> 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. Peter _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
