import processing.core.*; import processing.xml.*; import twitter4j.*; import edu.cmu.sphinx.frontend.util.Microphone; import edu.cmu.sphinx.recognizer.Recognizer; import edu.cmu.sphinx.result.Result; import edu.cmu.sphinx.util.props.ConfigurationManager; import edu.cmu.sphinx.util.props.PropertyException; import com.apple.cocoa.application.*; import com.apple.cocoa.foundation.*; import java.util.regex.Pattern.*; import java.util.regex.Matcher; import edu.cmu.sphinx.linguist.flat.FlatLinguist; import javax.swing.JLabel; import javax.swing.JComboBox; import java.awt.BorderLayout; import java.util.Random; import edu.cmu.sphinx.decoder.*; import edu.cmu.sphinx.linguist.lextree.*; import com.sun.speech.freetts.jsapi.*; import edu.cmu.sphinx.linguist.language.ngram.*; import com.sun.syndication.feed.synd.*; import com.sun.speech.freetts.lexicon.*; import twitter4j.internal.org.json.*; import com.sun.speech.engine.synthesis.text.*; import edu.cmu.sphinx.jsapi.*; import simpleSpeech.*; import org.jdom.xpath.*; import com.sun.speech.freetts.en.us.cmu_time_awb.*; import de.dfki.lt.freetts.*; import edu.cmu.sphinx.frontend.*; import com.sun.syndication.feed.synd.impl.*; import edu.cmu.sphinx.result.*; import org.jdom.input.*; import edu.cmu.sphinx.linguist.acoustic.*; import edu.cmu.sphinx.linguist.language.grammar.*; import edu.cmu.sphinx.linguist.dflat.*; import edu.cmu.sphinx.model.acoustic.WSJ_8gau_13dCep_8kHz_31mel_200Hz_3500Hz.*; import edu.cmu.sphinx.decoder.search.*; import com.sun.speech.engine.recognition.*; import edu.cmu.sphinx.frontend.filter.*; import edu.cmu.sphinx.frontend.transform.*; import edu.cmu.sphinx.util.*; import edu.cmu.sphinx.linguist.language.ngram.large.*; import edu.cmu.sphinx.util.props.*; import edu.cmu.sphinx.linguist.acoustic.trivial.*; import com.sun.speech.freetts.clunits.*; import org.jdom.adapters.*; import twitter4j.internal.async.*; import edu.cmu.sphinx.model.acoustic.WSJ_8gau_13dCep_16k_40mel_130Hz_6800Hz.*; import com.sun.syndication.feed.rss.*; import edu.cmu.sphinx.frontend.util.*; import edu.cmu.sphinx.linguist.dictionary.*; import com.sun.speech.freetts.diphone.*; import edu.cmu.sphinx.research.parallel.*; import edu.cmu.sphinx.frontend.feature.*; import twitter4j.*; import com.sun.syndication.feed.module.*; import com.sun.speech.freetts.cart.*; import twitter4j.http.*; import com.sun.speech.freetts.en.us.*; import com.sun.speech.engine.*; import com.sun.syndication.feed.atom.*; import twitter4j.conf.*; import com.sun.speech.freetts.en.*; import edu.cmu.sphinx.linguist.util.*; import twitter4j.internal.logging.*; import twitter4j.internal.http.*; import com.sun.syndication.feed.module.impl.*; import twitter4j.util.*; import edu.cmu.sphinx.frontend.endpoint.*; import edu.cmu.sphinx.decoder.pruner.*; import edu.cmu.sphinx.decoder.scorer.*; import org.jdom.*; import edu.cmu.sphinx.linguist.flat.*; import edu.cmu.sphinx.recognizer.*; import com.sun.speech.freetts.en.us.cmu_us_kal.*; import com.sun.speech.freetts.audio.*; import com.sun.speech.freetts.relp.*; import edu.cmu.sphinx.frontend.frequencywarp.*; import edu.cmu.sphinx.linguist.*; import com.sun.speech.engine.synthesis.*; import com.sun.syndication.io.impl.*; import org.jdom.output.*; import edu.cmu.sphinx.model.acoustic.TIDIGITS_8gau_13dCep_16k_40mel_130Hz_6800Hz.*; import com.sun.speech.freetts.*; import com.sun.syndication.feed.impl.*; import edu.cmu.sphinx.instrumentation.*; import com.sun.speech.freetts.util.*; import com.sun.syndication.io.*; import edu.cmu.sphinx.frontend.window.*; import org.jdom.transform.*; import org.jdom.filter.*; import twitter4j.api.*; import com.sun.syndication.feed.*; import edu.cmu.sphinx.linguist.acoustic.tiedstate.*; import java.applet.*; import java.awt.*; import java.awt.image.*; import java.awt.event.*; import java.io.*; import java.net.*; import java.text.*; import java.util.*; import java.util.zip.*; import java.util.regex.