I totally agree with the namespace clutter concern, but honestly, I would use
`atleast_nd` with its `pos` argument (I might rename it to `position`, `axis`,
or `axis_position`) any day over `at_least{1,2,3}d`, for which I had no idea
where the new axes would end up.
So, I’m in favour of including it, and optionally deprecating
`atleast_{1,2,3}d`.
Juan.
> On 11 Feb 2021, at 9:48 am, Sebastian Berg <[email protected]> wrote:
>
> On Wed, 2021-02-10 at 17:31 -0500, Joseph Fox-Rabinovitz wrote:
>> I've created PR#18386 to add a function called atleast_nd to numpy and
>> numpy.ma. This would generalize the existing atleast_1d, atleast_2d, and
>> atleast_3d functions.
>>
>> I proposed a similar idea about four and a half years ago:
>> https://mail.python.org/pipermail/numpy-discussion/2016-July/075722.html
>> <https://mail.python.org/pipermail/numpy-discussion/2016-July/075722.html>,
>> PR#7804. The reception was ambivalent, but a couple of folks have asked me
>> about this, so I'm bringing it back.
>>
>> Some pros:
>>
>> - This closes issue #12336
>> - There are a couple of Stack Overflow questions that would benefit
>> - Been asked about this a couple of times
>> - Implementation of three existing atleast_*d functions gets easier
>> - Looks nicer that the equivalent broadcasting and reshaping
>>
>> Some cons:
>>
>> - Cluttering up the API
>> - Maintenance burden (but not a big one)
>> - This is just a utility function, which can be achieved through
>> broadcasting and reshaping
>>
>
> My main concern would be the namespace cluttering. I can't say I use even the
> `atleast_2d` etc. functions personally, so I would tend to be slightly
> against the addition. But if others land on the "useful" side here (and it
> seemed a bit at least on github), I am also not opposed. It is a clean name
> that lines up with existing ones, so it doesn't seem like a big "mental load"
> with respect to namespace cluttering.
>
> Bike shedding the API is probably a good idea in any case.
>
> I have pasted the current PR documentation (as html) below for quick
> reference. I wonder a bit about the reasoning for having `pos` specify a
> value rather than just a side?
>
>
>
> numpy.atleast_nd(ary, ndim, pos=0)
> View input as array with at least ndim dimensions.
> New unit dimensions are inserted at the index given by pos if necessary.
> Parameters
> ary array_like
> The input array. Non-array inputs are converted to arrays. Arrays that
> already have ndim or more dimensions are preserved.
> ndim int
> The minimum number of dimensions required.
> pos int, optional
> The index to insert the new dimensions. May range from -ary.ndim - 1 to
> +ary.ndim (inclusive). Non-negative indices indicate locations before the
> corresponding axis: pos=0 means to insert at the very beginning. Negative
> indices indicate locations after the corresponding axis: pos=-1 means to
> insert at the very end. 0 and -1 are always guaranteed to work. Any other
> number will depend on the dimensions of the existing array. Default is 0.
> Returns
> res ndarray
> An array with res.ndim >= ndim. A view is returned for array inputs.
> Dimensions are prepended if pos is 0, so for example, a 1-D array of shape
> (N,) with ndim=4becomes a view of shape (1, 1, 1, N). Dimensions are appended
> if pos is -1, so for example a 2-D array of shape (M, N) becomes a view of
> shape (M, N, 1, 1)when ndim=4.
> See also
> atleast_1d
> <https://18298-908607-gh.circle-artifacts.com/0/doc/build/html/reference/generated/numpy.atleast_1d.html#numpy.atleast_1d>,
> atleast_2d
> <https://18298-908607-gh.circle-artifacts.com/0/doc/build/html/reference/generated/numpy.atleast_2d.html#numpy.atleast_2d>,
> atleast_3d
> <https://18298-908607-gh.circle-artifacts.com/0/doc/build/html/reference/generated/numpy.atleast_3d.html#numpy.atleast_3d>
> Notes
> This function does not follow the convention of the other atleast_*d
> functions in numpy in that it only accepts a single array argument. To
> process multiple arrays, use a comprehension or loop around the function
> call. See examples below.
> Setting pos=0 is equivalent to how the array would be interpreted by numpy’s
> broadcasting rules. There is no need to call this function for simple
> broadcasting. This is also roughly (but not exactly) equivalent to
> np.array(ary, copy=False, subok=True, ndmin=ndim).
> It is easy to create functions for specific dimensions similar to the other
> atleast_*d functions using Python’s functools.partial
> <https://docs.python.org/dev/library/functools.html#functools.partial>
> function. An example is shown below.
> Examples
> >>> np.atleast_nd(3.0, 4)
> array([[[[ 3.]]]])
> >>> x = np.arange(3.0)
> >>> np.atleast_nd(x, 2).shape
> (1, 3)
> >>> x = np.arange(12.0).reshape(4, 3)
> >>> np.atleast_nd(x, 5).shape
> (1, 1, 1, 4, 3)
> >>> np.atleast_nd(x, 5).base is x.base
> True
> >>> [np.atleast_nd(x) for x in ((1, 2), [[1, 2]], [[[1, 2]]])]:
> [array([[1, 2]]), array([[1, 2]]), array([[[1, 2]]])]
> >>> np.atleast_nd((1, 2), 5, pos=0).shape
> (1, 1, 1, 1, 2)
> >>> np.atleast_nd((1, 2), 5, pos=-1).shape
> (2, 1, 1, 1, 1)
> >>> from functools import partial
> >>> atleast_4d = partial(np.atleast_nd, ndim=4)
> >>> atleast_4d([1, 2, 3])
> [[[[1, 2, 3]]]]
>
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