Yes, I work at Metro when I’m not teaching at CalArts or making experimental art. I am responsible for the Python/Django code that makes Metro.net go.  I’ve been working on this for two+ years with an amazing team of five. It’s all part of a larger project to get Angelenos out of their cars and into buses and trains.

We’ve received plenty of awards, but this one feels good enough to share. We’re taking the Red Line subway to the festivities. Hope to see you there!

Need to make the switch to public transportation but feel like you have no understanding as to how the Metro system works? Metro’s website is a ridiculously user-friendly portal into the world of public transportation. Use it to plan trips, keep updated on traffic and even check out the Metro Art program.

This one is about chromatic aberration:  those phantom colors that bleed away from their source into neighboring colors. He explained that chromatic aberrations are generated by all lenses including camera lenses, raindrops, warm air pockets, even the retina of the eye. I guess it’s a form of unweaving the rainbow, of splitting white light into constituent colors when it passes through a substrate of different density.

It’s worth noting that this artifact comes from the optics: both digital and film cameras are equally susceptible. But maybe that’s a bit misleading as digital cameras often have different optics that film cameras. The smaller imaging surface means less glass.

He showed an example image looking out windows, black aluminum muntins against the snow. He described the blue and purple stripes in the dark area of the muntin. It was subtle, and to be honest I didn’t see the color he was talking about and started to worry that I was being set up. The other students said they could see it, but they had presumably been looking at similar images for the past semester.

Muntins on Flickr, with some rights reserved by Ted and Jen

Anyway, I believe it now. I’m still waiting to see it directly with my eyes, though I have seen examples of this in images shot by cameras with cheap optics. Here are a couple of examples.

1. Note the soft color shadows that you will find in the vicinity of hard lines in an image in these examples from Wikipedia:

Purple fringing (detail)

Chromatic aberrations around highlights

The script requires Python and mencoder.

Inspiration taken from Nic Collin’s Devil’s Music. Thanks for that, Nic!

Here’s what you’ll need to do.

1. Use QuickTime to export your video as still PNG frames. Use sequential naming, no spaces. Put these in a folder by themselves and remember the folder name.
2. Copy this script into a folder above the one containing the images. Call it “mersenne.py”
3. Try it out: open terminal, cd to the directory containing you script and type: python mersenne.py
4. You’ll get an error suggesting that you enter some information. Tell mersenne where it can find your still PNG images.
5. Specify a range of frame sequences to retain before desequencing. minimum 4 max 12 works for me!
6. open your preview file with this: mplayer review.avi

Here is that example: Mersenne Devil twister video

import os, sys
import random
from dircache import listdir
from pprint import pprint

"""
a. gen a list of images in sequence
b. gen a list of random numbers within a range random.randint(seq_minlen, seq_maxlen)
c. loop over random numbers list using the values to grab a slice of the image list. save as a tuple.
d. add the slice to a new list
e. randomize this new list
f. dump the list of tuples into a new list
g. make a new image sequence from this list and prepare a preview movie
"""

if len(sys.argv) != 5:
    print 'Usage: mersenne.py    
'
    sys.exit(1)

dir = sys.argv[1]
seq_minlen=int(sys.argv[2])
seq_maxlen=int(sys.argv[3])
outmov=sys.argv[4]

imgexts = {
    "jpg": "image/jpeg",
    "jpeg": "image/jpeg",
    "png": "image/png",
    "gif": "image/gif"
    }

def isimage(filename):
    """true if the filename's extension is in the content-type lookup"""
    filename = filename.lower()
    return filename[filename.rfind(".")+1:] in imgexts

images = [f for f in listdir(dir) if isimage(f)]
imgct = len(images)

b.

randlist=[]
for i in range(0,imgct):
    randy = random.randint(seq_minlen, seq_maxlen)
    randlist.append(randy)

c.

dup = images[:]
bin = []
while dup:
    randy = random.randint(seq_minlen, (seq_maxlen+1))
    tup = dup[0:(randy+1)]
    del dup[0:(randy+1)]
    bin.extend([tup])

e.

random.shuffle(bin)

f.

fin = []
listfile = "./list.txt"
twister = open(listfile, "w")
for list in bin:
    for img in list:
        pl = "%s/%s\n" %(dir,img)
        twister.write(pl)
        fin.extend(pl)
twister.close()

print file(listfile).read()

g.

cmd = "mencoder mf://@%s -vf scale=320:240 -mf w=64:h=48:fps=25:type=png -ovc lavc -lavcopts vcodec=mpeg4:mbd=2:trell -oac copy -o %s" %(listfile,outmov)
os.system(cmd)

this is wood

My point is that your understanding of this wood can be broken down into lists of cues based on your past experience of wood floors. Your tactile experience, olfactory experience, kinetic experience. This is a pretty unique floor, the timbers culled from some long gone forest. You probably haven’t interacted with too many of these, but you have enough experience to tell me a lot about it without actually interacting with this specific floor.

Now, imagine its your job to represent this floor. How will you do it? You have many tools available, narrative, images, persuasion, etc. At the most basic level your job is to convey enough of these cues to say ‘wood.’

wood

The digital media engineer is motivated by the economy of means. He wants to use the least amount of information possible to communicate wood-ness to you without distracting you with the means of that representation. The engineer’s job is to reduce these as much as possible. Information is the means.

How much information has something to do with the subject of your representation. Is this wood part of the foreground or background? Is it subject or object?

The Lossless Couple

Thanks, friends, for coming out to the screening. It was a blast!

highway stripe

highways stripes at 65mph seem to know something about economic hard times

look carefully at the water

it may appear to be a normal beach snap but look again. it’s actually 722 vertical stripes of video lined up horizontally along the x-axis. there is about 24 seconds of time captured here.

21 seconds in front of the fountain. the fountain in the mall at hollywood and highland is popular with tourists. here the z dimension (time) is swapped with x (width). starting with the leftmost vertical slice we scan across the x dimension to produce a smooth sequence of frames.