Boost.Python on OS X with NumPy [XifNet Wiki]

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== Boost.Python on OS X with NumPy ======

Ur python program too slow at calculating the boiling point of tungsten under a 600bar atmosphere of francium fluoride ? Too baaad...\\
Well, let's implement some part of it in C++ using [[http://boostorg.github.io/python/|Boost.Python]] with its NumPy brand new module. That's easy, I promise.\\
This tuto uses Python 3.4, installed from [[https://www.python.org/downloads/mac-osx/]]. Should be easily adapted to other versions.

===== Build Boost.Python =====

  - Download Boost (dev version) : ''git clone --recursive https://github.com/boostorg/boost.git modular-boost''
  - ''./bootstrap.sh --with-libraries=python --with-python-version=3.4 --with-python-root=/Library/Frameworks/Python.framework/Versions/3.4/bin/python3.4''
  - ''sudo ./b2 toolset=clang cxxflags="-stdlib=libc++ -std=c++0x" linkflags="-stdlib=libc++" -j2 install''
  - If it works, you should see ''/usr/local/lib/libboost_python3.a'' and ''/usr/local/lib/libboost_numpy3.a''.

===== Example Python module =====

Exemple Makefile :
<code make>
NAME = gauss_seidel
CXX = clang++
CXXFLAGS += -Wall -std=c++1y
PYTHON_VER = 3.4

NUMPY_ROOT = $(shell pip$(PYTHON_VER) show numpy | grep "Location:" | cut -d" " -f2-)
CXXFLAGS += -fPIC $(shell python$(PYTHON_VER)-config --cflags) -I$(NUMPY_ROOT)/numpy/core/include
LDFLAGS += -fPIC $(shell python$(PYTHON_VER)-config --ldflags)
#LDFLAGS += -lboost_python3 -lboost_numpy3  # Fails at module import
LDFLAGS += /usr/local/lib/libboost_python3.a /usr/local/lib/libboost_numpy3.a

all: $(NAME).o
	$(CXX) -shared $^ $(LDFLAGS) -o $(NAME).so

$(NAME).o: $(NAME).cpp

%.o: %.cpp
	$(CXX) -o $@ -c $< $(CXXFLAGS) 
</code>

Exemple code (''my_little_poney.cpp'') :
<code cpp>
#include <boost/python.hpp>
#include <boost/python/numpy.hpp>

namespace py = boost::python;
namespace np = boost::python::numpy;

np::ndarray shift_col (int k, np::ndarray M) {
	size_t n = M.shape(0), p = M.shape(1);
	np::ndarray R = np::empty(py::make_tuple(n,p), M.get_dtype());
	for (size_t i = 0; i < n; ++i) {
		for (size_t j = 0; j < p; ++j) {
			R[i][(j+k)%p] = M[i][j];
		}
	}
	return R;
}

BOOST_PYTHON_MODULE(my_little_poney) {
	np::initialize();
	py::def("shift_col", shift_col);
}
</code>

Then build with ''make''. It will create a ''my_little_poney.so'' dynamic lib which can be loaded as a python module with ''import my_little_poney''.

==== NumPy arrays ====

Here is the issue : all operation made with ''array[i][j]'' are executed through the python interpreter and are very inefficient.
You can use the ''xif::multiarr'' warper from [[http://dev.xif.fr:7979/xifutils/|xifutils]] to access directly and easily ''ndarray''s : <code cpp>
template <typename T, size_t dim>
struct nparray_accessor : public xif::multiarr<T,dim> {
	nparray_accessor (np::ndarray& ndarr) : xif::multiarr<T,dim>( 
		(T*)ndarr.get_data(), 
		[&ndarr] (size_t d) -> size_t { return ndarr.shape(d); }
	) {}
};
</code>
''nparray_accessor<T,dim>'' takes ''T'' as the data type (should match the ''ndarray'''s ''dtype'') and ''dim'' the number of dimensions.

Documentation about Boost.Python.NumPy : [[http://boostorg.github.io/python/develop/doc/html/numpy/]].