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This script mimics rsync which is available for the unix platform and have been ported to win32 one. It is a sort of advanced version of xcopy. Its aim is to selectively synchronize folders. More precisely it copy selective parts of a folder to a destination folder and in addition can remove parts of the destination folder that do not correspond to parts of the original folder. I like its capability to avoid copying files through the .cvsignore mechanism or the pattern matching mechanism and its capability to delete files that are no longer relevant, not to mention that because it's a python script anyone can easily fix or improve it as he whish.

Mpmath is a pure-Python library for multiprecision floating-point arithmetic. It provides an extensive set of transcendental functions, unlimited exponent sizes, complex numbers, interval arithmetic, numerical integration and differentiation, root-finding, linear algebra, and much more. Almost any calculation can be performed just as well at 10-digit or 1000-digit precision, and in many cases mpmath implements asymptotically fast algorithms that scale well for extremely high precision work. If available, mpmath will (optionally) use gmpy to speed up high precision operations.

tinypy is a minimalist implementation of python in 64k of code

I wrote dbtxt because I needed a small, flat database in a python environment that didn't depend upon any external libraries. Most libraries are contaminated with the GPL, and this needed to be OK for commercial distribution without any complications. So that's what we have here - a complete (though small) database system that depends on nothing at all other than the Python language and its internal libraries. The entire database comes in at about 20k bytes (that's right, "k", not hundreds of k or megabytes) and I was able to implement all the functions I needed. So I was happy. Will you be happy? Well, download it and read the docs and see what you think. The download, zipped, is about 13k. Yep. 13k. :-) By all means, if you have a need for the same kind of thing, feel free to make any use of dbtxt you please. I have released it as PD, so you can use it in projects that are commercial, GPL, BSD, PD, private, government... whatever you like. Below you'll find a basic description taken from beginning of the docs; in the archive you'll download there is complete documentation, two sample databases, a test program and the database engine itself.

ProfitPy is a set of libraries and tools for the development, testing, and execution of automated stock trading systems Specifically, ProfitPy is a collection of Python packages, modules, and scripts that work together to help you implement an automated stock trading program (or programs). The package features: A library with classes for accounts, orders, tickers, and more A GUI program for executing and plotting trades in real-time A GUI tool to collect ticker data for off-line use A CLI tool to back-test trading strategies Dozens of technical indicators and plotting widgets to display them An X11 keystroke tool for automating the TWS application

To attempt to explain it in the simplest terms, a continuation is a representation, at a particular point in a program, of everything the program is capable of doing subsequently. A continuation is a potential that depends on initial conditions. Rather than loop in a traditional way, it is possible to invoke the same continuation recursively with different initial conditions. One broad claim I have read is that continuations, in a theoretical sense, are more fundamental and underlie every other control structure. Don't worry if these ideas cause your brain to melt; that is a normal reaction.

Stackless Python is an alternative implementation of Python created by independent developer Christian Tismer. He started with the conventional Python language processor managed by the language's inventor, Guido van Rossum, and patched his own Stackless invention in place of a small but central part of Python's internals. Stackless Python is the result. This article introduces Tismer's technology and its significance. In future articles, you'll be able to read about how to make your own start at programming Stackless Python, as well as the prospects for a merger between Stackless and the main Python distribution.

In our last article on compression we showed you how to demonstrate run time encoding in Python. In this article we'll show you how to implement another kind of compression, Huffman encoding, which is useful when dealing with small sets of items, such as character strings.

Welcome to the PyPubSub / PMS project, an implementation of a pure python networked publish subscribe mechanism. Central is a message broker which dispatches messages between applications.

PyACTS- http://www.pyacts.org -- PyACTS is a collection of Python Modules that are very useful to Parallel Computing in a High Performance Computing environment. This packages incorporates several modules like PyBLACS (allows communication data for Linear Algebra), PyPBLAS (distributed Matrix Operations) and PyScaLAPACK (solve linear systems and get the eigenvalue problems). These libraries are part of PyACTS project that provide interfaces to the ACTS Collection. Also is provided a parrallel interpreter for using this package that implements message-based parallel programming using MPI.

he most popular cryptographic software for Windows is probably TrueCrypt. In this article I will explain how TrueCrypt works and as a by-product a working Python implementation will be provided. This article is written from a programmer perspective and the math will be kept to the minimum. The emphasis is on how TrueCrypt uses cryptographic primitives such as AES and SHA-1, not how the primitives themselves work.

What's the relation between these benefits and Stackless's implementation details? Here's a quick sketch: Continuations are the general-purpose concurrency construct. A continuation represents all the future computations of a particular program. Capturing all this control flow in a single conceptual object makes it programmable: It becomes possible to calculate or reason over the control flow. In particular, there's great scope for optimizing assignment of different calculations to different processes or threads or even hosts.

Few programming languages provide direct support for graphs as a data type, and Python is no exception. However, graphs are easily built out of lists and dictionaries. For instance, here's a simple graph (I can't use drawings in these columns, so I write down the graph's arcs):