On Wed, May 4, 2011 at 6:19 AM, Christoph Groth <c...@falma.de> wrote:
> Dear numpy experts, > > I have noticed that with Numpy 1.5.1 the operation > > m[::2] += 1.0 > > takes twice as long as > > t = m[::2] > t += 1.0 > > where "m" is some large matrix. This is of course because the first > snippet is equivalent to > > t = m[::2] > t += 1.0 > m[::2] = t > > I wonder whether it would not be a good idea to optimize > ndarray.__setitem__ to not execute an assignment of a slice onto itself. > Is there any good reason why this is not being done already? > > best, Christoph > You'd better time this in 1.6 too. ;) https://github.com/numpy/numpy/commit/f60797ba64ccf33597225d23b893b6eb11149860 The case of boolean mask indexing can't benefit so easily from this optimization, but I think could see a big performance benefit if combined __index__ + __i<op>__ operators were added to Python. Something to consider, anyway. -Mark
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