use of ij.gui.PlotWindow in project GDSC-SMLM by aherbert.
the class BenchmarkFilterAnalysis method showPlots.
private void showPlots() {
if (plots.isEmpty())
return;
// Display the top N plots
int[] list = new int[plots.size()];
int i = 0;
for (NamedPlot p : plots) {
Plot2 plot = new Plot2(p.name, p.xAxisName, COLUMNS[scoreIndex], p.xValues, p.yValues);
plot.setLimits(p.xValues[0], p.xValues[p.xValues.length - 1], 0, 1);
plot.setColor(Color.RED);
plot.draw();
plot.setColor(Color.BLUE);
plot.addPoints(p.xValues, p.yValues, Plot2.CROSS);
PlotWindow plotWindow = Utils.display(p.name, plot);
list[i++] = plotWindow.getImagePlus().getID();
}
new WindowOrganiser().tileWindows(list);
}
use of ij.gui.PlotWindow in project GDSC-SMLM by aherbert.
the class EMGainAnalysis method plotPMF.
private void plotPMF() {
if (!showPMFDialog())
return;
final int gaussWidth = 5;
int dummyBias = (int) Math.max(500, gaussWidth * _noise + 1);
double[] pmf = pdf(0, _photons, _gain, _noise, dummyBias);
double[] x = Utils.newArray(pmf.length, 0, 1.0);
double yMax = Maths.max(pmf);
// Truncate x
int max = 0;
double sum = 0;
double p = 1 - tail;
while (sum < p && max < pmf.length) {
sum += pmf[max];
if (sum > 0.5 && pmf[max] == 0)
break;
max++;
}
int min = pmf.length;
sum = 0;
p = 1 - head;
while (sum < p && min > 0) {
min--;
sum += pmf[min];
if (sum > 0.5 && pmf[min] == 0)
break;
}
//int min = (int) (dummyBias - gaussWidth * _noise);
pmf = Arrays.copyOfRange(pmf, min, max);
x = Arrays.copyOfRange(x, min, max);
// Get the approximation
double[] f = new double[x.length];
LikelihoodFunction fun;
double myNoise = _noise;
switch(approximation) {
case 3:
fun = new PoissonFunction(1.0 / _gain, true);
break;
case 2:
// The mean does not matter so just use zero
fun = PoissonGaussianFunction.createWithStandardDeviation(1.0 / _gain, 0, _noise);
break;
case 1:
myNoise = 0;
case 0:
default:
PoissonGammaGaussianFunction myFun = new PoissonGammaGaussianFunction(1.0 / _gain, myNoise);
myFun.setMinimumProbability(0);
fun = myFun;
}
double expected = _photons;
if (offset != 0)
expected += offset * expected / 100.0;
expected *= _gain;
//double sum2 = 0;
for (int i = 0; i < f.length; i++) {
// Adjust the x-values to remove the dummy bias
x[i] -= dummyBias;
f[i] = fun.likelihood(x[i], expected);
//sum += pmf[i];
//sum2 += f[i];
}
//System.out.printf("Approximation sum = %f : %f\n", sum ,sum2);
if (showApproximation)
yMax = Maths.maxDefault(yMax, f);
String label = String.format("Gain=%s, noise=%s, photons=%s", Utils.rounded(_gain), Utils.rounded(_noise), Utils.rounded(_photons));
Plot2 plot = new Plot2("PMF", "ADUs", "p");
plot.setLimits(x[0], x[x.length - 1], 0, yMax);
plot.setColor(Color.red);
plot.addPoints(x, pmf, Plot2.LINE);
if (showApproximation) {
plot.setColor(Color.blue);
plot.addPoints(x, f, Plot2.LINE);
}
plot.setColor(Color.magenta);
plot.drawLine(_photons * _gain, 0, _photons * _gain, yMax);
plot.setColor(Color.black);
plot.addLabel(0, 0, label);
PlotWindow win1 = Utils.display("PMF", plot);
// Plot the difference between the actual and approximation
double[] delta = new double[f.length];
for (int i = 0; i < f.length; i++) {
if (pmf[i] == 0 && f[i] == 0)
continue;
if (relativeDelta)
delta[i] = DoubleEquality.relativeError(f[i], pmf[i]) * Math.signum(f[i] - pmf[i]);
else
delta[i] = f[i] - pmf[i];
}
Plot2 plot2 = new Plot2("PMF delta", "ADUs", (relativeDelta) ? "Relative delta" : "delta");
double[] limits = Maths.limits(delta);
plot2.setLimits(x[0], x[x.length - 1], limits[0], limits[1]);
plot2.setColor(Color.red);
plot2.addPoints(x, delta, Plot2.LINE);
plot2.setColor(Color.magenta);
plot2.drawLine(_photons * _gain, limits[0], _photons * _gain, limits[1]);
plot2.setColor(Color.black);
plot2.addLabel(0, 0, label + ((offset == 0) ? "" : ", expected = " + Utils.rounded(expected / _gain)));
PlotWindow win2 = Utils.display("PMF delta", plot2);
if (Utils.isNewWindow()) {
Point p2 = win2.getLocation();
p2.y += win1.getHeight();
win2.setLocation(p2);
}
}
use of ij.gui.PlotWindow in project GDSC-SMLM by aherbert.
the class DiffusionRateTest method plotJumpDistances.
/**
* Plot a cumulative histogram and standard histogram of the jump distances.
