[Numpy-discussion] NEP 30 - Duck Typing for NumPy Arrays - Implementation

[Chris Barker]

On Mon, Sep 16, 2019 at 2:27 PM Stephan Hoyer <shoyer at gmail.com> wrote:

> On Mon, Sep 16, 2019 at 1:45 PM Peter Andreas Entschev <peter at entschev.com>
> wrote:
>
>> What would be the use case for a duck-array to implement __array__ and
>> return 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.
>

Exactly.

And I have implemented __array__ in classes that are NOT duck arrays at all
(an image class, for instance). But I also can see wanting to support both:

use me as a duck array
and
convert me into a proper numpy array.

OK -- looking again at the NEP, I see this suggested implementation:

    def duckarray(array_like):
        if hasattr(array_like, '__duckarray__'):
            return array_like.__duckarray__()
        return np.asarray(array_like)

So I see the point now, if a user wants a duck array -- they may not want
to accidentally coerce this object to a real array (potentially expensive).

but in this case, asarray() will only get called (and thus __array__ will
only get called), if __duckarray__ is not implemented. So the only reason
to impliment __array__ and raise and Exception is so that users will get
that exception is the specifically call asarray() -- why should they get
that??

I'm working on a PR with suggestion for this.

-CHB

-- 

Christopher Barker, Ph.D.
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