#!/usr/bin/env python
# Wenchang Yang (wenchang@princeton.edu)
# Tue Jun 29 16:14:32 EDT 2021
if __name__ == '__main__':
    from misc.timer import Timer
    tt = Timer(f'start {__file__}')
import sys, os.path, os, glob, datetime
import xarray as xr, numpy as np, pandas as pd, matplotlib.pyplot as plt
#more imports
import geoxarray
#from misc.landmask import flagland
from misc.wyregionmask import where_region
#
if __name__ == '__main__':
    tt.check('end import')
#
#start from here
def mask_region(da, region_file='region.txt'):
    df = pd.read_csv(region_file, sep='\s+')
    lon, lat = df['x'], df['y']
    if lon.min() < 0:
        lon = lon + 360
    return where_region(da, lon=lon, lat=lat)
#ifile = 'data_precip_FLOR_histRCP45_5ens_1860-2100_SouthAfrica.nc'
#ifile = 't_ref_max_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_5ens_1860-2100_SouthAsia.nc'
#ifile = 't_ref_max_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_5ens_1860-2100_SouthAsia_masked.nc'
#ifile = 'precip_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_5ens_1860-2100_Pernambuco_masked.nc'
#ifile = 'precip_AM2.5C360_amipHadISSTlong_chancorr_tigercpu_intelmpi_18_1080PE_10ens_1871-2021_Pernambuco_masked.nc'
#ifile = 't_ref_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_10ens_1991-2020_Argentina.nc'
#ifile = 'precip_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_10ens_1860-2100_CSA.nc'
#ifile = 't_ref_FLOR_HistRCP45_tigercpu_intelmpi_18_576PE_10ens_1860-2100_CSA.nc'
ifile = 't_ref_AM2.5C360_amipHadISSTrcp45_tigercpu_intelmpi_18_1080PE_3ens_1871-2050_CSA.nc'
#dsname =  'precip'
#dsname =  't_ref_max'
dsname =  't_ref'
daname = dsname
#units = 'mm/day'
units = 'K'
#years = slice('1981', '2010')
years = slice('1991', '2020')
#title = f'FLOR histRCP45 10ens {ifile.split(".")[-2].split("_")[-1]} {years.start}-{years.stop}'
title = f'AM2.5C360 AMIP 3ens {ifile.split(".")[-2].split("_")[-1]} {years.start}-{years.stop}'
tag = '_' + title.replace(' ', '_')
ofile_nc = __file__.replace('.py', f'{tag}.nc')
#selregion = lambda x: x.sel(lat=slice(20, 37), lon=slice(65, 85))



if os.path.exists(ofile_nc):
    ds = xr.open_dataset(ofile_nc)
    print('[loaded]:', ofile_nc)
else:
    print('loading...')
    da = xr.open_dataarray(ifile).sel(time=years).load()
    print('calculating...')
    #da = da.where(landflag(da)>0.5).geo.fldmean()
    #da.pipe(mask_region).isel(time=0, ens=0).plot(); plt.show(); sys.exit() #test
    da = da.pipe(mask_region)
    da = da.geo.fldmean()
    #da = da.pipe(selregion).pipe(lambda x: x.where(flagland(x)>0.5)).geo.fldmean()
    damean = da.groupby('time.month').mean(['time', 'ens'])
    da_quantile = da.groupby('time.month').quantile([0.025, 0.17, 0.83, 0.975], dim=['time', 'ens'])
    ds = xr.Dataset(dict(m=damean, q=da_quantile))
    print('saving...')
    ds.to_netcdf(ofile_nc)
    print('[saved]:', ofile_nc)
 
if __name__ == '__main__':
    from wyconfig import * #my plot settings
    #ds = ds.pipe(lambda x: x-273.15)
    fig, ax = plt.subplots()
    roll_time = True #False
    #func_units = lambda x: x.assign_attrs(units='mm/day')
    units = 'degC'
    func_units = lambda x: (x-273.15).assign_attrs(units=units)
    if roll_time:
        ##rotate dayofyear
        #ndays = 31+29+31+30+31+30 #Jan-Jun days
        #ds = ds.roll(dayofyear=-ndays, roll_coords=True)
        #ds = ds.assign_coords(dayofyear=ds.dayofyear.where(ds.dayofyear>ndays, other=ds.dayofyear+366))
        #rotate month
        nmonths = 6 
        ds = ds.roll(month=-nmonths, roll_coords=True)
        ds = ds.assign_coords(month=ds.month.where(ds.month>nmonths, other=ds.month+12))
    #ds.m.pipe(func_units).plot(color='k', lw=2, label='mean')
    ds.m.pipe(func_units).plot(color='k', lw=2, label='mean')
    for ii,qi in enumerate(ds['quantile'].values):
        ds.q.sel(quantile=qi).pipe(func_units).plot(ax=ax, lw=1, color=f'C{ii}', label=f'{qi*100:.1f}%')

    #xticks = np.cumsum([1, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30])
    xticks = np.arange(1,13)
    xticklabels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    if roll_time:
        xticks = np.roll(xticks, -6)
        xticks = np.where(xticks>nmonths, xticks, xticks+12)
        xticklabels = np.roll(xticklabels, -6)
    ax.set_xticks(xticks)
    #ax.set_xticklabels(xticklabels, ha='left')
    ax.set_xticklabels(xticklabels)
    """
    if roll_time:
        ax.set_xlim(1+ndays, 366+ndays)
    else:
        ax.set_xlim(1, 366)
    """
    ax.set_xlabel('')
    ax.set_ylabel(f'{daname} [{units}]')
    ax.set_title('')
    #ax.text(0,1, f' {360-lons.start}-{360-lons.stop}W, {lats.start}-{lats.stop}N, land', transform=ax.transAxes,
    #    ha='left', va='top')
    ax.legend(loc='upper right')
    #ax.legend(loc='lower right')
    ax.set_title(title, loc='left')
    
    #savefig
    if 'savefig' in sys.argv or 's' in sys.argv:
        figname = __file__.replace('.py', f'{tag}.png')
        if 'overwritefig' in sys.argv or 'o' in sys.argv:
            wysavefig(figname, overwritefig=True)
        else:
            wysavefig(figname)
    tt.check(f'**Done**')
    plt.show()