[Numpy-discussion] Histograms via indirect index arrays
luszczek at cs.utk.edu
Fri Mar 17 12:14:03 CST 2006
On Friday 17 March 2006 14:43, Tim Hochberg wrote:
> Piotr Luszczek wrote:
> >On Friday 17 March 2006 13:29, Travis Oliphant wrote:
> >>Piotr Luszczek wrote:
> >>>>Yes, this is intended (sort of --- this particular example isn't
> >>>>the reason for the behavior though).
> >>>>The issue is that the code g[idx] +=1 is equivalent in Python to
> >>>>g[idx] = g[idx] + 1
> >>>This is not what I read at:
> >>>These methods should attempt to do the operation in-place
> >>> (modifying self) and return the result (which could be, but does
> >>> not have to be, self).
> >>>What you describe is "lack of attempt" in which case the "fall
> >>> back" behavior gets triggered.
> >>The problems is that this explanation is very clear when we are
> >>talking about the syntax
> >>g += 1
> >>Then, there is a method of g that can be over-ridden to get the
> >>desired behavior. Now, what you are talking about is "indexing
> >>followed by in-place addition".
> >>i.e. at issue is
> >>how does python interpret
> >>g[idx] += 1
> >>How does this get compiled to byte-code?
> >>There are two possibilities:
> >>1) g[idx] creates a new object which then has 1 added to it using
> >>in-place addition.
> >> This would not produce the desired behavior as g[idx] is a
> >> copy of the data when idx is a
> >> general indexing array as it is in this case. So, you make a
> >>copy of those indices, add 1 to them
> >> and then do what with the resut?
> >>2) g[idx] += 1 gets converted to g[idx] = g[idx] + 1
> >> This appears to be effectively what Python actually does.
> >> Notice that there is no way for us to control this behavior
> >> because there is no __inplace_with_indexing_add__ operator to
> >> over-ride.
> >>There is no such single operation to over-ride for the object. In
> >>other words, I don't see anyay for us to even alter the object to
> >> get the behavior you want from that syntax. We can, of course,
> >> add a function or method to do that, but I we would have to extend
> >> Python to get the behavior you want here.
> >Hardly. At least from what I'm seeing happens on a small example.
> >'g[idx] += 1' becomes ('g' and 'idx' are generic objects):
> >__getitem__(self, idx)
> >__setitem__(result of __iadd__)
> >By design numpy returns views from __getitem__
> You are missing that idx is not a slice index. rather it is an index
> array (or list). In this case g[idx] does *not* return a view, it
> returns a copy. From this everything else follows.
You are missing that indexing with array doesn't have to make
a copy. From this everythin else follows.
In other words, It's a design decision that slices give views and
indirection with arrays (I don't care for lists) gives a copy.
I question things on conceptual level: both objects (the array an
the indexing array) are from numpy. What code would break
if the operation didn't return a copy?
> Conceivably g[idx] could return a psuedo-array object like flat does,
> but I suspect that might have bad performance characteristics.
> >In this case, it would be view into 'self' and 'idx' so the __iadd__
> >would just use the 'idx' directly rather than a copy.
> >Finally, __setitem__ doesn't do anything since 'self' and 'value'
> >will be the same.
> >Of course, this is just a quick draft. I don't know how it would
> > work in practice and in other cases.
> >>Note, however, if idx is slice syntax, then the operation is done
> >>without making copies because, for example,
> >>returns a "view" of the data (an array that shares the same memory)
> >>as the original array. Thus,
> >>g[1:10:2] += 1
> >>does an inplace add without data-copying.
> >>It may be possible to do what you want using zero-strided arrays,
> >> but I'll leave that for a more motivated contributor.
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