my_filename = ('snc_NAM-44_CCCma-CanESM2_rcp85_r1i1p1_'
    'CCCma-CanRCM4_r2_day_20410101-20451231.nc')
# for this demo, we will see where it will be a white christmas in 2041.
my_year, my_month, my_day = (2041, 12, 25)


# calculations based on the above values:

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

# extracts variable name from beginning of filename:
my_variable = my_filename[:my_filename.find('_')]

print("my_variable = {}".format(my_variable))

first_year = int(re.search(('([12][90][0-9]{2})[01][0-9]'
    '[0-3][0-9]\-[12][90][0-9]{2}[01][0-9][0-3][0-9]\.nc'), 
    my_filename).group(1))
print("first year in .nc file = {}".format(first_year))

month_lengths = (0,31,28,31,30,31,30,31,31,30,31,30)
month_cumulative = []
counter = 0
for length in month_lengths:
    counter += length
    month_cumulative.append(counter)
print("month_cumulative = {}".format(month_cumulative))
      
days_elapsed = (365 * (my_year - first_year) + 
                month_cumulative[my_month - 1] + my_day)
print(("{} days elapsed between Jan. 1, {} and specified date"
       "of {}-{}-{}").format(days_elapsed, first_year, 
                           my_year, my_month, my_day))
print("(leap days are not counted)")

import netCDF4
# netCDF4 is not part of standard Python,
# you may have to download and install it
import numpy as np

ncfile = netCDF4.Dataset(my_filename, 'r')
# note that you'll have to download the file yourself

# Let's have a look at this file.
print("Attributes:")
print(ncfile.ncattrs())
print("\nVariables:")
print(ncfile.variables)

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

print("Shape of nc_lons: {}".format(nc_lons.shape))
print("Shape of nc_lons: {}".format(nc_lats.shape))
print("Shape of nc_vars before specifying day: {}".\
      format(nc_vars.shape))
nc_vars = nc_vars[days_elapsed]
print("Shape of nc_vars after specifying day: {}".\
      format(nc_vars.shape))
print("Starting day of file: {}".format(\
       ncfile.variables['time'][0]))

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
%matplotlib inline

fig = plt.figure(figsize=(8,8))
ax = fig.add_axes([0.1,0.1,0.8,0.8])
m = Basemap(width=6000000,height=4000000,
            resolution='l',projection='stere',\
            lon_0=-93,lat_0=41, area_thresh=1000)
m.drawlsmask(land_color='#00441b',ocean_color='#8be5e5',
             lakes=True)
m.drawcoastlines()
m.drawstates()
m.drawcountries()
parallels = np.arange(0., 90, 10.)
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
meridians = np.arange(180.,360.,10.)
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
x, y = m(nc_lons, nc_lats)
clevs = range(1,101)
cs = m.contourf(x,y,nc_vars,clevs,cmap=plt.cm.Greens_r)
cbar = m.colorbar(cs,location='bottom',pad="5%")
cbar.set_label('% snow cover')
plt.title('Snow cover projected on Dec. 25, 2041')
plt.show()