Author: nannon

nannon-parrish

What stuck with me about Parrish's talk was the overall theme of the exploration of uncertainty. It's something thats commonly done in science/math areas, yet not so much in the humanities. Parrish gives many options to exploring the unknown, from the most literal way--for example, mapping out semantic space through n-grams, and finding the empty spaces--to more abstract ways, such as creating new words through splicing existing words together and generating new definitions. It was something I thought about a lot as I was working on the book project as well, since I felt pulled towards making something more generative/abstract, yet ultimately made something that explored connections between existing language. These two sides of the spectrum feel like the two types of infinity, one that exists between numbers and goes towards the infinitely small, and the other one that exists towards infinite largeness.

nannon-book

Reddit Bible

About

In short, the Reddit Bible compares questions found on 8 advice boards of reddit with interrogative sentences found in the bible.

After much wrangling with religious-text based concepts, I decided to create a "Reddit Advice" board by answering biblical questions with Reddit content. I scraped the Bible for interrogative sentences, and found similarities in questions scraped from Reddit advice boards, using sentence embedding. From there, I wanted to to answer the Bible Questions with Markov-chain generated answers based on the thread responses of the respective Reddit questions.

Unexpectedly, I loved the results of the "similar" reddit and bible questions--the seeming connections between the two almost hint at some sort of relation between the questions people are asking now and in Biblical times. Though I did go through with the Markov chained responses, the results were a bit jumble-y and seemed to take away from what I liked about the juxtaposed questions. Ultimately, I made the decision to cut the Markov chains, and to highlight the contrast in pairs of questions, as well as how similar the computer thinks the question pairs are.

Inspiration

I originally wanted to generate some sort of occult text, ie. Chinese Divination. I ended up pivoting to the more normative of religious texts, the Bible to be specific, since I have a lot of personal experience with this one. Prior to the "reddit advice" board, I actually had the opposite idea, of making a "christian advice" board where I would gather 4chan questions, and answer them with markov chain generated responses based on real christian advice forums. I scraped a christian advice forum, but the results were too few and inconsistent, so I knew I had to pivot a bit. That's when I flipped the idea and decided to reverse it to answering bible questions with reddit data. (4chan's threads were a little too inconsistent and lacking compared with reddits thousand-count response threads).

If anyone ever wants buttloads of  responses from a christian forum:  https://github.com/swlsheltie/christian-forum-data

Process

Once I solidified my concept, it was time to execute, one step at a time.

  1. Getting matching question pairs from Reddit and the Bible
    1. Getting questions from Reddit
      1. Reddit has a great API called PRAW
      2. Originally I only scraped r/advice, but towards the end, I decided to bump it up and scrape 8 different advice subreddits: r/advice, r/internetparents, r/legal_advice, r/need_a_friend, r/need_advice, r/relationship_advice, r/tech_support, r/social_skills
        1. Using PRAW, i looked at top posts of all time, with no limit
      3. r/Advice yielded over 1000 question sentences, and the other advice subreddits ranged more or less around that number.
      4. Lists with the lists of scraped questions:
        1. https://github.com/swlsheltie/reddit_bible/tree/master/lists%20of%20reddit%20questions
      5. # REDDIT QUESTIONS X BIBLE QUESTIONS
# ENTER A SUBREDDIT
#   FIND ALL QUESTIONS
# INFERSENT WITH BIBLE QUESTIONS 
# GET LIST OF [DISTANCE, SUBREDDIT, THREAD ID, SUBMISSION TEXT, BIBLE TEXT]
#   HIGHLIGHT SPECIFIC QUESTION
 
 
# load 400 iterations 
# format [distance, subreddit, reddit question, bible verse, bible question]
 
 
 
# get bible questions
from final_bible_questions import bible_questions
from split_into_sentences import split_into_sentences
 
# write to file
data_file= open("rqbq_data.txt", "w")
rel_advice_file = open("rel_advice.txt", "w")
legal_advice_file = open("legal_advice.txt", "w")
tech_support_file = open("tech_support.txt", "w")
need_friend_file = open("needfriend.txt", "w")
internetparents_file = open("internetparents.txt", "w")
socialskills_file = open("socialskills.txt", "w")
advice_file = open("advice.txt", "w")
needadvice_file = open("needadvice.txt", "w")
 
files = [rel_advice_file, legal_advice_file, tech_support_file, need_friend_file, internetparents_file, socialskills_file, advice_file, needadvice_file]
 
# libraries
import praw
import re
 
# reddit keys
reddit = praw.Reddit(client_id='---',
                     client_secret='---',
                     user_agent='script: http://0.0.0.0:8000/: v.0.0(by /u/swlsheltie)')
 
