gov.nih.nci.caintegrator.analysis.messaging.DataPointVector.java Source code

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Here is the source code for gov.nih.nci.caintegrator.analysis.messaging.DataPointVector.java

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/*L
 *  Copyright SAIC
 *
 *  Distributed under the OSI-approved BSD 3-Clause License.
 *  See http://ncip.github.com/stats-analysis-commons/LICENSE.txt for details.
 */

package gov.nih.nci.caintegrator.analysis.messaging;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;

import org.apache.commons.math.stat.descriptive.DescriptiveStatistics;

/**
 * This class holds a named list of data points. It is used to 
 * return expression values for a reporter.  The reporter name is set 
 * as the name of the vector and the DataPoint will contain the sample id
 * and the expression value. 
 * @author harrismic
 *
 */
public class DataPointVector implements Serializable {

    private static final long serialVersionUID = 1L;
    private List<DataPoint> dataPoints = new ArrayList<DataPoint>();
    private String name;

    public List<DataPoint> getDataPoints() {
        return dataPoints;
    }

    public DataPointVector(String name) {
        this.name = name;
    }

    public void setDataPoints(List<DataPoint> dataPoints) {
        this.dataPoints = dataPoints;
    }

    public String getName() {
        return name;
    }

    public void addDataPoint(DataPoint point) {
        dataPoints.add(point);
    }

    public int size() {
        return dataPoints.size();
    }

    public List<Double> getXValues() {
        return getXValues(false);
    }

    /**
     * Get a list of Doubles corresponding to the X values.
     * @return
     */
    public List<Double> getXValues(boolean includeNulls) {
        List<Double> xValues = new ArrayList<Double>();
        Double val = null;
        for (DataPoint point : dataPoints) {
            if (point != null) {
                val = point.getX();
                if ((val != null) || (includeNulls)) {
                    xValues.add(point.getX());
                }
            } else if (includeNulls) {
                xValues.add(null);
            }
        }
        return xValues;
    }

    /**
     * Get a list of Doubles corresponding to the Y values.
     * @return
     */
    public List<Double> getYValues(boolean includeNulls) {
        List<Double> yValues = new ArrayList<Double>();
        Double val = null;
        for (DataPoint point : dataPoints) {
            if (point != null) {
                val = point.getY();
                if ((val != null) || (includeNulls)) {
                    yValues.add(point.getY());
                }
            } else if (includeNulls) {
                yValues.add(null);
            }
        }
        return yValues;
    }

    public List<Double> getYValues() {
        return getYValues(false);
    }

    /**
     * Get a list of Doubles corresponding to the Z values.
     * @return
     */
    public List<Double> getZValues(boolean includeNulls) {
        List<Double> zValues = new ArrayList<Double>();
        Double val = null;
        for (DataPoint point : dataPoints) {
            if (point != null) {
                if ((val != null) || (includeNulls)) {
                    zValues.add(point.getZ());
                }
            } else {
                zValues.add(null);
            }
        }
        return zValues;
    }

    public List<Double> getZValues() {
        return getZValues(false);
    }

    public Double getMeanZ() {

        return computeMean(getZValues());

    }

    public Double getMeanY() {

        return computeMean(getYValues());

    }

    public Double getMeanX() {

        return computeMean(getXValues());

    }

    /**
     * Compute the mean of the values in the list
     * @param values
     * @return
     */
    private Double computeMean(List<Double> values) {

        DescriptiveStatistics stats = DescriptiveStatistics.newInstance();

        //       Add the data from the array
        for (Double value : values) {
            stats.addValue(value);
        }
        double mean = stats.getMean();
        return new Double(mean);
    }

    public Double getStdDeviationX() {

        return computeStdDeviation(getXValues());

    }

    public Double getStdDeviationY() {

        return computeStdDeviation(getYValues());

    }

    public Double getStdDeviationZ() {

        return computeStdDeviation(getZValues());

    }

    private Double computeStdDeviation(List<Double> values) {
        DescriptiveStatistics stats = DescriptiveStatistics.newInstance();

        //       Add the data from the array
        for (Double value : values) {
            stats.addValue(value);
        }

        //       Compute some statistics 
        //double mean = stats.getMean();
        //      double median = stats.getMedian();
        double std = stats.getStandardDeviation();
        return new Double(std);

    }

}