sum over bin range in python

I have a numpy array with shape:

In: imar.shape 
Out: (21, 77, 10000)

I want a binned sum on the last axis, with every bin containing 20 items.

The way I'm doing this now is:

np.sum(  开发者_JS百科imar.reshape([-1,500,20]), axis=2 ).reshape(imar.shape[:2])

It's fast, but seems error-prone if I get the arguments to reshape wrong. Is there a better way to do this?

I've looked at np.digitize,histogram,bincount, and some others, but those are value based; I want sum over a set of ranges.

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You have the right approach. I asked a similar question a while back:

How can I efficiently process a numpy array in blocks similar to Matlab's blkproc (blockproc) function

There are several approaches to handling the reshape. If you are careful and write a function to do it, you'll be alright. Of course, you need to be certain that you trim your input matrix if it isn't an integer multiple of your block size.