Efficiently get indices of histogram bins in Python

Question

Short Question

I have a large 10000x10000 elements image, which I bin into a few hundred different sectors/bins. I then need to perform some iterative calculation

Answer 1

I assume that the binning, done in the example with digitize, cannot be changed. This is one way to go, where you do the sorting once and for all.

vals = np.random.random(1e4)
nbins = 100
bins = np.linspace(0, 1, nbins+1)
ind = np.digitize(vals, bins)

new_order = argsort(ind)
ind = ind[new_order]
ordered_vals = vals[new_order]
# slower way of calculating first_hit (first version of this post)
# _,first_hit = unique(ind,return_index=True)
# faster way:
first_hit = searchsorted(ind,arange(1,nbins-1))
first_hit.sort()

#example of using the data:
for j in range(nbins-1):
    #I am using a plotting function for your f, to show that they cluster
    plot(ordered_vals[first_hit[j]:first_hit[j+1]],'o')

The figure shows that the bins are actually clusters as expected: