On Mon, Sep 16, 2019 at 1:45 PM Peter Andreas Entschev peter@entschev.com wrote:

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 array)?

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 chris.barker@noaa.gov wrote:

On Mon, Aug 12, 2019 at 4:02 AM Peter Andreas Entschev <

peter@entschev.com> wrote:

Apologies for the late reply. I've opened a new PR https://github.com/numpy/numpy/pull/14257 with the changes requested

thanks!

I've written a small PR on your PR:

https://github.com/pentschev/numpy/pull/1

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.

-CHB

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