Cython to Wrap Existing C Code

What is Cython ? It is an optimizing static compiler for both the Python programming language and the extended Cython programming language. It is used to make it easy to write C extensions for Python as easy as Python itself. It comes up with many helpful features :

• Writing a Python code that calls back and forth from and to C/C++ code.
• Easily tuning of readable Python code into plain C performance by adding static type declarations.
• Use of combined source code level debugging to find bugs in given Python, Cython and C code.
• Efficient interaction with large data sets, e.g. using multi-dimensional NumPy arrays.
• Integration with existing code and data from low-level or high-performance libraries and applications.

To make an extension with Cython is a tricky task to perform. Doing so, one needs to create a collection of wrapper functions. Assuming that the work code shown has been compiled into a C library called libwork. The code below will create a file named csample.pxd. Code #1 : Python3 1==

# cwork.pxd
#
# Declarations of "external" C 
# functions and structures

cdef extern from "work.h":
 
    int gcd(int, int)
    int divide(int, int, int *)
    double avg(double *, int) nogil
    
    ctypedef struct Point:
        double x
        double y
        
    double distance(Point *, Point *)

In Cython, the code above will work as a C header file. The initial declaration cdef extern from "work.h" declares the required C header file. Declarations that follow are taken from the header. The name of this file is cwork.pxd. Next target is to create a work.pyx file which will define wrappers that bridge the Python interpreter to the underlying C code declared in the cwork.pxd file. Code #2 : Python3 1==

# work.pyx
# Import the low-level C declarations
      
cimport cwork
# Importing functionalities from Python
# and the C stdlib
from cpython.pycapsule cimport * 
from libc.stdlib cimport malloc, free

# Wrappers
def gcd(unsigned int x, unsigned int y):
    return cwork.gcd(x, y)

def divide(x, y):
    cdef int rem
    quot = cwork.divide(x, y, &rem)
    return quot, rem

def avg(double[:] a):
    cdef:
        int sz
        double result

    sz = a.size

    with nogil:
        result = cwork.avg(<double *> &a[0], sz)

    return result

  Code #3 : Python3 1==

# Destructor for cleaning up Point objects
cdef del_Point(object obj):
    pt = <csample.Point *> PyCapsule_GetPointer(obj, "Point")
    free(<void *> pt)
    
# Create a Point object and return as a capsule
def Point(double x, double y):
    cdef csample.Point * p
    p = <csample.Point *> malloc(sizeof(csample.Point))
    
    if p == NULL:
        raise MemoryError("No memory to make a Point")
        
    p.x = x
    p.y = y
    
    return PyCapsule_New(<void *>p, "Point",
                         <PyCapsule_Destructor>del_Point)

def distance(p1, p2):
    pt1 = <csample.Point *> PyCapsule_GetPointer(p1, "Point")
    pt2 = <csample.Point *> PyCapsule_GetPointer(p2, "Point")
    
    return csample.distance(pt1, pt2)

  Finally, to build the extension module, create a work.py file. Code #4: Python3

# importing libraries
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext

ext_modules = [Extension('work', 
                         ['work.pyx'], 
                         libraries=['work'], 
                         library_dirs=['.'])]

setup(name = 'work extension module',
      cmdclass = {'build_ext': build_ext},
      ext_modules = ext_modules)

  Code #5 : Building resulting module for experimentation. bash

bash % python3 setup.py build_ext --inplace
running build_ext

cythoning work.pyx to work.c
building 'work' extension

gcc -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes
-I/usr/local/include/python3.3m -c work.c
-o build/temp.macosx-10.6-x86_64-3.3/work.o

gcc -bundle -undefined dynamic_lookup build/temp.macosx-10.6-x86_64-3.3/work.o
-L. -lwork -o work.so
bash %

Now, we have an extension module work.so. Let's see how it works. Code #6 : Python3

import sample
print ("GCD : ", sample.gcd(12, 8))

print ("\nDivision : ", sample.divide(42,10))

import array
arr = array.array('d',[1,2,3])
print ("\nAverage  : ", sample.avg(a)

pt1 = sample.Point(2,3)
pt2 = sample.Point(4,5)

print ("\npt1 : ", pt1)
print ("\npt2 : ", pt2)

print ("\nDistance between the two points : ", 
       sample.distance(pt1, pt2))

Output :

GCD : 4

Division : (4, 2)

Average : 2.0

pt1 : <capsule object "Point" at 0x1005d1e70>

pt2 : <capsule object "Point" at 0x1005d1ea0>

Distance between the two points : 2.8284271247461903

At a high level, using Cython is modeled after C. The .pxd files merely contain C definitions (similar to .h files) and the .pyx files contain implementation (similar to a .c file). The cimport statement is used by Cython to import definitions from a .pxd file. This is different than using a normal Python import statement, which would load a regular Python module.