On Tue, Feb 14, 2012 at 4:03 AM, Francesc Alted <franc...@continuum.io> wrote: > On Feb 14, 2012, at 1:50 AM, Wes McKinney wrote: > [clip] >> But: >> >> In [40]: timeit hist[i, j] >> 10000 loops, best of 3: 32 us per loop >> >> So that's roughly 7-8x slower than a simple Cython method, so I >> sincerely hope it could be brought down to the sub 10 microsecond >> level with a little bit of work. > > I vaguely remember this has shown up before. My hunch is that indexing in > NumPy is so powerful, that it has to check for a lot of different values for > indices (integers, tuples, lists, arrays…), and that it is all these checks > what is taking time. Your Cython wrapper just assumed that the indices where > integers, so this is probably the reason why it is that much faster. > > This is not to say that indexing in NumPy could not be accelerated, but it > won't be trivial, IMO. >
Given that __getitem__ and __setitem__ receive a 2-tuple of 1-dimensional integer arrays, should be pretty simple (dare I say trivial? :) ) to optimize for this use case? The abysmal performance of of __getitem__ and __setitem__ with 1d integer arrays is pretty high on my list of annoyances with NumPy (especially when take and put are so much faster), so you guys may see a pull request from me whenever I can spare the time to hack on it (assuming you don't beat me to it)! > -- Francesc Alted > > > > _______________________________________________ > 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
