Sebastian, Stefan and Marten -- thanks for the excellent summaries of the discussion.
In line with this consensus, I have drafted a revision of the NEP without __skip_array_function__: https://github.com/numpy/numpy/pull/13624
On Thu, May 23, 2019 at 5:28 PM Marten van Kerkwijk < firstname.lastname@example.org> wrote:
Hi Sebastian, Stéfan,
Thanks for the very good summaries!
An additional item worth mentioning is that by using `__skip_array_function__` everywhere inside, one minimizes the performance penalty of checking for `__array_function__`. It would obviously be worth trying to do that, but ideally in a way that is much less intrusive.
Furthermore, it became clear that there were different pictures of the final goal, with quite a bit of discussion about the relevant benefits of trying the limit exposure of the internal API and of, conversely, trying to (incrementally) move to implementations that are maximally re-usable (using duck-typing), which are themselves based around a smaller core (more in line with Nathaniel's NEP-22).
In the latter respect, Stéfan's example is instructive. The real implementation of `ones_like` is:
def ones_like(a, dtype=None, order='K', subok=True, shape=None): res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape) multiarray.copyto(res, 1, casting='unsafe') return res
The first step is here seems obvious: an "empty_like" function would seem to belong in the core. The second step less so: Stéfan's `res.fill(1)` seems more logical, as surely a class's method is the optimal way to do something. Though I do feel `.fill` itself breaks "There should be one-- and preferably only one --obvious way to do it." So, I'd want to replace it with `res[...] = 1`, so that one relies on the more obvious `__setitem__`. (Note that all are equally fast even now.)
Of course, in this idealized future, there would be little reason to even allow `ones_like` to be overridden with __array_function__...
All the best,
Marten _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion