guineu.modules.dataanalysis.variationCoefficient.VariationCoefficientTask.java Source code

Introduction

Here is the source code for guineu.modules.dataanalysis.variationCoefficient.VariationCoefficientTask.java

Source

/*
 * Copyright 2007-2013 VTT Biotechnology
 * This file is part of Guineu.
 *
 * Guineu 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 2 of the License, or (at your option) any later
 * version.
 *
 * Guineu 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 General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License along with
 * Guineu; if not, write to the Free Software Foundation, Inc., 51 Franklin St,
 * Fifth Floor, Boston, MA 02110-1301 USA
 */
package guineu.modules.dataanalysis.variationCoefficient;

import guineu.data.Dataset;
import guineu.data.PeakListRow;
import guineu.data.datamodels.VariationCoefficientDataModel;
import guineu.data.impl.VariationCoefficientData;
import guineu.main.GuineuCore;
import guineu.taskcontrol.AbstractTask;
import guineu.taskcontrol.TaskStatus;
import guineu.util.Tables.DataTable;
import guineu.util.Tables.DataTableModel;
import guineu.util.Tables.impl.PushableTable;
import guineu.util.internalframe.DataInternalFrame;
import java.awt.Dimension;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math.stat.descriptive.DescriptiveStatistics;

/**
 *
 * @author scsandra
 */
public class VariationCoefficientTask extends AbstractTask {

    private Dataset[] datasets;
    private double progress;

    public VariationCoefficientTask(Dataset[] datasets) {
        this.datasets = datasets;
    }

    public String getTaskDescription() {
        return "Coefficient of variation... ";
    }

    public double getFinishedPercentage() {
        return progress;
    }

    public void cancel() {
        setStatus(TaskStatus.CANCELED);
    }

    public void run() {
        try {
            this.variationCoefficient();
        } catch (Exception e) {
            setStatus(TaskStatus.ERROR);
            errorMessage = e.toString();
            return;
        }
    }

    public void variationCoefficient() {
        setStatus(TaskStatus.PROCESSING);
        try {
            progress = 0.0f;
            List<VariationCoefficientData> data = new ArrayList<VariationCoefficientData>();
            for (Dataset dataset : datasets) {
                VariationCoefficientData vcdata = new VariationCoefficientData();
                vcdata.variationCoefficient = getvariationCoefficient(dataset);
                vcdata.NumberIdentMol = getNumberIdentMol(dataset);
                vcdata.datasetName = dataset.getDatasetName();
                vcdata.numberMol = dataset.getNumberRows();
                vcdata.numberExperiments = dataset.getNumberCols();
                data.add(vcdata);
            }

            DataTableModel model = new VariationCoefficientDataModel(data);
            DataTable table = new PushableTable(model);
            table.formatNumbers(1);
            DataInternalFrame frame = new DataInternalFrame("Coefficient of variation", table.getTable(),
                    new Dimension(450, 450));
            GuineuCore.getDesktop().addInternalFrame(frame);
            frame.setVisible(true);

            progress = 1f;

        } catch (Exception ex) {
        }
        setStatus(TaskStatus.FINISHED);
    }

    private int getNumberIdentMol(Dataset dataset) {
        int cont = 0;
        for (PeakListRow row : dataset.getRows()) {
            if (!((String) row.getVar("getName")).toLowerCase().matches("unknown")) {
                cont++;
            }
        }
        return cont;
    }

    private double getvariationCoefficient(Dataset dataset) {
        DescriptiveStatistics superStats = new DescriptiveStatistics();
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (PeakListRow row : dataset.getRows()) {
            stats.clear();
            for (String experimentName : dataset.getAllColumnNames()) {
                Object value = row.getPeak(experimentName);
                if (value != null && value instanceof Double) {
                    stats.addValue((Double) value);
                } else {

                    try {
                        stats.addValue(Double.valueOf((String) value));
                    } catch (Exception e) {
                    }
                }
            }
            if (stats.getMean() > 0) {
                double value = stats.getStandardDeviation() / stats.getMean();
                superStats.addValue(value);
            }
        }
        return superStats.getMean();
    }
}