Performing a test (to determine whether the loop should be aborted) after looking at each element would cause `np.any` to run slower if all elements are zero, or if the first non-zero element is near the end of the array.  Possibly a separate method that operates in this fashion is needed?

Phillip

On Mon, Jul 2, 2018 at 11:03 AM, Dieter Werthmüller <dieter@werthmuller.org> wrote:

Dear devs,

Sorry for posting a numpy-issue on the scipy-list. I am not (yet) subscribed to the numpy list, but I believe that the two community have a big enough overlap so it shouldn't matter too much where I post it.

I recently encountered the issue that I found that np.any(x) is sort of veeeery slow for big arrays, even if every element has a non-zero value and I only need a True/False response. So my thinking was that if np.any(x) encounters the first non-zero value it should simply return True, which should take basically no time at all if every element in the array is non-zero.

Searching for it I found the following two old issues on numpy:

1. https://github.com/numpy/numpy/issues/2269

It is an issue from 2010, but got some traffic again in 2016 and 2017.


2. https://github.com/numpy/numpy/issues/3446

This is a related issue from 2013, reporting a potential performance regression between numpy 1.6.2 and 1.7.0, that got some traffic in 2016 again as well.


I just wanted to ask about the opinions of devs more familiar with these two functions (np.all(), np.any()). Would there be better ways to check if any element in a big (1D or higher dimensions)-array is non-zero (or the reverse with np.all)?


Thanks,
Dieter

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