On Sun, Nov 4, 2018 at 8:45 AM Marten van Kerkwijk < m.h.vankerkw...@gmail.com> wrote:
> Does the above make sense? I realize that the same would be true for > `__array_ufunc__`, though there the situation is slightly trickier since it > is not as easy to bypass any further override checks. Nevertheless, it does > seem like it would be correct to do the same there. (And if we agree this > is the case, I'd quite happily implement it -- with the merger of > multiarray and umath it has become much easier to do.) > Marten actually implemented a draft version of this already in https://github.com/numpy/numpy/pull/12328 :). I found reading over the PR helpful for understand this proposal. I guess the practical import of this change is that it makes it (much?) easier to write __array_function__ for ndarray subclasses: if there's a function where NumPy's default function works fine, you don't need to bother with returning anything other than NotImplemented from __array_function__. It's sort of like NotImplementedButCoercible, but only for ndarray subclasses. One minor downside is that this might make it harder to eventually deprecate and/or contemplate removing checks for 'mean' methods in functions like np.mean(), because __array_function__ implementers might still be relying on this. But so far, I think this makes sense. The PR includes additional changes to np.core.overrides, but I'm not sure if those are actually required here (or rather only possible due to this change). I guess they are needed if you want to be able to count on ndarray.__array_function__ being called after subclass __array_function__ methods. I'm not sure I like this part: it means that ndarray.__array_function__ actually gets called when other arguments implement __array_function__. For interactions with objects that aren't ndarray subclasses this is entirely pointless and would unnecessarily slow things down, since ndarray._array_function__ will always return NotImplemented.
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