public void calAutoCorrelation_RingMean(
int
nRefType) { // crosscorrelation coefficients between pixel valuse and the means and
// medians of the pixels located on the cocentric rings with radius up to
// m_nRMax
m_nRefType = nRefType;
int numRings = m_cvRings.size();
calRefPixels();
int r, c;
int len = m_cIMSC.m_nTotalSteps + 1;
double[] pixels0 = new double[len];
double[] meanr = new double[len];
double[] medianr = new double[len];
int index = 0;
Point p;
index = 0;
while (true) {
p = m_cIMSC.getPosition();
pixels0[index] = m_pnPixelsLR[p.y][p.x];
index++;
if (m_cIMSC.done()) break;
m_cIMSC.move();
}
ImageShape shape;
Histogram hist = CommonMethods.getDefaultHistogram(m_pnPixelsLR);
int y, y0 = 0;
int nRef;
for (c = 0; c < numRings - 1; c++) {
index = 0;
m_cIMSC.reset();
y0 = m_cIMSC.getPosition().y;
shape = m_cvRings.get(c);
while (true) {
p = m_cIMSC.getPosition();
nRef = getRefPixel(p);
y = p.y;
if (y > y0) {
IJ.showStatus(
"calAutoCorrelation_RingMean: r="
+ PrintAssist.ToString(c + 1)
+ " y="
+ PrintAssist.ToString(y));
y0 = y;
}
CommonMethods.fillHistogram(m_pnPixels, nRef, shape, p, hist);
meanr[index] = hist.getMean();
medianr[index] = hist.getPercentileValue();
index++;
if (m_cIMSC.done()) break;
m_cIMSC.move();
}
m_pdAutoCorr_mean[c] = CommonStatisticsMethods.crossCorrelationCoefficient(pixels0, meanr, 0);
m_pdAutoCorr_median[c] =
CommonStatisticsMethods.crossCorrelationCoefficient(pixels0, medianr, 0);
}
}
void calRefPixels() {
int[] nRange = new int[2];
CommonMethods.getDataRange(m_pnPixels, nRange);
MeanSem0 ms = CommonStatisticsMethods.buildMeanSem(m_pnPixels);
int nMin;
switch (m_nRefType) {
case export_AutoCorrelation.originalRef:
m_pnPixelsLR = new int[h][w];
CommonStatisticsMethods.copyArray(m_pnPixels, m_pnPixelsLR);
break;
case export_AutoCorrelation.globalMeanRef:
m_pnPixelsLR = new int[h][w];
CommonStatisticsMethods.copyArray(m_pnPixels, m_pnPixelsLR);
CommonStatisticsMethods.shiftArray(m_pnPixelsLR, -(int) (ms.mean + 0.5));
break;
case export_AutoCorrelation.globalMedianRef:
m_pnPixelsLR = new int[h][w];
CommonStatisticsMethods.copyArray(m_pnPixels, m_pnPixelsLR);
CommonStatisticsMethods.shiftArray(
m_pnPixelsLR, -CommonStatisticsMethods.getQuantile(m_pnPixels, 0.5));
break;
case export_AutoCorrelation.localMeanRef:
m_pnPixelsLR = CommonMethods.getPixelValus_LocalReference(impl, m_cRefRing, m_nRefType);
break;
case export_AutoCorrelation.localMedianRef:
m_pnPixelsLR = CommonMethods.getPixelValus_LocalReference(impl, m_cRefRing, m_nRefType);
break;
default:
m_pnPixelsLR = new int[h][w];
CommonStatisticsMethods.copyArray(m_pnPixels, m_pnPixelsLR);
break;
}
updateHistograms();
}
int calCompensatedPixels() {
int numRings = m_cvRings.size();
CommonStatisticsMethods.copyArray(m_pnPixels, m_pnPixelsCompensated);
CommonMethods.displayPixels(m_pnPixelsCompensated, "before compensation", impl.getType());
m_cIMSC.reset();
Point pt;
int index, step = 0, pixel, i, r0;
double dm, dsur;
ImageShape shape;
while (true) {
pt = m_cIMSC.getPosition();
index = getPercentileIndex(step);
dsur = m_pcRingMeanSems_Mean[index][m_nRMax].mean;
r0 = m_pnAveRadius[index];
//
// m_pnPixelsCompensated[pt.y][pt.x]-=(int)(m_cPixelMeanSems_trail[index].mean-dsur);
