On Fri, 2020-04-24 at 11:34 +0100, Eric Wieser wrote: > Perhaps worth mentioning that we've discussed this sort of API > before, in > https://github.com/numpy/numpy/pull/11897. > > Under that proposal, the api would be something like: > > * `copy=True` - always copy, like it is today > * `copy=False` - copy if needed, like it is today > * `copy=np.never_copy` - never copy, throw an exception if not > possible > > I think the discussion stalled on the precise spelling of the third > option. > > `__array__` was not discussed there, but it seems like adding the > `copy` > argument to `__array__` would be a perfectly reasonable extension. >
One thing to note is that `__array__` is actually asked to return a copy AFAIK. I doubt it always does, but if it does not I assume the object should and could provide `__array_interface__`. Under that assumption, it would be an opt-out right now since NumPy allows copies by default here. Defining things along copy does seem sensible, though I do not know how it would play with some of the current array-likes choosing to refuse `__array__`. - Sebastian > Eric > > On Fri, 24 Apr 2020 at 03:00, Juan Nunez-Iglesias <j...@fastmail.com> > wrote: > > > 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. > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion