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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.

sychotic is an innovative optimizing compiler for Python code. It has unique features, the most important of which is that it breaks through the ConstantTimeBarrier. It is very easy to use and has an interface similar to that of the popular Psyco project. Below, you can see a usage example. You can also learn about HowItWorks, the ProjectHistory and KnownIssues. You can also browse the easy-to-understand source especially the alysis.py analyzer module and the dingo.py runtime bootstrap. There is an introductory screencast available (less than 5 minutes long). Usage

There are two applications that require using the NumPy array type in C extension modules: Access to numerical libraries: Extension modules can be used to make numerical libraries written in C (or languages linkable to C, such as Fortran) accessible to Python programs. The NumPy array type has the advantage of using the same data layout as arrays in C and Fortran. Mixed-language numerical code: In most numerical applications, only a small part of the total code is CPU time intensive. Only this part should thus be written in C, the rest can be written in Python. NumPy arrays are important for the interface between these two parts, because they provide equally simple access to their contents from Python and from C. This document is a tutorial for using NumPy arrays in C extensions.

GUESS is an exploratory data analysis and visualization tool for graphs and networks. The system contains a domain-specific embedded language called Gython (an extension of Python, or more specifically Jython) which supports the operators and syntactic sugar necessary for working on graph structures in an intuitive manner. An interactive interpreter binds the text that you type in the interpreter to the objects being visualized for more useful integration. GUESS also offers a visualization front end that supports the export of static images and dynamic movies

A python refactoring library and IDE. The IDE uses the library for providing features like refactorings and coding assists.

Neat script I wrote for a friend to annotate a Python script with the types observed during a run of the program

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.

PyScripter and most Python IDEs redirect sys.stdout.  Also GUI apps like PyScripter have no standard output.  What you need to do is PyObject *f = PySys_GetObject("stdout") and then use PyFile_WriteString for writing to the sys.stdout. This is what Python itself is doing and I think this is the best way

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.

UliPad uses Mixin and Plugin technique as its architecture. Most of its classes can be extended via mixin and plugin components, and finally become an integrity class when creating the instance. So UliPad is very dynamic. You can write the new features in new files, and hardly need to modify the existing code. And if you want to extend the existing classes, you could write mixins and plugins, and this will be bound to the target class that I call "Slot Class". This technique will make the changes centralized and easily managed.

This is something I always forget how to do, and it's kind of hard to Google or search the Python docs because you can't search for **.The point is, when using **kwargs, you have to use the ** prefix not only in the function definition, but also in the call, prefixed to the variable you want to use as a keyword dictionary.

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.

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.

Following my thoughts yesterday, here are some VIM python scripts to add python breakpoint and debugging features to VIM. With this set up the F7 key will set a breakpoint on a line of code, Shift-F7 will remove all breakpoints and Shift-F12 will execute a script in the python debugger. This only runs on windows as far as I know, because it uses the 'start' command to launch the debugger in a seperate process without VIM waiting for it to finish. This allows you to look through the source code (and fix it) while the debugging is still in progress.