#!/usr/bin/env python
# Wenchang Yang (wenchang@princeton.edu)
# Wed Sep 18 21:10:30 EDT 2019
from datetime import datetime
import os.path, sys, os
#import matplotlib.pyplot as plt
import xarray as xr, numpy as np, pandas as pd
import geoxarray

expnames = ['m6p0sol_CTL1860_tigercpu_intelmpi_18_576PE',
    'p6p0sol_CTL1860_tigercpu_intelmpi_18_576PE',
    'm6p0sol_CTL1860_recover0501_tigercpu_intelmpi_18_576PE',
    'CTL1860_newdiag_tigercpu_intelmpi_18_576PE']
year_ranges = [range(101, 597+1),
    range(101, 400+1),
    range(501, 900+1),
    range(1, 1000+1)]
data_name = 't_surf'
tag = 'tropMean'

def get_tropMean(expname, data_name, years):
    '''get global mean of a variable from model output'''
    ofile = f'{expname}.{data_name}.{tag}.{years[0]:04d}-{years[-1]:04d}.nc'
    if os.path.exists(ofile):
        print('[exists]:', ofile)
        return
    ifiles = [f'/tigress/wenchang/MODEL_OUT/{expname}/POSTP/{year:04d}0101.atmos_month.nc'
        for year in years]
    
    # open dataset
    with  xr.open_mfdataset(ifiles) as ds:
        da = ds[data_name]
    attrs = da.attrs # save the raw attrs
    
    # fldmean
    da = da.rename({'grid_xt': 'lon', 'grid_yt': 'lat'}) \
        .sel(lat=slice(-30, 30)) \
        .geo.fldmean().load()
    
    # save to netcdf
    da.attrs = attrs
    encoding = {data_name: {'zlib': True, 'complevel': 1}}
    da.to_dataset(name=data_name) \
        .to_netcdf(ofile, encoding=encoding)
    print('[saved]:', ofile)

if __name__ == '__main__':
    tformat = '%Y-%m-%d %H:%M:%S'
    t0 = datetime.now()
    print()
    print('[time start]:', t0.strftime(tformat))

    for expname,years in zip(expnames, year_ranges):
        print(expname, data_name, tag)
        get_tropMean(expname, data_name, years)

    t1 = datetime.now()
    dt = t1 - t0
    print('[time end]:', t1.strftime(tformat))
    print('[time used]:', f'{dt.seconds:,} seconds')
    print()