/*
 * Skyline.java
 *
 * Copyright (C) 2002-2006 Alexei Drummond and Andrew Rambaut
 *
 * This file is part of BEAST.
 * See the NOTICE file distributed with this work for additional
 * information regarding copyright ownership and licensing.
 *
 * BEAST is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2
 * of the License, or (at your option) any later version.
 *
 *  BEAST is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with BEAST; if not, write to the
 * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
 * Boston, MA  02110-1301  USA
 */

package dr.evolution.coalescent;

import dr.evolution.tree.Tree;
import dr.evolution.util.Units;
import dr.math.Binomial;

/**
 * Skyline plot derived from a strictly bifurcating tree
 * or a coalescent interval.
 *
 * This class provides the "classic" skyline plot method by
 * Pybus, Rambaut and Harvey 2000. Genetics 155:1429-1437, as well
 * as the "generalized" skyline plot method  described in
 * Strimmer and Pybus. 2001. MBE submitted.
 * 
 * @version $Id: Skyline.java,v 1.15 2005/05/24 20:25:56 rambaut Exp $
 *
 * @author Korbinian Strimmer
 */
public class Skyline implements Units {
	//
	// Public stuff
	//
	
	Skyline() {}

	/**
	 * Construct skyline plot from tree
	 *
	 * @param epsilon smoothing parameter (if set < 0 then epsilon will be optimized)
	 */
	public Skyline(Tree tree, double epsilon)
	{
		this(new TreeIntervals(tree), 1.0, epsilon);
	}

	/**
	 * Construct skyline plot from tree
	 *
	 * @param epsilon smoothing parameter (if set < 0 then epsilon will be optimized)
	 */
	public Skyline(Tree tree, double mutationRate, double epsilon)
	{
		this(new TreeIntervals(tree), mutationRate, epsilon);
	}


	/**
	 * Construct skyline plot from given coalescent intervals
	 * 
	 * @param epsilon smoothing parameter (if set < 0 then epsilon will be optimized)
	 */
	public Skyline(IntervalList intervals, double mutationRate, double epsilon)
	{
		init(intervals, mutationRate, epsilon);
	}
	
	void init(IntervalList intervals, double mutationRate, double epsilon) {
		if (!intervals.isBinaryCoalescent()) {
			throw new IllegalArgumentException("All intervals must contain only a single coalescent");
		}
		
		mu = mutationRate;
		
		size = intervals.getIntervalCount();
		
		this.intervals = intervals;
		
		// population size in each coalescent interval
		populationSize = new double[size];
				
		// cumulative interval sizes
		cis = new double[size];
		
		maxTime = 0.0;
		for (int i = 0; i < size; i++) {
			cis[i] = maxTime;
			maxTime += intervals.getInterval(i) / mu;
		}
		
		if (epsilon == 0.0) {
			/* init with classic skyline plot */
			computeClassic();
		} else if (epsilon > 0.0) {
			/* init with generalized skyline plot */
			computeGeneralized(epsilon);
		} else {
			// find optimal generalized skyline plot
			optimize();
		}
	}
	
	public String toString()
	{
		StringBuffer buffer = new StringBuffer("Skyline Plot ");
		if (eps == 0.0) {
			buffer.append("(classic): ");
		} else {
			buffer.append("(generalized): ");
			buffer.append("epsilon = " + eps + ", ");
		}
		
		buffer.append("log L = " + getLogLikelihood());
		if (params > size-2) {
			buffer.append("\tlog L(AICC) not available");
		} else {
			buffer.append("\tlog L(AICC) = " + getAICC());
		}
		
		return buffer.toString();
	}

	/**
	 * Compute classic skyline plot
	 */
	public void computeClassic()
	{
		int i = 0;
		int k = 0;
		do {
		
			double popSize = 0.0;
			boolean done = false;
			
			k = i;
			
			do {
				double w = intervals.getInterval(i) / mu;
				int n = intervals.getLineageCount(i);
				if (n < 0) n = 0;
				done = intervals.getIntervalType(i) == IntervalType.COALESCENT;
				
				popSize += w * Binomial.choose2(n);
				i++;
			} while (i < size && !done);
			
			for (int j = k; j < i; j++) {
				populationSize[j] = popSize;
			}
		} while (i < size);

		params = k;
		eps = 0.0;
	}

	/**
	 * Compute generalized skyline plot
	 */
	public void computeGeneralized(double epsilon)
	{
		params = 0;
		double cw = 0; //cumulative w
		
		for (int i = 0; i < size; i++) {
		
			int n = intervals.getLineageCount(i);
			if (n < 0) n = 0;
			
			double w = intervals.getInterval(i) / mu;
			
			int start = i;
			int k = 1;
			while ((w < epsilon && i < size-1) || 
				intervals.getIntervalType(i) != IntervalType.COALESCENT) {
				i++;
				k++;
				w += intervals.getInterval(i) / mu;
				