*; public class attraction_Test4 extends PApplet { Twitter myTwitter; Listen reading; Flock flock; Particle ball; Particle arriver; int max_width = 1500; int max_height = 900; String s = ""; Flock finding = new Flock(); Flock finding2 = new Flock(); Flock finding3 = new Flock(); Flock finding4 = new Flock(); public void loadTwitter() { myTwitter = new Twitter("subcultured", "cl0ckw0rk"); try { String searchKeys = "love OR hate OR sad OR happy"; // Search twitter for tweets matching the query println("Searching Twitter for " + searchKeys); Query tquery = new Query(searchKeys); tquery.setRpp(100); QueryResult result = myTwitter.search(tquery); for (int i = 0; i < 6; i++) // Loop though 4 pages of search results { ArrayList tweets = (ArrayList) result.getTweets(); println("tweets found : " + tweets.size()); // Set the next page for the search results tquery.setPage(tquery.getPage() + 1); // Create a wanderer class for each of the search results and add it to the array for (int k = 0; k < tweets.size(); k++) { int colCount = 0; int colRed = 0; int colGreen = 0; int colBlue = 0; Particle ball = new Particle(new PVector(random(max_width), random(max_height)) ); // Get the tweet text String tweetText = tweets.get(k).toString(); ball.setTweet(tweetText); // Set the tweet text in the class // convert the text to lowercase (for searching) tweetText = tweetText.toLowerCase(); if (tweetText.contains("love")) { colRed += 216; colGreen += 20; colBlue += 20; colCount++; finding.addParticle(ball); } if (tweetText.contains("hate")) { colRed += 129; colGreen += 160; colBlue += 162; colCount++; finding2.addParticle(ball); } if (tweetText.contains("sad")) { colRed += 84; colGreen += 151; colBlue += 180; colCount++; finding3.addParticle(ball); } if (tweetText.contains("happy")) { colRed += 229; colGreen += 212; colBlue += 16; colCount++; finding4.addParticle(ball); } if (colCount > 1) { colRed = colRed / colCount; colGreen = colGreen / colCount; colBlue = colBlue / colCount; } ball.setColor(color(colRed, colGreen, colBlue)); } } println("Number of balls: " + finding.particles.size()); } catch (TwitterException te) { println("Couldn't connect: " + te); } } public void setup() { size(max_width, max_height); background(0); noStroke(); smooth(); loadTwitter(); reading = new Listen(this,"sample.config.xml", myTwitter); frameRate(30); } public void dispose() { reading.dispose(); } public void draw() { background(0); int findingSize = finding.particles.size(); for (int m = 0; m < findingSize; m++) { Particle ball = (Particle) finding.particles.get(m); } finding.run(); int finding2Size = finding2.particles.size(); for (int m = 0; m < finding2Size; m++) { Particle ball = (Particle) finding2.particles.get(m); } finding2.run(); int finding3Size = finding3.particles.size(); for (int m = 0; m < finding3Size; m++) { Particle ball = (Particle) finding3.particles.get(m); } finding3.run(); int finding4Size = finding4.particles.size(); for (int m = 0; m < finding4Size; m++) { Particle ball = (Particle) finding4.particles.get(m); } finding4.run(); } public void listenEvent(Listen _l) { int k = -5; int minDistance = 3; background(0); s = _l.readString();//returns the tweet value of "love" "hate" "angry" "sad" etc for (int c = 0; c < finding.particles.size(); c++) { if (s.contains("love")){ //if the Listen passed result = word love if (finding.particles.contains("love")){ ball.seek(new PVector(width/2,height/2)); } } } for (int c = 0; c < finding2.particles.size(); c++) { if (s.contains("hate")){ if (finding2.particles.contains("hate")){ ball.arrive(new PVector(width/2,height/2)); } } } for (int c = 0; c < finding3.particles.size(); c++) { if (s.contains("sad")){ if (finding3.particles.contains("sad")){ ball.arrive(new PVector(width/2,height/2)); } } } for (int c = 0; c < finding2.particles.size(); c++) { if (s.contains("happy")){ if (finding2.particles.contains("happy")){ ball.arrive(new PVector(width/2,height/2)); } } } } // The Boid class class Particle { PVector loc; PVector vel; PVector acc; float r; float maxforce; // Maximum steering force float maxspeed; // Maximum speed float wander_theta; float wander_radius; int col; String tweet; // bigger = more edgier, hectic float max_wander_offset = 0.3f; // bigger = faster turns float max_wander_radius = 3.5f; Particle(PVector l) { acc = new PVector(0,0); vel = new PVector(random(-1,1),random(-1,1)); loc = l.get(); r = 2.0f; wander_theta = random(TWO_PI); wander_radius = random(max_wander_radius); } public void run(ArrayList particles) { update(); borders(); move(); //flock(particles); // We don't want them to flock. render(); } public void render() { fill(col); ellipse(loc.x, loc.y, 20, 20); } public void move() { float wander_offset = random(-max_wander_offset, max_wander_offset); wander_theta += wander_offset; loc.x += cos(wander_theta); loc.y += sin(wander_theta); } public void stayInsideCanvas() { loc.x %= width; loc.y %= height; } // We accumulate a new acceleration each time based on three rules public void flock(ArrayList particles) { PVector sep = separate(particles); // Separation PVector ali = align(particles); // Alignment PVector coh = cohesion(particles); // Cohesion // Arbitrarily weight these forces