On Jul 5, 2016 11:21 PM, "Ralf Gommers" <ralf.gomm...@gmail.com> wrote: > > > > On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith <n...@pobox.com> wrote: > >> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz" <jfoxrabinov...@gmail.com> wrote: >> > >> > Hi, >> > >> > I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a >> > function np.atleast_nd in PR#7804 >> > (https://github.com/numpy/numpy/pull/7804). >> > >> > As a result of this PR, I have a couple of questions about >> > `np.atleast_3d`. `np.atleast_3d` appears to do something weird with >> > the dimensions: If the input is 1D, it prepends and appends a size-1 >> > dimension. If the input is 2D, it appends a size-1 dimension. This is >> > inconsistent with `np.atleast_2d`, which always prepends (as does >> > `np.atleast_nd`). >> > >> > - Is there any reason for this behavior? >> > - Can it be cleaned up (e.g., by reimplementing `np.atleast_3d` in >> > terms of `np.atleast_nd`, which is actually much simpler)? This would >> > be a slight API change since the output would not be exactly the same. >> >> Changing atleast_3d seems likely to break a bunch of stuff... >> >> Beyond that, I find it hard to have an opinion about the best design for these functions, because I don't think I've ever encountered a situation where they were actually what I wanted. I'm not a big fan of coercing dimensions in the first place, for the usual "refuse to guess" reasons. And then generally if I do want to coerce an array to another dimension, then I have some opinion about where the new dimensions should go, and/or I have some opinion about the minimum acceptable starting dimension, and/or I have a maximum dimension in mind. (E.g. "coerce 1d inputs into a column matrix; 0d or 3d inputs are an error" -- atleast_2d is zero-for-three on that requirements list.) >> >> I don't know how typical I am in this. But it does make me wonder if the atleast_* functions act as an attractive nuisance, where new users take their presence as an implicit recommendation that they are actually a useful thing to reach for, even though they... aren't that. And maybe we should be recommending folk move away from them rather than trying to extend them further? >> >> Or maybe they're totally useful and I'm just missing it. What's your use case that motivates atleast_nd? > > I think you're just missing it:) atleast_1d/2d are used quite a bit in Scipy and Statsmodels (those are the only ones I checked), and in the large majority of cases it's the best thing to use there. There's a bunch of atleast_2d calls with a transpose appended because the input needs to be treated as columns instead of rows, but that's still efficient and readable enough.
I know people *use* it :-). What I'm confused about is in what situations you would invent it if it didn't exist. Can you point me to an example or two where it's "the best thing"? I actually had statsmodels in mind with my example of wanting the semantics "coerce 1d inputs into a column matrix; 0d or 3d inputs are an error". I'm surprised if there are places where you really want 0d arrays converted into 1x1, or want to allow high dimensional arrays to pass through - and if you do want to allow high dimensional arrays to pass through, then transposing might help with 2d cases but will silently mangle high-d cases, right? -n
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