# enter subreddit here ------------------------------------------------------------------------------------action required
list_subreddits = ["relationship_advice", "legaladvice", "techsupport", "needafriend", "internetparents", "socialskills", "advice", "needadvice"]
 
relationshipadvice = {}
legaladvice={}
techsupport={}
needafriend={}
internetparents={}
socialskills={}
advice={}
needavice={}
 
subreddit_dict_list=[relationshipadvice, legaladvice, techsupport, needafriend, internetparents, socialskills, advice, needavice]
 
relationshipadvice_questions = []
legaladvice_questions =[]
techsupport_questions=[]
needafriend_questions=[]
internetparents_questions=[]
socialskills_questions=[]
advice_questions=[]
needavice_questions=[]
 
questions_list_list=[relationshipadvice_questions, legaladvice_questions, techsupport_questions, needafriend_questions, internetparents_questions, socialskills_questions, advice_questions, needavice_questions]
 
 
# sub_reddit = reddit.subreddit('relationship_advice')
 
for subreddit in list_subreddits:
    counter=0
    print(subreddit)
    i = list_subreddits.index(subreddit)
    sub_reddit = reddit.subreddit(subreddit)
    txt_file_temp = []
 
    for submission in sub_reddit.top(limit=1000):
        # print(submission)
        print("...getting from reddit", counter)
        submission_txt = str(reddit.submission(id=submission).selftext.replace('\n', ' ').replace('\r', ''))
        txt_file_temp.append(submission_txt)
        counter+=1
    print("gottem")
    for sub_txt in txt_file_temp:
        print("splitting")
        sent_list = split_into_sentences(sub_txt)
 
        for sent in sent_list:
            print("grabbing questions")
            if sent.endswith("?"):
                questions_list_list[i].append(sent)
    print("writing file")
    files[i].write(str(questions_list_list[i]))
    print("written file, next")
 
# for list_ in questions_list_list:
#     print("\n")
#     print(list_subreddits[questions_list_list.index(list_)])
#     print(list_)
#     print("\n")
    2. Getting questions from the bible
      1. Used the King James Version, because of this awesome text file, that only has the text in it (no verse numbers, etc) (would bite me in the ass later on)
      2. Found some code on Stack Overflow that allowed me to get a list of the sentences in the bible
      3. Originally used RITA to get the question sentences, then towards the end (since rita --> python was too much of a hassle), I just went through found all sentences that ended with a "?". 
        1. file = open("bible.txt", "r")
empty= open("bible_sent.txt", "w")
bible = file.read()
 
from nltk import tokenize
import csv
import re
 
alphabets= "([A-Za-z])"
prefixes = "(Mr|St|Mrs|Ms|Dr)[.]"
suffixes = "(Inc|Ltd|Jr|Sr|Co)"
starters = "(Mr|Mrs|Ms|Dr|He\s|She\s|It\s|They\s|Their\s|Our\s|We\s|But\s|However\s|That\s|This\s|Wherever)"
acronyms = "([A-Z][.][A-Z][.](?:[A-Z][.])?)"
websites = "[.](com|net|org|io|gov)"
 
# final_output=[]
 
def split_into_sentences(text):
    text = " " + text + "  "
    text = text.replace("\n"," ")
    text = re.sub(prefixes,"\\1",text)
    text = re.sub(websites,"\\1",text)
    if "Ph.D" in text: text = text.replace("Ph.D.","PhD")
    text = re.sub("\s" + alphabets + "[.] "," \\1 ",text)
    text = re.sub(acronyms+" "+starters,"\\1 \\2",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]" + alphabets + "[.]","\\1\\2\\3",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]","\\1\\2",text)
    text = re.sub(" "+suffixes+"[.] "+starters," \\1 \\2",text)
    text = re.sub(" "+suffixes+"[.]"," \\1",text)
    text = re.sub(" " + alphabets + "[.]"," \\1",text)
    if "”" in text: text = text.replace(".”","”.")
    if "\"" in text: text = text.replace(".\"","\".")
    if "!" in text: text = text.replace("!\"","\"!")
    if "?" in text: text = text.replace("?\"","\"?")
    text = text.replace(".",".")
    text = text.replace("?","?")
    text = text.replace("!","!")
    text = text.replace("",".")
    sentences = text.split("")
    sentences = sentences[:-1]
    sentences = [s.strip() for s in sentences]
    return (sentences)
    # final_output.append(sentences)
 
print(split_into_sentences(bible))
 
# with open('christian_forums1.csv', newline='') as csvfile:
#     reader = csv.reader(csvfile)
#     for row in reader:
#         for i in range(2): 
#             if (i==1) and (row[i]!= ""):
#                 input_txt = row[0]
#                 # print(text)
#                 # list_sent = tokenize.sent_tokenize(text)
#                 # sentences.append(list_sent)
#                 list_sent= split_into_sentences(input_txt)
#                 final_output.append(list_sent)
 
