This is good stuff, but I can't help thinking that if I needed to do an any/all test on a number of arrays with common and/or combos -- I'd probably write a Cython function to do it.
It could be a bit tricky to make it really general, but not bad for a couple specific dtypes / use cases. -just a thought... Also -- how does this work with numexpr? It would be nice if it could handle these kinds of cases. -Chris On Thu, Sep 5, 2013 at 1:54 AM, Graeme B. Bell <g...@skogoglandskap.no>wrote: > > > Hi Julian, > > Thanks for the post. It's great to hear that the main numpy function is > improving in 1.8, though I think there is still plenty of value here for > performance junkies :-) > > I don't have 1.8beta installed (and I can't conveniently install it on my > machines just now). If you have time, and have the beta installed, could > you try this and mail me the output from the benchmark? I'm curious to > know. > > # git clone https://github.com/gbb/numpy-fast-any-all.git > # cd numpy-fast-any-all > # python test-fast-any-all.py > > Graeme > > > On Sep 4, 2013, at 7:38 PM, Julian Taylor <jtaylor.deb...@googlemail.com> > wrote: > > >> > >> The result is 14 to 17x faster than np.any() for this use case.* > > > > any/all and boolean operations have been significantly speed up by > > vectorization in numpy 1.8 [0]. > > They are now around 10 times faster than before, especially if the > > boolean array fits into one of the cpu caching layers. > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov
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