What would be the use case for a duck-array to implement __array__ and
return a NumPy array? Unless I'm missing something, this seems
redundant and one should just use array/asarray functions then. This
would also prevent error-handling, what if the developer intentionally
wants a NumPy-like array (e.g., the original array passed to the
duckarray function) or an exception (instead of coercing to a NumPy
Dask arrays are a good example. They will want to implement __duck_array__ (or whatever we call it) because they support duck typed versions of NumPy operation. They also (already) implement __array__, so they can converted into NumPy arrays as a fallback. This is convenient for moderately sized dask arrays, e.g., so you can pass one into a matplotlib function.
On Mon, Sep 16, 2019 at 9:25 PM Chris Barker <firstname.lastname@example.org> wrote:
> On Mon, Aug 12, 2019 at 4:02 AM Peter Andreas Entschev <email@example.com> wrote:
>> Apologies for the late reply. I've opened a new PR
>> https://github.com/numpy/numpy/pull/14257 with the changes requested
> I've written a small PR on your PR:
> Essentially, other than typos and copy editing, I'm suggesting that a duck-array could choose to implement __array__ if it so chooses -- it should, of course, return an actual numpy array.
> I think this could be useful, as much code does require an actual numpy array, and only that class itself knows how best to convert to one.
> Christopher Barker, Ph.D.
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