#!/usr/bin/env python
# Wenchang Yang (wenchang@princeton.edu)
# Wed Mar  4 19:10:44 EST 2020
if __name__ == '__main__':
    from misc.timer import Timer
    tt = Timer(f'start {__file__}')
import sys, os.path, os, datetime
import xarray as xr, numpy as np, pandas as pd
#import matplotlib.pyplot as plt
import geoxarray
from xtc import tc_basins
from misc.cim import cim, sem
maindir = '/tigress/wenchang/analysis/seedTC'
if __name__ == '__main__':
    tt.check('end import')

# MDR lons and lats
# ventilation index
def get_pTang(basin='NA', dterm=None):
    '''ventilation index to TC genesis probability (based on Tang and Emanuel.
    dterm: which of Vshear, chi or PI is using full values, otherwise monthly mean climatology from ctl1990.'''
    thisdir = os.path.abspath( os.path.dirname(__file__) )
    ctldir = os.path.join(os.path.dirname(thisdir), 'HiRAM_CTL1990')
    if dterm is None:
        ofile = f'{thisdir}/data_pTang_{basin}.nc'
    else:
        ofile = f'{thisdir}/data_pTang_only{dterm}_{basin}.nc'
    if os.path.exists(ofile):
        print('[exists]:', ofile)
        return xr.open_dataset(ofile)['p']
    bs = tc_basins()
    if dterm == 'Vshear':
        Vshear = xr.open_dataset(f'{thisdir}/data.Vshear.nc')['Vshear']#.groupby('time.month').mean('time')
        chi = xr.open_dataset(f'{ctldir}/data.chi.nc')['chi'].groupby('time.month').mean('time')
        V_PI = xr.open_dataset(f'{ctldir}/data.PI.nc')['vmax'].groupby('time.month').mean('time')
        vi = Vshear.groupby('time.month') * (chi/V_PI)
    elif dterm == 'chi':
        Vshear = xr.open_dataset(f'{ctldir}/data.Vshear.nc')['Vshear'].groupby('time.month').mean('time')
        chi = xr.open_dataset(f'{thisdir}/data.chi.nc')['chi']#.groupby('time.month').mean('time')
        V_PI = xr.open_dataset(f'{ctldir}/data.PI.nc')['vmax'].groupby('time.month').mean('time')
        vi = (Vshear/V_PI) * chi.groupby('time.month')
    elif dterm == 'PI':
        Vshear = xr.open_dataset(f'{ctldir}/data.Vshear.nc')['Vshear'].groupby('time.month').mean('time')
        chi = xr.open_dataset(f'{ctldir}/data.chi.nc')['chi'].groupby('time.month').mean('time')
        V_PI = xr.open_dataset(f'{thisdir}/data.PI.nc')['vmax']#.groupby('time.month').mean('time')
        vi = Vshear*chi/V_PI.groupby('time.month')
    else:
        ifile = f'{thisdir}/data.VI.nc'
        vi = xr.open_dataset(ifile)['VI']
    vi.load()
    vi = (vi
        # NA basin
        .where( bs.mask(vi)==bs.map_keys(basin) )
        # lat range 10-30N
        .where(vi.lat>10).where(vi.lat<30)
        )
    vi0 = 0.014
    n = np.log(9)/np.log(0.1/0.014)
    p = vi.pipe(lambda x: 1./(1. + (x/vi0)**n)).geo.fldmean()
    long_name = 'TC genesis probability'
    p = p.assign_attrs(long_name=long_name)
    p.to_dataset(name='p').to_netcdf(ofile)
    print('[saved]:', ofile)

    return p 


if __name__ == '__main__':
    from wyconfig import * #my plot settings
    basin = 'NA'
    model = 'HIRAM'
    expname = 'plus2K'
    figname = __file__.replace('.py', f'_{basin}_{tt.today()}.png')
    dc = {v:get_pTang(basin=basin, dterm=v) for v in [None, 'Vshear', 'chi', 'PI']}
    for key,da in dc.items():
        if key is None:
            label = 'full'
            color = 'k'
        else:
            label = f'only {key}'
            color = None
        da.groupby('time.year').mean('time').plot(label=label, color=color)
    plt.legend()
    title = f'{model} {expname} {basin} TC genesis probability'
    plt.title(title)


    plt.savefig(figname)
    print('[saved]:', figname)

    tt.check('**done**')
    plt.show()