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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