[Numpy-discussion] Add pad_to_shape and pad_to_match for simpler padding if a target size is already known

[Ralf Gommers]

On Sat, Apr 24, 2021 at 6:25 PM czorio4 <czorio4 at gmail.com> wrote:

> Hello Ralf,
>
> Thanks for the feedback!
>
> Instead of the two separate arguments, would it be possible to define it
> like:
>
>     def pad_to(x, target, ...)
>
> where the `target` can be either a tuple or an ndarray (and/or some other
> array-like), and the function determines the shape based on what input type
> was provided.
> My worry here is that this might introduce some more room for bugs with
> input types.
>
> Alternatively (and probably simpler, too), drop the entire `pad_to_match`
> function, and instead have the user explicitly provide the other array's
> shape:
>
>     padded = np.pad_to(x, y.shape, ...)
>

Yes I agree, that's an even better idea. +1 for avoiding unions when the
solution is as simple as using `.shape`.

Cheers,
Ralf


> Kind regards,
> Mathijs
>
> Op za 24 apr. 2021 om 17:54 schreef Ralf Gommers <ralf.gommers at gmail.com>:
>
>>
>>
>> On Thu, Apr 15, 2021 at 11:54 AM czorio4 <czorio4 at gmail.com> wrote:
>>
>>> Hello all,
>>>
>>> I'm only two weeks late with this message about my pull request
>>> <https://github.com/numpy/numpy/pull/18707> that adds functions that
>>> allow the user to pad to a target shape, instead of adding a certain amount
>>> to each axis.
>>>
>>
>> No worries at all, things often go at "whenever I have some time after
>> life stops getting in the way" pace here.
>>
>>
>>> For example:
>>>
>>>     x = np.ones((3, 3))
>>>     # We want an output shape of (10, 10)
>>>     padded = np.pad_to_shape(x, (10, 10))
>>>     print(padded.shape)
>>>     prints: (10, 10)
>>>
>>> Whereas with the current implementation of np.pad you'd need to first
>>> figure out the difference in axis sizes. The proposed function would
>>> perform this step for you. As this function passes to np.pad internally, I
>>> made sure to include the arguments in the signature that np.pad uses for
>>> padding modes.
>>>
>>> Finally, I've added a logical extension of this function; pad_to_match,
>>> this takes another array and pads the input array to match.
>>>
>>
>> Adding this functionality makes sense - if we find a good place outside
>> of the main namespace, as was also remarked on in your PR. Luckily there is
>> plan to create such a space, as submodules under `numpy.lib`. This draft PR
>> needs to be finished for that: https://github.com/numpy/numpy/pull/18447
>>
>>
>>> These calls would be equivalent:
>>>
>>>     x = np.ones((3, 3))
>>>     y = np.zeros((10, 10))
>>>     padded_to_shape = np.pad_to_shape(x, y.shape)
>>>     padded_to_match = np.pad_to_match(x, y)
>>>
>>
>> Question, why two functions with slighty awkward (double underscores)
>> names rather than:
>>
>>     def pad_to(x, shape=None, like=None):
>>
>> Note also the `like` instead of `match`; this seems more consistent with
>> functions like `ones_like` and the `like=` keyword which also take a shape
>> from another array.
>>
>>
>>> For additional functionality description, I refer to the pull request.
>>>
>>> I'm not too familiar with mailing lists, so I hope this is how things
>>> work.
>>>
>>
>> Yep, you got it all right. Mailing lists are starting to get
>> old-fashioned, one of these days we'll move to something more modern.
>>
>> Cheers,
>> Ralf
>>
>>
>>
>>> Kind Regards,
>>> Mathijs
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