for (i = 0; i < numRings; i++) {
pixel = (int) (m_pcRingMeanSems_Mean[index][i].mean - dsur);
if (pixel < 1) break;
shape = m_cvRings.get(i);
shape.setCenter(pt);
ImageShapeHandler.addValue(m_pnPixelsCompensated, shape, -pixel);
}
if (m_cIMSC.done()) break;
m_cIMSC.move();
step++;
}
CommonMethods.displayPixels(m_pnPixelsCompensated, "after compensation", impl.getType());
return 1;
}
public void buildIPOGContourShapes(int[][] pixelsCompensated, int[][] pnScratch, int cutoff) {
int h = pixelsCompensated.length, w = pixelsCompensated[0].length;
intRange xRange3 = new intRange(), yRange3 = new intRange();
IPOGaussianNodeHandler.getRanges3(IPOGNode.IPOGs, xRange, yRange);
xRange.setCommonRange(new intRange(0, w - 1));
yRange.setCommonRange(new intRange(0, h - 1));
CommonStatisticsMethods.copyArray(
pixelsCompensated,
pnScratch,
yRange.getMin(),
yRange.getMax(),
xRange.getMin(),
xRange.getMax());
IPOGaussianNodeHandler.getSuperImposition(pnScratch, IPOGNode.IPOGs);
if (pnScratch[cy][cx] < cutoff) {
cContourShape = new CircleImage(3);
cContourShape.setCenter(new Point(cx, cy));
cContourShape.setFrameRanges(new intRange(0, w - 1), new intRange(0, h - 1));
} else {
ArrayList<Point> contour =
ContourFollower.getContour_Out(
pnScratch, w, h, new Point(cx, cy), cutoff, Integer.MAX_VALUE, true);
cContourShape = ImageShapeHandler.buildImageShape(contour);
}
}
public IntPair getDataPosition(int ranking0) { // ascending, ranking starts from 0
if (ranking0 >= m_nSize) {
IJ.error("ranking is larger than the data size: in getDataPosition");
return null;
}
IntPair ip = null;
int r, c, num = m_dvDataLo.size(), nt;
ArrayList<Double> dvt;
dvt = m_dvDataLo;
nt = dvt.size();
num = nt;
r = -1;
while (num <= ranking0) {
r++;
if (r < m_nDim) dvt = m_pdvData[r];
else dvt = m_dvDataHi;
nt = dvt.size();
num += nt;
}
if (r >= m_nDim) r = -2;
int ranking = ranking0 - (num - nt);
c = CommonStatisticsMethods.getRankingIndex(dvt, ranking, 1);
ip = new IntPair(r, c);
return ip;
}
void calAveRadius() {
int len = m_pcRingMeanSems_Mean.length;
double sd = CommonStatisticsMethods.buildMeanSem(m_pnPixels).getSD();
int i, j, numRings = m_cvRings.size();
for (i = 0; i < len; i++) {
for (j = 0; j < numRings; j++) {
if (m_pcRingMeanSems_Mean[i][j].mean < 2 * m_pcRingMeanSems_Mean[i][j].getSD()) break;
m_pnAveRadius[i] = j;
}
}
}
int refinePixelStatitics() {
stampIPOs();
int[][] pixelsTemp = m_pnPixels;
m_pnPixels = m_pnPixelsCompensated;
m_cIMSC.reset();
int len = m_cIMSC.m_nTotalSteps + 1;
int i, c, step = 0;
Point p = m_cIMSC.getPosition();
int index = 0;
ImageShape shape;
Histogram hist;
double mean, median;
int y, y0 = 0;
int pixel, nRef;
double dsur;
int numRings = m_cvRings.size();
while (true) {
p = m_cIMSC.getPosition();
y = p.y;
if (y > y0)
IJ.showStatus(
"refinePixelStatistics "
+ PrintAssist.ToString(y)
+ "-th line of "
+ PrintAssist.ToString(h)
+ " lines!");
index = getPercentileIndex(step);
dsur = m_pcRingMeanSems_Mean[index][m_nRMax].mean;
ImageShapeHandler.copyElements(m_pnPixelsCompensated, m_pnPixelsTemp, m_cvRings, p);
for (i = 0; i < numRings; i++) {
pixel = (int) (m_pcRingMeanSems_Mean[index][i].mean - dsur);
if (pixel < 1) break;
shape = m_cvRings.get(i);