# list_sent = split_into_sentences(bible)
# for sent in list_sent:
#     # print(sent)
#     empty.write(sent+"\n")
# empty.close()
# print(list_sent)
      4. Results: around 1000+ Bible questions, find them here
    3. Get matching pairs!!! 
      1. My boyfriend suggested that I use sentence embeddings to find the best matching pairs. This library is the one i used. It was super easy to install+use! 4.75 Stars
      2. Infersent pumps out a 2d matrix containing each vector of each sentence in the list of sentences that you provide. I gave it the list of bible questions, then ran a loop to get the embeddings of all 8 subreddit question lists.
      3. Then another loop with matrix multiplication to get each matrix with the distances between the bible vs. [respective] subreddit sentences.
      4. Since the matrix contains such precise information about how close two sentences, are I wanted to visualize this data. I saved the "distances," and used circle size to show how close they are.
        1. I didn't have that much time to visually design the book, which I regret, and the circle sizes were obviously not that communicative about what they represented. I ended up including a truncated version of the distances on each page.
    4. Markov chaining the responses
        1. Now that I had my reddit questions to their bible question counterparts, I wanted to get a mishmash of the respective response threads of the submissions that the questions came from.
        2. submission = reddit.submission(pair["thread_id"])
for top_level_comment in submission.comments:
    if isinstance(top_level_comment, MoreComments):
        continue
    # print(top_level_comment.body)
    comment_body.append(top_level_comment.body)
 
comment_body_single_str="".join(comment_body)
comment_body = split_into_sentences(comment_body_single_str)
 
# print(" ".join(comment_body))
text_model = markovify.Text(comment_body)
for i in range(10):
    print(text_model.make_sentence())
        3. This was an example of my result:

          1.  Submission Data: {'thread_id': '7zg0rt', 'submission': 'Deep down, everyone likes it when someone has a crush on them right?   So why not just tell people that you like them if you do? Even if they reject you, deep down they feel good about themselves right? '}

            Markov Result: Great if you like it, you generally say yes, even if they have a hard time not jumping for joy.

            Us guys really do love it when someone has a crush on them right?

            I think it's a much more nuanced concept.

            In an ideal world, yesDepends on the maturity of the art.

            But many people would not recommend lying there and saying "I don't like you", they would benefit from it, but people unequipped to deal with rejection in a more positive way.

            If it's not mutual, then I definitely think telling them how you feel, they may not be friends with you or ignore you, or not able to answer?

            I've always been open about how I feel, and be completely honest.

            In an ideal world, yesDepends on the maturity of the chance to receive a big compliment.

            So while most people would argue that being honest there.

            I think that in reality it's a much more nuanced examples within relationships too.

        4. While this was ok, I felt like the Markov data just probably wasn't good (extensive) enough to sound that different from the source. It didn't seem like this would add anything to the concept, so I decided to cut it.
    5. Organizing the matching pairs 
      1. The part that I probably had the most difficulty with was trying to organize the lists into [least distant] to [most distant] pairs. Seemed really easy in my head, but for reason, I just had a lot of trouble executing.
        1. However, it paid off in the end, as I was able to get random pairs from only the closest related 100 pairs from each of the 8 subreddit/bible lists.
        2. import numpy
import torch
import json
 
 
from rel_advice import rel_advice
from legal_advice import legal_advice
from techsupport import techsupport
from needfriend import needfriend
from internetparents import internet_parents
from socialskills import socialskills
from advice import advice
from needadvice import needadvice
 
 
from final_bible_questions import bible_questions
from bible_sent import bible_sentences
 
final_pairs= open("final_pairs.txt", "w")
# final_pairs_py= open("final_pairs_py.txt", "w")
 
with open("kjv.json", "r") as read_file:
    bible_corpus = json.load(read_file)
 
rqbq_data_file = open("rqbq_data.txt", "w")
subreddit_questions = [rel_advice, legal_advice, techsupport, needfriend, internet_parents, socialskills, advice, needadvice]
list_subreddits = ["relationship_advice", "legaladvice", "techsupport", "needafriend", "internetparents", "socialskills", "advice", "needadvice"]
 
bible_verses=[]
# for 
 
 
from models import InferSent
V = 2
MODEL_PATH = 'encoder/infersent%s.pkl' % V
params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,
                'pool_type': 'max', 'dpout_model': 0.0, 'version': V}
 
print("HELLO", MODEL_PATH)
infersent = InferSent(params_model)
infersent.load_state_dict(torch.load(MODEL_PATH))
 
W2V_PATH = 'dataset/fastText/crawl-300d-2M.vec'
infersent.set_w2v_path(W2V_PATH)
 
with open("encoder/samples.txt", "r") as f:
    sentences = f.readlines()
 
infersent.build_vocab_k_words(K=100000)
infersent.update_vocab(bible_sentences)
 
print("embed bible")
temp_bible = bible_questions 
embeddings_bible = infersent.encode(temp_bible, tokenize=True)
normalizer_bible = numpy.linalg.norm(embeddings_bible, axis=1)
normalized_bible = embeddings_bible/normalizer_bible.reshape(1539,1)
 
