On Tue, Oct 14, 2014 at 6:19 PM, Daniele Nicolodi <dani...@grinta.net> wrote: >> On Mo, 2014-10-13 at 13:35 +0200, Daniele Nicolodi wrote: >> > I have a C++ application that collects float, int or complex data in a >> > possibly quite large std::vector. The application has some SWIG >> > generated python wrappers that expose this vector to python. However, >> > the standard way in which SWIG exposes the data is to create a touple >> > and pass this to python, where it is very often converted to a numpy >> > array for processing. Of course this is not efficient. > > Boost Python may be an option as the codebase already depends on Boost, > but probably not yet on Boost Python. Can you point me to the relevant > documentation, and maybe to an example? One of the problems I have is > that the current wrapping is done auto-magically with SWIG and I would > like to deviate the less possible from that patter.
Some time ago I needed to do something similar. I fused the NumPy C API and Boost.Python with a small bit of code which I then open-sourced as part of a slightly larger library. The most relevant part for you is here: https://github.com/jzwinck/pccl/blob/master/NumPyArray.hpp In particular, it offers this function: // use already-allocated storage for the array // (it will not be initialized, and its format must match the given dtype) boost::python::object makeNumPyArrayWithData( boost::python::list const& dtype, unsigned count, void* data); What is dtype? For that you can use another small widget in my library: https://github.com/jzwinck/pccl/blob/master/NumPyDataType.hpp So the outline for your use case: you have a C array or C++ vector of contiguous plain old data. You create a NumPyDataType(), call append() on it with your data type, then pass all of the above to makeNumPyArrayWithData(). The result is a boost::python::object which you can return from C++ to Python with zero copies made of your data. Even if you don't decide to use Boost.Python, maybe you will find the implementation instructive. The functions I described take only a few lines. John Zwinck _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
