jwatbivariatestatstablemodel.java

一个用于排队系统仿真的开源软件,有非常形象的图象仿真过程!

package jmt.engine.jwat.workloadAnalysis.tables;

import javax.swing.table.AbstractTableModel;

import jmt.engine.jwat.MatrixOsservazioni;
import jmt.engine.jwat.StatBivariate;
import jmt.gui.jwat.JWATConstants;
//TODO: al modello deve essere passati i nomi delle variabili e le statisctiche bivariate
public class JWatBivariateStatsTableModel extends AbstractTableModel implements JWATConstants{
	private String[] names = new String[0];
	private MatrixOsservazioni matrix = null;
	//private DecimalFormat format = new DecimalFormat("####.####E0");
	
	public JWatBivariateStatsTableModel(MatrixOsservazioni m){
		matrix = m;
	}
	
	/**
	 * Returns name for each column (given its index) to be displayed inside
	 * table header
	 */
	public String getColumnName(int columnIndex) {
		if(matrix != null) names = matrix.getVariableNames();
		if (columnIndex < names.length)
			return names[columnIndex];
		else
			return null;
	}
	
	public int getRowCount() {
		if(matrix == null) return 0;
		return names.length;
	}

	public int getColumnCount() {
		return names.length;
	}

	public Object getValueAt(int rowIndex, int columnIndex) {
		if(matrix != null){
			StatBivariate b = matrix.getBivStatObj();
		
			if(rowIndex == columnIndex) return null;
			return defaultFormat.format(b.getCovariance(rowIndex,columnIndex));
		}
		return null;
	}
	
	public Class getColumnClass(int index) { 
		return String.class;
	}
	
	public String[] getNames(){
		if(matrix != null) return matrix.getVariableNames();
		return names;
	}

	public void setMatrixObs(MatrixOsservazioni m){
		matrix = m;
		if(matrix == null) names = new String[0];
		else names = matrix.getVariableNames();
		this.fireTableStructureChanged();
	}
}