 
 
 
pairs = {}
for question_list in range(len(subreddit_questions)):
    pairs[list_subreddits[question_list]]={}
 
    print("setting variables: ", list_subreddits[question_list])
    temp_reddit = subreddit_questions[question_list] #TRIM THESE TO TEST SMALLER LIST SIZES
 
 
    print("embed", list_subreddits[question_list], "questions")
    embeddings_reddit = infersent.encode(temp_reddit, tokenize=True)
 
    print("embed_reddit dim: ",embeddings_reddit.shape)
    print("embed_bible dim: ", embeddings_bible.shape)
 
    normalizer_reddit = numpy.linalg.norm(embeddings_reddit, axis=1)
 
 
    print("normalizer_reddit dim: ", normalizer_reddit.shape)
    print("normalizer_bible dim: ", normalizer_bible.shape)
 
    temp_tuple = normalizer_reddit.shape
 
    normalized_reddit = embeddings_reddit/normalizer_reddit.reshape(temp_tuple[0],1)
 
 
    print("normalized_reddit dim:", normalized_reddit)
    print("normalized_bible dim:", normalized_bible)
 
 
    print("normed normalized_reddit dim: ", numpy.linalg.norm(normalized_reddit, ord=2, axis=1))
    print("normed normalized_bible dim: ", numpy.linalg.norm(normalized_bible, ord=2, axis=1))
 
    reddit_x_bible = numpy.matmul(normalized_reddit, normalized_bible.transpose())
    print("reddit x bible", reddit_x_bible)
 
    matrix = reddit_x_bible.tolist()
    distances = []
    distances_double=[]
    distances_index = []
    bible_indeces=[]
 
 
    for reddit_row in matrix:
        closest = max(reddit_row)
        bible_indeces.append(reddit_row.index(closest))
        distances.append(closest)
        distances_double.append(closest)
        cur_index = matrix.index(reddit_row)
 
        final_pairs.write("\n-------\n" + "distance: "+ str(closest)+"\n" +str(list_subreddits[question_list])+"\n"+subreddit_questions[question_list][cur_index]+"\n"+ bible_questions[reddit_row.index(closest)]+"\n-------\n")
 
 
    distances.sort()
    distances.reverse()
    for distance in distances: 
        inde_x = distances_double.index(distance)
        distances_index.append(inde_x)
 
    pairs[list_subreddits[question_list]]["distances"]=distances
    pairs[list_subreddits[question_list]]["distances_indexer"]=distances_index
    pairs[list_subreddits[question_list]]["bible_question"]=bible_indeces
    # print(pairs)
rqbq_data_file.write(str(pairs))
rqbq_data_file.close()
 
 
 
# for pair in pairs: 
#     # print( "\n-------\n", reddit_questions[pair],"\n", bible_questions[pairs[pair]], "\n-------\n")
#     # export_list.append([max_nums[counter], pair, pairs[pair], reddit_questions[pair],  bible_questions[pairs[pair]]])
#     counter+=1
 
# # final_pairs_py.write(str(export_list))
# # final_pairs_py.close()
# final_pairs.close()
 
        # nums.append(closest)
        # max_nums.append(closest)
        # for distance in max_nums:    
        # row = nums.index(distance) #matrix row 
        # column = matrix[row].index(distance)
        # pairs[row]= column
        # pairs[list_subreddits[question_list]][closest]={}
 
        # reddit_bodies.append()
 
# export_list = []
#     nums=[]
#     max_nums = []
#         max_nums.sort()
#     max_nums.reverse()
 
 
# load 400 iterations 
# format [distance, subreddit, reddit question, bible verse, bible question]
 
# build dictionary in loop, and keep list of min distances 
 
# final_pairs.write(str(pairs))
 
# counter = 0
 
# bible_x_reddit = numpy.matmul(embeddings_bible, reddit_trans)
# print(bible_x_reddit)
  2. Basil.js
    1. Using Basil's CSV method, I was able to load pair data into the book. 
      1. from rqbq_data import rqbq_dictionary
from bibleverses import find_verse
import random
import csv
 
 
from rel_advice import rel_advice
from legal_advice import legal_advice
from techsupport import techsupport
from needfriend import needfriend
from internetparents import internet_parents
from socialskills import socialskills
from advice import advice
from needadvice import needadvice
 
from final_bible_questions import bible_questions
 
 
 
# print(len(rqbq_dictionary))
# print(rqbq_dictionary.keys())
 
list_subreddits = ["relationship_advice", "legaladvice", "techsupport", "needafriend", "internetparents", "socialskills", "advice", "needadvice"]
subreddit_questions = [rel_advice, legal_advice, techsupport, needfriend, internet_parents, socialskills, advice, needadvice]
 
 
write_csv=[]
 
def getPage():
    subreddit_index=random.randint(0,7)
    subreddit = list_subreddits[subreddit_index]
    print("subreddit: ", subreddit)
    length = len(rqbq_dictionary[subreddit]["distances"])
    print("length: ", length)
    random_question = random.randint(0,500) #SPECIFY B AS CUT OFF FOR REDDIT/BIBLE ACCURACY. 1=MOST ACCURATE, LENGTH-1 = LEAST ACCURATE 
    print("random question num: ", random_question)
    print("distance of random question: ", rqbq_dictionary[subreddit]["distances"][random_question])
    print("index of random question: ", rqbq_dictionary[subreddit]["distances_indexer"][random_question])
    index_rand_q=rqbq_dictionary[subreddit]["distances_indexer"][random_question]
    index_rand_q_bible = rqbq_dictionary[subreddit]["bible_question"][index_rand_q]
    # print(index_rand_q, index_rand_q_bible)
    print("question: ", subreddit_questions[subreddit_index][index_rand_q])
    print("verse: ", bible_questions[index_rand_q_bible])
    verse = find_verse(bible_questions[index_rand_q_bible])
    write_csv.append([rqbq_dictionary[subreddit]["distances"][random_question], subreddit, subreddit_questions[subreddit_index][index_rand_q], verse, bible_questions[index_rand_q_bible]])
# getPage()
 
