I have put a pull request implementing numpy.guvectorize up for review: https://github.com/numpy/numpy/pull/8054
Cheers, Stephan On Tue, Sep 13, 2016 at 10:54 PM, Travis Oliphant <tra...@continuum.io> wrote: > There has been some discussion on the Numba mailing list as well about a > version of guvectorize that doesn't compile for testing and flexibility. > > Having this be inside NumPy itself seems ideal. > > -Travis > > > On Tue, Sep 13, 2016 at 12:59 PM, Stephan Hoyer <sho...@gmail.com> wrote: > >> On Tue, Sep 13, 2016 at 10:39 AM, Nathan Goldbaum <nathan12...@gmail.com> >> wrote: >> >>> I'm curious whether you have a plan to deal with the python functional >>> call overhead. Numba gets around this by JIT-compiling python functions - >>> is there something analogous you can do in NumPy or will this always be >>> limited by the overhead of repeatedly calling a Python implementation of >>> the "core" operation? >>> >> >> I don't think there is any way to avoid this in NumPy proper, but that's >> OK (it's similar to the existing overhead of vectorize). >> >> Numba already has guvectorize (and it's own version of vectorize as >> well), which already does exactly this. >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > > -- > > *Travis Oliphant, PhD* > *Co-founder and CEO* > > > @teoliphant > 512-222-5440 > http://www.continuum.io > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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