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 <ns...@case.edu> wrote: > Hi all, > > I opened a Pull Request > <https://link.getmailspring.com/link/58478f5e-3390-4c6d-8aa4-0b8724fc0...@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-8aa4-0b8724fc0...@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-8aa4-0b8724fc0...@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] > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
