private void drawGraph(
final double[] params, final double[] boxSizes, final double[] boxCountSums) {
final int samples = 100;
final float[] px = new float[samples];
final float[] py = new float[samples];
double[] a = Tools.getMinMax(boxSizes);
final double xmin = a[0], xmax = a[1];
a = Tools.getMinMax(boxCountSums);
double ymin = a[0], ymax = a[1];
final double inc = (xmax - xmin) / ((double) samples - 1);
double tmp = xmin;
for (int i = 0; i < samples; i++) {
px[i] = (float) tmp;
tmp += inc;
}
for (int i = 0; i < samples; i++) {
py[i] = (float) CurveFitter.f(CurveFitter.STRAIGHT_LINE, params, px[i]);
}
a = Tools.getMinMax(py);
ymin = Math.min(ymin, a[0]);
ymax = Math.max(ymax, a[1]);
final Plot plot = new Plot("Plot", "-log(box size)", "log(box count)", px, py);
plot.setLimits(xmin, xmax * 0.9, ymin, ymax * 1.1);
plot.draw();
plot.addPoints(boxSizes, boxCountSums, Plot.CIRCLE);
plot.addPoints(px, py, Plot.LINE);
final CurveFitter cf = new CurveFitter(boxSizes, boxCountSums);
cf.doFit(CurveFitter.STRAIGHT_LINE);
plot.addLabel(
0.1, 0.1, "Slope: " + IJ.d2s(params[1], 4) + "\n" + "R²: " + IJ.d2s(cf.getRSquared(), 4));
final ImagePlus plotImage = new ImagePlus("Plot", plot.getProcessor());
plotImage.show();
return;
}
public int setup(String arg, ImagePlus imp) {
// IJ.register(Average_Oversampled.class);
if (IJ.versionLessThan("1.32c")) return DONE;
if (this.pre) { // before finding means
imp.unlock();
this.imp = imp;
this.nslices = imp.getNSlices();
this.width = imp.getWidth();
this.height = imp.getHeight();
this.slicecols = new float[nslices][width];
this.pre = false;
return (DOES_ALL + DOES_STACKS + FINAL_PROCESSING);
} else { // find SD after finding means of columns
float sum; // sum of pixel values column
float avg; // average pixel value of a column
double devsum; // sum of deviations
double devavg; // standard deviation
double[] columns = new double[width]; // x-axis of plot
double[] sdevs = new double[width]; // y-axis of plot
for (int i = 0; i < width; i += 1) {
sum = 0; // reset with each column
avg = 0; // reset with each column
devsum = 0; // reset with each column
devavg = 0; // reset with each column
columns[i] = i; // building x-axis
for (int s = 0; s < nslices; s += 1) { // sum of column means
sum = sum + slicecols[s][i];
}
avg = sum / nslices; // mean of one column across all slices
for (int s = 0; s < nslices; s += 1) { // standard deviation
devsum = devsum + Math.pow((slicecols[s][i] - avg), 2); // square diff
}
devavg = Math.sqrt(devsum / nslices);
sdevs[i] = devavg; // building y-axis
}
Plot plot = new Plot("Average SD by Column", "Columns", "Standard Deviation", columns, sdevs);
plot.show();
// calculate CTN
double ctn;
double sdevssum = 0;
for (double sd : sdevs) {
sdevssum = sdevssum + sd;
}
ctn = sdevssum / width;
// display CTN on results table
ResultsTable rt = ResultsTable.getResultsTable();
// rt.incrementCounter();
rt.addValue("Column Temporal Noise", ctn);
rt.show("Results");
this.pre = false;
return DONE;
}
}