for i in range(15):
    getPage()
 
with open('redditxBible.csv', 'w', newline='') as f:
        writer = csv.writer(f)
        writer.writerow(["distance", "subreddit", "reddit_question", "verse", "bible_question"])
        writer.writerows(write_csv)
      2. Example of one of the books' CSV: redditxBible
    2. As said earlier, not having the Bible verse data was a pain when I realized it would be nice to have the verses. So I had to run each bible question in this code to get the verses: 
      1. import json
 
with open("kjv.json", "r") as read_file:
    bible_corpus = json.load(read_file)
 
sample = " went up against the king of Assyria to the river Euphrates: and king Josiah went against him; and he slew him at Megiddo, when he had seen him."
 
def find_verse(string):
    for x in bible_corpus["books"]:
        for chapter  in x["chapters"]: 
            # print(chapter["verses"])
            for verse in chapter["verses"]:
                if string in verse["text"]:
                    return (verse["name"])
    3. Designing the book
      1. Unfortunately, I didn't save many iterations from my design process, but I did play with having two columns, and other ways of organizing the text+typography.
    5. I had used Basil.js before in Kyu's class last year, so that was really helpful in knowing how to auto-resize the text box sizes.
      1. That way, I was able to get exact distances between all the text boxes.
    6.  I had some trouble with rotating the text boxes and getting the locations after rotation.
    7. The circle was drawn easily by mapping the distance between sentences to a relative size of the page.
Examples

00-nannon

01-nannon

02-nannon

03-nannon

05-nannon

Thoughts

Overall, i really enjoyed making this book. This project was super interesting in that I feel like I really had to apply more programming knowledge than previous projects in order to combine all the parts to get fully usable code. They couldn't only work disparately, they had to able to work all together too. Piping all the parts together was definitely the toughest part. I only wish I had more time to design the book, but i will probably continue working on that onwards.

See a list of ALL (8000+) the pairs here

nannon-chaine-justaline

Augmented Reality Fun With Friends Treasure Hunt

by nannon & chaine

How it works:

  • A two player game.
  • Player 1 starts by "hiding" coins around the area. Coins can be drawn anywhere.
  • Once coins are all hidden, switch players
  • Player 2 now needs to find all the coins
  • Player 2 finds the coins by circling them.
  • Game ends when all coins are found.

nannon-LookingOutwards03

Seated Catalog of Feelings

I came across this exhibit recently at the Cooper Hewitt, and loved how simple yet exciting this experiential piece was. It is a solo experience, yet not isolated, as the artists Eric Gunther + Sosolimited carefully show you what the user is experiencing through communicative + artful projections.

In these disembodied days, where the majority of the experiences we're tuned into happen from the neck up, I am attracted to things that remind us to listen to the world with our bodies.

This piece is not only fun + whimsical, but also smart because it relinquishes so much to the viewer. It relies on the viewers' own imagination to create for themselves in their heads the feeling of "a cricket rubbing its arms together, or "making love to a snail on a bicycle seat." With the piece's carefully crafted physical + auditory sensations, it asks the viewer to take the extra step by placing themselves in the context of a given text. The text is often ambiguous enough that the viewer needs to jump to an immediate conclusion about what the sentence means (in the case of the snail example, are you a human, snail, or the bicycle seat?)

However, what I think makes the piece go above and beyond is the consideration for creating a visualization that outside/passing participants can also see. It extends the experience just a little bit (and the viewer themselves cannot see this), so that the experience remains vague, yet enticing.

https://medium.com/sosolimited/organized-vibrations-db2c782534c6

nannon-Body

Face Face Revolution 

Vimeo

(sadly the "levels" label disappears!!)

 
 
 

-----
GIF

 
 
 

----
Still

 
 
 
 

Though I was super impressed/awed by all the examples shown in class of digital interactive artworks, I personally had a hard time coming up with an idea for this project. My DDR concept still seems a bit boring to me, but I'm glad I got it to work in a reasonable amount of time (relative to how stressed I was about previous assignmentS). The game itself is mildly fun, if a little hard. In coming up with this concept, I wanted to make sure I wasn't scaling out of proportion, so I looked at the templates carefully first before trying to figure out what to do, which really helped me scope down my project. Moving forwards, if this project were to be improved, I think it'd be really fun to try to get people to contort their faces to match what comes up in the grid--though I can imagine that being 1) really hard to play and 2) needing to randomize the grid in a really careful way i.e. the mouth is never above the eyes.

