In [1]:

    

import sys
from glob import glob

import mkserialisable
mkserialisable.mk_netcdf()
import netCDF4
from mpl_toolkits.basemap import Basemap

import seasonalmean

data_dir = '/data/cmip5/output1/MOHC/HadGEM2-ES/rcp85/mon/atmos/' \
           'Amon/r1i1p1/v20111215/tas/'
files = glob(data_dir + 'tas_Amon_HadGEM2-ES_rcp85_r1i1p1_*-*.nc')

# get variables for plotting
d = netCDF4.Dataset(files[0])
lon = d.variables['lon'][:]
lat = d.variables['lat'][:]
d.close()


    

In [101]:

    

y0 = 2010
y1 = 2200
m0 = 12
# time methods
for parallel in (False, True):
    print ('parallel' if parallel else 'serial') + ':',
    %timeit seasonalmean.run(files, 'tas', y0, m0, y1, parallel)
# check they give the same results
results_serial = seasonalmean.run(files, 'tas', y0, m0, y1, False)
results_parallel = seasonalmean.run(files, 'tas', y0, m0, y1, True)
print 'results equal:', (results_serial == results_parallel).all()
results = results_parallel
mn, mx = results.min(), results.max()


    

    




serial: 1 loops, best of 3: 1.64 s per loop
parallel: 1 loops, best of 3: 1.41 s per loop
results equal: True

In [111]:

    

def plot (i):
    tas = results[i]
    m = Basemap(projection = 'merc', llcrnrlat = -80, urcrnrlat = 80,
                llcrnrlon = lon[0], urcrnrlon = lon[-1], lat_ts = 0)
    m.drawcoastlines()
    x, y = m(*np.meshgrid(lon, lat))
    m.contourf(x, y, tas, levels = range(mn, mx, int((mx - mn) / 50)))
    m1 = m0 + 3 - 1
    y = y0 + i
    plt.title('{0}/{1} to {2}/{3}'.format(m0, y, m1 % 12, y + m1 / 12))
    plt.colorbar()


    

In [124]:

    

n = 10
for i in xrange(0, len(results), int(float(len(results)) / (n - 1))):
    # create a new figure so we don't plot everything on the same graph
    figure()
    plot(i) # uses the last created figure