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.