Section 22.11. Other Extending Tools

22.11. Other Extending Tools

In closing the extending topic, I should mention that there are alternatives to SWIG, some of which have a loyal user base of their own. This section briefly introduces some of the more popular tools in this domain; as usual, search the Web for more details on these and more. All of the following are currently third-party tools that must be installed separately like SWIG, though Python 2.5 is scheduled to incorporate the ctypes extension as a standard library module by the time you read this.


Just as a sip is a smaller swig in the drinking world, so too is the SIP system a lighter alternative to SWIG in the Python world (in fact, it was named on purpose for the joke). According to its web page, SIP makes it easy to create Python bindings for C and C++ libraries. Originally developed to create the PyQt Python bindings for the Qt toolkit, it can be used to create bindings for any C or C++ library. SIP includes a code generator and a Python support module.

Much like SWIG, the code generator processes a set of specification files and generates C or C++ code, which is compiled to create the bindings extension module. The SIP Python module provides support functions to the automatically generated code. Unlike SWIG, SIP is specifically designed for bringing together Python and C/C++; SWIG also generates wrappers for many other scripting languages.


The ctypes system is a foreign function interface (FFI) module for Python. It allows Python scripts to access and call compiled functions in a binary library file directly and dynamically, by writing dispatch code in Python instead of generating or writing the integration C wrapper code we've studied in this chapter.

According to its web site, ctypes allows Python to call functions exposed from DLLs and shared libraries and has facilities to create, access, and manipulate complex C datatypes in Python. The net effect is to wrap libraries in pure Python. It is also possible to implement C callback functions in pure Python; ctypes now includes an experimental code generator feature that allows automatic creation of library wrappers from C header files. ctypes works on Windows, Mac OS X, Linux, Solaris, FreeBSD, and OpenBSD. It may run on additional systems, provided that the libffi package it employs is supported. For Windows, ctypes contains a package, which allows Python code to call and implement custom COM interfaces.


The Boost.Python system is a C++ library that enables seamless interoperability between C++ and the Python programming language through an IDL-like model. Using it, developers generally write a small amount of C++ wrapper code to create a shared library for use in Python scripts. Boost.Python handles references, callbacks, type mappings, and cleanup tasks. Because it is designed to wrap C++ interfaces nonintrusively, C++ code need not be changed to be wrapped. Like other tools, this makes the system useful for wrapping existing libraries, as well as developing new extensions from scratch.

Writing interface code for large libraries can be more tedious than the generation approaches of SWIG and SIP, but it's easier than manually wrapping libraries and may afford greater control than a fully automated wrapping tool. In addition, the Pyste system provides a Boost.Python code generator, in which users specify classes and functions to be exported using a simple interface file, which is Python code. Pyste uses GCCXML to parse all the headers and extract the necessary information to generate C++ code.


Pyrex is a language specifically for writing Python extension modules. It lets you write code that mixes Python and C datatypes anyway you want, and it compiles it into a C extension for Python. In principle, developers need not deal with the Python/C API at all, because Pyrex takes care of things such as error-checking and reference counts automatically.

Technically, Pyrex is a distinct language that is Python-like, with extensions for mixing in C datatype declarations. However, almost any Python code is also valid Pyrex code. The Pyrex compiler converts Python code into C code, which makes calls to the Python/C API. In this aspect, Pyrex is similar to the older Python2C conversion project. By combining Python and C code, Pyrex offers a very different approach than the integration code generation or coding schemes of other systems.


The CXX system is roughly a C++ version of Python's usual C API, which handles reference counters, exception translation, and much of the type checking and cleanup inherent in handcoded C++ extensions. As such, CXX lets you focus on the application-specific parts of your code. CXX also exposes parts of the C++ Standard Template Library containers to be compatible with Python lists and tuples.


Finally, the Modulator system is a simple Python-coded GUI that generates skeleton boilerplate code for C extension modules and types. Users select components to be supported in the GUI, and Modulator generates the initial C code; simply edit to insert type-specific parts of extension functions. Modulator is available in the Tools directory of the Python source distribution.

At the end of the next chapter, we will return to extending in the context of integration at large, and we'll compare Python C integration techniques to very different approaches such as COM, CORBA, and Jython. First, though, we need to shift our perspective 180 degrees to explore the other mode of Python/C integration discussed in the next chapter: embedding.

Mixing Python and C++

Python's standard implementation is currently coded in C, so all the normal rules about mixing C programs with C++ programs apply to the Python interpreter. In fact, there is nothing special about Python in this context, but here are a few pointers.

When embedding Python in a C++ program, there are no special rules to follow. Simply link in the Python library and call its functions from C++. Python's header files automatically wrap themselves in extern "C" {...} declarations to suppress C++ name mangling. Hence, the Python library looks like any other C component to C++; there is no need to recompile Python itself with a C++ compiler.

When extending Python with C++ components, Python header files are still C++ friendly, so Python API calls in C++ extensions work like any other C++-to-C call. But be sure to wrap the parts of your extension code made visible to Python with extern "C" declarations so that they may be called by Python's C code. For example, to wrap a C++ class, SWIG generates a C++ extension module that declares its initialization function this way, though the rest of the module is pure C++.

The only other potential complication involves C++ static or global object constructor methods when extending. If Python (a C program) is at the top level of a system, such C++ constructors may not be run when the system starts up. This behavior may vary per compiler, but if your C++ objects are not initialized on startup, make sure that your main program is linked by your C++ compiler, not by C.

If you are interested in Python/C++ integration in general, be sure to consult the C++ Special Interest Group (SIG) pages at for information about work in this domain. The CXX system, for instance, makes it easier to extend Python with C++.

Programming Python
Programming Python
ISBN: 0596009259
EAN: 2147483647
Year: 2004
Pages: 270
Authors: Mark Lutz

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