On Sat, Aug 13, 2011 at 7:17 PM, Mark Wiebe <mwwi...@gmail.com> wrote:
> On Thu, Aug 11, 2011 at 1:37 PM, Benjamin Root <ben.r...@ou.edu> wrote: > >> On Thu, Aug 11, 2011 at 10:33 AM, Olivier Delalleau <sh...@keba.be>wrote: >> >>> 2011/8/11 Benjamin Root <ben.r...@ou.edu> >>> >>>> >>>> >>>> On Thu, Aug 11, 2011 at 8:37 AM, Olivier Delalleau <sh...@keba.be>wrote: >>>> >>>>> Maybe confusing, but working as expected. >>>>> >>>>> >>>>> When you write: >>>>> matched_to[np.array([0, 1, 2])] = 3 >>>>> it calls __setitem__ on matched_to, with arguments (np.array([0, 1, >>>>> 2]), 3). So numpy understand you want to write 3 at these indices. >>>>> >>>>> >>>>> When you write: >>>>> matched_to[:3][match] = 3 >>>>> it first calls __getitem__ with the slice as argument, which returns a >>>>> view of your array, then it calls __setitem__ on this view, and it fills >>>>> your matched_to array at the same time. >>>>> >>>>> >>>>> But when you write: >>>>> matched_to[np.array([0, 1, 2])][match] = 3 >>>>> it first calls __getitem__ with the array as argument, which retunrs a >>>>> *copy* of your array, so that calling __setitem__ on this copy has no >>>>> effect >>>>> on your original array. >>>>> >>>>> -=- Olivier >>>>> >>>>> >>>> Right, but I guess my question is does it *have* to be that way? I >>>> guess it makes some sense with respect to indexing with a numpy array like >>>> I >>>> did with the last example, because an element could be referred to multiple >>>> times (which explains the common surprise with '+='), but with boolean >>>> indexing, we are guaranteed that each element of the view will appear at >>>> most once. Therefore, shouldn't boolean indexing always return a view, not >>>> a copy? Is the general case of arbitrary array selection inherently >>>> impossible to encode in a view versus a slice with a regular spacing? >>>> >>> >>> Yes, due to the fact the array interface only supports regular spacing >>> (otherwise it is more difficult to get efficient access to arbitrary array >>> positions). >>> >>> -=- Olivier >>> >>> >> This still bothers me, though. I imagine that it is next to impossible to >> detect this situation from numpy's perspective, so it can't even emit a >> warning or error. Furthermore, for someone who makes a general function to >> modify the contents of some externally provided array, there is a >> possibility that the provided array is actually a copy not a view. >> Although, I guess it is the responsibility of the user to know the >> difference. >> >> I guess that is the key problem. The key advantage we are taught about >> numpy arrays is the use of views for efficient access. It would seem that >> most access operations would use it, but in reality, only sliced access do. >> Everything else is a copy (unless you are doing fancy indexing with >> assignment). Maybe with some of the forthcoming changes that have been done >> with respect to nditer and ufuncs (in particular, I am thinking of the >> "where" kwarg), maybe we could consider an enhancement allowing fancy >> indexing (or at least boolean indexing) to produce a view? Even if it is >> less efficient than a view from slicing, it would bring better consistency >> in behavior between the different forms of indexing. >> >> Just my 2 cents, >> Ben Root >> > > I think it would be nice to evolve the NumPy indexing and array > representation towards the goal of indexing returning a view in all cases > with no exceptions. This would provide a much nicer mental model to program > with. Accomplishing such a transition will take a fair bit of time, though. > > -Mark > > Mark, It is good to know that there is a chance to make this possible, eventually. However, I just thought of a possible barrier that might have to be overcome before achieving this. Because it has always been very clear that non-slicing produces copies, I can easily imagine situations where developers have come to depend on this copying behavior. While I think most copies are unintended (but unnoticed because it was read-only), it is quite possible that there are situations where this copy behavior is entirely intended. Therefore, changing this behavior may break code in subtle ways. I am not saying that it shouldn't be done (clarity and simplicity should be paramount), but one should tread carefully here. My 2 cents, Ben Root
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
