For our June meeting we will have 3 x 20-minute "Rolling Thunder Talks," followed by 1/2 hour of lightning talks before heading to Prohibition for beers and socializing. Our location sponsor is SunGard and our food sponsor is SIG -- big thanks to both companies.
Our three featured talks are as follows:
1. NetworkX by Jay Ilustre
Visualizing and analyzing graphs and networks is a useful tool in many problem areas. Working with graphs and visualizing them with Python is made powerful and relatively easy using the library NetworkX. Jay will give a brief overview of NetworkX, http://networkx.lanl.gov, and how it is used at SIG to view graphs of large scale workflows.
2. Windows Monitoring Tools in Python by Tim Gross
Tim will be presenting on his current project developing monitoring tools for existing Windows services in Python. He'll be demonstrating the use of Python-based tools to debug Windows processes and the use of Python to hook Win32 system calls and monitor for file system changes. Tim works by day for the Burns Group, an AEC engineering firm where he serves as sysadmin, expert in Building Information Modeling, and in-house developer in Python, C#, and AutoLisp. By night, he is the lead developer for LegalCap, a startup aimed at providing novel document creation and management capabilities for the legal industry.
3. Interfacing Python and R by Bob Lannon
R is a strongly functional programming language and a general
environment for data manipulation, statistical analysis and
graphical display. It is similar to S, originally developed at
The creation of a free, open source alternative to S was a
tremendous boon to researchers from many fields, and papers on R packages now dominate journals related to statistical
analytic computing. More recently, R has been enthusiastically
adopted by many of the best known analytics-driven companies
today, including Google and Facebook.
Powerful and dynamic by itself, R also benefits from an
extremely active and robust contributor community, providing
and maintaining custom packages for even the most esoteric
One problem, though: R. Is. Ugly. Even the most loving
admirers of R cringe at some of its syntax and behaviors.
ENTER THE PYTHON.
RPy2 was developed to make the use of R simple from a Python
user's perspective. With only minimal knowledge of R,
Pythonistas can gain access to tools that are at the cutting
(and bleeding) edge of statistical analysis. It offers casual
users a high level interface with an embedded R process,
offering the convenience of class mappings to familiar Python
objects. For larger scale and computationally intensive
projects, the more advanced user can utlitize a separate, low-
level interface to R's memory management and garbage
Excited? Sweet. Let's get started using a pirate's favorite
statistical analysis software with RPy2!"
Looking forward to seeing you all.