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