shape.setCenter(p);
ImageShapeHandler.addValue(m_pnPixelsTemp, shape, pixel);
}
nRef =
(int) ImageShapeHandler.getMean(m_pnPixelsTemp, m_cRefRing, m_pnStamp, m_nExcludingType);
CommonMethods.fillHistogram(
m_pnPixelsTemp,
nRef,
m_cvRings.get(numRings - 1),
p,
m_cvHists.get(numRings - 1),
m_pnStamp,
m_nExcludingType);
CommonMethods.fillHistograms(m_pnPixelsTemp, nRef, m_cvRings, p, m_cvHists);
for (i = 0; i < numRings; i++) {
hist = m_cvHists.get(i);
mean = hist.getMeanSem().mean;
median = hist.getPercentileValue();
m_pdRingMean[step][i] = mean;
m_pdRingMedian[step][i] = median;
}
// m_pnPixels_trail[step]=m_pnPixelsTemp[p.y][p.x]-nRef;
if (m_cIMSC.done()) break;
m_cIMSC.move();
step++;
}
for (c = 0; c < numRings; c++) {
m_pcMeanSems_Median[c] = CommonStatisticsMethods.buildMeanSem(m_pdRingMedian, c);
m_pcMeanSems_Mean[c] = CommonStatisticsMethods.buildMeanSem(m_pdRingMean, c);
}
if (m_bPresetPoints) {
len = pixelsOfPresetPoints.size();
int num = len / m_nPercentileDivision;
int rI, rF;
for (i = 0; i < m_nPercentileDivision; i++) {
rI = Math.max(0, (int) ((i - .5) * num));
rF = Math.min(len - 1, (int) ((i + .5) * num));
for (c = 0; c < numRings; c++) {
m_pcRingMeanSems_Median[i][c] =
CommonStatisticsMethods.buildMeanSem(m_pdRingMedian, rI, rF, 1, c);
m_pcRingMeanSems_Mean[i][c] =
CommonStatisticsMethods.buildMeanSem(m_pdRingMean, rI, rF, 1, c);
}
}
}
m_pnPixels = pixelsTemp;
return 1;
}
public void exportRingMeanSem(String path) {
int r, c;
int len = w * h;
double[] pixels0 = new double[len];
double[] pixelsr = new double[len];
int numRings = m_cvRings.size();
MeanSem0 pcMeanSems_MeanNR[] = new MeanSem0[numRings];
MeanSem0 pcMeanSems_MedianNR[] = new MeanSem0[numRings];
len = m_cIMSC.getTotalSteps() + 1;
Point p;
int pixel;
int index = 0;
while (true) {
p = m_cIMSC.getPosition();
if (m_bPresetPoints) {
pixel = pixelsOfPresetPoints.get(index);
}
pixel = m_pnPixelsLR[p.y][p.x];
for (c = 0; c < numRings; c++) {
if (pixel == 0) {
m_pdRingMedian[index][c] = pixel;
m_pdRingMean[index][c] = pixel;
} else {
m_pdRingMedian[index][c] /= pixel;
m_pdRingMean[index][c] /= pixel;
}
}
if (m_cIMSC.done()) break;
m_cIMSC.move();
index++;
}
for (c = 0; c < numRings; c++) {
pcMeanSems_MedianNR[c] = CommonStatisticsMethods.buildMeanSem(m_pdRingMedian, c);
pcMeanSems_MeanNR[c] = CommonStatisticsMethods.buildMeanSem(m_pdRingMean, c);
}
Formatter fm = QuickFormatter.getFormatter(path);
PrintAssist.printString(fm, "dist", 6);
PrintAssist.printString(fm, "mean", 12);
PrintAssist.printString(fm, "sem", 12);
PrintAssist.printString(fm, "median", 12);
PrintAssist.printString(fm, "sem", 12);
PrintAssist.printString(fm, "meanNR", 12);
PrintAssist.printString(fm, "semNR", 12);
PrintAssist.printString(fm, "medianNR", 12);
PrintAssist.printString(fm, "semNR", 12);
PrintAssist.printString(fm, "num", 8);
PrintAssist.endLine(fm);
/* PrintAssist.printNumber(fm, 0, 6, 0);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.mean, 12, 3);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.sem, 12, 4);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.mean, 12, 3);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.sem, 12, 4);
PrintAssist.printNumber(fm, 1, 12, 4);
