My work has been developing visualizations that utilize the new physical display in different ways. The first visualizes air traffic over four major airports in the US, drawing moving lines to indicate aircraft taking off and landing. The second, entitled Order/Disorder, displayed below aggregates a number of sources for natural disaster information and draws the disasters as “tears in the fabric” of the world. Lastly, I am working to create software that reacts to the environment and responds by flashing a series of animations developed by Marshall Reese.

A program in Processing communicates with the hardware and serves as a hub accepting drawing commands from other software via OSC. A number of scripts in Python perform data aggregation, scraping, parsing and animation for the flight tracker. A patch in Pd performs sound analysis and drives the canvases through OSC.

“Order/Disorder is a software visualization of destructive and restorative forces in the world. The software runs on an electronically-controlled tapestry of woven fiberoptic threads created by New York-based artists Ligorano/Reese. Two computer programs, named order and disorder, modify the tapestry throughout the day in response to natural and man-caused events such as earthquakes, biohazards, and aircraft accidents. Order seeks to weave a rainbow-gradated pattern representing peace and wholeness while Disorder seeks to destroy and unravel the tapestry by “tearing” the fabric and weaving in aberrant threads.”

Below are a few useful tools:

Princeton’s Wordnet is a massive linguistic database containing not just definitions, but how words are related to each other.

The Python Natural Language Toolkit, which provides tools for parsing and understanding natural language semantics.

The Carnegie Mellon Pronouncing Dictionary, which breaks words down into their phonemes, providing information regarding pronouncing, rhyming, and syllable counts.

Having found no simple resource to provide syllable counts for common words, I want to share a quick solution I wrote in Python, which uses the CMU dictionary. Simply download the dictionary to the same directory as this script, naming it cmu_pron.txt.

f = open("cmu_pron.txt")

f = open("cmu_pron.txt")
lines = f.readlines()
f.close()
words = {}

for line in lines:
	pieces = line.split()
	if pieces[0] == ";;;":
		continue
	words[pieces[0]] = pieces[1:]

def num_syllables(word):
	global words
	key = word.upper()
	plist = words[key]
	return len(filter(lambda c: c in ("0","1","2"),"".join(plist)))

print "alphabet: %s" % num_syllables("alphabet")

The result for alphabet is indeed 3. There are probably more efficient ways to do this, but it gets the job done.