package gdsc.smlm.function.gaussian;

/*----------------------------------------------------------------------------- 
 * GDSC SMLM Software
 * 
 * Copyright (C) 2013 Alex Herbert
 * Genome Damage and Stability Centre
 * University of Sussex, UK
 * 
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *---------------------------------------------------------------------------*/

/**
 * Evaluates an 2-dimensional elliptical Gaussian function for a configured number of peaks.
 * <p>
 * The single parameter x in the {@link #eval(int, double[])} function is assumed to be a linear index into
 * 2-dimensional
 * data. The dimensions of the data must be specified to allow unpacking to coordinates.
 * <p>
 * Data should be packed in descending dimension order, e.g. Y,X : Index for [x,y] = MaxX*y + x.
 */
public class NBEllipticalGaussian2DFunction extends EllipticalGaussian2DFunction
{
	/**
	 * Constructor
	 * 
	 * @param npeaks
	 *            The number of peaks
	 * @param maxx
	 *            The maximum x value of the 2-dimensional data (used to unpack a linear index into coordinates)
	 * @param maxy
	 *            The maximum y value of the 2-dimensional data (used to unpack a linear index into coordinates)
	 */
	public NBEllipticalGaussian2DFunction(int npeaks, int maxx, int maxy)
	{
		super(npeaks, maxx, maxy);
	}

	/*
	 * (non-Javadoc)
	 * 
	 * @see gdsc.smlm.function.gaussian.Gaussian2DFunction#copy()
	 */
	@Override
	public Gaussian2DFunction copy()
	{
		return new NBEllipticalGaussian2DFunction(npeaks, maxx, maxy);
	}

	/*
	 * (non-Javadoc)
	 * 
	 * @see gdsc.smlm.fitting.function.gaussian.EllipticalGaussian2DFunction#eval(int, double[])
	 */
	public double eval(final int x, final double[] dyda)
	{
		// Track the position of the parameters
		int apos = 0;
		int dydapos = 0;

		// First parameter is the background level 
		double y_fit = a[BACKGROUND];

		// Unpack the predictor into the dimensions
		final int x1 = x / maxx;
		final int x0 = x % maxx;

		for (int j = 0; j < npeaks; j++)
		{
			y_fit += gaussian(x0, x1, dyda, apos, dydapos, peakFactors[j]);
			apos += 6;
			dydapos += PARAMETERS_PER_PEAK;
		}

		return y_fit;
	}

	@Override
	public boolean evaluatesBackground()
	{
		return false;
	}
}