PrintAssist.printNumber(fm, 0, 12, 4);
PrintAssist.printNumber(fm, 1, 12, 4);
PrintAssist.printNumber(fm, 0, 12, 4);
PrintAssist.printNumber(fm, len, 8, 0);
PrintAssist.endLine(fm);*/
for (c = 0; c < numRings; c++) {
PrintAssist.printNumber(fm, c + 1, 6, 0);
PrintAssist.printNumber(fm, m_pcMeanSems_Mean[c].mean, 12, 3);
PrintAssist.printNumber(fm, m_pcMeanSems_Mean[c].sem, 12, 4);
PrintAssist.printNumber(fm, m_pcMeanSems_Median[c].mean, 12, 3);
PrintAssist.printNumber(fm, m_pcMeanSems_Median[c].sem, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MeanNR[c].mean, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MeanNR[c].sem, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MedianNR[c].mean, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MedianNR[c].sem, 12, 4);
PrintAssist.printNumber(fm, len, 8, 0);
PrintAssist.endLine(fm);
}
fm.close();
path = FileAssist.getExtendedFileName(path, " -all percentile");
fm = QuickFormatter.getFormatter(path);
int i;
for (i = 0; i < m_nPercentileDivision; i++) {
PrintAssist.printString(fm, "dist" + PrintAssist.ToString(i), 8);
PrintAssist.printString(fm, "mean" + PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "SD" + PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "median" + PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "SD" + PrintAssist.ToString(i), 12);
/*
PrintAssist.printString(fm, "meanNR"+PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "semNR"+PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "medianNR"+PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "semNR"+PrintAssist.ToString(i), 12);
*
*/
PrintAssist.printString(fm, "num" + PrintAssist.ToString(i), 10);
}
PrintAssist.printString(fm, "dist", 8);
PrintAssist.printString(fm, "meanAllPts", 12);
PrintAssist.printString(fm, "SD_AllPts", 12);
PrintAssist.printString(fm, "medianAPts", 12);
PrintAssist.printString(fm, "SD_AllPts", 12);
/*
PrintAssist.printString(fm, "meanNR"+PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "semNR"+PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "medianNR"+PrintAssist.ToString(i), 12);
PrintAssist.printString(fm, "semNR"+PrintAssist.ToString(i), 12);
*
*/
PrintAssist.printString(fm, "numAllPts", 10);
PrintAssist.endLine(fm);
/*
for(i=0;i<m_nPercentileDivision;i++){
PrintAssist.printNumber(fm, 0, 8, 0);
PrintAssist.printNumber(fm, m_cPixelMeanSems_trail[i].mean, 12, 3);
PrintAssist.printNumber(fm, m_cPixelMeanSems_trail[i].getSD(), 12, 4);
PrintAssist.printNumber(fm, m_cPixelMeanSems_trail[i].mean, 12, 3);
PrintAssist.printNumber(fm, m_cPixelMeanSems_trail[i].getSD(), 12, 4);
/*
PrintAssist.printNumber(fm, 1, 12, 4);
PrintAssist.printNumber(fm, 0, 12, 4);
PrintAssist.printNumber(fm, 1, 12, 4);
PrintAssist.printNumber(fm, 0, 12, 4);
*
*/
/* PrintAssist.printNumber(fm, m_cPixelMeanSems_trail[i].n, 10, 0);
}
PrintAssist.printNumber(fm, 0, 8, 0);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.mean, 12, 3);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.getSD(), 12, 4);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.mean, 12, 3);
PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.getSD(), 12, 4);
/*
PrintAssist.printNumber(fm, 1, 12, 4);
