public void readXMLData() {
energy = new double[0];
magnetization = new double[0];
numberOfPoints = 0;
String filename = "ising_data.xml";
JFileChooser chooser = OSPFrame.getChooser();
int result = chooser.showOpenDialog(null);
if (result == JFileChooser.APPROVE_OPTION) {
filename = chooser.getSelectedFile().getAbsolutePath();
} else {
return;
}
XMLControlElement xmlControl = new XMLControlElement(filename);
if (xmlControl.failedToRead()) {
control.println("failed to read: " + filename);
} else {
// gets the datasets in the xml file
Iterator it = xmlControl.getObjects(Dataset.class, false).iterator();
while (it.hasNext()) {
Dataset dataset = (Dataset) it.next();
if (dataset.getName().equals("magnetization")) {
magnetization = dataset.getYPoints();
}
if (dataset.getName().equals("energy")) {
energy = dataset.getYPoints();
}
}
numberOfPoints = magnetization.length;
control.println("Reading: " + filename);
control.println("Number of points = " + numberOfPoints);
}
calculate();
plotFrame.repaint();
}
/**
* Gets the template location at which the best match occurs in a rectangle and along a line. May
* return null.
*
* @param target the image to search
* @param searchRect the rectangle to search within the target image
* @param x0 the x-component of a point on the line
* @param y0 the y-component of a point on the line
* @param slope the slope of the line
* @param spread the spread of the line (line width = 1+2*spread)
* @return the optimized template location of the best match, if any
*/
public TPoint getMatchLocation(
BufferedImage target, Rectangle searchRect, double x0, double y0, double theta, int spread) {
wTarget = target.getWidth();
hTarget = target.getHeight();
// determine insets needed to accommodate template
int left = wTemplate / 2, right = left;
if (wTemplate % 2 > 0) right++;
int top = hTemplate / 2, bottom = top;
if (hTemplate % 2 > 0) bottom++;
// trim search rectangle if necessary
searchRect.x = Math.max(left, Math.min(wTarget - right, searchRect.x));
searchRect.y = Math.max(top, Math.min(hTarget - bottom, searchRect.y));
searchRect.width = Math.min(wTarget - searchRect.x - right, searchRect.width);
searchRect.height = Math.min(hTarget - searchRect.y - bottom, searchRect.height);
if (searchRect.width <= 0 || searchRect.height <= 0) {
peakHeight = Double.NaN;
peakWidth = Double.NaN;
return null;
}
// set up test pixels to search (rectangle plus template)
int xMin = Math.max(0, searchRect.x - left);
int xMax = Math.min(wTarget, searchRect.x + searchRect.width + right);
int yMin = Math.max(0, searchRect.y - top);
int yMax = Math.min(hTarget, searchRect.y + searchRect.height + bottom);
wTest = xMax - xMin;
hTest = yMax - yMin;
if (target.getType() != BufferedImage.TYPE_INT_RGB) {
BufferedImage image = new BufferedImage(wTarget, hTarget, BufferedImage.TYPE_INT_RGB);
image.createGraphics().drawImage(target, 0, 0, null);
target = image;
}
targetPixels = new int[wTest * hTest];
target.getRaster().getDataElements(xMin, yMin, wTest, hTest, targetPixels);
// get the points to search along the line
ArrayList<Point2D> searchPts = getSearchPoints(searchRect, x0, y0, theta);
if (searchPts == null) {
peakHeight = Double.NaN;
peakWidth = Double.NaN;
return null;
}
// collect differences in a map as they are measured
HashMap<Point2D, Double> diffs = new HashMap<Point2D, Double>();
// find the point with the minimum difference from template
double matchDiff = largeNumber; // larger than typical differences
int xMatch = 0, yMatch = 0;
double avgDiff = 0;
Point2D matchPt = null;
for (Point2D pt : searchPts) {
int x = (int) pt.getX();
int y = (int) pt.getY();
double diff = getDifferenceAtTestPoint(x, y);
diffs.put(pt, diff);
avgDiff += diff;
if (diff < matchDiff) {
matchDiff = diff;
xMatch = x;
yMatch = y;
matchPt = pt;
}
}
avgDiff /= searchPts.size();
peakHeight = avgDiff / matchDiff - 1;
peakWidth = Double.NaN;
double dl = 0;
int matchIndex = searchPts.indexOf(matchPt);
// if match is not exact, fit a Gaussian and find peak
if (!Double.isInfinite(peakHeight) && matchIndex > 0 && matchIndex < searchPts.size() - 1) {
