#!/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 misc.cim import cim, sem #confidence interval of the mean
#
if __name__ == '__main__':
    tt.check('end import')
    
#start from here
def get_ntc(basin='NA'):
    ifile = '/tigress/wenchang/analysis/TC/HIRAM/amipHadISST_tigercpu_intelmpi_18_540PE/netcdf/HIRAM.amipHadISST_tigercpu_intelmpi_18_540PE.tc_tracks.TS.1971-2018.nc'
    ds = xr.open_dataset(ifile)
    if 'vmax' in ds and 'windmax' not in ds:
        ds = ds.rename(vmax='windmax')
    da = ds.pipe(tc_count, basin=basin, ws=17)
    return da

#annual cycle
years = slice('1980', '2018')
def get_cycle(basin='NA', years=years):
    da = get_ntc(basin=basin)
    da = da.sel(time=years)
    # stack dims of ['en', 'time'] and use the stacked 'time' as the new coord
    if 'en' in da.dims:
        da = da.stack(s=[...]).pipe(lambda x: x.assign_coords(s=x.time)).rename(s='time')
    da_mclim = da.groupby('time.month').mean('time')
    da_cim = da.groupby('time.month').map(cim, dim='time')
    da_sem = da.groupby('time.month').map(sem, dim='time')
    ds_mclim = xr.Dataset(dict(
        mclim=da_mclim,
        cim=da_cim,
        sem=da_sem
        ))
    return ds_mclim

 
if __name__ == '__main__':
    from wyconfig import * #my plot settings
    source = 'HiRAM_amipHadISST'
    basin = 'NA'
    figname = f'data_ntc_{basin}_{source}_{tt.today()}.png'
    da = get_ntc(basin=basin)
    da.groupby('time.year').sum('time').plot(hue='en')
    plt.title(source, loc='left')
    """
    ds_ = get_cycle(basin=basin, years=years)
    long_name = f'N_TC({basin}, {source})'
    ax = ds_.rename(mclim=long_name).to_dataframe().plot.bar(y=long_name, yerr='sem', rot=0)
    ax.legend(loc='upper left')
    """
    plt.savefig(figname)
    print('[saved]:', figname)

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