PrintAssist.printNumber(fm, 0, 12, 4);
PrintAssist.printNumber(fm, 1, 12, 4);
PrintAssist.printNumber(fm, 0, 12, 4);
*
*/
// PrintAssist.printNumber(fm, m_cPixelMeanSem_trail.n, 10, 0);
// PrintAssist.endLine(fm);
for (c = 0; c < numRings; c++) {
for (i = 0; i < m_nPercentileDivision; i++) {
PrintAssist.printNumber(fm, m_dvRs.get(c), 8, 2);
PrintAssist.printNumber(fm, m_pcRingMeanSems_Mean[i][c].mean, 12, 3);
PrintAssist.printNumber(fm, m_pcRingMeanSems_Mean[i][c].getSD(), 12, 4);
PrintAssist.printNumber(fm, m_pcRingMeanSems_Median[i][c].mean, 12, 3);
PrintAssist.printNumber(fm, m_pcRingMeanSems_Median[i][c].getSD(), 12, 4);
/*
PrintAssist.printNumber(fm, pcMeanSems_MeanNR[c].mean, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MeanNR[c].sem, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MedianNR[c].mean, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MedianNR[c].sem, 12, 4);
*/
PrintAssist.printNumber(fm, m_pcRingMeanSems_Mean[i][c].n, 10, 0);
}
PrintAssist.printNumber(fm, m_dvRs.get(c), 8, 0);
PrintAssist.printNumber(fm, m_pcMeanSems_Mean[c].mean, 12, 3);
PrintAssist.printNumber(fm, m_pcMeanSems_Mean[c].getSD(), 12, 4);
PrintAssist.printNumber(fm, m_pcMeanSems_Median[c].mean, 12, 3);
PrintAssist.printNumber(fm, m_pcMeanSems_Median[c].getSD(), 12, 4);
/*
PrintAssist.printNumber(fm, pcMeanSems_MeanNR[c].mean, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MeanNR[c].sem, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MedianNR[c].mean, 12, 4);
PrintAssist.printNumber(fm, pcMeanSems_MedianNR[c].sem, 12, 4);
*/
PrintAssist.printNumber(fm, m_pcMeanSems_Mean[c].n, 10, 0);
PrintAssist.endLine(fm);
}
fm.close();
path = FileAssist.getExtendedFileName(path, "all percentile");
}
public void calAutoCorrelation(
int
nRefType) { // computing the cross correlation coefficient between pixel values of points
// separated by the radius of rings
m_nRefType = nRefType;
int numRings = m_cvRings.size();
calRefPixels();
m_cPixelMeanSems = CommonStatisticsMethods.buildMeanSem(m_pnPixelsLR);
int len;
double mean = m_cPixelMeanSems.mean, sem2 = m_cPixelMeanSems.sem2;
double medianMean[] = new double[numRings - 1];
double medianSem2[] = new double[numRings - 1];
double crossProduct[] = new double[numRings - 1];
double numCrossPairs[] = new double[numRings - 1];
double crossProduct_median[] = new double[numRings - 1];
double numCrossPairs_median[] = new double[numRings - 1];
ArrayList<Double> medians[] = new ArrayList[numRings - 1];
int i, j, pixel0;
for (i = 0; i < numRings - 1; i++) {
crossProduct[i] = 0;
numCrossPairs[i] = 0;
crossProduct_median[i] = 0;
numCrossPairs_median[i] = 0;
medianMean[i] = 0;
medianSem2[i] = 0;
medians[i] = new ArrayList();
}
int[][] pixels_LMC = new int[h][w];
ImageShape shape;
ArrayList<Point> points = new ArrayList();
ArrayList<Point> scanningTrace = new ArrayList();
double dp, median;
Point p0, p;
int index = 0, y0 = 0, y;
Histogram hist;
int nRef, mean1;
while (true) {
p0 = new Point(m_cIMSC.getPosition());
y = p0.y;
scanningTrace.add(p0);
pixel0 = m_pnPixelsLR[p0.y][p0.x];
nRef = getRefPixel(p0);
dp = pixel0 - mean;
CommonMethods.fillHistograms(m_pnPixels, nRef, m_cvRings, p0, m_cvHists);
for (i = 0; i < numRings - 1; i++) {
