max_award_per_year = 3e5  # above this amount just bin in to top bin
years_per_bin = 1.0 
award_bin_size = 10000
start_year = np.array([a.year + (a.month - 1)/12. for a in awards['StartDate']])
year_bins = np.arange(np.floor(start_year.min()), np.ceil(start_year.max())+1, years_per_bin)
awarded_per_year = np.array(awards['AwardedAmountToDate']/awards['DurationYears'])
awarded_per_year_mean_per_bin = np.zeros(year_bins.size - 1)
awarded_per_year_median_per_bin = np.zeros(year_bins.size - 1)
year_per_bin = np.zeros(year_bins.size - 1)
for i in np.arange(year_bins.size - 1):
    year_per_bin[i] = (year_bins[i+1] + year_bins[i])/2.0
    curmask = (start_year >= year_bins[i]) & (start_year < year_bins[i+1])
    awarded_per_year_mean_per_bin[i] = awarded_per_year[curmask].mean()
    awarded_per_year_median_per_bin[i] = np.median(awarded_per_year[curmask])
awarded_per_year[awarded_per_year > (max_award_per_year - 1.0)] = max_award_per_year - 1.0
award_bins = np.arange(0., max_award_per_year + 1., award_bin_size)
H, xedges, yedges = np.histogram2d(start_year, awarded_per_year, bins=[year_bins, award_bins])
# H needs to be rotated and flipped
H = np.rot90(H)
H = np.flipud(H)
# Mask zeros
Hmasked = np.ma.masked_where(H==0,H) # Mask pixels with a value of zero
# Plot 2D histogram using pcolor
fig2 = plt.figure(figsize=(12,5))
plt.pcolormesh(xedges,yedges,Hmasked)
plt.plot(year_per_bin, awarded_per_year_mean_per_bin, 'r', label='mean', linewidth=2)
plt.plot(year_per_bin, awarded_per_year_median_per_bin, 'm', label='median',linewidth=3)
plt.xlabel('Start Year for each grant')
plt.ylabel('$/year awarded to date')
plt.xlim(1970, 2015)
plt.ylim(0, max_award_per_year)
plt.legend(loc='upper left')
cbar = plt.colorbar()
cbar.ax.set_ylabel('# of grants')