 
 
 
 

//
// a template for receiving face tracking osc messages from
// Kyle McDonald's FaceOSC https://github.com/kylemcdonald/ofxFaceTracker
//
// 2012 Dan Wilcox danomatika.com
// for the IACD Spring 2012 class at the CMU School of Art
//
// adapted from from Greg Borenstein's 2011 example
// http://www.gregborenstein.com/
// https://gist.github.com/1603230
//
import oscP5.*;
OscP5 oscP5;
 
 
PFont intersect50;
PFont intersect25;
 
 
//game bools
boolean pressplay =false;
boolean gameOver = false;
 
int level;
 
// num faces found
int found;
 
// pose
float poseScale;
PVector posePosition = new PVector();
PVector poseOrientation = new PVector();
 
 
 
 
 
// gesture
float mouthHeight;
float mouthWidth;
float eyeLeft;
float eyeRight;
float eyebrowLeft;
float eyebrowRight;
float jaw;
float nostrils;
 
 
//mouth, left eye, right eye, nose
String[] parts = {"mouth", "left eye", "right eye","nose"};
int partsActive; 
 
// grid variables
int[][] grid = new int[10][10];
 
int gridNum = 2;
 
int gridSize = 700;
int margin = 100;
 
int sqSize;
int randX;
int randY;
int points;
//timer
 
int time;
int wait = 8000;
float countdown;
 
 
void setup() {
  // Uncomment the following two lines to see the available fonts 
  //String[] fontList = PFont.list();
  //printArray(fontList);
  //String[] fontList = PFont.list();
  //for (int i =300;i<700; i++) { // println(fontList[i]); //} intersect50 = createFont("IntersectB44Solid", 120); intersect25 = createFont("IntersectB24", 30); translate(0,0); size(700,700); frameRate(30); oscP5 = new OscP5(this, 8338); oscP5.plug(this, "found", "/found"); oscP5.plug(this, "poseScale", "/pose/scale"); oscP5.plug(this, "posePosition", "/pose/position"); oscP5.plug(this, "poseOrientation", "/pose/orientation"); oscP5.plug(this, "mouthWidthReceived", "/gesture/mouth/width"); oscP5.plug(this, "mouthHeightReceived", "/gesture/mouth/height"); oscP5.plug(this, "eyeLeftReceived", "/gesture/eye/left"); oscP5.plug(this, "eyeRightReceived", "/gesture/eye/right"); oscP5.plug(this, "eyebrowLeftReceived", "/gesture/eyebrow/left"); oscP5.plug(this, "eyebrowRightReceived", "/gesture/eyebrow/right"); oscP5.plug(this, "jawReceived", "/gesture/jaw"); oscP5.plug(this, "nostrilsReceived", "/gesture/nostrils"); points = 0; getNewRand(); //timer time = millis();//store the current time println("time", time); //game level =1; partsActive = int(random(4)); } void draw() { if (!pressplay && !gameOver) { gameStart(); } else if (pressplay && !gameOver){ game(); } else if (pressplay && gameOver) { gameend(); } } void gameStart(){ background(1, 34, 160); textAlign(CENTER, CENTER); textFont(intersect50); fill(133,242,231); text("face", width/2-3,height/2-100); text("face", width/2-3, height/2); fill(0,222,190); text("face", width/2, height/2-100+3); text("face", width/2, height/2+3); textFont(intersect25); int passedMillis = millis() - time; // calculates passed milliseconds if(passedMillis >= 300){
      time = millis();
      fill(1, 34, 160);  // if more than 215 milliseconds passed set fill color to red
  }
  else {
    fill (0,222,190);
  }
   text("free play", width/2, 490);
   if (keyPressed) {
     if (key=='p' || key=='P') {
       pressplay = true;
     }
   }
}
 
void gameend() {
  background(1, 34, 160);
   textAlign(CENTER, CENTER);
   textFont(intersect50);
   fill(133,242,231);
   text("game", width/2-3,height/2-100);
   text("over", width/2-3, height/2);
   fill(0,222,190);
   text("game", width/2, height/2-100+3);
   text("over", width/2, height/2+3);
   textFont(intersect25);
   int passedMillis = millis() - time; // calculates passed milliseconds
  if(passedMillis >= 300){
      time = millis();
      fill(1, 34, 160);  // if more than 215 milliseconds passed set fill color to red
  }
  else {
    fill (0,222,190);
  }
   text("good bye", width/2, 490);
   if (keyPressed) {
     if (key=='s' || key=='S') {
       gameOver =false;
       pressplay = false;
       //level =0;
       //points =0;
       setup();
     }
   }
 
