I can add a keyword-only argument that lets you put the new dims before or after the existing ones. I am not sure how to specify arbitrary patterns for the new dimensions, but that should take care of most use cases.

The use case that motivated this function in the first place is that I am doing some processing on 4D arrays and I need to reduce them but return a result with the original dimensionality (but not shape). atleast_nd seemed like a better solution than atleast_4d.

-Joe

On Wed, Jul 6, 2016 at 3:41 AM, josef.pktd@gmail.com wrote:

On Wed, Jul 6, 2016 at 3:29 AM, josef.pktd@gmail.com wrote:

On Wed, Jul 6, 2016 at 2:21 AM, Ralf Gommers ralf.gommers@gmail.com wrote:

On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith njs@pobox.com wrote:

On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz" jfoxrabinovitz@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.

As Ralph pointed out its usage in statsmodels. I do find them useful as replacement for several lines of ifs and reshapes

We stilll need in many cases the atleast_2d_cols, that appends the newaxis if necessary.

roughly the equivalent of

if x.ndim == 1: x = x[:, None] else: x = np.atleast_2d(x)

Josef

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

statsmodels has currently very little code with ndim >2, so I have no overview of possible use cases, but it would be necessary to have full control over the added axis since axis have a strict meaning and stats still prefer Fortran order to default numpy/C ordering.

Josef

Ralf

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