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I love python for its beautiful code and practicality. But it’s not going to win a pure speed race with most languages. Most people think of speed and ease-of-use as polar opposites - probably because they remember the pain of writing C. Cython tries to eliminate that duality and lets you have python syntax with C data types and functions - the best of both worlds. Keeping in mind that I’m by no means an expert at this, here are my notes based on my first real experiment with Cython:

Linda is an widely studied distributed computing environment, centered around the notion of a tuple space. A tuple space is a bag (also called a multi-set) of tuples. A tuple is an ordered, typed chunk of data. Tuple spaces exist independently of processes in the system, and the data placed into a tuple space also exist independently. See "Generative communication in Linda" (1985) and "Multiple tuple spaces in Linda" both by David Gelernter for more information on Linda.

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

> Is there a way to find the number of processors on a machine (on linux/ > windows/macos/cygwin) using python code (using the same code/cross > platform code)? From processing <http://cheeseshop.python.org/pypi/processing/0.34> : def cpuCount():     '''     Returns the number of CPUs in the system     '''     if sys.platform == 'win32':         try:             num = int(os.environ['NUMBER_OF_PROCESSORS'])         except (ValueError, KeyError):             pass     elif sys.platform == 'darwin':         try:             num = int(os.popen('sysctl -n hw.ncpu').read())         except ValueError:             pass     else:         try:             num = os.sysconf('SC_NPROCESSORS_ONLN')         except (ValueError, OSError, AttributeError):             pass     if num >= 1:         return num     else:         raise NotImplementedError --

Osh (Object SHell) is a tool that integrates the processing of structured data, database access, and remote access to a cluster of nodes. These capabilities are made available through a command-line interface (CLI) and a Python application programming interface (API). Osh processes streams of Python objects using simple commands. Complex data processing is achieved by command sequences in which the output from one command is passed to the input of the next. This is similar to composing Unix commands using pipes. However, Unix commands pass strings from one command to the next, and the commands (grep, awk, sed, etc.) are heavily string-oriented. Osh commands send primitive Python types such as strings and numbers; composite types such as tuples, lists and maps; objects representing files, dates and times; or even user-defined objects.

Document options Document options requiring JavaScript are not displayed Rate this pageHelp us improve this contentLevel: IntroductoryDavid Mertz (mertz@gnosis.cx), Applied Metaphysician, Gnosis Software, Inc. 01 Mar 2001Although users usually think of Python as a procedural and object-oriented language, it actually contains everything you need for a completely functional approach to programming. This article discusses general concepts of functional programming, and illustrates ways of implementing functional techniques in Python. We'd better start with the hardest question: "What is functional programming (FP), anyway?" One answer would be to say that FP is what you do when you program in languages like Lisp, Scheme, Haskell, ML, OCAML, Clean, Mercury, or Erlang (or a few others). That is a safe answer, but not one that clarifies very much. Unfortunately, it is hard to get a consistent opinion on just what FP is, even from functional programmers themselves. A story about elephants and blind men seems apropos here. It is also safe to contrast FP with "imperative programming" (what you do in languages like C, Pascal, C++, Java, Perl, Awk, TCL, and most others, at least for the most part).

Python in Maya Done RightPymel makes python scripting with Maya work the way it should. Maya's command module is a direct translation of mel commands into python commands. The result is a very awkward and unpythonic syntax which does not take advantage of python's strengths -- particulary, a flexible, object-oriented design. Pymel builds on the cmds module by organizing many of its commands into a class hierarchy, and by customizing them to operate in a more succinct and intuitive way. Powerful new classes

IPython on resizeable Win32 Console If you are on win32, you might want to use Console, an open source replacement for that inflexible old win32 terminal. Here's how. Install IPython using the .exe installer. Download stable version of Console (1.5 at this time). Create a shortcut to console.exe, right-click -> properties. Set target to e.g. C:\opt\Console\console.exe -c "/k python c:\python25\scripts\ipython.py -p sh" you can also create a tab dedicated to Ipython (and have other dedicated to other shells or command-line app). go to edit->settings->tabs : click on the add button and fill the following fields : * title : ipython ico : C:\Python24\py.ico shell : cmd.exe /k "ipython" start-up dir : whatever is good for you. note that python.exe need to be in the path Using IPython with Version 2 of Console use the -r option for creating the shortcut, e.g. C:\opt\Console2\console.exe -r "/k python c:\python25\scripts\ipython.py -p sh" Some advantages of Console2 over normal console (for IPython use) Distinctive taskbar icon Can reside in system tray There are many other benefits, so it's well worth setting up Example config for Console-2.00b120-Beta (you need to edit the xml file): http://vvtools.googlecode.com/svn/trunk/config/console.xml If you have problems with ctrl+C killing Console2 immediately, launch ipython from a normal Console2 session manually.