#!/usr/bin/env python
# coding: utf-8

# # Probando CDO
# 
# ## Integrantes: 
# 
# * Carla Gulizia
# * Natalia Zazulie
# * Natalia Montroull
# 
# ## Instalación:
# 
#     # Instalar cdo desde la terminal
#     $ sudo apt-get install cdo
# 
#     # Instalar con conda el python-cdo:
#     $conda install -c conda-forge cdo=1.7.1
# 
#     # Es necesaria una libreriaa extra para cdo:
#     sudo apt-get install libjpeg9

# In[12]:


# Load required module
import numpy as np
import netCDF4
from cdo import *
from matplotlib import pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# In[13]:


path = '../../../data/'
file = path + 'pr_Amon_CanESM2_historical_r1i1p1_193101-200512_SA.nc'


# In[14]:


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)


# In[20]:


pr_box.shape


# In[15]:


#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)


# In[16]:


prSA_anom = np.squeeze(cdo.sub(input = '-fldmean ' + file + ' -timmean -selyear,1971/2000 -fldmean ' + file, returnArray='pr', options = '-L'))


# In[17]:


pr_anu = cdo.yearmean (input= '-fldmean ' + file, returnArray='pr')


# In[1]:


#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())