				//System.out.println(ci.getInterval(i));
			} 
			
			//System.out.println("w=" + w + " k=" + k + "  i=" + i);
			
			// if remainder is smaller than epsilon
			// continue pooling until the end
			if (maxTime - cw - w < epsilon) {				
				for (int j = i+1; j < size; j++)
				{
					i++;
					k++;
					w += intervals.getInterval(i) / mu;
				}

			}
			
			double m = w * Binomial.choose2(n) / k;
			
			// assign the same pop.size to all sub intervals
			for (int j = start; j < start+k; j++) {
				populationSize[j] = m;
			}
			params++;
			cw += w;
		}
		eps = epsilon;
	}

	/**
	 * Optimize generalized skyline plot
	 */
	public void optimize()
	{
		// this is the naive way of doing this ...
		
		double besteps = getMaxTime();
		computeGeneralized(besteps);
		double bestaicc = getAICC();
		
		int GRID = 1000;
		double delta = besteps/GRID;
		
		double MINEPS = 1e-6;
		// Why MINEPS?
		// Because most "clock-like" trees are not properly
		// clock-like for a variety of reasons, i.e. the heights
		// of the tips are not exactly zero.
		
		
		
		eps = eps - delta;
		while(eps > MINEPS)
		{
			computeGeneralized(eps);
			double aicc = getAICC();
			
			if (aicc > bestaicc && params < size-1)
			{
				besteps = eps;
				bestaicc = aicc;
			}
			eps = eps - delta;
		}
		
		computeGeneralized(besteps);
	}

	/**
	 * Compute log-likelihood
	 */
	public double getLogLikelihood()
	{
		double logL = 0.0;
				
		for (int i = 0; i < size; i++) {
		
			double w = intervals.getInterval(i);
			double m = populationSize[i];
			
			double n = intervals.getIntervalCount();
			double nc2 = n*(n-1.0)/2.0;
			
			if (intervals.getIntervalType(i) == IntervalType.COALESCENT) {
				logL += Math.log(nc2/m) - w*nc2/m;
			} else {
				logL -=  w*nc2/m;
			}
		}

		return logL;
	}

	/**
	 * Compute AICC-corrected log-likelihood
	 */
	public double getAICC()
	{
		double logL = getLogLikelihood();
	
		return AICC(logL, params, size);
	}

	/**
	 * Second-order Akaike (AICC) correction (Hurvich and Tsai 1989)
	 *
	 * @param l    log-likelihood
	 * @param k    number of inferred parameters
	 * @param n    sample size 
	 *
	 * @return     l - k - (k(k+1))/(n - k - 1)   
	 */
	public static double AICC(double l, int k, int n)
	{
		if (k > n-2) throw new IllegalArgumentException("k must be smaller than n-1");
		
		return  l - k - (double) (k*(k+1.0))/ (double) (n - k - 1.0) ;
	}

	/**
	 * Find interval corresponding to a specific time
	 */
	public double findInterval(double time)
	{
		if (time < 0) throw new IllegalArgumentException("Negative values for time are not allowed");
		
		for (int i = 0; i < size-1; i++)
		{
			if (time >= cis[i] && time < cis[i+1]) return i;
		}
		
		return size-1;
	}
	 
	/**
	 * Returns the largest value of time defined in this plot 
	 * (= maximum value for epsilon)
	 */
	public double getMaxTime()
	{
		return maxTime;
	}


	/**
	 * Returns the largest estimate of population size.
	 */
	public double getMaxPopulationSize() {
		double max = 0.0;
		for (int i = 0; i < size; i++) {
			if (populationSize[i] > max) {
				max = populationSize[i];
			}
		}

		return max;
	}

	/**
	 * Returns the coalescent intervals in this skyline plot.
	 */
	public IntervalList getIntervals() {
		return intervals;
	}

	/**
	 * Returns the number of intervals in this skyline plot.
	 */
	public int getSize() {
		return size;
	}
	
	/**
	 * Returns the number of composite intervals (=number of parameters).
	 */
	public int getParameterCount() {
		return params;
	}

	/**
	 * Returns epsilon
	 */
	public double getEpsilon() {
		return eps;
	}
	
	/**
	 * Returns the population size in interval i.
	 */
	public double getPopulationSize(int i) {
		return populationSize[i];
	}

	/**
	 * Return the units
	 */
	public final Type getUnits() {
		return intervals.getUnits();
	} 

	/**
	 * Sets the units
	 */
	public final void setUnits(Type units) {
		throw new IllegalArgumentException("Can't set skyline's units");
	}


	// private
	
	private IntervalList intervals;
	private int size;
	private double maxTime;
	private double eps;
	private double mu;
	private int params;
	
	/** cummulative interval sizes */
	private double[] cis;
	
	/** estimated population size in a coalescent interval */
	private double[] populationSize; 


}
