Free python programs software for linux

rope is a Python IDE. Python ropes main goal is to provide features like auto-completion, refactorings, content assistance, and outlines.

Refactoring: In recent years refactoring has become a basic task of everyday programing, specially in java community. In the agile programing methodologies, like Extreme Programing, Refactoring is one of the core practices.
Some IDEs support some basic refactorings like PyDev (which uses bicycle repair man). These IDEs have a limited set of refactorings and fail when doing refactorings that need to know the type of objects in the source code (specially for relatively large projects). rope tries to provide a rich set of refactorings. Some of the refactorings require type inferencing which is described later.
Auto Completion: One of the basic features of modern IDEs is the availability of auto-completion. Some Python IDEs have auto-completion support but in a limited form. Since the type of many variables cannot be deduced from simple analysis of the source code. Auto-completing modules names, class names, static methods, class methods, function names and variable names are easy. But auto-completing the methods and attributes of an object is hard. Because the IDE needs to know the type of the object that cannot be achieved easily most of the time in dynamic languages. rope uses Type Inferencing algorithms to solve this problem.
Type Inferencing: One disadvantage of dynamic languages like python is that you cannot know the type of variables by a simple analysis of program source code most of the time. Knowing the type of variables is very essential for providing many of the refactorings and auto-completions. rope will use type inferencing to overcome this problem.

Static type inferencing uses program source code to guess the type of objects. But type inferencing python programs is very hard. There have been some attempts though not very successful (examples: psycho: only str and int types, StarKiller: wasnt released and ShedSkin: good but limited). They where mostly directed at speeding up python programs by transforming its code to other typed languages rather than building IDEs. Such algorithms might be helpful.

There is another approach toward type inferencing. That is the analysis of running programs. This dynamic approach records the types variables are assigned to during the program execution. Although this approach is a lot easier to implement than the alternative, it is limited. Only the parts of the program that are executed are analyzed. If developers write unit tests and use test driven development this approach works very well.

Python Call Graph (pycallgraph) is a Python library that creates call graphs for Python programs

pycallgraph is quite new. It seems to work well and I will most likely improve it in the near future!

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 commandsplus is a Python module that complements the existing commands module with an extra function getstatusoutputerror, which separates the standard output from the standard error and makes it easier to evaluate return status.

Python commandsplus also includes a useful function that protects a command line argument from shell metacharacters.

Python Audio Tools are a collection of audio handling programs which work from the command line. These include programs for CD extraction, track conversion from one audio format to another, track renaming and retagging, track identification, CD burning from tracks, and more.
Supports internationalized track filenames and metadata using Unicode. Works with high-definition, multi-channel audio as well as CD-quality. Track conversion uses multiple CPUs or CPU cores if available to greatly speed the transcoding process. Track metadata can be retrieved from FreeDB or FreeDB-compatible servers. Audio formats supported are:
- WAV
- AIFF
- Sun AU
- FLAC
- WavPack
- Monkeys Audio
- MP3
- MP2
- Ogg Vorbis
- Ogg Speex
- M4A
Easy to install and use:
I dont like programs with lots of obscure dependencies, so Ive written Python Audio Tools to require as few external libraries and programs as possible. Most formats require only one or two common executables and some require none at all. Installation utilizes Pythons built-in distutils, but a provided makefile reduces the process to a simple "make install" command.
The programs that make up Python Audio Tools provide a consistent interface for painless switching between them. man pages are included, as is a lengthy manual for both users and programmers. See the short tutorial in the documentation for an illustration of their usage.
These Python Audio Tools are designed with Unix-like systems in mind. Although most of it is written in pure Python, the libcdio library is used to read from a CD-ROM drive. If one is willing to forgo the CD-based tools, Python Audio Tools is fully portable so long as the required helper applications are present. I have used it on the i386, x86_64 and ppc architectures without difficulty.
Standards compliant
Because Python Audio Tools sticks very close to established, documented standards, it has no trouble interoperating with other audio-handling programs. If you want to rip CDs with Exact Audio Copy, batch convert them to M4As with these tools and play them back in iTunes, thats no problem.
Enhancements:
- Revamped the track metadata system.
- Improved the file detection routines.
- Minor bugfixes.

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

GnuPGInterface is a Python module to interface with GnuPG . It concentrates on interacting with GnuPG via filehandles, providing access to control GnuPG via versatile and extensible means.
This module is based on GnuPG::Interface, a Perl module by the same author.
To install this distribution, simply run the following commands:
python setup.py build
python setup.py install
See pydoc GnuPGInterface for module documentation. Only recent
versions of Python include the executable pydoc.
Enhancements:
- GnuPGInterface.py, NEWS, setup.py: bump version to 0.3.2 and news

python-amarok is a fairly complete amaroK remote control class for Python.

python-amarok is extremely useful for amaroK scripts and other applications and makes it possible to remote control amaroK by calling methods from an amaroKProxy instance.

In addition, it implements the Observer/Observable pattern so you can deal with events in an object oriented, loosely coupled fashion.

Even better, you can call an amaroKProxy instances collection.getSong(filename) method to get a Song object, with track, artist, album name, and elegant "Artist - Track name" strings.