使用cython简单包装C代码

Question

I have a number of C functions, and I would like to call them from python. cython seems to be the way to go, but I can't really find an example of how exactly this is done. My C function looks like this:

void calculate_daily ( char *db_name, int grid_id, int year,
                       double *dtmp, double *dtmn, double *dtmx, 
                       double *dprec, double *ddtr, double *dayl, 
                       double *dpet, double *dpar ) ;

All I want to do is to specify the first three parameters (a string and two integers), and recover 8 numpy arrays (or python lists. All the double arrays have N elements). My code assumes that the pointers are pointing to an already allocated chunk of memory. Also, the produced C code ought to link to some external libraries.

Answer 1

Here's a tiny but complete example of passing numpy arrays to 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 skim Cython build and 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 fo 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 also distutils doc and/or SO questions on distutils .)

Like make , setup.py will rerun cython f.pyx and g++ -c ... f.cpp if 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 -> fo
g++ -c ... fc.cpp -> fc.o
cc-lib fo fc.o -> dynamic library f.so .
Modify the cc-lib-mac wrapper below 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 any SciKit .

See also: Cython for NumPy users and 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 np
cimport numpy as np

cdef extern from "fc.h": 
    int fc( int N, double* a, double* b, double* z )  # z = a + b

def 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+python

import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
# from Cython.Build import cythonize

ext_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.py

import numpy as np
import f  # loads f.so from cc-lib: f.pyx -> f.c + fc.o -> f.so

N = 3
a = np.arange( N, dtype=np.float64 )
b = np.arange( N, dtype=np.float64 )
z = np.ones( N, dtype=np.float64 ) * np.NaN

fret = f.fpy( N, a, b, z )
print "fpy -> fc z:", z

!

#--------------------------------------------------------------------------------
cat >cc-lib-mac <<\!
#!/bin/sh
me=${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 1
esac

# Logically this is simple, compile and link,
# but platform-dependent, layers upon layers, gloom, doom

base=${1%.c*}
base=${base%.o}
set -x

g++ -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

Answer 2

The following Cython code from http://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])

Answer 3

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).

Answer 4

你应该看看Ctypes它可能是最容易使用的东西，如果你想要的只是一个功能。