Cython allows Python code to be compiled to C/C++ extensions for improved performance. It is a superset of Python that adds static type declarations for variables and functions. This allows Cython code to be compiled to efficient C/C++ code while retaining the syntax and functionality of Python. The document provides an overview of Cython and examples demonstrating how to install Cython, write basic Cython modules, optimize Python code with static types, call C functions from Cython, and interface with C/C++ libraries.

1. INTRODUCTION TO
CYTHON
John
Saturday, December 21, 2013

2. Overview
• a superset of the Python language which give
high-level, OO functional, and dynamic
programming.
• The source code translate to optimized C/C++
code and compiled as Python extension
modules.
• One word, Cython is Python with C data
types.

3. Installing Cython
• Pythonxy already include Cython.
• Use easy_install or pip install Cython from
PYPI.
$ python easy_install.py Cython
$ pip install Cython

4. First example: Building a Cython
module “hello”
hello.pyx
def say_hello_to(name):
print (“Hello %s!” %
name)
setup.py
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
ext_modules = [Extension("hello",
["hello.pyx"])]
Run command line build the module
$ python setup.py build_ext --inplace
--compiler=mingw32 --inplace
# How to run the function:
>>> from hello import say_hello_to
>>> say_hello_to(“John”)
Hello John
setup(
name = 'Hello world app',
cmdclass = {'build_ext': build_ext},
ext_modules = ext_modules
)

5. Static type declarations
Cython can compile pure python code. To improve
performance, use cdef add static type declarations
# test1.pyx, 35% speedup
def f(x):
return x**2-x
def integrate_f(a, b, N):
s=0
dx = (b-a)/N
for i in range(N):
s += f(a+i*dx)
return s*dx
# 4 time speedup over python version
def f(double x):
return x**2-x
def integrate_f(double a, double b, int
N):
cdef int i
cdef double s, dx
s=0
dx = (b-a)/N
for i in range(N):
s += f(a+i*dx)
return s*dx

6. Typing function
#declare c-style function
150 times speedup
cdef double f(double x)
except ? -2:
return x**2 - x
>>> import hello
>>> hasattr(hello,'f')
False
# if use cpdef instead of cdef, a
Python wrapper is also created
annotation tell you why your
code take time
$ cython.py -a hello.pyx
a html (hello.html) is created,
Click the yellow, you will get
why the Python API is called
here

7. Calling C function
A complete list of these
cimport file see LibsitepackagesCythonIncludes
from libc.math cimport sin
cdef double f(double x):
return sin(x*x)
If Cython do not provide read-to-use
declaration, access C code by cdef
# instruct Cython generate C
code that include math.h
header file
# C compiler will see it at
compile time
cdef extern from “math.h”:
double sin(double)
cdef double f(double x):
return sin(x*x)

8. Using C libraries
Step 1: redefine .pxd head file
Step 2: create a pyx define
Queue class in Python
# file: cqueue.pxd
# copy most part of C head file here
cdef extern from "libcalg/queue.h":
ctypedef struct Queue:
pass
ctypedef void* QueueValue
Queue* queue_new()
void queue_free(Queue* queue)
# file: queue.pyx
cimport cqueue
cdef class Queue:
cdef cqueue.Queue
*_c_queue
def __cinit__(self):
self._c_queue =
cqueue.queue_new()

9. Using C libraries - cont
or step 3.2: include the lib in
step 3.1: change the setup.py
the option
$ CFLAGS="change
I/usr/local/otherdir/calg/include"
ext_modules =
[Extension("queue
LDFLAGS="", ["queue.pyx"])]
L/usr/local/otherdir/calg/lib" python
setup.py build_ext -i
to
ext_modules =
[ Extension("queu
e", ["queue.pyx"],
libraries=["calg"]) ]
calg lib see Simon Howard, C Algorithms library,
http://c-algorithms.sourceforge.net/

10. Using C++ in Cython
• Brief overview of C++ support in Cython(Cython
v0.13)
– C++ objects can now be dynamically allocated
with new and del keywords.
– C++ objects can be stack-allocated.
– C++ classes can be declared with the new
keyword cppclass.
– Templated classes are supported.
– Overloaded functions are supported.
– Overloading of C++ operators (such as operator+,
operator[],...) is supported.

11. Review of this Slides
1. introduce the Cython
2. How to install Cython
3. An example show how to compile a Cython
project
4. Optimize the pure Python code with Cython
5. Call C function in Python code
6. Use C library in Python code