# Load required module
import numpy as np
import netCDF4
from cdo import *
from matplotlib import pyplot as plt
%matplotlib inline
path = '../../../data/'
file = path + 'pr_Amon_CanESM2_historical_r1i1p1_193101-200512_SA.nc'
cdo = Cdo()
# Compute the South America mean monthly precipitation and return it as a numpy array:
mean_pr = np.squeeze(cdo.fldmean(input=file, returnArray='pr')) # Field Mean pr SA (da una serie temporal)
years = cdo.showyear(input=file)
pr_box.shape
(900,)
#pr_ene = np.squeeze(cdo.fldmean(input=file, returnArray='pr'))
pr_box = np.squeeze(cdo.fldmean (input='-sellonlatbox,280,300,-40,-20 ' + file, returnArray='pr'))
pr_box.shape
plt.plot(pr_box*60*60*24)
[<matplotlib.lines.Line2D at 0x7fbe377c63c8>]
prSA_anom = np.squeeze(cdo.sub(input = '-fldmean ' + file + ' -timmean -selyear,1971/2000 -fldmean ' + file, returnArray='pr', options = '-L'))
pr_anu = cdo.yearmean (input= '-fldmean ' + file, returnArray='pr')
#Este css es trabajo de @LorenaABarba y su grupo
from IPython.core.display import HTML
css_file = '../../css/personal.css'
HTML(open(css_file, "r").read())