shape = m_cvRings.get(i);
shape.setCenter(p0);
shape.setFrameRanges(
new intRange(p0.x, w - 1),
new intRange(
p0.y,
h - 1)); // this is to avoid double counting the pair of points with given distance
shape.getInnerPoints(points);
shape.setFrameRanges(new intRange(0, w - 1), new intRange(0, h - 1));
len = points.size();
for (j = 0; j < len; j++) {
p = points.get(j);
crossProduct[i] += dp * (m_pnPixels[p.y][p.x] - nRef - mean);
}
numCrossPairs[i] += len;
numCrossPairs_median[i] += 1;
hist = m_cvHists.get(i);
median = hist.getPercentileValue();
medianMean[i] += median;
medianSem2[i] += median * median;
medians[i].add(median);
if (y > y0) {
IJ.showStatus("computing autocorrelation: " + PrintAssist.ToString(y) + "-th line");
IJ.showProgress(y / h);
y0 = y;
}
}
if (m_cIMSC.done()) break;
m_cIMSC.move();
index++;
}
double dAutoCorr_mean, meanMedian, cp;
MeanSem0 ms = new MeanSem0();
int num;
for (i = 0; i < numRings - 1; i++) {
m_pdAutoCorr_mean[i] = crossProduct[i] / (Math.sqrt(sem2 * sem2) * (numCrossPairs[i]));
num = medians[i].size();
medianMean[i] /= num;
medianSem2[i] /= num;
ms.updateMeanSquareSum(num, medianMean[i], medianSem2[i]);
meanMedian = ms.mean;
cp = 0;
for (j = 0; j < num; j++) {
p = scanningTrace.get(j);
cp += (m_pnPixelsLR[p.y][p.x] - mean) * (medians[i].get(j) - meanMedian);
}
m_pdAutoCorr_median[i] = cp / (Math.sqrt(sem2 * ms.sem2) * (num));
}
}
void calPixelStatistics() {
calRefPixels();
int len = m_cIMSC.m_nTotalSteps + 1;
int numRings = m_cvRings.size();
m_pdRingMean = new double[len][numRings];
m_pdRingMedian = new double[len][numRings];
int i, c;
Point p = m_cIMSC.getPosition();
int index = 0;
// ImageShape shape;
Histogram hist;
double mean, median;
int y, y0 = 0;
int nRef;
while (true) {
p = m_cIMSC.getPosition();
y = p.y;
if (y > y0)
IJ.showStatus(
"calPixelStatistics "
+ PrintAssist.ToString(y)
+ "-th line of "
+ PrintAssist.ToString(h)
+ " lines!");
// m_cRefRing.setCenter(p);
nRef = getRefPixel(p);
CommonMethods.fillHistograms(m_pnPixels, nRef, m_cvRings, p, m_cvHists);
for (i = 0; i <= m_nRMax; i++) {
hist = m_cvHists.get(i);
mean = hist.getMeanSem().mean;
median = hist.getPercentileValue();
m_pdRingMean[index][i] = mean;
m_pdRingMedian[index][i] = median;
}
if (m_cIMSC.done()) break;
m_cIMSC.move();
index++;
}
m_cPixelMeanSems = CommonStatisticsMethods.buildMeanSem(m_pnPixelsLR);
// m_cPixelMeanSem_trail=CommonStatisticsMethods.buildMeanSem(m_pnPixels_trail);
for (c = 0; c <= m_nRMax; c++) {
m_pcMeanSems_Median[c] = CommonStatisticsMethods.buildMeanSem(m_pdRingMedian, c);
m_pcMeanSems_Mean[c] = CommonStatisticsMethods.buildMeanSem(m_pdRingMean, c);
}
if (m_bPresetPoints) {
len = pixelsOfPresetPoints.size();
int num = len / m_nPercentileDivision;
int rI, rF;
for (i = 0; i < m_nPercentileDivision; i++) {
rI = Math.max(0, (int) ((i - .5) * num));
rF = Math.min(len - 1, (int) ((i + .5) * num));
//
// m_cPixelMeanSems_trail[i]=CommonStatisticsMethods.buildMeanSem(m_pnPixels_trail,rI,rF,1);
for (c = 0; c < numRings; c++) {
m_pcRingMeanSems_Median[i][c] =
CommonStatisticsMethods.buildMeanSem(m_pdRingMedian, rI, rF, 1, c);
m_pcRingMeanSems_Mean[i][c] =
CommonStatisticsMethods.buildMeanSem(m_pdRingMean, rI, rF, 1, c);
}
}
}
}