Simple wrapping of C code with cython

Here's a tiny but complete example of passing numpy arraysto an external C function, logically

fc( int N, double* a, double* b, double* z )  # z = a + b

using Cython.(This is surely well-known to those who know it well.Comments are welcome.Last change: 23 Feb 2011, for Cython 0.14.)

First read or skimCython buildand Cython with NumPy .

2 steps:

• python f-setup.py build_ext --inplace
  turns f.pyx and fc.cpp -> f.so, a dynamic library
• python test-f.py
import f loads f.so; f.fpy( ... ) calls the C fc( ... ).

python f-setup.py uses distutils to run cython, compile and link:
cython f.pyx -> f.cpp
compile f.cpp and fc.cpp
link f.o fc.o -> f.so,a dynamic lib that python import f will load.

For students, I'd suggest: make a diagram of these steps,look through the files below, then download and run them.

(distutils is a huge, convoluted package used to make Python packages for distribution, and install them.Here we're using just a small part of it to compile and link to f.so.This step has nothing to do with Cython, but it can be confusing;simple mistakes in a .pyx can cause pages of obscure error messages from g++ compile and link.See alsodistutils docand/orSO questions on distutils .)

Like make, setup.py will reruncython f.pyx and g++ -c ... f.cppif f.pyx is newer than f.cpp.
To cleanup, rm -r build/ .

An alternative to setup.py would be to run the steps separately, in a script or Makefile:
cython --cplus f.pyx -> f.cpp # see cython -h
g++ -c ... f.cpp -> f.o
g++ -c ... fc.cpp -> fc.o
cc-lib f.o fc.o -> dynamic library f.so.
Modify the cc-lib-mac wrapperbelow for your platform and installation: it's not pretty, but small.

For real examples of Cython wrapping C,look at .pyx files in just about anySciKit .

See also:Cython for NumPy usersand SO questions/tagged/cython .

To unpack the following files,cut-paste the lot to one big file, say cython-numpy-c-demo,then in Unix (in a clean new directory) run sh cython-numpy-c-demo.

#--------------------------------------------------------------------------------cat >f.pyx <<\!# f.pyx: numpy arrays -> extern from "fc.h"# 3 steps:# cython f.pyx  -> f.c# link: python f-setup.py build_ext --inplace  -> f.so, a dynamic library# py test-f.py: import f gets f.so, f.fpy below calls fc()import numpy as npcimport numpy as npcdef extern from "fc.h":     int fc( int N, double* a, double* b, double* z )  # z = a + bdef fpy( N,    np.ndarray[np.double_t,ndim=1] A,    np.ndarray[np.double_t,ndim=1] B,    np.ndarray[np.double_t,ndim=1] Z ):    """ wrap np arrays to fc( a.data ... ) """    assert N <= len(A) == len(B) == len(Z)    fcret = fc( N, <double*> A.data, <double*> B.data, <double*> Z.data )        # fcret = fc( N, A.data, B.data, Z.data )  grr char*    return fcret!#--------------------------------------------------------------------------------cat >fc.h <<\!// fc.h: numpy arrays from cython , double*int fc( int N, const double a[], const double b[], double z[] );!#--------------------------------------------------------------------------------cat >fc.cpp <<\!// fc.cpp: z = a + b, numpy arrays from cython#include "fc.h"#include <stdio.h>int fc( int N, const double a[], const double b[], double z[] ){    printf( "fc: N=%d a[0]=%f b[0]=%f \n", N, a[0], b[0] );    for( int j = 0;  j < N;  j ++ ){        z[j] = a[j] + b[j];    }    return N;}!#--------------------------------------------------------------------------------cat >f-setup.py <<\!# python f-setup.py build_ext --inplace#   cython f.pyx -> f.cpp#   g++ -c f.cpp -> f.o#   g++ -c fc.cpp -> fc.o#   link f.o fc.o -> f.so# distutils uses the Makefile distutils.sysconfig.get_makefile_filename()# for compiling and linking: a sea of options.# http://docs.python.org/distutils/introduction.html# http://docs.python.org/distutils/apiref.html  20 pages ...# https://stackoverflow.com/questions/tagged/distutils+pythonimport numpyfrom distutils.core import setupfrom distutils.extension import Extensionfrom Cython.Distutils import build_ext# from Cython.Build import cythonizeext_modules = [Extension(    name="f",    sources=["f.pyx", "fc.cpp"],        # extra_objects=["fc.o"],  # if you compile fc.cpp separately    include_dirs = [numpy.get_include()],  # .../site-packages/numpy/core/include    language="c++",        # libraries=        # extra_compile_args = "...".split(),        # extra_link_args = "...".split()    )]setup(    name = 'f',    cmdclass = {'build_ext': build_ext},    ext_modules = ext_modules,        # ext_modules = cythonize(ext_modules)  ? not in 0.14.1    # version=    # description=    # author=    # author_email=    )# test: import f!#--------------------------------------------------------------------------------cat >test-f.py <<\!#!/usr/bin/env python# test-f.pyimport numpy as npimport f  # loads f.so from cc-lib: f.pyx -> f.c + fc.o -> f.soN = 3a = np.arange( N, dtype=np.float64 )b = np.arange( N, dtype=np.float64 )z = np.ones( N, dtype=np.float64 ) * np.NaNfret = f.fpy( N, a, b, z )print "fpy -> fc z:", z!#--------------------------------------------------------------------------------cat >cc-lib-mac <<\!#!/bin/shme=${0##*/}case $1 in"" )    set --  f.cpp fc.cpp ;;  # default: g++ these-h* | --h* )    echo "$me [g++ flags] xx.c yy.cpp zz.o ...    compiles .c .cpp .o files to a dynamic lib xx.so"    exit 1esac# Logically this is simple, compile and link,# but platform-dependent, layers upon layers, gloom, doombase=${1%.c*}base=${base%.o}set -xg++ -dynamic -arch ppc \    -bundle -undefined dynamic_lookup \    -fno-strict-aliasing -fPIC -fno-common -DNDEBUG `# -g` -fwrapv \    -isysroot /Developer/SDKs/MacOSX10.4u.sdk \    -I/Library/Frameworks/Python.framework/Versions/2.6/include/python2.6 \    -I${Pysite?}/numpy/core/include \    -O2 -Wall \    "$@" \    -o $base.so# undefs: nm -gpv $base.so | egrep '^ *U _+[^P]'!# 23 Feb 2011 13:38

The following Cython code fromhttp://article.gmane.org/gmane.comp.python.cython.user/5625 doesn't require explicit casts and also handles non-continous arrays:

def fpy(A):    cdef np.ndarray[np.double_t, ndim=2, mode="c"] A_c    A_c = np.ascontiguousarray(A, dtype=np.double)    fc(&A_c[0,0])

Basically you can write your Cython function such that it allocates the arrays (make sure you cimport numpy as np):

cdef np.ndarray[np.double_t, ndim=1] rr = np.zeros((N,), dtype=np.double)

then pass in the .data pointer of each to your C function. That should work. If you don't need to start with zeros you could use np.empty for a small speed boost.

See the Cython for NumPy Users tutorial in the docs (fixed it to the correct link).