}
 
void game() {
 
if (points<40) {
   level =1;
 }
 
 else if (points<70) {
   level =2;
 }
 
 else if (points<100) {
   level =3;
 }
 
   else if (points<130) {
   level =4;
 }
 
 else {
 level =5;
 }
 
  pushMatrix();
  background(0);
   translate(sqSize/2,sqSize/2);
 
 
  fill(0);
 
  gridNum=level+1;
  sqSize = (gridSize-margin*2)/gridNum;
  //500/3
  //
  println(sqSize);
 
  for (int i=0; i<gridNum; i++) {
        for (int j=0; j<gridNum; j++) { //size/ grid size stroke(133,242,231); //noFill();222 //println(i, j, sqSize, sqSize*j+margin); rect(sqSize*j+margin, sqSize*i+margin, sqSize, sqSize); } } textFont(intersect25); fill (0,222,190); textAlign(LEFT); text("level "+str(level), -50,-50); fill(133,242,231); rect(sqSize*randX+margin, sqSize*randY+margin, sqSize, sqSize); fill(0); rect(sqSize*randX+margin, sqSize*randY+margin, sqSize-20, sqSize-20); fill(133,242,231); rect(sqSize*randX+margin, sqSize*randY+margin, sqSize-40, sqSize-40); //rect(300,300,40,40); popMatrix(); pushMatrix(); textFont(intersect25,40); textAlign(CENTER,CENTER); fill(0); text(parts[partsActive], sqSize*randX+margin+sqSize/2, sqSize*randY+margin+sqSize/2); popMatrix(); //write points count fill(133,242,231); textSize(60); //println(points); text(str(points), 350, 630); //timer if(millis() - time >= wait){
      gameOver = true;
      //also update the stored time
 
    }
    else {
      countdown = (millis()-time);
 
      //println("wait", wait);
      //println("test", millis()-time);
 
      countdown = countdown/wait;
      //println("countdown", countdown);
    }
    //println((3*PI)/2, ((3*PI)/2)*countdown);
    pushMatrix();
    noFill();
    stroke(133,242,231);
    strokeWeight(7);
    arc(600, 70, 30,30, 3*(PI/2)*countdown, 3*(PI/2), OPEN);
    popMatrix();
 
    strokeWeight(1);
 
  //translate(width,0);
  //  scale(-1,1);
  //facial data
  if(found > 0) {
 
    pushMatrix();
 
    translate(posePosition.x, posePosition.y);
    scale(poseScale);
    noFill();
    ellipse(-20, eyeLeft * -9, 7, 7);
    ellipse(20, eyeRight * -9, 7, 7);
    ellipse(0, 20, 7, 7);
    ellipse(0, nostrils * -1, 7, 7);
    rectMode(CENTER);
    fill(0);
    rect(-20, eyebrowLeft * -5, 25, 5);
    rect(20, eyebrowRight * -5, 25, 5);
    popMatrix();
 
 
    //gamify your LEFT eye
    float realMouthX = (posePosition.x);
    float realMouthY = posePosition.y+((eyeLeft*7)*poseScale);
 
 
    //gamify your LEFT eye
    float realLeftEyeX = (posePosition.x-(20*poseScale));
    float realLeftEyeY = posePosition.y+((eyeLeft*-9)*poseScale);
 
     //gamify your RIGHT eye
    float realRightEyeX = (posePosition.x+(20*poseScale));
    float realRightEyeY = posePosition.y+((eyeLeft*-9)*poseScale);
 
     //gamify your NOOOSE
    float realNoseX = (posePosition.x);
    float realNoseY = posePosition.y+((eyeLeft*-1)*poseScale);
 
 
    //translate(125,125);
    stroke(255,0,0);
 
 
    //MOUTH POINTS
     if (partsActive==0) {
      ellipse(realMouthX, realMouthY, 20,20);
        if (realMouthX >= sqSize*randX+margin && realMouthX <= sqSize*randX+sqSize+margin && realMouthY>= sqSize*randY+margin && realMouthY<= sqSize*randY+sqSize+margin) { //println("hello"); points+=10; getNewRand(); } } //LEFT EYE POINTS if (partsActive==1) { ellipse(realLeftEyeX, realLeftEyeY, 20,20); if (realLeftEyeX >= sqSize*randX+margin && realLeftEyeX <= sqSize*randX+sqSize+margin && realLeftEyeY>= sqSize*randY+margin && realLeftEyeY<= sqSize*randY+sqSize+margin) { //println("hello"); points+=10; getNewRand(); } } //RIGHT EYE POINTS if (partsActive==2) { ellipse(realRightEyeX, realRightEyeY, 20,20); if (realRightEyeX >= sqSize*randX+margin && realRightEyeX <= sqSize*randX+sqSize+margin && realRightEyeY>= sqSize*randY+margin && realRightEyeY<= sqSize*randY+sqSize+margin) { //println("hello"); points+=10; getNewRand(); } } if (partsActive==3) { ellipse(realNoseX, realNoseY, 20,20); if (realNoseX >= sqSize*randX+margin && realNoseX <= sqSize*randX+sqSize+margin && realNoseY>= sqSize*randY+margin && realNoseY<= sqSize*randY+sqSize+margin) {
        //println("hello");
        points+=10;
        getNewRand();
      }  
    }
 
  }
}
 
 
 
void getNewRand() {
  randX = int(random(0,gridNum));
  randY = int(random(0,gridNum));
  partsActive = int(random(4));
  time = millis();
}
 
void mouseClicked() {
  randX = int(random(0,gridNum));
  randY = int(random(0,gridNum));
  partsActive = int(random(4));
  time = millis();
}
 
