I think having some function for common cases like moving average and spectrogram would be good.  Having a jumping-off point and simple reference for testing against could encourage someone to make a faster implementation down the road.

-Todd

On Mon, Aug 26, 2019 at 12:24 AM Stephan Hoyer <shoyer@gmail.com> wrote:

I would be very interested to see the “sliding window view” function merged into np.lib.stride_tricks.

I don’t think it makes sense to add a suite of dedicated functions for sliding window calculations that wrap that function. If we are going to go down the path of adding sliding window calculations into a NumPy, they should use efficient algorithms, like those found in the “bottleneck” package.

Best,

Stephan

On Sun, Aug 25, 2019 at 3:33 PM Nicholas Georgescu <nsg27@case.edu> wrote:

Hi all,

I opened a Pull Request to include this package in numpy, along with the associated sliding window function in this PR.

The function picks the fastest method to do a moving average if there is no weighting, but with weights it resorts to the second-fastest method which has an easier implementation.  It also contains a binning option which cuts the number of points down by a factor of n rather than by subtracting n.  The details are in the package documentation and PR.

Thanks,

Nicholas

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