Free python extension software for linux

One of the features of Python that makes it so powerful is the ability to take existing libraries, written in C or C++, and make them available as Python extension modules. Such extension modules are often called bindings for the library.
SIP is a tool that makes it very easy to create Python bindings for C and C++ libraries. Python-SIP was originally developed to create PyQt, the Python bindings for the Qt toolkit, but can be used to create bindings for any C or C++ library.
SIP comprises a code generator and a Python module. The code generator processes a set of specification files and generates C or C++ code which is then compiled to create the bindings extension module. The SIP Python module provides support functions to the automatically generated code.
The specification files contains a description of the interface of the C or C++ library, i.e. the classes, methods, functions and variables. The format of a specification file is almost identical to a C or C++ header file, so much so that the easiest way of creating a specification file is to edit the corresponding header file.
SIP makes it easy to exploit existing C or C++ libraries in a productive interpretive programming environment. SIP also makes it easy to take a Python application (maybe a prototype) and selectively implement parts of the application (maybe for performance reasons) in C or C++.
Enhancements:
- This release adds support for consolidated and composite modules.
- It adds support for pickling classes and enums.

Python XHTML is a simple Python module for the generation of valid XHTML.

ToDo:

* include tests for all methods in xhtml_test.py
* ensure that all text that is added to the document is xml safe
* include methods for added w3.org validation link buttons
* complete support for XHTML 1.1 specification

Python in Scheme is a scheme library that allows you to run Python code within Scheme.

Python in Scheme project uses the Python/C API to embed a Python interpreter.

Python is an interpreted, interactive, object-oriented programming language. Python is often compared to Perl, Tcl, Scheme or Java.

Python combines remarkable power with very clear syntax. It has modules, classes, exceptions, very high level dynamic data types, and dynamic typing. There are interfaces to many system calls and libraries, as well as to various windowing systems (X11, Motif, Tk, Mac, MFC). New built-in modules are easily written in C or C++. Python is also usable as an extension language for applications that need a programmable interface.

The Python implementation is portable: it runs on many brands of UNIX, on Windows, OS/2, Mac, Amiga, and many other platforms. If your favorite system isnt listed here, it may still be supported, if theres a C compiler for it.

The Python implementation is copyrighted but freely usable and distributable, even for commercial use.

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.

Pythons simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception.

When the program doesnt catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on.

The debugger is written in Python itself, testifying to Pythons introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective.

Python chess module project is a Python chess move adjudicator module.
Python chess module does not know how to play chess, but does understand the rules enough that it can watch moves and verify that they are correct.
It features high abstraction, understands various notations (including algebraic, long algebraic, and standard algebraic notation), does disambiguation, and supports saving and loading the state of a game.
Main features:
- high abstraction
- understands various notations, including algebraic, long algebraic, and standard algebraic notation (as in PGN); does disambiguation
- supports saving and loading of the state of a game
- not a trivial move processor; understands the intracies of the game
Enhancements:
- Bug with en passant moves fixed.

FirePhish Anti-Phishing Extension is an Anti-Phishing Toolbar which utilizes the Open Phishing Database to provide the user with information and tools to protect against phishing attacks.

python-gdkinput is a Python binding for the gdk_input_add and gdk_input_remove functions missing in PyGtk. his group of functions is not present in the Gtk+/Python "PyGtk" binding, and one needs it to do asynchronous I/O in a Gtk+ based GUI.

Plans for the future:

A priori, its finished. Maybe add gdk_input_add_full if I have requests for it.
Bug corrections (I probably have made mistakes when using the reference counting system of Python); Monitor memory leaks.