I've got a numpy 2D histogram that bins data points with two spacial coordinates (x_array and y_array) and averages the values of a third, data_array component within each bin.
The code is as follows:
xbins = 240 #image is 240x240
ybins = 240
H, xedges, yedges = np.histogram2d(y_array, x_array, bins=[ybins, xbins], weights=data_array)
count, x, y = np.histogram2d(y_array, x_array, bins=[ybins, xbins])
H/=count
extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
plt.imshow(H, extent=extent, interpolation='nearest')
etc. (The x's and y's are reversed in some places, I know... it's just the way I got it to work).
What I need to do now is oversample (or grid) this data, i.e. sample a few times with overlapping bins such that the whole sampling moves over, and also up (separately), half a bin-width, to achieve a smoother image.
I can obviously increase the number of bins to increase the resolution, but that only works to a point... at a certain point there are bins without any data points, as they are distributed randomly.
I've been working on this one for a while and I'm just stumped. Thanks for any help you can provide.