Hi everyone,
> One bit of expressivity we would miss is “copy if necessary, but otherwise
> don’t bother”, but there are workarounds to this.
After a side discussion with Stéfan van der Walt, we came up with
`allow_copy=True`, which would express to the downstream library that we don’t
mind waiting, but that zero-copy would also be ok.
> This sounds like the sort of thing that is use case driven. If enough
> projects want to use it, then I have no objections to adding the keyword.
> OTOH, we need to be careful about adding too many interoperability tricks as
> they complicate the code and makes it hard for folks to determine the best
> solution. Interoperability is a hot topic and we need to be careful not put
> too leave behind too many experiments in the NumPy code. Do you have any
> other ideas of how to achieve the same effect?
Personally, I don’t have any other ideas, but would be happy to hear some!
My view regarding API/experiment creep is that `__array__` is the oldest and
most basic of all the interop tricks and that this can be safely maintained for
future generations. Currently it only takes `dtype=` as a keyword argument, so
it is a very lean API. I think this particular use case is very natural and
I’ve encountered the reluctance to implicitly copy twice, so I expect it is
reasonably common.
Regarding difficulty in determining the best solution, I would be happy to
contribute to the dispatch basics guide together with the new kwarg. I agree
that the protocols are getting quite numerous and I couldn’t find a single
place that gathers all the best practices together. But, to reiterate my point:
`__array__` is the simplest of these and I think this keyword is pretty safe to
add.
For ease of discussion, here are the API options discussed so far, as well as a
few extra that I don’t like but might trigger other ideas:
np.asarray(my_duck_array, allow_copy=True) # default is False, or None ->
leave it to the duck array to decide
np.asarray(my_duck_array, copy=True) # always copies, but, if supported by the
duck array, defers to it for the copy
np.asarray(my_duck_array, copy=‘allow’) # could take values ‘allow’, ‘force’,
’no’, True(=‘force’), False(=’no’)
np.asarray(my_duck_array, force_copy=False, allow_copy=True) # separate
concepts, but unclear what force_copy=True, allow_copy=False means!
np.asarray(my_duck_array, force=True)
Juan.
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