here
daname = get_kws_from_argv('daname', 't_surf')
#func = lambda x: x.load().geo.fldmean()
#funcname = 'glbmean'
#func = funcs['nino34']
#funcname = 'nino34'
func = funcs['annualmean']
funcname = 'annualmean'

cleanup = False #remove cache or not

das = dict()

model = 'FLOR'
"""
#ctl1990_v201905
label = 'FLOR_ctl_1990_v201905'
expname = 'CTL1990_v201905_tigercpu_intelmpi_18_576PE'
da = get_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, years=range(1901,2000+1))
das[label] = da
#ctl1990_v201905_FA
label = 'FLOR_ctl_1990_v201905_FA'
expname = 'CTL1990_v201905_FA_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)#, years=range(100,201))
das[label] = da
#ctl1990_v201905_FA05
label = 'FLOR_ctl_1990_v201905_FA05'
expname = 'CTL1990_v201905_FA05_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)#, years=range(100,201))
das[label] = da
#ctl1990_v201905_FA05trop
label = 'FLOR_ctl_1990_v201905_FA05trop'
expname = 'CTL1990_v201905_FA05trop_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)#, years=range(100,201))
das[label] = da
#ctl1990_v201905_FAtrop
label = 'FLOR_ctl_1990_v201905_FAtrop'
expname = 'CTL1990_v201905_FAtrop_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)#, years=range(100,201))
das[label] = da
"""

#ctl1860_v202407
label = 'FLOR_ctl_1860_v202407'
expname = 'CTL1860_v202407_tigercpu_intelmpi_18_576PE'
da = get_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, years=range(1901,2000+1))
das[label] = da
#ctl1860_v202407_FA
label = 'FLOR_ctl_1860_v202407_FA'
expname = 'CTL1860_v202407_FA_tigercpu_intelmpi_18_576PE'
da = get_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname, years=range(1901,2000+1))
das[label] = da
#ctl1860_v202407_FAtrop
label = 'FLOR_ctl_1860_v202407_FAtrop'
expname = 'CTL1860_v202407_FAtrop_tigercpu_intelmpi_18_576PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname)#, years=range(1901,2000+1))
das[label] = da

model = 'CM2.1p1'
#ctl1860_v202410
label = 'CM2p1_ctl_1860_v202407'
expname = 'CTL1860_v202410_tigercpu_intelmpi_18_80PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname) #, years=range(1901,2000+1))
das[label] = da
#ctl1860_v202410_FAtrop
label = 'CM2p1_ctl_1860_v202407_FAtrop'
expname = 'CTL1860_v202410_FAtrop_tigercpu_intelmpi_18_80PE'
da = update_modelout_data(daname=daname, model=model, expname=expname, func=func, funcname=funcname) #, years=range(1901,2000+1))
das[label] = da

#units
units = da.attrs['units']
 
def wyplot(da, label, ax=None, **kws):
    if ax is None: ax = plt.subplots()[1]
    da_ctl = das['FLOR_ctl_1990_v201905'] if 'FLOR_ctl_1990_v201905' in label else None #default ctl case
    da_ctl = das['FLOR_ctl_1860_v202407'] if 'FLOR_ctl_1860_v202407' in label else da_ctl #adjust the ctl case
    da_ctl = das['CM2p1_ctl_1860_v202407'] if 'CM2p1_ctl_1860_v202407' in label else da_ctl #adjust the ctl case
    #da = da.groupby('time.year').mean('time')
    daa = da.mean('time') - da_ctl.mean('time') #anomal from the original control
    #da = da.pipe(lowpass)
    #da = da.sel(year=slice(1900,2000))
    daa.assign_attrs(units=units).plot(ax=ax, **kws)

    
 
if __name__ == '__main__':
    from wyconfig import * #my plot settings
    from geoplots import mapplot
    if daname in ('t_surf', 't_ref'):
        vmax = 4
    elif daname == 'slp':
        vmax = 3
    elif daname == 'precip':
        vmax = 2 
    else:
        vmax = None
    if daname == 'precip':
        cmap = 'BrBG'
    else:
        cmap = None
    for label,da in das.items():
        if label in ('FLOR_ctl_1990_v201905','FLOR_ctl_1860_v202407', 'CM2p1_ctl_1860_v202407'): continue #skip the ctl case
        fig,ax = plt.subplots()
        year_start = da.time[0].item().year
        year_stop = da.time[-1].item().year
        wyplot(da, label, ax=ax, robust=True, levels=21, vmax=vmax, cmap=cmap, extend='both')
        plt.sca(ax)
        mapplot()

        ax.set_title(f"{label} {daname} anomaly: {year_start}-{year_stop}")

        #savefig
        if 'savefig' in sys.argv or 's' in sys.argv:
            figname = __file__.replace('.py', f'__{daname}_{label}.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()