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. For 3D/nD I can see that you'd need more control over where the dimensions go, but 1D/2D are fine. Ralf
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