Not to discourage you from making the suggestion https://github.com/data-apis/array-api/issues, but my experience is that the array API consortium tends to make the reverse argument, that if something isn't already implemented in one (or ideally most) array libraries, then it's less likely to be slated for inclusion. The general exception to this is for things for which there is a clear user need but no pre-existing APIs, for example, the new isdtype() function or the new inspection APIs added in the 2023.12 version of the standard, which are needed to make portable array code easier to write. Although I haven't checked if libraries like pytorch already have this feature in tile().
If you do want to propose this for the array API, I would be sure to 1. show that there is a clear user need 2. argue that this is not difficult for most array libraries to implement (this applies generally, not just for this one suggestion) Also FWIW, tile() is new in the 2023.12 standard (just released a few days ago), so that potentially strengthens the argument that an axis keyword was missed during standardization and should be added. Aaron Meurer On Thu, Feb 29, 2024 at 4:42 PM Nathan <nathan.goldb...@gmail.com> wrote: > > Hi, > > I think the thing to do is argue that this should be included in the array > API: > > https://data-apis.org/array-api/latest/API_specification/generated/array_api.tile.html#array_api.tile > > Once that’s settled we can add it to NumPy. > > In general there’s a feeling that there are already too many keywords in the > API and now that the array API is a thing, we can point to that as a place to > hash out API decisions. > > Including syntax in the array API also encourages more libraries to adopt > your preferred syntax. > > Nathan > > On Thu, Feb 29, 2024 at 4:12 PM <e...@evanw.org> wrote: >> >> Hoping to get some more feedback on my recent PR [0] which has stagnated a >> bit for the past few weeks. >> >> This adds an `axis` keyword argument to np.tile which may be an int or tuple >> of ints, much like np.sum or np.roll. >> >> This is my first contribution to numpy :) >> >> Thanks, >> Evan >> >> [0]: https://github.com/numpy/numpy/pull/25703 >> _______________________________________________ >> NumPy-Discussion mailing list -- numpy-discussion@python.org >> To unsubscribe send an email to numpy-discussion-le...@python.org >> https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ >> Member address: nathan12...@gmail.com > > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: asmeu...@gmail.com _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
