my_path = r'F:\Data\snc-climate'
my_month, my_day = (12, 25) # will extract data every year on Christmas Day
my_variable = 'snc'

from __future__ import print_function # to increase compatibility with Python 3.x

import netCDF4 # Not part of standard Python, you may have to download and install it
import numpy as np
import re
import glob
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
%matplotlib inline

fig = plt.figure(figsize=(12,12))
ax = fig.add_axes([0.1,0.1,0.8,0.8])
m = Basemap(width=6000000,height=5200000,
            resolution='l',projection='stere',\
            lon_0=-93,lat_0=44, area_thresh=1000)
m.bluemarble()
m.drawstates()
m.drawcountries()

datatxt = 'Data: Canadian Centre for Climate Modeling and Analysis, Model CCGM4/CanESM2/RCP8.5'
plt.text(0.01, 0.02,datatxt, horizontalalignment='left',
         verticalalignment='center',transform=ax.transAxes, fontdict={'family' : 'sans',
        'color'  : 'white',
        'weight' : 'bold',
        'size'   : 10,
        })

plt.text(0.99, 0.02,'www.prooffreader.com', horizontalalignment='right',
         verticalalignment='center',transform=ax.transAxes, fontdict={'family' : 'serif',
        'color'  : 'white',
        'weight' : 'bold',
        'size'   : 10,
        })

plt.text(0.02, 0.98,'Projected White Christmases until the year 2100', horizontalalignment='left',
         verticalalignment='top',transform=ax.transAxes, fontdict={'family' : 'sans',
        'color'  : 'black',
        'weight' : 'bold',
        'size'   : 18,
        }, bbox=dict(boxstyle="square", fc="w"))

plt.savefig('back.png')
plt.show()

#make list of cumulative month lengths for calculations
month_lengths = (0,31,28,31,30,31,30,31,31,30,31,30) # 0 & Jan-Nov
month_cumulative = []
counter = 0
for length in month_lengths:
    counter += length
    month_cumulative.append(counter)

# get list of .nc files in directory
nc_files = glob.glob(my_path+'/*.nc')

for my_filename in nc_files:
    
    ncfile = netCDF4.Dataset(my_filename, 'r')

    to_find = '([12][90][0-9]{2})[01][0-9][0-3][0-9]\-([12][901][0-9]{2})[01][0-9][0-3][0-9]\.nc'
    first_year = int(re.search(to_find, my_filename).group(1))
    last_year = int(re.search(to_find, my_filename).group(2))

    nc_lons = np.array(ncfile.variables['lon'])
    nc_lats = np.array(ncfile.variables['lat'])
    nc_vars = np.array(ncfile.variables[my_variable])

    for my_year in range(first_year, last_year + 1):
        print(my_year, " ", end="")
        days_elapsed = 365 * (my_year - first_year) + month_cumulative[my_month - 1] + my_day
        nc_vars_this_year = nc_vars[days_elapsed]
        fig = plt.figure(figsize=(12,12))
        ax = fig.add_axes([0.1,0.1,0.8,0.8])
        m = Basemap(width=6000000,height=5200000,
            resolution='l',projection='stere',\
            lon_0=-93,lat_0=44, area_thresh=1000)
        x, y = m(nc_lons, nc_lats)

        cdict = {'red':   [(0.0,  0.0, 0.0), # color map from black to white
                           (1.0,  1.0, 1.0)],
                 'green': [(0.0,  0.0, 0.0),
                           (1.0,  1.0, 1.0)],
                 'blue':  [(0.0,  0.0, 0.0),
                           (1.0,  1.0, 1.0)]}

        my_cmap = LinearSegmentedColormap('CustomMap', cdict)

        clevs = range(0,101)

        cs = m.contourf(x,y,nc_vars_this_year,clevs,cmap=my_cmap)
        plt.text(0.91, 0.35,'Dec.25,', horizontalalignment='center',
                 verticalalignment='bottom',transform=ax.transAxes, fontdict={'family' : 'monospace',
                'color'  : 'white',
                'weight' : 'bold',
                'size'   : 23,
                })
        plt.text(0.91, 0.34,str(my_year), horizontalalignment='center',
                 verticalalignment='top',transform=ax.transAxes, fontdict={'family' : 'monospace',
                'color'  : 'white',
                'weight' : 'bold',
                'size'   : 27,
                })
        plt.text(0.02, 0.98,'Projected White Christmases until the year 2099', horizontalalignment='left',
                 verticalalignment='top',transform=ax.transAxes, fontdict={'family' : 'sans',
                'color'  : 'black',
                'weight' : 'bold',
                'size'   : 18,
                }, bbox=dict(boxstyle="square", fc="k"))
        plt.savefig('mask'+str(my_year)+'.png')
        #plt.show()
        plt.close()

from PIL import Image

background = 'back.png'

for year in range(2011, 2101):
    maskname = 'mask' + str(year) + '.png'
    back = Image.open(background, 'r')
    mask = Image.open(maskname, 'r')
    mask = mask.convert("L")
    back.paste(mask, mask=mask)
    back.save('snc'+str(year)+'.png')