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Safari | Python Developer's Handbook -> Summary





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Summary

This chapter provides a general overview of some important advanced Python topics that you might need to use on a regular basis. They are image manipulation, sounds, restricted environment, Numeric Python, regular expressions, and threads.

Python has comprehensive support for handling image files. The foundation of this structure is based on the Python Imaging Library (commonly known as PIL). Its framework is cross-platform, which allows it to perform image manipulation and processing in different systems using the same code. Besides PIL, some other modules (such as imghdr, GD, WBMP, and PyOpenGL )can help you manipulate graphic and image files.

winsound, wave, sndhdr, aifc, and Audiodev are some of the Python modules that provide audio support for your programs by allowing you to listen to your favorite audio CDs and read/write audio files (such as .wav, .aifc, and so on). All these modules are part of the standard distribution. However, there are some great third-party Python audio modules too. The PythonWare Sound Toolkit (PST) reads sound files in different formats, and plays them on a variety of hardware platforms. It is really cool!

Restricted execution is the basic framework in Python that allows the segregation of trusted and untrusted code. Two modules implement Python support to restricted execution: rexec and Bastion. These modules prevent access to critical operations mostly because a program running in trusted mode can create an execution environment in which untrusted code can be executed with limited privileges. The idea is to use a program that runs in trusted mode to create an execution environment in which you can define limits to be applied on the execution of the untrusted code.

Python is also extensively used for scientific computing because it enables a rapid prototyping and execution of a number of functions. The Python Numerical Extensions (commonly known as NumPy) provides efficient operations on large multi dimensional arrays because it adds a fast and compact multidimensional array language facility to Python. NumPy has also proven to be the correct powerful choice when talking about scientific computing with Python. Other scientific extensions, such as ScientificPython, Pyfort, RNG, pyclimate, GmatH, and Real are also part of the constant work of many Python developers who want to turn Python into a more complete scientific language.

While I'm talking about scientific and school projects, there is a very important project that recommends the idea of teaching Python to young people at schools. The project is titled "Computer Programming for Everybody," and it was created by Guido van Rossum.

Regular expressions are stringscontaining a mix of text and special charactersthat let you define complicated pattern matching and replacement rules for other strings. You can, for example, search for a specific pattern of data in a whole text file, and substitute it for other text.

Python has two threading interfaces: the thread module and the threading module. The use of these native threading built-in modules enables the code to be portable across all platforms that support Python. The thread module supports lightweight process threads. It offers a low-level interface for working with multiple threads. On the other hand, the threading module provides high-level threading interfaces on top of the thread module. Besides these two modules, Python also implements the Queue module, which is a synchronized queue class that is used in thread programming to move Python objects between multiple threads in a safe way. Besides these two implementations, Python developers can use microthreads too. This technology implements threading by tweaking the execution order of Python's virtual machine, rather than by interrupting the processor.


Last updated on 1/30/2002
Python Developer's Handbook, © 2002 Sams Publishing

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2002, O'Reilly & Associates, Inc.

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