[Numpy-discussion] Is there a defined way to "unpad" an array, and if not, should there be?

[Eric Wieser]

Some other options here that avoid the need for a new function:

* Add a `return_view` argument to `pad`, such that for `padded, orig =
np.pad(arr, ..., return_view=True)`, `orig == arr` and `orig.base is
padded`. This is useful if `padded` is modified in place, but less useful
otherwise. It has the advantage of not having to recompute the slices, as
pad already has them.
* Accept a `slice` object directly in `np.pad`; for `sl = np.s_[2:-20,
4:-40]`, `padded = np.pad(array, sl)`, we have `padded[sl] == array`.

The second idea seems promising to me, but perhaps there are corner cases I
haven't thought of that it wouldn't help with.

Eric

On Tue, 13 Apr 2021 at 09:26, Ralf Gommers <ralf.gommers at gmail.com> wrote:

>
>
> On Tue, Apr 13, 2021 at 3:37 AM Jeff Gostick <jgostick at gmail.com> wrote:
>
>> It is great to hear that this might be useful.  I would LOVE to create a
>> PR on this idea and contribute back to numpy...but let's not get ahead of
>> ourselves :-)
>>
>> Regarding the name, I kinda like "unpad" since it relates directly to
>> "pad", analogous to "ravel" and "unravel" for instance.  Or maybe "depad".
>> Although, it's possible to use this on any array, not just a previously
>> padded one, so maybe tying it too directly to "pad" is not right, in which
>> case "trim" and "crop" are both perfect.  I must admit that I find it odd
>> that these functions are not in numpy already.  I just searched the docs
>> and they show up as keyword args for a few functions but are otherwise
>> conspicuously absent.  Also, funnily, there is a link to "padding arrays"
>> but it is basically empty:
>> https://numpy.org/doc/stable/reference/routines.padding.html.
>>
>> Alternatively, I don't hate the idea of passing negative pad widths into
>> "pad".  I actually tried this at one point to see if there was a hidden
>> functionality there, to no avail.
>>
>> BTW, we just adding a custom "unpad" function to our PoreSpy package for
>> this purpose:
>> https://github.com/PMEAL/porespy/blob/dev/porespy/tools/_unpadfunc.py
>>
>>
>>
>> On Mon, Apr 12, 2021 at 9:15 PM Stephan Hoyer <shoyer at gmail.com> wrote:
>>
>>> On Mon, Apr 12, 2021 at 5:12 PM Jeff Gostick <jgostick at gmail.com> wrote:
>>>
>>>> I guess I should have clarified that I was inquiring about proposing a
>>>> 'feature request'.  The github site suggested I open a discussion on this
>>>> list first.  There are several ways to effectively unpad an array as has
>>>> been pointed out, but they all require more than a little bit of thought
>>>> and care, are dependent on array shape, and honestly error prone.  It would
>>>> be very valuable to me to have such a 'predefined' function, so I was
>>>> wondering if (a) I was unaware of some function that already does this and
>>>> (b) if I'm alone in thinking this would be useful.
>>>>
>>>
>>> Indeed, this is a fair question.
>>>
>>> Given that this is not entirely trivial to write correctly, I think it
>>> would be reasonable to add the inverse operation for pad() into NumPy. This
>>> is generally better than encouraging users to write their own thing.
>>>
>>> From a naming perspective, here are some possibilities:
>>> unpad
>>> trim
>>> crop
>>>
>>> I think "trim" would be pretty descriptive, probably slightly better
>>> than "unpad."
>>>
>>
> I'm not a fan of `trim`. We already have `clip` which sounds similar.
>
> `unpad` looks like the only one that's completely unambiguous.
>
> `crop` sounds like an image processing function, and what we don't want is
> something like Pillow's `crop(left, top, right, bottom)`.
>
> Cheers,
> Ralf
>
>
> _______________________________________________
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion at python.org
>>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mail.python.org/pipermail/numpy-discussion/attachments/20210413/134530b2/attachment-0001.html>