#!/usr/bin/env python
# Wenchang Yang (wenchang@princeton.edu)
# Thu May 21 11:24:22 EDT 2020
if __name__ == '__main__':
    from misc.timer import Timer
    tt = Timer(f'start {__file__}')
import sys, os.path, os, glob
import xarray as xr, numpy as np, pandas as pd
#import matplotlib.pyplot as plt
#more imports
from xtc import tc_count
from xtc.mask import get_landflag
from misc.cim import cim, sem #confidence interval of the mean
#
if __name__ == '__main__':
    tt.check('end import')
    
#start from here
thisdir = os.path.dirname(__file__)
def get_nseed(basin='NA'):
    #ifile = '/tigress/wenchang/analysis/TC/HIRAM/amipHadISST_tigercpu_intelmpi_18_540PE/netcdf/HIRAM.amipHadISST_tigercpu_intelmpi_18_540PE.tc_tracks.allstorms.1971-2018.nc'
    ifile = '/tigress/wenchang/analysis/TC/HIRAM/CTL1990s_v201910_2xCO2_tigercpu_intelmpi_18_540PE/netcdf/tc_tracks.allstorms.0101-0150.nc'
    ofile = os.path.join(thisdir, f'data_nseed_{basin}.nc')
    if os.path.exists(ofile):
        print('[exists]:', ofile)
        return xr.open_dataset(ofile)['N_SEED']
    seed_kinds = [6, 12, 18, 24] # least hours of life
    ds = (xr.open_dataset(ifile)
        # genesis locations within the tropics
        .pipe(lambda x: x.where(x.lat.isel(stage=0)>-30).where(x.lat.isel(stage=0)<30))
    )
    n_ocean = get_landflag(ds).pipe(lambda x: x<0.5).sum('stage')
    # calculate # of seed with different threshold of life hours
    n_seed = [ds.where(n_ocean>(seed_life//6)).pipe(tc_count, basin=basin)
        for seed_life in seed_kinds]
    #if 'vmax' in ds and 'windmax' not in ds:
    #    ds = ds.rename(vmax='windmax')
    n_seed = xr.concat(n_seed, dim=pd.Index(seed_kinds, name='life'))
    n_seed = n_seed.rename('N_SEED')

    #save to nc
    n_seed.to_dataset().to_netcdf(ofile)
    print('[saved]:', ofile)

    return n_seed

if __name__ == '__main__':
    from wyconfig import * #my plot settings
    source = 'HiRAM_2xCO2'
    basin = 'NA'
    fig, ax = plt.subplots()
    figname = __file__.replace('.py', f'_{basin}_{tt.today()}.png')
    da = get_nseed(basin=basin)
    da.groupby('time.year').sum('time').plot(hue='life')
    plt.title(f'{basin} N_SEED: {source}')

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

    tt.check(f'**Done**')
    plt.show()