Launched a new app for Moby, Systems inc. called Moby Trips (download from the iTunes Store). This app builds upon Simple Location Sharing by adding features such as waypoints (pins), photos, and recording your path via GPS. The app uses the same, flexible, nodejs backend which powers Simple Location Sharing.Â I’m continuing to work on this app in my free time, so any feature suggestions are welcome!
Trips can be reloaded into the map.
Your trips are saved in the history, automatically.
Moby Trips Homescreen
TwoÂ interesting challenges I ran into were the manor in which iOS’s Asset Library class handles deleted files, and keeping the server and iOS client synchronized throughout areas of patchy cell service. Next time I may try to use Meteor’s Open DDP protocol to overcome this challenge, as REST wrangling became much more trouble than expected.
Future improvements include synchronization of the data across multiple devices, a more comprehensive HTML5 viewer, the ability to enable and disable live streaming, and exporting of your data to various formats such as GPX and Open Street Maps.
Moby Simple Location Sharing is an app for iOS and Android which helps you share your live location with friends. This project contains several components, including a node.js backend, two apps, and two websites in HTML5 (the viewer app and the homepage). Try it out from Google Play or the iTunes Store!
Screenshot of the Moby Simple Location Sharing iOS app.
Iâ€™ve been working on a side project,Â Relisten. Â This is a web application for Facebook and Spotify users that aggregates listening history into weekly playlists that can be â€œrelistenedâ€ through Spotify. The app came from my usage of Last.fm and the realization that no such tools exist for data on Facebook.
Handlebars is currently the onlyÂ templateÂ engine integrated with Meteor. Notably absent from Handlebar’s feature set is the ability to pass context between blocks. Furthermore, the scope of a parent block is absolutely inaccessible from the child’s context (no super, no this.parent).
One can get around any restriction imposed by Handlebar’s markup by writing a custom block helper. The theory behind block helpers is to keep logic separate from the design markup. Every block in Handlebars (if, each, with, list…) calls a block helper behind the scenes. The block helper is a function which takes the block’s HTML content as an argument, as well as any arguments listed in the block’s declaration, and uses these arguments to return a string which is rendered in place of the block. It’s sort of like a macro.
In the case of a tree, using a block helper is absolutely the way to go.
I spent the final days of the summer exploring theÂ FourierÂ transform with Nicolas Avrutin by writingÂ an image <-> audio converterÂ in Python/Numpy. This was more an exercise in learning Numpy, but the result is pretty cool:
This is the spectrograph of an 18 second audio file generated by our script. The ghostly vertical lines are due to interpolation issues, translating an image sampled at a certain rate into an image with far fewer pixels columns than necessary. Color represents intensity of the sound.
We implemented a full color version by using a three channel WAV file (usually used for surround sound or secondary language purposes). We had to write a custom spectrograph generator, as SOX will not combine the channels into a color image. Here’s SOX’s spectrograph:
And our own spectrograph:
Work has been halted on this project while Nick and I deal with school, but this could be used to embed a faint image into music.
I wrote a nifty class which wraps URLLib behind an interface for RESTful web services. The result is a set of one-liners that can be used to import data from compatible APIs.
The goal of this project is to easily import and process data from disparate APIs without needing to read thousands of pages of poorly written documentation. Just import the models you need and hit the ground running.
Think of it as Yahoo Pipes, but local and in Python instead of a graphical programming language.
My first assignment at Cooper Union was to make something in AutoCad, a program used by engineers worldwide to draft architectural plans and machine parts. I decided to write a script in Python that generates the lines of an L-System fractal (as described in The Computational Beauty of Nature by Flake) I used the SDXF library for Python to write the AutoCad DXF file format. You can check out my source code here ->Â autol.py
Back when I was working on my FRC robotics team’s website, I wrote a simple page to display all of the subversion repositories related to my team. Realizing that there were no existing simple subversion repository index page generators, I decided to make one withÂ human readable code and post it here.
See it in action! Â http://svn.jperr.com
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.Â 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 have completed a research paper on the how integrated information behaves during artificial evolution of neural networks in Polyworld. Mad props to Virgil Griffith (with his integrated information calculator) and Larry Yeager (author of the aforementioned Polyworld) for their incredible insight and willingness to help out a mere high school student.
Download the full paper as PDF (400k)
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 (Ï†). Observed data is analyzed, interpreted, and compared to more con- ventional properties of the artificial neural network. The data analysis shows that Ï† increases over evolutionary time and is therefore promising as a heuristic measure.
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