de.biomedical_imaging.traJ.features.PowerLawFeature.java Source code

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Here is the source code for de.biomedical_imaging.traJ.features.PowerLawFeature.java

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/*
The MIT License (MIT)
    
Copyright (c) 2015-2016 Thorsten Wagner (wagner@biomedical-imaging.de)
    
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
    
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
    
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/

package de.biomedical_imaging.traJ.features;

import java.util.ArrayList;

import org.apache.commons.lang3.ArrayUtils;

import de.biomedical_imaging.traJ.Trajectory;
import de.biomedical_imaging.traj.math.PowerLawCurveFit;

/**
 * Fits the function y = 4*a*x^b to the MSD data and calculates the exponent alpha (b) and the diffusion coefficient (a).
 * @author Thorsten Wagner
 *
 */
public class PowerLawFeature extends AbstractTrajectoryFeature {

    private Trajectory t;
    private int minlag;
    private int maxlag;
    private AbstractMeanSquaredDisplacmentEvaluator msdeval;
    private int evaluateIndex = 0;
    private boolean useInitialGuess;
    private double initalDiffusionCoefficient;
    private double initalAlpha;
    private double fps;
    private double timelag;

    /**
     * @param t Trajectory for which alpha and diffusion coefficient is to be calculated.
     * @param minlag Minimum timelag for the MSD curve calculation
     * @param maxlag Maximum timelag for the MSD curve calculation
     */
    public PowerLawFeature(Trajectory t, double fps, int minlag, int maxlag) {
        this.t = t;
        this.minlag = minlag;
        this.maxlag = maxlag;
        this.fps = fps;
        this.timelag = 1 / fps;
        msdeval = new MeanSquaredDisplacmentFeature(null, 0);
        ((MeanSquaredDisplacmentFeature) msdeval).setOverlap(false);
        evaluateIndex = 0;
        useInitialGuess = false;

    }

    /**
     * 
     * @param t Trajectory for which alpha and diffusion coefficient is to be calculated.
     * @param minlag Minimum timelag for the MSD curve calculation
     * @param maxlag Maximum timelag for the MSD curve calculation
     * @param initalAlpha Initial guess for alpha
     * @param initialDiffusionCoefficient Initial guess for the diffusion coefficient.
     */
    public PowerLawFeature(Trajectory t, double fps, int minlag, int maxlag, double initalAlpha,
            double initialDiffusionCoefficient) {
        this.t = t;
        this.minlag = minlag;
        this.maxlag = maxlag;
        this.fps = fps;
        this.timelag = 1 / fps;
        msdeval = new MeanSquaredDisplacmentFeature(null, 0);
        ((MeanSquaredDisplacmentFeature) msdeval).setOverlap(false);
        evaluateIndex = 0;
        useInitialGuess = true;
        this.initalAlpha = initalAlpha;
        this.initalDiffusionCoefficient = initialDiffusionCoefficient;
    }

    /**
     * @return An double array with the elements [0]= alpha, [1]=diffusion coefficient [2]=goodness of fit
     */
    @Override
    public double[] evaluate() {

        ArrayList<Double> xDataList = new ArrayList<Double>();
        ArrayList<Double> yDataList = new ArrayList<Double>();
        msdeval.setTrajectory(t);
        double[][] data = new double[maxlag - minlag + 1][3];

        for (int i = minlag; i <= maxlag; i++) {
            msdeval.setTimelag(i);
            data[i - minlag][0] = i * timelag;
            double[] res = msdeval.evaluate();
            data[i - minlag][1] = res[evaluateIndex];
            data[i - minlag][2] = (int) res[2];

        }

        //Weightening
        for (int i = 0; i < (maxlag - minlag + 1); i++) {
            double x = data[i][0];
            double y = data[i][1];
            int np = (int) data[i][2];

            for (int j = 0; j < np; j++) {
                xDataList.add(x);
                yDataList.add(y);
            }
        }

        double[] xData = ArrayUtils.toPrimitive(xDataList.toArray(new Double[0]));
        double[] yData = ArrayUtils.toPrimitive(yDataList.toArray(new Double[0]));

        PowerLawCurveFit pwFit = new PowerLawCurveFit();

        if (useInitialGuess) {
            pwFit.doFit(xData, yData, initalAlpha, initalDiffusionCoefficient);
        } else {
            pwFit.doFit(xData, yData);
        }
        result = new double[] { pwFit.getAlpha(), pwFit.getDiffusionCoefficient(), pwFit.getGoodness() };

        return result;
    }

    /**
     * If a custim mean squared displacement evaluator is used, evaluateIndex should be the array index of the MSD value.
     * @param evaluateIndex
     */
    public void setEvaluateIndex(int evaluateIndex) {
        this.evaluateIndex = evaluateIndex;
    }

    public void setMeanSquaredDisplacmentEvaluator(AbstractMeanSquaredDisplacmentEvaluator msdeval) {
        this.msdeval = msdeval;
    }

    @Override
    public String getName() {

        return "Power-Law-Feature";
    }

    @Override
    public void setTrajectory(Trajectory t) {
        this.t = t;
        result = null;

    }

    @Override
    public String getShortName() {
        return "POWER";
    }

}