package stoppers;

import java.lang.*;
import java.io.*;
import main.BetterTokenizer;
import main.Cell;
import main.GeneralInput;
import main.Model;

public class TwoCellDifferenceStop extends SimpleStop	{
	int			nodeNum;

	/** The threshold below which the concentration of a node is considered 0.
		This is used to say whether a cell has switched low. */
	float		lowThreshold; 
	/** The threshold above which the concentration of a node is considered 1.
		This is used to say whether a cell has switched high. */
	float		highThreshold;
	
	final static private int	THRESHOLDS = 0, ABSOLUTE_DIFFERENCE = 1, MULTIPLICATIVE_DIFFERENCE = 2;
	int			mode = THRESHOLDS;
	
	float		interval;
	int			xPosLow, yPosLow, xPosHigh, yPosHigh;	

	float		lastScoreTime = 0;
	float		fadeAvg = 0;
	float		averagingTime = 10;
	float		lastHighVal, lastLowVal;
	
	float		punishSlope = 10;

	// No arg constructor for instantiating by name
	public TwoCellDifferenceStop() { this(1); }
	
	public TwoCellDifferenceStop(float stop_time) {
		stopTime = stop_time;
	}
	
	public void init(Model model) {
		super.init(model);
		score = 0;
	}
	
	public boolean stop(float time) {		
		// trivial reasons to return
		if(time < lastTime + interval)	return false;
		if(time > stopTime) return true;

		float	timestep_score = 0;

		float low_val = model.cells[yPosLow * model.arrayWidth + xPosLow].getValue(nodeNum);
		float high_val = model.cells[yPosHigh * model.arrayWidth + xPosHigh].getValue(nodeNum);
		
		float temp = (time - lastTime) / averagingTime;
		if(temp > 1) temp = 1;
		fadeAvg -= fadeAvg * temp;
		
		// Add in pattern scoring and stability scoring
		// In this stability scoring attempt, I am only punishing movement away from a good solution, not
		// movement towards a good solution
		if(mode == THRESHOLDS)
		{
			if(high_val < highThreshold) fadeAvg += (highThreshold - high_val) * punishSlope * temp;
			if(low_val > lowThreshold) fadeAvg += (low_val - lowThreshold) * punishSlope * temp;

			// Stability scoring
			if(high_val < lastHighVal)
				fadeAvg += (lastHighVal - high_val) * punishSlope * temp;
			if(low_val > lastLowVal)
				fadeAvg += (low_val - lastLowVal) * punishSlope * temp;
		}
		else if(mode == ABSOLUTE_DIFFERENCE)
		{
		
		}
		else if(mode == MULTIPLICATIVE_DIFFERENCE)
		{
		
		}
		
		lastHighVal = high_val;
		lastLowVal = low_val;
		
		score = fadeAvg;
		lastTime = time;

		return false;	// Only stop when run out of time
	}
	
	public boolean didPass(float s)  { return didPass(); }
	public boolean didPass()  {
		if(score < Cutoff) return true;

		float low_val = model.cells[yPosLow * model.arrayWidth + xPosLow].getValue(nodeNum);
		float high_val = model.cells[yPosHigh * model.arrayWidth + xPosHigh].getValue(nodeNum);
				
		if(mode == THRESHOLDS)
		{
			if(low_val > lowThreshold || high_val < highThreshold) return false;
		}
		else if(mode == ABSOLUTE_DIFFERENCE)
		{
			if(high_val - low_val < lowThreshold) return false;
		}
		else if(mode == MULTIPLICATIVE_DIFFERENCE)
		{
			if(low_val > 0 && high_val / low_val < lowThreshold) return false;
			else if(high_val <= 0) return false;
		}
		return true;
	}

	public void reset() {
		score = 0;
		lastScoreTime = 0;
		fadeAvg = 0;
		super.reset();
	}

	protected void loadParameter(String info, BetterTokenizer tokenizer) throws Exception {
		if(info.equals("Node")) { GeneralInput.nextToken(tokenizer); nodeNum = model.cells[0].getNodeNum(tokenizer.sval); }
		else if(info.equals("LowCellXPos")) { GeneralInput.nextToken(tokenizer); xPosLow = ((int)tokenizer.nval); }
		else if(info.equals("LowCellYPos")) { GeneralInput.nextToken(tokenizer); yPosLow = ((int)tokenizer.nval); }
		else if(info.equals("HighCellXPos")) { GeneralInput.nextToken(tokenizer); xPosHigh = ((int)tokenizer.nval); }
		else if(info.equals("HighCellYPos")) { GeneralInput.nextToken(tokenizer); yPosHigh = ((int)tokenizer.nval); }
		else if(info.equals("LowThreshold")) { GeneralInput.nextToken(tokenizer); lowThreshold = ((float)tokenizer.nval); }
		else if(info.equals("Threshold")) { GeneralInput.nextToken(tokenizer); lowThreshold = ((float)tokenizer.nval); }
		else if(info.equals("HighThreshold")) { GeneralInput.nextToken(tokenizer); highThreshold = ((float)tokenizer.nval); }
		else if(info.equals("AveragingTime")) { GeneralInput.nextToken(tokenizer); averagingTime = ((float)tokenizer.nval); }
		else if(info.equals("PunishSlope")) { GeneralInput.nextToken(tokenizer); punishSlope = ((float)tokenizer.nval); }
		else if(info.equals("Interval")) { GeneralInput.nextToken(tokenizer); interval = ((float)tokenizer.nval); }
		else if(info.equals("Mode"))
		{
			GeneralInput.nextToken(tokenizer);
			if(tokenizer.sval.equals("Thresholds")) mode = THRESHOLDS;
			else if(tokenizer.sval.equals("Absolute")) mode = ABSOLUTE_DIFFERENCE;
			else if(tokenizer.sval.equals("Multiplicative")) mode = MULTIPLICATIVE_DIFFERENCE;
			else System.out.println("Unrecognized mode <" + tokenizer.sval +"> in TwoCellDifferenceStop");
		}
		else super.loadParameter(info,tokenizer);
	}
}