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Cython to Wrap Existing C Code

  • Last Updated : 29 Mar, 2019

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 :

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  • 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 :

# 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 :

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

Code #3 :

# 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",
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 file.

Code #4:

# importing libraries
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
ext_modules = [Extension('work'
setup(name = 'work extension module',
      cmdclass = {'build_ext': build_ext},
      ext_modules = ext_modules)

Code #5 : Building resulting module for experimentation.

bash % python3 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
bash %

Now, we have an extension module Let’s see how it works.

Code #6 :

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.

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