sep.mult(1.5f); ali.mult(1.0f); coh.mult(1.0f); // Add the force vectors to acceleration acc.add(sep); acc.add(ali); acc.add(coh); } // Method to update location public void update() { // Update velocity vel.add(acc); // Limit speed vel.limit(maxspeed); loc.add(vel); // Reset accelertion to 0 each cycle acc.mult(0); } public void seek(PVector target) { acc.add(steer(target,false)); } public void arrive(PVector target) { acc.add(steer(target,true)); } // A method that calculates a steering vector towards a target // Takes a second argument, if true, it slows down as it approaches the target public PVector steer(PVector target, boolean slowdown) { //maxspeed = ms; //maxforce = mf; PVector steer; // The steering vector PVector desired = target.sub(target,loc); // A vector pointing from the location to the target float d = desired.mag(); // Distance from the target is the magnitude of the vector // If the distance is greater than 0, calc steering (otherwise return zero vector) if (d > 0) { // Normalize desired desired.normalize(); // Two options for desired vector magnitude (1 -- based on distance, 2 -- maxspeed) if ((slowdown) && (d < 100.0f)) desired.mult(maxspeed*(d/100.0f)); // This damping is somewhat arbitrary else desired.mult(maxspeed); // Steering = Desired minus Velocity steer = target.sub(desired,vel); steer.limit(maxforce); // Limit to maximum steering force } else { steer = new PVector(0,0); } return steer; } // Wraparound public void borders() { if (loc.x < -r) loc.x = width+r; if (loc.y < -r) loc.y = height+r; if (loc.x > width+r) loc.x = -r; if (loc.y > height+r) loc.y = -r; } // Separation // Method checks for nearby boids and steers away public PVector separate (ArrayList particles) { float desiredseparation = 20.0f; PVector steer = new PVector(0,0,0); int count = 0; // For every boid in the system, check if it's too close for (int i = 0 ; i < particles.size(); i++) { Particle other = (Particle) particles.get(i); float d = PVector.dist(loc,other.loc); // If the distance is greater than 0 and less than an arbitrary amount (0 when you are yourself) if ((d > 0) && (d < desiredseparation)) { // Calculate vector pointing away from neighbor PVector diff = PVector.sub(loc,other.loc); diff.normalize(); diff.div(d); // Weight by distance steer.add(diff); count++; // Keep track of how many } } // Average -- divide by how many if (count > 0) { steer.div((float)count); } // As long as the vector is greater than 0 if (steer.mag() > 0) { // Implement Reynolds: Steering = Desired - Velocity steer.normalize(); steer.mult(maxspeed); steer.sub(vel); steer.limit(maxforce); } return steer; } // Alignment // For every nearby boid in the system, calculate the average velocity public PVector align (ArrayList particles) { float neighbordist = 25.0f; PVector steer = new PVector(0,0,0); int count = 0; for (int i = 0 ; i < particles.size(); i++) { Particle other = (Particle) particles.get(i); float d = PVector.dist(loc,other.loc); if ((d > 0) && (d < neighbordist)) { steer.add(other.vel); count++; } } if (count > 0) { steer.div((float)count); } // As long as the vector is greater than 0 if (steer.mag() > 0) { // Implement Reynolds: Steering = Desired - Velocity steer.normalize(); steer.mult(maxspeed); steer.sub(vel); steer.limit(maxforce); } return steer; } // Cohesion // For the average location (i.e. center) of all nearby boids, calculate steering vector towards that location public PVector cohesion (ArrayList particles) { float neighbordist = 25.0f; PVector sum = new PVector(0,0); // Start with empty vector to accumulate all locations int count = 0; for (int i = 0 ; i < particles.size(); i++) { Particle other = (Particle) particles.get(i); float d = loc.dist(other.loc); if ((d > 0) && (d < neighbordist)) { sum.add(other.loc); // Add location count++; } } if (count > 0) { sum.div((float)count); return steer(sum,false); // Steer towards the location } return sum; } public int getColor() { return col; } public void setTweet(String text) { tweet = text; } public void setColor(int c) { col = c; } } // The Flock (a list of Boid objects) class Flock { ArrayList particles; // An arraylist for all the boids Flock() { particles = new ArrayList(); // Initialize the arraylist } public void run() { for (int i = 0; i < particles.size(); i++) { Particle b = (Particle) particles.get(i); b.run(particles); // Passing the entire list of boids to each boid individually } } public void addParticle(Particle b) { particles.add(b); } } static public void main(String args[]) { PApplet.main(new String[] { "--bgcolor=#FFFFFF", "attraction_Test4" }); } }