// fill data arrays
Point2D pt = searchPts.get(matchIndex - 1);
double diff = diffs.get(pt);
xValues[0] = -pt.distance(matchPt);
yValues[0] = avgDiff / diff - 1;
xValues[1] = 0;
yValues[1] = peakHeight;
pt = searchPts.get(matchIndex + 1);
diff = diffs.get(pt);
xValues[2] = pt.distance(matchPt);
yValues[2] = avgDiff / diff - 1;
// determine approximate offset (dl) and width (w) values
double pull = -xValues[0] / (yValues[1] - yValues[0]);
double push = xValues[2] / (yValues[1] - yValues[2]);
if (Double.isNaN(pull)) pull = LARGE_NUMBER;
if (Double.isNaN(push)) push = LARGE_NUMBER;
dl = 0.3 * (xValues[2] - xValues[0]) * (push - pull) / (push + pull);
double ratio = dl > 0 ? peakHeight / yValues[0] : peakHeight / yValues[2];
double w = dl > 0 ? dl - xValues[0] : dl - xValues[2];
w = w * w / Math.log(ratio);
// set parameters and fit to x data
dataset.clear();
dataset.append(xValues, yValues);
double rmsDev = 1;
for (int k = 0; k < 3; k++) {
double c = k == 0 ? w : k == 1 ? w / 3 : w * 3;
f.setParameterValue(0, peakHeight);
f.setParameterValue(1, dl);
f.setParameterValue(2, c);
rmsDev = fitter.fit(f);
if (rmsDev < 0.01) { // fitter succeeded (3-point fit should be exact)
dl = f.getParameterValue(1);
peakWidth = f.getParameterValue(2);
break;
}
}
}
double dx = dl * Math.cos(theta);
double dy = dl * Math.sin(theta);
double xImage = xMatch + searchRect.x - left - trimLeft + dx;
double yImage = yMatch + searchRect.y - top - trimTop + dy;
return new TPoint(xImage, yImage);
}
/** Initializes the table. */
protected void init() {
dataTable
.getColumnModel()
.addColumnModelListener(
new TableColumnModelListener() {
public void columnAdded(TableColumnModelEvent e) {
/** empty block */
}
public void columnRemoved(TableColumnModelEvent e) {
/** empty block */
}
public void columnSelectionChanged(ListSelectionEvent e) {
/** empty block */
}
public void columnMarginChanged(ChangeEvent e) {
refreshTable();
}
public void columnMoved(TableColumnModelEvent e) {
refreshTable();
}
});
// assemble statistics data array
Dataset workingData = dataTable.getWorkingData();
double[] xstats = getStatistics(workingData.getXPoints());
double[] ystats = getStatistics(workingData.getValidYPoints());
if (statsData == null) {
statsData = new Object[xstats.length][0];
}
for (int i = 0; i < xstats.length; i++) {
String label = ToolsRes.getString("Table.Entry.Count"); // $NON-NLS-1$
if (i == 5) {
statsData[i] =
new Object[] {label, new Integer((int) xstats[i]), new Integer((int) ystats[i])};
} else {
switch (i) {
case 0:
label = ToolsRes.getString("Table.Entry.Max");
break; //$NON-NLS-1$
case 1:
label = ToolsRes.getString("Table.Entry.Min");
break; //$NON-NLS-1$
case 2:
label = ToolsRes.getString("Table.Entry.Mean");
break; //$NON-NLS-1$
case 3:
label = ToolsRes.getString("Table.Entry.StandardDev");
break; //$NON-NLS-1$
case 4:
label = ToolsRes.getString("Table.Entry.StandardError"); // $NON-NLS-1$
}
statsData[i] = new Object[] {label, new Double(xstats[i]), new Double(ystats[i])};
}
}
// set and configure table model and header
setModel(tableModel);
setGridColor(Color.blue);
setEnabled(false);
setTableHeader(null); // no table header
labelRenderer = new LabelRenderer();
setAutoResizeMode(JTable.AUTO_RESIZE_OFF);
setDefaultRenderer(Double.class, new ScientificRenderer(3));
ListSelectionModel selectionModel = dataTable.getSelectionModel();
selectionModel.addListSelectionListener(
new ListSelectionListener() {
public void valueChanged(ListSelectionEvent e) {
Dataset workingData = dataTable.getWorkingData();
double[] xstats = getStatistics(workingData.getXPoints());
double[] ystats = getStatistics(workingData.getValidYPoints());
int i = 0;
for (; i < xstats.length - 1; i++) {
statsData[i][1] = new Double(xstats[i]);
statsData[i][2] = new Double(ystats[i]);
}
statsData[i][1] = new Integer((int) xstats[i]);
statsData[i][2] = new Integer((int) ystats[i]);
refreshTable();
}
});
refreshCellWidths();
}
/**
* Gets the template location at which the best match occurs in a rectangle. May return null.
