[Numpy-discussion] State-of-the-art to use a C/C++ library from Python
nevion at gmail.com
Wed Aug 31 13:20:28 EDT 2016
I just wanted to follow up on the C++ side of OP email - Cython has quite a
few difficulties working with C++ code at the moment. It's really more of
a C solution most of the time and you must split things up into a mostly C
call interface (that is the C code Cython can call) and limit
exposure/complications with templates and complex C++11+ constructs. This
may change in the longer term but in the near, that is the state.
I used to use Boost.Python but I'm getting my feet wet with Pybind (which
is basically the same api but works more as you expect it to with it's
signature/type plumbing (including std::shared_ptr islanding), with some
other C++11 based improvements, and is header only + submodule friendly!).
I also remembered ndarray thanks to Neal's post but I haven't figured out
how to leverage it better than pybind, at the moment. I'd be interested to
see ndarray gain support for pybind interoperability...
On Wed, Aug 31, 2016 at 1:08 PM, David Morris <othalan at othalan.net> wrote:
> On Wed, Aug 31, 2016 at 2:28 PM, Michael Bieri <mibieri at gmail.com> wrote:
>> Hi all
>> There are several ways on how to use C/C++ code from Python with NumPy,
>> as given in http://docs.scipy.org/doc/numpy/user/c-info.html .
>> Furthermore, there's at least pybind11.
>> 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.
> I have been delighted with Cython for this purpose. Great integration
> with NumPy (you can access numpy arrays directly as C arrays), very python
> like syntax and amazing performance.
> Good luck,
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion