import sys
import numpy as np
#import Scientific.IO.NetCDF as s
import matplotlib.pyplot as plt
import mpl_toolkits.basemap as bm
from scipy.stats.stats import pearsonr
from netCDF4 import Dataset
path = "/ouce-home/staff/sedm4922/"
#for fname in glob.glob(path):
    #print(fname)

workdir='/ouce-home/staff/sedm4922/biases/'
model = ['chirps']

for i, m in enumerate(model):
	precip_f='/ouce-home/staff/sedm4922/biases/inputs/'+m+'_short.nc'
	print precip_f
	precip_mod = Dataset(precip_f, 'r')
	precip_model=precip_mod.variables['precip'][:]
	precip_model = np.squeeze(precip_model)
	lat = precip_mod.variables['latitude'][:]
	lon = precip_mod.variables['longitude'][:]
	print precip_model.shape
	precip_model = np.mean(precip_model, axis=1)
	precip_model = np.mean(precip_model, axis=1)
	precip_model = np.mean(precip_model, axis=0)
	print precip_model.shape
	print precip_model
	exit()