maybe https://bitbucket.org/memotype/cffiwrap or https://github.com/andrewleech/cfficloak helps?
C. 2016-09-02 11:16 GMT+02:00 Nathaniel Smith <n...@pobox.com>: > On Fri, Sep 2, 2016 at 1:16 AM, Peter Creasey > <p.e.creasey...@googlemail.com> wrote: > >> Date: Wed, 31 Aug 2016 13:28:21 +0200 > >> From: Michael Bieri <mibi...@gmail.com> > >> > >> I'm not quite sure which approach is state-of-the-art as of 2016. How > would > >> you do it if you had to make a C/C++ library available in Python right > now? > >> > >> In my case, I have a C library with some scientific functions on > matrices > >> and vectors. You will typically call a few functions to configure the > >> computation, then hand over some pointers to existing buffers containing > >> vector data, then start the computation, and finally read back the data. > >> The library also can use MPI to parallelize. > >> > > > > Depending on how minimal and universal you want to keep things, I use > > the ctypes approach quite often, i.e. treat your numpy inputs an > > outputs as arrays of doubles etc using the ndpointer(...) syntax. I > > find it works well if you have a small number of well-defined > > functions (not too many options) which are numerically very heavy. > > With this approach I usually wrap each method in python to check the > > inputs for contiguity, pass in the sizes etc. and allocate the numpy > > array for the result. > > FWIW, the broader Python community seems to have largely deprecated > ctypes in favor of cffi. Unfortunately I don't know if anyone has > written helpers like numpy.ctypeslib for cffi... > > -n > > -- > Nathaniel J. Smith -- https://vorpus.org > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
