I'd like to have a look at the implementation of iadd in numpy, but I'm having a real hard time to find the corresponding code.
I'm basically stuck at https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/number.c#L487 Could someone give me a pointer where to find it? Respectively, could someone point me to some documentation where the (folder/file) structure of the numpy sources is explained? Sebastian On Thu, Sep 6, 2012 at 2:58 PM, Nathaniel Smith <n...@pobox.com> wrote: > On Thu, Sep 6, 2012 at 1:41 AM, Sebastian Berg > <sebast...@sipsolutions.net> wrote: >> Hey, >> >> No idea if this is simply not support or just a bug, though I am >> guessing that such usage simply is not planned. > > I think that's right... currently numpy simply makes no guarantees > about what order ufunc loops will be performed in, or even if they > will be performed in any strictly sequential order. In ordinary cases > this lets it make various optimizations, but it means that you can't > count on any specific behaviour for the unusual case where different > locations in the output array are stored in overlapping memory. > > Fixing this would require two things: > (a) Some code to detect when an array may have internal overlaps (sort > of like np.may_share_memory for axes). Not entirely trivial. > (b) A "fallback mode" for ufuncs where if the code in (a) detects that > we are (probably) dealing with one of these arrays, it processes the > operations in some predictable order without buffering. > > I suppose if someone wanted to come up with these two pieces, and it > didn't look like it would cause slowdowns in common cases, the code in > (b) avoided creating duplicate code paths that increased maintenance > burden, etc., then probably no-one would object to making these arrays > act in a better defined way? I don't think most people are that > worried about this though. Your original code would be much clearer if > it just used np.sum... > > -n > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
