On Wed, Apr 24, 2013 at 4:11 AM, Sebastian Berg <[email protected]> wrote: > On Tue, 2013-04-23 at 23:33 -0400, [email protected] wrote: >> On Tue, Apr 23, 2013 at 6:16 PM, Sebastian Berg >> <[email protected]> wrote: >> > On Tue, 2013-04-23 at 12:13 -0500, Jonathan Helmus wrote: >> >> Back in December it was pointed out on the scipy-user list[1] that >> >> numpy has a percentile function which has similar functionality to >> >> scipy's stats.scoreatpercentile. I've been trying to harmonize these >> >> two functions into a single version which has the features of both. >> >> Scipy PR 374[2] introduced a version which look the parameters from >> >> both the scipy and numpy percentile function and was accepted into Scipy >> >> with the plan that it would be depreciated when a similar function was >> >> introduced into Numpy. Then I moved to enhancing the Numpy version with >> >> Pull Request 2970 [3]. With some input from Sebastian Berg the >> >> percentile function was rewritten with further vectorization, but >> >> neither of us felt fully comfortable with the final product. Can >> >> someone look at implementation in the PR and suggest what should be done >> >> from here? >> >> >> > >> > Thanks! For me the main question is the vectorized usage when both >> > haystack (`a`) and needle (`q`) are vectorized. What I mean is for: >> > >> > np.percentile(np.random.randn(n1, n2, N), [25., 50., 75.], axis=-1) >> > >> > I would probably expect an output shape of (n1, n2, 3), but currently >> > you will get the needle dimensions first, because it is roughly the same >> > as >> > >> > [np.percentile(np.random.randn(n1, n2, N), q, axis=-1) for q in [25., 50., >> > 75.]] >> > >> > so for the (probably rare) vectorization of both `a` and `q`, would it >> > be preferable to do some kind of long term behaviour change, or just put >> > the dimensions in `q` first, which should be compatible to the current >> > list? >> >> I don't have much of a preference either way, but I'm glad this is >> going into numpy. >> We can work with it either way. >> >> In stats, the most common case will be axis=0, and then the two are >> the same, aren't they? >> >> What I like about the second version is unrolling (with 2 or 3 >> quantiles), which I think will work >> >> u, l = np.random.randn(2,5) >> or >> res = np.percentile(...) >> func(*res) >> >> The first case will be nicer when there are lots of percentiles, but I >> guess I won't need it much except for axis=0. >> >> Actually, I would prefer the second version, because it might be a bit >> more cumbersome to get the individual percentiles out if the axis is >> somewhere in the middle, however I don't think I have a case like >> that. >> > > I never thought about the axis being where to insert the dimensions of > the quantiles. That would be a third option. It feels simpler to me to > just always use the end (or the start) though.
If the choices are start or end, then I prefer start for unpacking. Josef > > - Sebastian > >> The first version would be consistent with reduceat, and that would be >> more numpythonic. I would go for that in numpy. >> >> my 2.5c >> >> Josef >> >> > >> > Regards, >> > >> > Sebastian >> > >> >> Cheers, >> >> >> >> - Jonathan Helmus >> >> >> >> >> >> [1] http://thread.gmane.org/gmane.comp.python.scientific.user/33331 >> >> [2] https://github.com/scipy/scipy/pull/374 >> >> [3] https://github.com/numpy/numpy/pull/2970 >> >> _______________________________________________ >> >> NumPy-Discussion mailing list >> >> [email protected] >> >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> >> > >> > >> > _______________________________________________ >> > NumPy-Discussion mailing list >> > [email protected] >> > http://mail.scipy.org/mailman/listinfo/numpy-discussion >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
