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Python library for arbitrary-precision floating-point arithmetic

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

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.

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.

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

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.

pylize is a Python script that makes the creation of on-screen presentations a matter of a few minutes. It generates a template master document, which you can edit with your favourite text or HTML editor. The master document is then processed by pylize to generate HTML files for every slide plus a file for the table of contents. You can view the presentation with any CSS-capable webbrowser.

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:

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

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()