What would be the use case for a duck-array to implement __array__ and
return a NumPy array?
some users need a genuine, actual numpy array (for passing to Cyton code, for example).
if __array__ is not implemented, how can they get that from an array-like object??
Only the author of the array-like object knows how best to make a numpy array out of it.
Unless I'm missing something, this seems
redundant and one should just use array/asarray functions then.
but if the object does not impliment __array__, then user's can't use the array/asarray functions!
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)?
I'm really confused now -- if a end-user wants a duckarray, they should call duckarray() -- if they want an actual numpy array, they should call .asarray().
Why would anyone want an Exception? If you don't want an array, then don't call asarray()
If you call duckarray(), and the object has not implemented __duckarray__, then you will get an exception -- whoch you should.
If you call __array_, and __array__ has not been implimented, then you will get an exception.
what is the potential problem here?
Which makes me think -- why should Duck arrays ever implement an __array__ method that raises an Exception? why not jsut not impliment it? (unless you wantt o add some helpful error message -- which I did for the example in my PR.
(PR to the numpy repo in progress)
-CHB
Christopher Barker, Ph.D.
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