[Numpy-discussion] ENH: Add Moving Average Function

[Stephan Hoyer]

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 at case.edu> wrote:

> Hi all,
>
> I opened a Pull Request
> <https://link.getmailspring.com/link/58478F5E-3390-4C6D-8AA4-0B8724FC079A@getmailspring.com/0?redirect=https%3A%2F%2Fgithub.com%2Fnumpy%2Fnumpy%2Fpull%2F13923&recipient=bnVtcHktZGlzY3Vzc2lvbkBweXRob24ub3Jn> to
> include this package in numpy
> <https://link.getmailspring.com/link/58478F5E-3390-4C6D-8AA4-0B8724FC079A@getmailspring.com/1?redirect=https%3A%2F%2Fpypi.org%2Fproject%2Fmvgavg%2F&recipient=bnVtcHktZGlzY3Vzc2lvbkBweXRob24ub3Jn>,
> along with the associated sliding window function in this PR
> <https://link.getmailspring.com/link/58478F5E-3390-4C6D-8AA4-0B8724FC079A@getmailspring.com/2?redirect=https%3A%2F%2Fgithub.com%2Fnumpy%2Fnumpy%2Fissues%2F7753&recipient=bnVtcHktZGlzY3Vzc2lvbkBweXRob24ub3Jn>
> .
>
> 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
> [image: Sent from Mailspring]
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