On Sun, Jun 3, 2018 at 4:25 PM Marten van Kerkwijk < m.h.vankerkw...@gmail.com> wrote:
> I think one might still want to know *where* the type occurs (e.g., as an > output or index would have different implications). > This in certainly true in general, but given the complete flexibility of __array_function__ there's no way we can make every check convenient. The best we can do is make it easy to handle the common cases, where the argument position does not matter. > Possibly, a solution would rely on the same structure as used for the > "dance". But as a general point, I don't see the advantage of passing types > rather than arguments - less information for no benefit. > Maybe this is premature optimization, but there will certainly be fewer unique types than arguments to check for types. I suspect this may make for a noticeable difference in performance in use cases involving a large number of argument. For example, suppose np.concatenate() is called on a list of 10,000 dask arrays. Now dask.array.Array.__array_function__ needs to check all arguments to decide whether it can use dask.array.concatenate() or needs to return NotImplemented. By using the `types` argument, it only needs to do isinstance() checks on the single argument in `types`, rather than all 10,000 overloaded function arguments.
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