I continued to work with headlines and news. This time I used an API to grab the most popular headlines on and pick a random word to replace with a definition from Oxford dictionary. Sometimes the results don’t make sense but every now an then an interesting replacement occurs. Here are some examples of the output (my favorites are in bold):

Read More

RWET – Cut up 2: Sets and dictionaries

This week I continued to work with news and tried to make some lightly cut up headlines. What I ended up with is a script that reads in a whole article, splits it into headlines and body (kind of hacky), finds words unique to each article and then randomly replaces words in the headlines with these unique words. Here are some results:

I had to run this a bunch of times to get good results but I was happy with the way it created this news blending effect. I could see working more on distorting news articles.

The code is a bit sloppy this time but I was able to to make some light usage of dictionaries and sets. I found myself relying on lists a lot out of familiarity but could begin to see how other structures would be useful.


Reading and Writing – Now with python!

This week we started out with python. We were asked to make a UNIX command-like python script for text manipulation. In the previous assignment I struggled with line length and that kind of came up again. I played around with the .replace() method and escape characters. I thought that by inserting “\n” into a line it would then be considered a new line in the rest of the script but it did not seem to work that way. Then I decided to try to remove a specific list of words (in this case conjunctions) and I was able to get that working in a for-loop. Eventually I did something similar to the two columns of words I made last week.

Read More

RWET – Terminal Assignment

This week for Reading and Writing electronic set we were asked to do something creative with command line text manipulation. As a source material I started working with two thesis papers I wrote in college. One was for a Transportation Geography class that dealt with the impact of mobile phones on transit preferences and the other was for a class on Public Finance where I had written about GPS (GNSS) systems and the economic structure behind them. I’ve done a lot of work related to transportation and to a certain extend behavioral economics while at ITP so I thought going back and working with these half remembered papers would be interesting.

First I converted the files from their “.Docx” format to “.txt” to use with terminal. This worked but resulted in really long lines. Apparently in this instance the lines were broken where there had been paragraph breaks. I used the cut command to try and break up these lines into words or even sentences but had a hard time with that. Eventually I used Fold to force the paragraphs into lines 80 units long. Fold had broken the paragraphs at odd points so there were fractions of words at the beginning and end of each line. I kept playing around with the cut command and figured out that I could take the second word off of each line and come up with a list of full words that were pseudo randomly selected from each paper. I though comparing a random selection of words from each paper could be interesting to see how they differ. I guess I was curious if the topic or my writing style would stand out under this comparison. I took both lists, sorted them and pasted the two columns next to each other in a separate file. Alone this wasn’t very interesting but when I ran different grep searches for words it would sometimes yield some interesting stuff.

Grep eco

become about

become about

becomes achieve

ecosystem as

for become

incentive economic

second not

socio-economic of

was second


Grep tech

technology. of

whose technology

willing technology

with technology

with technology

world technology


I thought this was an OK application of terminal but I felt like I could have gotten more out of this if I hadn’t spent so much time struggling with the weird line lengths. I would have preferred being able to pair words from both texts in a more complete and contextual way. However I was happy that I figured out how to compress most of this process into a couple lines in terminal. Mostly it was:

fold <CapstoneEcon.txt | cut -d ‘ ‘ -f 2 | sort >AlpListwordsEcon.txt

fold <geogcap.txt | cut -d ‘ ‘ -f 2 | sort >GeogFoldSort.txt

paste AlpListwordsEcon.txt GeogFoldSort.txt >CombinedEconGeog.txt