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import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.util import add_cyclic_point
import cmaps

# Rangos de contornos
clevs=np.arange(-25,26,2.5)
nrows=4
ncols=1

# Define the figure and each axis for the 3 rows and 3 columns
fig, axs = plt.subplots(nrows=nrows,ncols=ncols,
subplot_kw={'projection': ccrs.PlateCarree(central_longitude=-180)},
figsize=(6,20))

# getting the original colormap using cm.get_cmap() function
orig_map=cmaps.NEO_div_vegetation_c

# reversing the original colormap using reversed() function
reversed_map = orig_map.reversed()
# axs is a 2 dimensional array of `GeoAxes`.  We will flatten it into a 1-D array
axs=axs.flatten()

#Loop over all of the models
for i,olrms in enumerate(F_mjo):
# Select the week 1 forecast from the specified model
data=PCP_MJO[i]

# Add the cyclic point
data,lons=add_cyclic_point(data,coord=ds['lon'])

# Contour plot
cs=axs[i].contourf(lons,ds['lat'],data,clevs,
transform = ccrs.PlateCarree(),
cmap=reversed_map,extend='both')

# Draw the coastines for each subplot
axs[i].coastlines()
gl = axs[i].gridlines(color='black',alpha=0.2)
axs[i].set_extent([50,-60, -35, 35], crs=ccrs.PlateCarree(central_longitude=-180))

# Longitude labels
if i == 3:
axs[i].set_xticks(np.arange(50,301,50), crs=ccrs.PlateCarree())
lon_formatter = cticker.LongitudeFormatter()
axs[i].xaxis.set_major_formatter(lon_formatter)
else:
axs[i].set_xticks(np.arange(50,301,50), crs=ccrs.PlateCarree())
axs[i].set_xticklabels([])

# Latitude labels
axs[i].set_yticks(np.arange(-35,36,20), crs=ccrs.PlateCarree())
lat_formatter = cticker.LatitudeFormatter()
axs[i].yaxis.set_major_formatter(lat_formatter)
# Title each subplot with the name of the model
axs[i].set_ylabel(olrms)

axs[i].set_title(str(days[i])+' days',loc='right',fontsize=10)
# Adjust the location of the subplots on the page to make room for the colorbar
fig.subplots_adjust(bottom=0.05, top=0.4, left=0.1, right=0.9,
wspace=0.01, hspace=0.3)

# Add a colorbar axis at the bottom of the graph
cbar_ax = fig.add_axes([0.2, 0.005, 0.6, 0.015])

# Draw the colorbar
rango= np.arange(-25,26,5)
cbar=fig.colorbar(cs, cax=cbar_ax,orientation='horizontal',ticks=rango)
#plt.title('OLR ($W/m^{2}$)')
cbar.set_label('OLR ($W/m^{2}$)')