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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.

When running "python test.py" the interpreter will first precompile the test.py source file, and then execute it. When running "python test.pyc", the interpreter will go straight to the execution of the script.

RRDTool is a really good back-end for storing time-series data. If you are developing tools that need a data repository and graphing capabilities, this provides you both. You create an RRD and then you begin inserting data values at regular intervals. You then call the graphing API to have a graph displayed. The neat thing about this data storage is its “round robin” nature. You define various time spans, and the granularity at which you want them stored. A fixed binary file is created, which never grows in size over time. As you insert more data, it is inserted into each span. As results are collected, they are averaged and rolled into successive time spans. It makes a much more efficient system than using your own complex data structures, relational database, or file system storage.

Easily create Google charts using python syntax and datasets. Python wrapper for the Google Chart API. The wrapper can render the URL of the Google chart, based on your parameters, or it can render an HTML img tag to insert into webpages on the fly. Made for dynamic python websites (Django,Zope,CGI,etc.) that need on the fly chart generation without any extra modules. The wrapper SHOULD also work with Eastwood the Google Chart API workalike. G = GChart('p3', [5,10])

For drawing you can use pygraphviz (also available at networkx.lanl.gov) or the built-in drawing tools.

PyNGL (pronounced "pingle") is a Python language module used to visualize scientific data, with an emphasis on high quality 2D visualizations. A working knowledge of Python is assumed.

This is a project to develop a cross-platform pythonic GUI API.  The goals of this project are:

googlecalendarsync is a tool written in python to bidirectional synchronize a local iCal (.ics) file with Google Calendar

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

On Mon, May 5, 2008 at 8:22 PM, Reckoner <recko...@gmail.com> wrote: > is there a unit step (heaviside) function in sympy? > I need to work a conditional into a symbolic expression. We have sign which is basically the same thing:

I'm declaring this one to be the winner! (Not that my opinion counts for anythin, but I'm just impressed: a1, b1 = zip(*ab) (or a1, b1 = apply(zip,ab)?) BTW: this process isn't 100% inverse. It has the side effect of turning lists or strings (non-tuple sequences) into tuples, if they are supplied as the original sequences.

Note that it's more rebust to run methods on the public IPython api.I.e. do ip = ipshell.api and then ip.magic('px import os')You can explore the api interactively by playing with _ip object.

One of my recent research tasks required me to retrieve various information from del.icio.us, a well-known social bookmarking service. My programming language of choice is Python, and so I wrote a basic Python module for getting the data I needed.

Last week we looked at the itertools module and how its iterator based functions can be faster than doing things from scratch. One of the examples showed a function using itertools.takewhile to be much faster than one using itertools.groupby. Some feedback we got, asked, what then is the point of groupby if it's just a slower version of other functionality?

Louie provides Python programmers with a straightforward way to dispatch signals between objects in a wide variety of contexts. It is based on PyDispatcher, which in turn was based on a highly-rated recipe in the Python Cookbook. Louie is licensed under The BSD License.