See it in action!   http://svn.jperr.com

Download svnindex.py

The code was developed and tested using Python2.6, but I see no reason why it shouldn’t work in Python3.0. Likewise, I’ve developed and tested this using UNIX based machines (Mac 10.6 and Debian Linux), but it should work under Windows. The script requires the command line “svn” client to be installed and accessible by the user under which Python runs, and you must have the standard python “time” and “commands” modules installed. 99.9% of Python installations will have them pre-installed.

All you need to do to get svnindex.py running on your site:

• Download svnindex.py (link above)
• Modify the settings at the top of the file
• Set up a daily cronjob to run the generation script. Something like `python /path/to/svnindex.py` works just fine.

I began this endeavor by exploring the Mandelbrot set with Zlata over the MIT Spark weekend. In the wee hours of Sunday morning, the first grainy images of the Mandelbrot set came into view. Uplifted by this success, we toiled tirelessly to create a full color, zoomable, and downright awesome fractal generator.

One of the best renderings from our Mandelbrot generator.

View Demo

Adding color was one of the toughest challenges we encountered. The standard RGB color notation is great for most purposes, but it really does fall short for generating the spectrum. In order to more easily generate a smooth color spectrum, we switched the project to HSL color and were able to simply scale the “H” (hue) property with each point’s iteration count.

Another interesting problem which still exists as a “bug” in the demo applet above is the lower limit imposed by the floating point datatype. As you zoom in farther and farther, the coordinates shrink down to absurdly small and precise values. Eventually, the measly floating point becomes imprecise, and the display seems pixelated. An easy fix would be switching all datatypes to doubles or bignums, but that would just take too much effort. It works!

Next I tackled the Lorenz Attractor. One of many such “strange attractors” that visualize the changes in a dynamic system by plotting the system’s state in “system space.” In the case of the Lorenz Attractor, fluid convection is represented in 3-space by the following set of equations:
From hypertextbook.com:
dx/dt = σ(y − x)
dy/dt = ρx − y − xz
dz/dt = xy − βz
As stated above, “x” is the convective flow, “y” is the horizontal temperature distribution, and “z” is the vertical temperature distribution of the system. Curiously, the orbit of point (x,y,z) over time will not “settle down” or form a repetitious cycle given certain critical values for σ, ρ, and β. Instead, the orbit forms a magnificent, infinitely complex object known as a strange attractor.

The Lorenz Attractor

View Demo

My algorithm works by increasing an “error” variable by y0 – y1 every iteration. Should the error ever rise above x0-x1, the error is decreased by x0 – x1 and the drawn Y value is increased by 1.

We are leaning towards writing a bare-bones server using Python’s WSGI specification. The specifics are still being worked out, but this new web presence will need to:

• Serve a “presentable” site to the public complete with teacher bios, course descriptions, syllabus, and possibly a calendar.
• Serve special class pages with editable sections and some sort of source code browser to authenticated users.

Since every teacher at Stuy would rather edit files in Emacs than bother with a buggy AJAX admin interface, most configuration and static content will be kept in plain-text files. Again, the specifics have not been worked out. More to come!

Download the full paper as PDF (400k)

Abstract:

Evolution has proven to be a wildly successful autonomous process for creating intelligent systems in the natural world and in simulation. Since the early 1960s, re- searchers have used artificial evolution to find ingenious and novel solutions to complex problems such as series prediction and flight control. Recently, artificial evolution has been applied to neural networks with the aim of evolving more robust artificial intel- ligence. Several metrics have been proposed to chart the emergence of intelligence in these evolved networks. This work analyzes the behavior of a new metric, integrated information (φ). Observed data is analyzed, interpreted, and compared to more con- ventional properties of the artificial neural network. The data analysis shows that φ increases over evolutionary time and is therefore promising as a heuristic measure.

Experimental Data:

Results from Integrated Information experiments. The data set includes anatomical neural network sampled from 5,202 Polyworldians, at death, from three genetically independent simulations. Each data point represents a 200 time step bin size. *First sample for which p < 0.01

Click to visit stuypulse.com

The new site contains a novel CMS developed from scratch. Using a seamless interface, team members can add and edit pages and subpages to an infinite depth, create and display custom “widgets” for both sidebars, and easily update header and footer information. Integration with our existing SMF forum login system means that team members can use their forum credentials to log into the CMS.

Harvey Citron wanted a Flashy new site to show off his amazing custom guitars and basses. Keeping with the “film strip” design of his previous site, I created a general swf file that loads images dynamically from a MYSQL database. This is the first dynamic Flash app I have ever made.

I wrote a nifty little administrative interface that allows Harvey to add instruments, delete them, and modify them. He can also change any other static looking page on the site. No framework was used, just raw PHP.