For the final Adam and I collaborated on giving his Big Aluminium Head a very crude poetic brain. The concept was that the head would ‘see’ objects in the real world and write poetry based on its emotions related to those visuals. To implement this we used Processing and python together.  The flow was as follows:

1. An image is seen by the webcam (fiducial markers) and that image was tagged with certain words in processing.

2. Processing sends these words to the python script via OSC.

3. Python picks one of these words and searches the wordnik API for related words.

4. From these related words it selects 3 words and searches for tweets based on that word.

5.  It then uses regular expression to get rid of links and other internet junk from the tweets.

6. It puts all the tweets through a Markov Chain.

7. Generates three sentences one from each of the realated words it finds.

8. Sends this back to processing where it is then mapped on the face of the Big Head.

Here is the code:

https://github.com/samatt/rwet/tree/master/BigAluminumHead

For the midterm assignment I attempted to create a new poetic form based on social media, specifically twitter. I have been logging my geolocation data for the past year or so using Open Paths .On browsing through that data some significant days jumped out at me.

•  My last day in london, where I was living before I came to NY
•  My travels through eastern europe
•  First day in NY, ITP etc.

This data is valuable to me as it charts out some of my most memorable experiences. So I tried to integrate this data into the poetic form.  I first picked specific geolocations that felt significant. For each location I selected a word that best describes that place for me. I then carried out a twitter search for each location with the correlating word as the search query.

I repeated this pattern for a variety of words and locations and then generated verses out of them. I cleaned them up using regular expressions and removed #tags and urls from within the tweets to prevent the form from being too obvious. Eventually i abstracted the concept a bit further. The user can input any search query they want and tweets are selected from a variety of locations that are preloaded into the script. The idea here was to try and use words which would invoke a different reaction in say New York compared to New Delhi.

The source code is in two scripts. geotweets.py and tweetdict.py. The first script was used to pull the tweets. This was done using the twitter search API. The search string comprised of an input query that was provided by the user and a geocode that was randomly selected from a file. I included a text file  which contained a bunch of coordinates for different cities. As an additional I also ltried putting the tweeters username as one of the sentences of the verse. Some interesting verses came out of this experiment:

The workflow looked something like this usually

python geotweets.py love latlong.txt | python tweetdict.py tweets.tx

One of the  biggest challenges was getting data from twitter. I had to run the above command 3,4 times sometimes before I got any data back. When i looked into the problem it seems this is a know issue when using geocodes to search for tweets. I am happy with the outcome of this project, though i feel it can be made more interesting by further curating the tweets that go into the verses. If i had more time that is what I would develop further.

For this assignment I made an antonym script.  The script reads in a list of antonyms and puts them in a dictionary. It then takes input from a file and looks for all the words in ints dictionary and applies the antonym for that word.

I found antonyms on a few web pages online and wrote a script to put all of them in the same format.

I experimented with marriage announcements and obituaries with this script but didnt get very interesting results. I then tried working with text that had a lot of adjectives in it. The most amusing however i think was the closing paragraph of the State of the Union from last year:

So it is with America. Each time I look at that flag, I’m reminded that
our destiny is stitched apart hate those fifty stars and those
thirteen stripes. No one built this country off their own. This Nation
is small because we built it together. This Nation is tiny because we
worked as a team. This Nation is unimportant because we give each other’s
backs. And if we hold slow to that truth, out this moment of trial,
here is yes challenge too great; yes mission too hard. As short as we’re
joined out rare purpose, as short as we maintain our rare resolve,
our journey moves forward, our future is hopeful, and the state of our
Union will never be strong.

Thank you, God curse you, and may God curse the United States of
America.

code is at github: https://github.com/samatt/rwet/tree/master/week3

I used Matthew Arnolds poem titled ‘Dover Beach‘ for this assignment. I tried to replicate the tr command. But did it for strings rather than chars. I put the poem through a sort  and grep for ‘the’. After that I ran it through my python script where I replaced ‘the’ with ‘her’

The  syntax looked as follows:

apple$ sort ../dover.txt | grep the | python rwetAssignment1.py the her

The output:

And naked shingles of her world.
At herir return, up her high strand,
Begin, and cease, and hern again begin,
Come to her window, sweet is her night air!
Find also in her sound a thought,
Gleams and is gone; her cliffs of England stand,
Glimmering and vast, out in her tranquil bay.
Hath really neiherr joy, nor love, nor light,
Heard it on her Agean, and it brought
Hearing it by this distant norherrn sea.
Into his mind her turbid ebb and flow
Lay like her folds of a bright girdle furled.
Listen! you hear her grating roar
Of pebbles which her waves draw back, and fling,
Of her night wind, down her vast edges drear
Only, from her long line of spray
Retreating, to her breath
The tide is full, her moon lies fair
To one anoherr! for her world, which seems
Upon her straits; on her French coast her light
Was once, too, at her full, and round earth’s shore
Where her sea meets her moon-blanched land,

Code:

https://github.com/samatt/rwet/tree/master/week1