I think you can use ffi.from_buffer and ffi.cast from cffi.
On Fri, Sep 2, 2016 at 8:53 AM Carl Kleffner <cmkleff...@gmail.com> wrote: > fork / extension of cffiwrap: > > > *"cfficloak - A simple but flexible module for creating object-oriented, > pythonic CFFI wrappers.This is an extension of > https://bitbucket.org/memotype/cffiwrap > <https://bitbucket.org/memotype/cffiwrap>"* > > 2016-09-02 13:46 GMT+02:00 Sebastian Haase <seb.ha...@gmail.com>: > >> How do these two relate to each other !? >> - Sebastian >> >> >> On Fri, Sep 2, 2016 at 12:33 PM, Carl Kleffner <cmkleff...@gmail.com> >> wrote: >> >>> 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 >>> >>> >> >> _______________________________________________ >> 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 >
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