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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.

PyObject *mainmod = PyImport_AddModule("__main__"); PyObject *foo = PyImport_ImportModule("foo"); Py_INCREF(foo); //Increment foo module since PyModule_AddObject() steals reference PyModule_AddObject(mainmod, "foo", foo);

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

I hope this article encourages you to think about your command line programs in a different light, and to treat them as first class objects. Using inheritance to share code is so common in other areas of development that it is hardly given a second thought in most cases. As has been shown with the SQLiteAppBase programs, the same technique can be just as powerful when applied to building command line programs, saving development time and testing effort as a result. CommandLineApp has been used as the foundation for dozens of types of programs, and could be just what you need the next time you have to write a new command line program.

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.