Prior art data point: scikit-image.util.crop : https://scikit-image.org/docs/dev/api/skimage.util.html#skimage.util.crop
вт, 13 апр. 2021 г., 11:37 Eric Wieser <wieser.eric+nu...@gmail.com>: > 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.gomm...@gmail.com> wrote: > >> >> >> On Tue, Apr 13, 2021 at 3:37 AM Jeff Gostick <jgost...@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 <sho...@gmail.com> wrote: >>> >>>> On Mon, Apr 12, 2021 at 5:12 PM Jeff Gostick <jgost...@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@python.org >>>> https://mail.python.org/mailman/listinfo/numpy-discussion >>>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@python.org >>> https://mail.python.org/mailman/listinfo/numpy-discussion >>> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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