Python library for arbitrary-precision floating-point arithmetic

The taskbar module is really experimental - and I think this is one of the first questions about it :)    tb.RunningApplication.Button(i).Click() - doesn't use ClickInput() 

In short: run your existing Python software much faster, with no change in your source. Think of Psyco as a kind of just-in-time (JIT) compiler, a little bit like what exists for other languages, that emit machine code on the fly instead of interpreting your Python program step by step. The difference with the traditional approach to JIT compilers is that Psyco writes several version of the same blocks (a block is a bit of a function), which are optimized by being specialized to some kinds of variables (a "kind" can mean a type, but it is more general). The result is that your unmodified Python programs run faster. Benefits 2x to 100x speed-ups, typically 4x, with an unmodified Python interpreter and unmodified source code, just a dynamically loadable C extension module. Drawbacks Psyco currently uses a lot of memory. It only runs on Intel 386-compatible processors (under any OS) right now. There are some subtle semantic differences (i.e. bugs) with the way Python works; they should not be apparent in most programs.

The module comes with a demo application which is written in wxWidgets. The demo emulates any terminal program inside a text box. The following are the screenshots of emulating bash, emacs and vi using TermEmulator and the demo. successfully emulated(ran in a text box) bash, emacs, vi and some other terminal programs.

Provides callback for updating terminal screen

aspects.py library provides means to intercept function calls. Functions and methods (also in Python standard library and third party code) can be wrapped so that when they are called, the wrap is invoked first. Depending on the wrap, the execution of the original function can be omitted, or the function can be called arbitrarily many times. Wraps are able to modify the arguments and the return value of the original function. In the terminology of aspect-oriented programming, the library allows applying advices (wraps) to call join points of methods and functions in around fashion.

This tutorial teaches the fundamental concepts of symbolic debuggers. We will be using Winpdb to debug Python scripts. The concepts that you learn here, however, are fundamental to working with debuggers in general. At the end of the exercise, you should feel comfortable with using symbolic debuggers and have an inherent understanding of why these tools prove to reduce time and effort needed in debugging without altering the code itself to do so.

The Definitive Guide to Django: Web Development Done Right (Paperback)

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:

Your example works as you describe on recent matplotlib versions. I suspect you are using an old one. The preferred way of handling missing points in numpy, and therefore in matplotlib and pylab, however, is via masked arrays.import pylabimport numpy as npfrom numpy import maa = [1,2,3,4,5]b = np.array([6,2,np.nan,1,9])bm = ma.masked_where(np.isnan(b), b)pylab.plot(a,bm)pylab.show()There are many other examples of masked array use in the examples directory of the matplotlib distribution.EricFernando Abilleira wrote:> Dear sourceforge community,> > I come from a Matlab environment so I am used to plotting matrices that > contain NaN elements. This is very useful because in some cases one > doesn't have data for the entire matrix. If one tries plotting the data, > the NaN elements won't be plotted.

I didn’t want to make a new build tool. Honestly. The main reason that I created Paver is...

One of the main difficulties I've had using parser generators has been the difficulty of figuring out why a grammar didn't work. Fixing shift-reduce and reduce-reduce conflicts seemed like voodoo to me, and though I slightly understand better how to fix such things now it's still a different mode of thinking that I don't want to try to get into when I just want to parse something simple. PyMeta uses a variation on the Parsing Expression Grammar (PEG) approach to parsing. The chief consequence of this is there's no possibility of ambiguity in a parse: a successful parse will yield exactly one result, and you can trace the control flow through the grammar to figure out how it got there.

Metaclass programming in PythonPushing object-oriented programming to the next level

New to AOP? You may find this "Introduction to Aspect-Oriented Programming" (PDF) by Ken Wing Kuen Lee of the Hong Kong University of Science and Technology interesting.

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.