Python 2d-histogram success rate per bin

Question

I have data in arrays x, y and w where 'x' and 'y' indicate position and 'w' is a weight of either 1 or 0 indicating success or failure. I'm trying to create a 2d histogram where each bin of the histogram is coloured based on the percentage of successes in that bin (i.e. # of successes in bin divided by total points in bin). I've played around with numpy.histogram2d quite a bit and can get density plots going, but this is not the same as the % of success scheme I'm aiming for. Please note normed=True in the numpy.histogram2d argument does not alleviate this problem.

(To clarify on the difference, a density plot would indicate large 'colour value' if there is a larger number of successes in the bin regardless of how many failures are in the same bin. I would like to have the percentage of successes instead, so a large number of failures in the same bin would give a smaller 'colour value'. I apologise for poor terminology).

Thank you very much to anyone who can help!

Example of current code that doesn't do what I'm aiming for:

import matplotlib.pyplot as plt
import numpy as np
plt.figure(1)
H, xedges, yedges = np.histogram2d(x, y, bins=50, weights=w, normed=True)
extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
plt.imshow(H, extent=extent,interpolation='nearest')
plt.colorbar()
plt.show()

Jeff Tratner Jeff Tratner · Accepted Answer · 2013-04-27T10:43:11

I'm pretty sure this works, but you don't give data, so it's hard to check. normed=True gives you densities, if you don't pass normed=True, you get weighted sample counts, so if you divide your weighted version (which is really just #successes per bin) by unweighted (# of elements in each bin), you'll end up with % successes.

import matplotlib.pyplot as plt
import numpy as np
plt.figure(1)
H, xedges, yedges = np.histogram2d(x, y, bins=50, weights=w)
H2, _, _ = np.histogram2d(x,y, bins=50)
extent = [0,1, xedges[-1], xedges[0]]
plt.imshow(H/H2, extent=extent,interpolation='nearest')
plt.colorbar()
plt.show()

This could leave nan in the final histogram, so you might want to make a decision for those cases.

Python 2d-histogram success rate per bin

1 Answers