/*
@(#)SuccessRunsExperiment.java

Copyright (C) 2005  Kyle Siegrist
Department of Mathematical Sciences
University of Alabama in Huntsville

This program is part of Virtual Laboratories in Probability and Statistics,
http://www.math.uah.edu/stat/, a project partially supported by the
National Science Foundation under grant number DUE-0089377.

This program is licensed under a Creative Commons License. Basically, you are free to copy,
distribute, and modify this program, and to make commercial use of the program.
However you must give proper attribution.
See http://creativecommons.org/licenses/by/2.0/ for more information.
*/

package edu.uah.math.experiments;
import java.awt.FlowLayout;
import java.awt.event.MouseEvent;
import java.awt.Color;
import java.io.Serializable;
import javax.swing.JToolBar;
import javax.swing.JComboBox;
import javax.swing.event.ChangeEvent;
import java.awt.event.ItemEvent;
import java.awt.Dimension;
import edu.uah.math.distributions.Domain;
import edu.uah.math.distributions.GeometricDistribution;
import edu.uah.math.distributions.RandomVariable;
import edu.uah.math.devices.RecordTable;
import edu.uah.math.devices.Timeline;
import edu.uah.math.devices.RandomVariableGraph;
import edu.uah.math.devices.RandomVariableTable;
import edu.uah.math.devices.Parameter;

/**
* This class models the sucess-runs Markov chain.  A sequence of Bernoulli trials is
* performed.  The state at any time is the length of the current run of successes.
* The limiting distribution is geometric.
* @author Kyle Siegrist
* @version December, 2005
*/
public class SuccessRunsExperiment extends Experiment{
	//Variables
	private int initialState = 0, currentState = 0;
	private double prob = 0.5;
	//Objects
	private RecordTable recordTable = new RecordTable(new String[] {"Time", "X"});
	private JToolBar toolBar = new JToolBar("Parameter Toolbar");
	private GeometricDistribution limitDistribution = new GeometricDistribution(prob, 1);
	private Timeline chain = new Timeline(limitDistribution.getDomain(), "x");
	private RandomVariable state = new RandomVariable(limitDistribution, "X");
	private RandomVariableGraph stateGraph = new RandomVariableGraph(state);
	private RandomVariableTable stateTable = new RandomVariableTable(state);
	private JComboBox modelChoice = new JComboBox();
	//Scrollbars and labels
	private Parameter probScroll = new Parameter(0.1, 0.95, 0.01, prob, "Probability of success", "p");

	/**
	* This method initialize the experiment, including the toolbar, record table, Markov chain,
	* histogram, and data table.
	*/
	public void init(){
		super.init();
		setName("Ehrenfest Experiment");
		//Scrollbar
		probScroll.getSlider().addChangeListener(this);
		probScroll.applyDecimalPattern("0.00");
		//Toolbar
		toolBar.setLayout(new FlowLayout(FlowLayout.LEFT));
		toolBar.add(probScroll);
		addToolBar(toolBar);
		//Urn Chain
		chain.setToolTipText("Markov chain: at time 0, click to set the initial state");
		chain.setPointSize(6);
		chain.setMargins(35, 20, 20, 20);
		chain.setMinimumSize(new Dimension(100, 30));
		chain.addMouseListener(this);
		chain.setCurrentTimeColor(Color.red);
		addComponent(chain, 0, 0, 2, 1, 10, 0);
		//Random variable graph
		addComponent(stateGraph, 0, 1, 2, 1);
		//Record Table
		recordTable.setDescription("X: state");
		addComponent(recordTable, 0, 2, 1, 1);
		//Data Table
		addComponent(stateTable, 1, 2, 1, 1);
		//Final actions
		validate();
		reset();
	}

	/**
	* This method returns basic information about the applet, including copyright
	* information, descriptive information, and instructions.
	* @return applet infomration
	*/
	public String getAppletInfo(){
		return super.getAppletInfo() + "\n\n"
			+ "Visit http://www.math.uah.edu/stat/applets/SuccessRunsExperiment.xhtml for more information\n"
			+ "about the applet and for a mathematical discussion of the success-runs experiment.";
	}

	/**
	* This method handles the scrollbar events for changing the number of states.
	* @param e the change event
	*/
	public void stateChanged(ChangeEvent e){
	    if (e.getSource() == probScroll.getSlider()){
			prob = probScroll.getValue();
			limitDistribution.setProbability(1 - prob);
			chain.setDomain(limitDistribution.getDomain());
			reset();
		 }
	}

	/**
	* This method sets the initial state when the user clicks on a ball.
	* @param e the mouse event
	*/
	public void mouseClicked(MouseEvent e){
		if (e.getSource() == chain && getTime()  == 0){
			int x = (int)Math.rint(chain.getXScale(e.getX()));
			int u = (int)chain.getDomain().getUpperBound();
			if (x < 0) x = 0;
			else if (x > u) x = u;
			initialState = x;
			reset();
		}
	}

	/**
	* This method performs the next step in the process. A note is played, depending on
	* the state.
	*/
	public void step(){
		doExperiment();
		playNote(currentState);
		update();
	}

	/**
	* This method resets the experiment, including the Markov chain, the record table
	* the histogram and data table.
	*/
	public void reset(){
		super.reset();
		currentState = initialState;
		chain.resetData();
		chain.addTime(currentState, Color.red);
		chain.setCurrentTime(currentState);
		recordTable.reset();
		recordTable.addRecord(new double[]{getTime(), initialState});
		state.reset();
		stateGraph.reset();
		stateTable.reset();
	}

	/**
	* This method defines the experiment. The Markov chain moves to the next state.
	*/
	public void doExperiment(){
		super.doExperiment();
		double p = Math.random();
		if (p <= prob) currentState++;
		else currentState = 0;
		state.setValue(currentState);
	}

	/**
	* This method updates the experiment, including the record table, Markov chain,
	* histogram, and data table.
	*/
	public void update(){
		super.update();
		recordTable.addRecord(new double[]{getTime(), currentState});
		chain.resetData();
		chain.addTime(initialState, Color.blue);
		chain.addTime(currentState, Color.red);
		chain.setCurrentTime(currentState);
		stateGraph.repaint();
		stateTable.repaint();
	}

}