Objects that represent potential behavior (functions and methods) can be invoked, or called. We can test an object's callability with the callable() function:

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

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

For example edge labels can be added using matplotlib "text" objects like this: import networkx as nx import pylab as plot K=nx.XGraph(name="Konigsberg", multiedges=True, selfloops=False) K.add_edges_from([("A","B","Honey Bridge"),     ("A","B","Blacksmith's Bridge"),     ("A","C","Green Bridge"),     ("A","C","Connecting Bridge"),     ("A","D","Merchant's Bridge"),     ("C","D","High Bridge"),     ("B","D","Wooden Bridge")]) pos=nx.spring_layout(K) nx.draw_nx(K,pos) xa,ya=pos['A'] xb,yb=pos['B'] plot.text((xa+xb)/2,(ya+yb)/2,"Blacksmith's Bridge") plot.show() With a little work you can get the label rotated and exactly how you want it positioned.  You can also set the node positions directly in the "pos" dictionary above.

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.

Use a staticmethod when you know which class you want to access as you are writing the code.

Use a classmethod if you have a class hierarchy and want the method to operate on the actual class used in the call rather than the class where it was defined:

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.

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.