Tutorial¶
The Basics of Cython¶
The fundamental nature of Cython can be summed up as follows: Cython is Python with C data types.
Cython is Python: Almost any piece of Python code is also valid Cython code. (There are a few Limitations, but this approximation will serve for now.) The Cython compiler will convert it into C code which makes equivalent calls to the Python/C API.
But Cython is much more than that, because parameters and variables can be declared to have C data types. Code which manipulates Python values and C values can be freely intermixed, with conversions occurring automatically wherever possible. Reference count maintenance and error checking of Python operations is also automatic, and the full power of Python’s exception handling facilities, including the try-except and try-finally statements, is available to you – even in the midst of manipulating C data.
Cython Hello World¶
As Cython can accept almost any valid python source file, one of the hardest things in getting started is just figuring out how to compile your extension.
So lets start with the canonical python hello world:
print "Hello World"
So the first thing to do is rename the file to helloworld.pyx
. Now we
need to make the setup.py
, which is like a python Makefile (for more
information see Source Files and Compilation). Your setup.py
should look like:
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
setup(
cmdclass = {'build_ext': build_ext},
ext_modules = [Extension("helloworld", ["helloworld.pyx"])]
)
To use this to build your Cython file use the commandline options:
$ python setup.py build_ext --inplace
Which will leave a file in your local directory called helloworld.so
in unix
or helloworld.dll
in Windows. Now to use this file: start the python
interpreter and simply import it as if it was a regular python module:
>>> import helloworld
Hello World
Congratulations! You now know how to build a Cython extension. But So Far this example doesn’t really give a feeling why one would ever want to use Cython, so lets create a more realistic example.
pyximport
: Cython Compilation the Easy Way¶
If your module doesn’t require any extra C libraries or a special
build setup, then you can use the pyximport module by Paul Prescod and
Stefan Behnel to load .pyx files directly on import, without having to
write a setup.py
file. It is shipped and installed with
Cython and can be used like this:
>>> import pyximport; pyximport.install()
>>> import helloworld
Hello World
Since Cython 0.11, the pyximport
module also has experimental
compilation support for normal Python modules. This allows you to
automatically run Cython on every .pyx and .py module that Python
imports, including the standard library and installed packages.
Cython will still fail to compile a lot of Python modules, in which
case the import mechanism will fall back to loading the Python source
modules instead. The .py import mechanism is installed like this:
>>> pyximport.install(pyimport = True)
Fibonacci Fun¶
From the official Python tutorial a simple fibonacci function is defined as:
Now following the steps for the Hello World example we first rename the file
to have a .pyx extension, lets say fib.pyx
, then we create the
setup.py
file. Using the file created for the Hello World example, all
that you need to change is the name of the Cython filename, and the resulting
module name, doing this we have:
Build the extension with the same command used for the helloworld.pyx:
$ python setup.py build_ext --inplace
And use the new extension with:
>>> import fib
>>> fib.fib(2000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597
Primes¶
Here’s a small example showing some of what can be done. It’s a routine for finding prime numbers. You tell it how many primes you want, and it returns them as a Python list.
primes.pyx
:
You’ll see that it starts out just like a normal Python function definition,
except that the parameter kmax
is declared to be of type int
. This
means that the object passed will be converted to a C integer (or a
TypeError.
will be raised if it can’t be).
Lines 2 and 3 use the cdef
statement to define some local C variables.
Line 4 creates a Python list which will be used to return the result. You’ll
notice that this is done exactly the same way it would be in Python. Because
the variable result hasn’t been given a type, it is assumed to hold a Python
object.
Lines 7-9 set up for a loop which will test candidate numbers for primeness until the required number of primes has been found. Lines 11-12, which try dividing a candidate by all the primes found so far, are of particular interest. Because no Python objects are referred to, the loop is translated entirely into C code, and thus runs very fast.
When a prime is found, lines 14-15 add it to the p array for fast access by
the testing loop, and line 16 adds it to the result list. Again, you’ll notice
that line 16 looks very much like a Python statement, and in fact it is, with
the twist that the C parameter n
is automatically converted to a Python
object before being passed to the append method. Finally, at line 18, a normal
Python return statement returns the result list.
Compiling primes.pyx with the Cython compiler produces an extension module which we can try out in the interactive interpreter as follows:
>>> import primes
>>> primes.primes(10)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
See, it works! And if you’re curious about how much work Cython has saved you, take a look at the C code generated for this module.
Language Details¶
For more about the Cython language, see Language Basics. To dive right in to using Cython in a numerical computation context, see Cython for NumPy users.