Free python remote software for linux

Python milter module provides a python interface to Sendmails libmilter that exploits all its features. Milters can run on the same machine as sendmail, or another machine.
The milter can even run with a different operating system or processor than sendmail. Sendmail talks to the milter via a local or internet socket, and keeps the milter informed of events as it processes a mail connection.
At any point, the milter can cut the conversation short by telling sendmail to ACCEPT, REJECT, or DISCARD the message. After receiving a complete message from sendmail, the milter can again REJECT or DISCARD it, but it can also ACCEPT it with changes to the headers or body.
Enhancements:
- The spf module has been moved to the pyspf package.
- This will cause some confusion when upgrading, since 0.8.6 still has the module and will conflict with pyspf, whereas 0.8.7 does not have spf by itself, and will stop doing SPF if you dont install pyspf.

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 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.

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.

pythondr project is a simple python library for parsing the TV-channel info at http://dr.dk.

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.

Dee-Python project is an implementation of a truly relational database language with Python. Inspired by Date and Darwens Third Manifesto, it addresses the problems of impedance mismatch between programming languages and databases and the weakness of SQL.

Mind The Gap

Most of todays programs handle data in one way or another and often this data is stored in some kind of relational database. To read and modify this data, a program must bridge the gap between its representation and the one used by the dialect of SQL that the database provides. This bridge typically comprises a database API that sends queries as text strings, often accompanied by some kind of table-to-object mapper that has to coerce data and relationships in both directions, usually with elaborate layers of abstraction in an effort to keep the two sides loosely coupled.

"Yet by obscuring the true data source these solutions end up throwing away the most compelling feature of relational databases; the ability for the data to be queried."

Microsoft, DLinq .NET Language-Integrated Query for Relational Data, May 2006.

This approach not only adds complexity and increases the need for data transformations but, most importantly, it destroys the significant advantages provided by the relational model of data. The relational model is built upon predicate logic which brings the power of formal reasoning to data: it is the only sound foundation available.