scripts/channelClasses/mock_ec/ModelEeg.java

/////////////////////////////////////////////////////////////////
// This script generates (somewhat) realistic EC data.
//
// Run with (for example)
//    java -cp build:lib/derby.jar -Dserver.cache.enable=false -Djava.library.path=c frontendClasses/CLI -scriptChannel mock_ec/ModelEeg -reviewSeries -paradigm ec -binZ site -transform power -display "TiledStack()" -v
/////////////////////////////////////////////////////////////////
package mock_ec;

import java.io.*;
import java.util.*;

import epochClasses.*;
import generalClasses.*;
import recordingClasses.Recording;
import seriesClasses.*;
import seriesClasses.seriesGeneration.Erlang;
import channelClasses.ChannelScript;
import static channelClasses.Channel.*;

/////////////////////////////////////////////////////////////////
/** This script generates (somewhat) realistic EC data.
 * This script generates (somewhat) realistic EC data.  The EEG is as
 * generated by the model.  The channels comprise:
 * <ol><li>three identical timeseries (for testing variance intrinsic 
 *         to fits)</li>
 *     <li>three simulations with same parameters (for testing effect of
 *         different but equivalent data)</li>
 * </ol>
 *
 * <p>The intended effect is:
 * <ul><li>Two alpha sources, nearly overlapping in time, frequency 
 *         and topography</li>
 * </ul>
 */
public class ModelEeg extends ChannelScript
{
    /** Recording instance to be operated on */
    Recording rec = null;
    /** Time series */
    ArrayList<SeriesAnalog> list = new ArrayList<SeriesAnalog>();
    /** Events.  May be left empty */
    ArrayList<Event> ev = new ArrayList<Event>();

    ////////////////////////////////////////////////////////////////////
    /** Initialize instance by setting its parameters to default values.
     */
    public ModelEeg(Recording rec) {
        this.rec = rec;
    } // ModelEeg

    ////////////////////////////////////////////////////////////////////
    /** Update recording data by performing channel-oriented operations.
     */
    public void update() {
        // Template - used to encapsulate all sampling characteristics
        float x0 = 10.0f;            // in seconds: times start at x0
        float xDelta = 0.004f;       // in seconds: times increment by xDelta
        float duration = 120.0f;     // in seconds: times end at x0+duration
        int nIndexes = Math.round(duration/xDelta);
        SeriesAnalog template = new SeriesAnalog(new SiteSet(), // sites
                                                 x0,            // x0
                                                 xDelta,        // xDelta
                                                 new Units(Unit.s),  // xUnits
                                                 nIndexes,      // # samples
                                                 new Units(Unit.uV), // yUnits
                                                 DataMode.EEG); // DataMode

        // Channels resulting that are either equal or equivalent
        String[] labels = {"A1", "A2", "A3", "B", "C", "D"};

        // Generate timeseries "A"
        float t0 = 0.090f;
        SeriesAnalog seriesA = getEegWithAlpha(template, t0);

        // Generate time series
        for(int site=0; site<labels.length; site++) {    
            // What modality?  What is the SiteSet?
            DataMode mode = DataMode.EEG;
            SiteSet ss = new SiteSet(new Site(labels[site]));

            // Append series to result
            if(site<labels.length/2)
                list.add(seriesA.clone().setSites(ss));
            else
                list.add(getEegWithAlpha(template, t0).setSites(ss));
        }

        // Add synthetic time series to the currently empty Recording
        replaceAllSeries(rec, list);
        replaceAllEvents(rec, ev);
    } // update


    ////////////////////////////////////////////////////////////////////
    /** Dump summary of this class or object
     * @return String representation of this object
     */
    public String toString() {
        String s = "<<<"+this.getClass().toString()+">>>\n";
        return s;
    } // toString


    ////////////////////////////////////////////////////////////////////
    /** Generate pseudo EEG containing alpha
     */
    private SeriesAnalog getEegWithAlpha(SeriesAnalog template, float t0) {
        float lnorm = 2.8f;
        return SeriesAnalog.getModelledEegTimeseries(template,t0,lnorm);
    } // getEegWithAlpha
}