*
* @param target the image to search
* @param searchRect the rectangle to search within the target image
* @return the optimized template location at which the best match, if any, is found
*/
public TPoint getMatchLocation(BufferedImage target, Rectangle searchRect) {
wTarget = target.getWidth();
hTarget = target.getHeight();
// determine insets needed to accommodate template
int left = wTemplate / 2, right = left;
if (wTemplate % 2 > 0) right++;
int top = hTemplate / 2, bottom = top;
if (hTemplate % 2 > 0) bottom++;
// trim search rectangle if necessary
searchRect.x = Math.max(left, Math.min(wTarget - right, searchRect.x));
searchRect.y = Math.max(top, Math.min(hTarget - bottom, searchRect.y));
searchRect.width = Math.min(wTarget - searchRect.x - right, searchRect.width);
searchRect.height = Math.min(hTarget - searchRect.y - bottom, searchRect.height);
if (searchRect.width <= 0 || searchRect.height <= 0) {
peakHeight = Double.NaN;
peakWidth = Double.NaN;
return null;
}
// set up test pixels to search (rectangle plus template)
int xMin = Math.max(0, searchRect.x - left);
int xMax = Math.min(wTarget, searchRect.x + searchRect.width + right);
int yMin = Math.max(0, searchRect.y - top);
int yMax = Math.min(hTarget, searchRect.y + searchRect.height + bottom);
wTest = xMax - xMin;
hTest = yMax - yMin;
if (target.getType() != BufferedImage.TYPE_INT_RGB) {
BufferedImage image = new BufferedImage(wTarget, hTarget, BufferedImage.TYPE_INT_RGB);
image.createGraphics().drawImage(target, 0, 0, null);
target = image;
}
targetPixels = new int[wTest * hTest];
target.getRaster().getDataElements(xMin, yMin, wTest, hTest, targetPixels);
// find the rectangle point with the minimum difference
double matchDiff = largeNumber; // larger than typical differences
int xMatch = 0, yMatch = 0;
double avgDiff = 0;
for (int x = 0; x <= searchRect.width; x++) {
for (int y = 0; y <= searchRect.height; y++) {
double diff = getDifferenceAtTestPoint(x, y);
avgDiff += diff;
if (diff < matchDiff) {
matchDiff = diff;
xMatch = x;
yMatch = y;
}
}
}
avgDiff /= (searchRect.width * searchRect.height);
peakHeight = avgDiff / matchDiff - 1;
peakWidth = Double.NaN;
double dx = 0, dy = 0;
// if match is not exact, fit a Gaussian and find peak
if (!Double.isInfinite(peakHeight)) {
// fill data arrays
xValues[1] = yValues[1] = peakHeight;
for (int i = -1; i < 2; i++) {
if (i == 0) continue;
double diff = getDifferenceAtTestPoint(xMatch + i, yMatch);
xValues[i + 1] = avgDiff / diff - 1;
diff = getDifferenceAtTestPoint(xMatch, yMatch + i);
yValues[i + 1] = avgDiff / diff - 1;
}
// estimate peakHeight = peak of gaussian
// estimate offset dx of gaussian
double pull = 1 / (xValues[1] - xValues[0]);
double push = 1 / (xValues[1] - xValues[2]);
if (Double.isNaN(pull)) pull = LARGE_NUMBER;
if (Double.isNaN(push)) push = LARGE_NUMBER;
dx = 0.6 * (push - pull) / (push + pull);
// estimate width wx of gaussian
double ratio = dx > 0 ? peakHeight / xValues[0] : peakHeight / xValues[2];
double wx = dx > 0 ? dx + 1 : dx - 1;
wx = wx * wx / Math.log(ratio);
// estimate offset dy of gaussian
pull = 1 / (yValues[1] - yValues[0]);
push = 1 / (yValues[1] - yValues[2]);
if (Double.isNaN(pull)) pull = LARGE_NUMBER;
if (Double.isNaN(push)) push = LARGE_NUMBER;
dy = 0.6 * (push - pull) / (push + pull);
// estimate width wy of gaussian
ratio = dy > 0 ? peakHeight / yValues[0] : peakHeight / yValues[2];
double wy = dy > 0 ? dy + 1 : dy - 1;
wy = wy * wy / Math.log(ratio);
// set x parameters and fit to x data
dataset.clear();
dataset.append(pixelOffsets, xValues);
double rmsDev = 1;
for (int k = 0; k < 3; k++) {
double c = k == 0 ? wx : k == 1 ? wx / 3 : wx * 3;
f.setParameterValue(0, peakHeight);
f.setParameterValue(1, dx);
f.setParameterValue(2, c);
rmsDev = fitter.fit(f);
if (rmsDev < 0.01) { // fitter succeeded (3-point fit should be exact)
dx = f.getParameterValue(1);
peakWidth = f.getParameterValue(2);
break;
}
}
if (!Double.isNaN(peakWidth)) {
// set y parameters and fit to y data
dataset.clear();
dataset.append(pixelOffsets, yValues);
for (int k = 0; k < 3; k++) {
double c = k == 0 ? wy : k == 1 ? wy / 3 : wy * 3;
f.setParameterValue(0, peakHeight);
f.setParameterValue(1, dx);
f.setParameterValue(2, c);
rmsDev = fitter.fit(f);
if (rmsDev < 0.01) { // fitter succeeded (3-point fit should be exact)
dy = f.getParameterValue(1);
peakWidth = (peakWidth + f.getParameterValue(2)) / 2;
break;
}
}
if (rmsDev > 0.01) peakWidth = Double.NaN;
}
}
double xImage = xMatch + searchRect.x - left - trimLeft + dx;
double yImage = yMatch + searchRect.y - top - trimTop + dy;
return new TPoint(xImage, yImage);
}