// OSC CALLBACK FUNCTIONS
 
public void found(int i) {
  //println("found: " + i);
  found = i;
}
 
public void poseScale(float s) {
  //println("scale: " + s);
  poseScale = s;
}
 
public void posePosition(float x, float y) {
  //println("pose position\tX: " + x + " Y: " + y );
  posePosition.set(x, y, 0);
}
 
public void poseOrientation(float x, float y, float z) {
  //println("pose orientation\tX: " + x + " Y: " + y + " Z: " + z);
  poseOrientation.set(x, y, z);
}
 
public void mouthWidthReceived(float w) {
  //println("mouth Width: " + w);
  mouthWidth = w;
}
 
public void mouthHeightReceived(float h) {
  //println("mouth height: " + h);
  mouthHeight = h;
}
 
public void eyeLeftReceived(float f) {
  //println("eye left: " + f);
  eyeLeft = f;
}
 
public void eyeRightReceived(float f) {
  //println("eye right: " + f);
  eyeRight = f;
}
 
public void eyebrowLeftReceived(float f) {
  //println("eyebrow left: " + f);
  eyebrowLeft = f;
}
 
public void eyebrowRightReceived(float f) {
  //println("eyebrow right: " + f);
  eyebrowRight = f;
}
 
public void jawReceived(float f) {
  //println("jaw: " + f);
  jaw = f;
}
 
public void nostrilsReceived(float f) {
  //println("nostrils: " + f);
  nostrils = f;
}
 
// all other OSC messages end up here
void oscEvent(OscMessage m) {
  if(m.isPlugged() == false) {
    println("UNPLUGGED: " + m);
  }
}

 

nannon-viewing04

Spectacle is most easily recognized in works that milk the most out of current technology--it's usually flashy, engaging, easy to understand, for capital gain.

Speculation pushes the edge of understanding + technology--it is not self conscious, can also be flashy, but also confusing, uncomfortable, in the midst of coming to terms with itself.

Trimalchio, AES+F, 2010

Image result for aes+f trimalchio

AES+F is an art collective that plays a lot of faux realistic 3D through 3D modeling and photo manipulation. This particular piece, Trimalchio, was made in 2010. It's hard to categorize this as either speculation or spectacle because I think it's so much both (maybe it does reclaim spectacle into speculation). At first glance, the aesthetic is very beautiful, with many references to renaissance/greek god type imagery. But as you watch the videos, the references to all kinds of humanity + culture come out of nowhere, seamlessly blending into this utopic world. I think AES+F uses 3D in an interesting way because it's not the tip top frontier of 3D imaging, yet the blend of photo+manipulation makes it look extremely real. It's the photography that brings it out of uncanny valley, and the very intentional play with scale, imagery, and context that bring it into the weird, which I think is awesome.

nannon-reading03

At first thought, I want to say that my interests lie with last word art. There's something extremely daunting about making something that radically transforming from a medium or concept that most people already take for granted. At the same time, people around me are often very first-word oriented; looking at my recent work for this class, I believe that I've been drawn into the first-word realm of things.

Technology has shaped culture at almost every step of the way--of course, the obvious examples are things such as agriculture, industrial revolution, the internet, etc. But something I think that is very interesting happening in our culture today is the way the internet has shaped very different cultures in the West and in China. Technology today being tied up largely in software has also widened an intellectual + educational gap, exacerbating existing income/wage/socioeconomic gaps. On the flip side, I think small sub cultures can drive technologies as well. For example, niche music and art tech is drive by those who need it and make the case and perhaps build it.

The phrase technologically novel immediately brings to mind countless examples that exist in today's world. AR/VR, blockchain, etc are examples of technologies that people are working immensely hard to try to find uses for, but 99% of what's made/exists will not be remembered beyond the year its made. However, I do think that this kind of research will influence the 1% of products that are the last word, and that the last word wouldn't have existed without the first word. In addition, obsoletism has never been more pervasive than today, and software changes so fast that the foundational value of products can't be based purely on its technology. I think last word stuff is not only technologically novel, but also conceptually, philosophically, and aesthetically challenging.

nannon-telematic

A kitchen chat room where you show up as a chair. Inspired by my own kitchen, where my roommates and friends have had many long nights of fun chats, deep talks, and laughs. Also doubles as a place where chairs can comfortably hang out with other chairs.

 

 

The regular chair is taken directly from my kitchen, but in this digital kitchen, you can be any chair you want. 

Kitchen Chat v1 (New):  Use arrow keys to move around!
Kitchen Chat V0 (Broken) :

(Different possibilities of chairs)

 

 

Sept 28:I wanted this chat room to be a literal room where users could move around and chat with each other. However, one of the biggest obstacles was getting the images to load correctly. In addition, I had a lot of trouble keeping track of who was in the room--as such, when a new user joins a room, they won't see people who arrived previously, but users can see new people joining the room live. To be continued.

Oct 2 Update: 
The chat works! Used the agar.io template as a base, instead of the drawing template. Helped a lot, since I realized the agar.io template solved all my previous problems with users showing up. The hardest part was learning how it worked, and understanding the socket code. Will be adding new chairs (currently only 3), and a prettier log in page, including explanations for each chair) soon.