import ee

import numpy as np

ee.Initialize()

import xarray as xr

import sys

#data_dir = '/home/cccr/msingh/data/merra_aod_global_1980_2020/MERRA2_100.tavg1_2d_aer_Nx.1981_monmean.nc4'

import sys

#dates = sys.argv[1]# '2020-03-23'

#datee = sys.argv[2]# '2020-04-24'

#ds_merra2 = xr.open_dataset(data_dir)

from datetime import timedelta, date

import pandas as pd

def daterange(start_date, end_date):

for n in range(int((end_date - start_date).days)):

yield start_date + timedelta(n)

start_date = date(2003, 1, 1)

end_date = date(2021, 1, 1)

#end_date = date(2000, 12, 31)

#end_date = date(2021, 1, 1)

times = pd.date_range('2003-01-01', '2021-01-01')

print(times, times.shape)

#sys.exit()

evi = np.zeros((times.shape[0]))

ndsi = np.zeros((times.shape[0]))

ndwi = np.zeros((times.shape[0]))

ndvi = np.zeros((times.shape[0]))

monsoon_region = ee.Geometry.Polygon([

[[74, 14], [87, 14],[87, 28], [74, 28], [74, 14]]

]);

cnt = 0

for single_date in daterange(start_date, end_date):

print(single_date.strftime("%Y-%m-%d"), (single_date + timedelta(days=1)).strftime("%Y-%m-%d"))

dates = single_date.strftime("%Y-%m-%d")

datee = (single_date + timedelta(days=1)).strftime("%Y-%m-%d")

collection_evi = ee.ImageCollection('MODIS/MYD09GA_006_EVI').select('EVI').filterDate(dates,datee ).mean()

collection_ndsi = ee.ImageCollection('MODIS/MYD09GA_006_NDSI').select('NDSI').filterDate(dates,datee ).mean()

collection_ndwi = ee.ImageCollection('MODIS/MYD09GA_006_NDWI').select('NDWI').filterDate(dates,datee ).mean()

collection_ndvi = ee.ImageCollection('MODIS/MYD09GA_006_NDVI').select('NDVI').filterDate(dates,datee ).mean()

data = collection_evi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('EVI')

evi[cnt] = ee.Number(data).getInfo()

data = collection_ndsi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('NDSI')

ndsi[cnt] = ee.Number(data).getInfo()

data = collection_ndwi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('NDWI')

ndwi[cnt] = ee.Number(data).getInfo()

data = collection_ndvi.reduceRegion(ee.Reducer.mean(), monsoon_region, scale=500).get('NDVI')

ndvi[cnt] = ee.Number(data).getInfo()

cnt = cnt+1

np.savetxt('evi.out', evi, delimiter=',')

np.savetxt('ndsi.out', ndsi, delimiter=',')

np.savetxt('ndwi.out', ndwi, delimiter=',')

np.savetxt('ndvi.out', ndvi, delimiter=',')

ds = xr.DataArray(evi, coords=[times], dims=["time"])

ds.to_netcdf('evi_.nc')

ds = xr.DataArray(ndsi, coords=[times], dims=["time"])

ds.to_netcdf('ndsi_.nc')

ds = xr.DataArray(ndwi, coords=[time], dims=["time"])

ds.to_netcdf('ndwi_.nc')

ds = xr.DataArray(ndvi, coords=[times], dims=["time"])

ds.to_netcdf('ndvi_.nc')