*
* @param title
* the title
* @param jumpDistances
* the jump distances
* @param dimensions
* the number of dimensions for the jumps
* @param steps
* the steps
*/
private void plotJumpDistances(String title, DoubleData jumpDistances, int dimensions) {
// Cumulative histogram
// --------------------
double[] values = jumpDistances.values();
String title2 = title + " Cumulative Jump Distance " + dimensions + "D";
double[][] jdHistogram = JumpDistanceAnalysis.cumulativeHistogram(values);
Plot2 jdPlot = new Plot2(title2, "Distance (um^2)", "Cumulative Probability", jdHistogram[0], jdHistogram[1]);
PlotWindow pw2 = Utils.display(title2, jdPlot);
if (Utils.isNewWindow())
idList[idCount++] = pw2.getImagePlus().getID();
// Plot the expected function
// This is the Chi-squared distribution: The sum of the squares of k independent
// standard normal random variables with k = dimensions. It is a special case of
// the gamma distribution. If the normals have non-unit variance the distribution
// is scaled.
// Chi ~ Gamma(k/2, 2) // using the scale parameterisation of the gamma
// s^2 * Chi ~ Gamma(k/2, 2*s^2)
// So if s^2 = 2D:
// 2D * Chi ~ Gamma(k/2, 4D)
double estimatedD = simpleD * simpleSteps;
double max = Maths.max(values);
double[] x = Utils.newArray(1000, 0, max / 1000);
double k = dimensions / 2.0;
double mean = 4 * estimatedD;
GammaDistribution dist = new GammaDistribution(k, mean);
double[] y = new double[x.length];
for (int i = 0; i < x.length; i++) y[i] = dist.cumulativeProbability(x[i]);
jdPlot.setColor(Color.red);
jdPlot.addPoints(x, y, Plot.LINE);
Utils.display(title2, jdPlot);
// Histogram
// ---------
title2 = title + " Jump " + dimensions + "D";
int plotId = Utils.showHistogram(title2, jumpDistances, "Distance (um^2)", 0, 0, Math.max(20, values.length / 1000));
if (Utils.isNewWindow())
idList[idCount++] = plotId;
// Recompute the expected function
for (int i = 0; i < x.length; i++) y[i] = dist.density(x[i]);
// Scale to have the same area
if (Utils.xValues.length > 1) {
final double area1 = jumpDistances.size() * (Utils.xValues[1] - Utils.xValues[0]);
final double area2 = dist.cumulativeProbability(x[x.length - 1]);
final double scaleFactor = area1 / area2;
for (int i = 0; i < y.length; i++) y[i] *= scaleFactor;
}
jdPlot = Utils.plot;
jdPlot.setColor(Color.red);
jdPlot.addPoints(x, y, Plot.LINE);
Utils.display(WindowManager.getImage(plotId).getTitle(), jdPlot);
}
use of ij.gui.PlotWindow in project GDSC-SMLM by aherbert.
the class BenchmarkSpotFit method summariseResults.
private void summariseResults(TIntObjectHashMap<FilterCandidates> filterCandidates, long runTime, final PreprocessedPeakResult[] preprocessedPeakResults, int nUniqueIDs) {
createTable();
// Summarise the fitting results. N fits, N failures.
// Optimal match statistics if filtering is perfect (since fitting is not perfect).
StoredDataStatistics distanceStats = new StoredDataStatistics();
StoredDataStatistics depthStats = new StoredDataStatistics();
// Get stats for all fitted results and those that match
// Signal, SNR, Width, xShift, yShift, Precision
createFilterCriteria();
StoredDataStatistics[][] stats = new StoredDataStatistics[3][filterCriteria.length];
for (int i = 0; i < stats.length; i++) for (int j = 0; j < stats[i].length; j++) stats[i][j] = new StoredDataStatistics();
final double nmPerPixel = simulationParameters.a;
double tp = 0, fp = 0;
int failcTP = 0, failcFP = 0;
int cTP = 0, cFP = 0;
int[] singleStatus = null, multiStatus = null, doubletStatus = null, multiDoubletStatus = null;
singleStatus = new int[FitStatus.values().length];
multiStatus = new int[singleStatus.length];
doubletStatus = new int[singleStatus.length];
multiDoubletStatus = new int[singleStatus.length];
// Easier to materialise the values since we have a lot of non final variables to manipulate
final int[] frames = new int[filterCandidates.size()];
final FilterCandidates[] candidates = new FilterCandidates[filterCandidates.size()];
final int[] counter = new int[1];
filterCandidates.forEachEntry(new TIntObjectProcedure<FilterCandidates>() {
public boolean execute(int a, FilterCandidates b) {
frames[counter[0]] = a;
candidates[counter[0]] = b;
counter[0]++;
return true;
}
});
for (FilterCandidates result : candidates) {
// Count the number of fit results that matched (tp) and did not match (fp)
tp += result.tp;
fp += result.fp;
for (int i = 0; i < result.fitResult.length; i++) {
if (result.spots[i].match)
cTP++;
else
cFP++;
final MultiPathFitResult fitResult = result.fitResult[i];
if (singleStatus != null && result.spots[i].match) {
// Debugging reasons for fit failure
addStatus(singleStatus, fitResult.getSingleFitResult());
addStatus(multiStatus, fitResult.getMultiFitResult());
addStatus(doubletStatus, fitResult.getDoubletFitResult());
addStatus(multiDoubletStatus, fitResult.getMultiDoubletFitResult());
}
if (noMatch(fitResult)) {
if (result.spots[i].match)
failcTP++;
else
failcFP++;
}
// We have multi-path results.
// We want statistics for:
// [0] all fitted spots
// [1] fitted spots that match a result
// [2] fitted spots that do not match a result
addToStats(fitResult.getSingleFitResult(), stats);
addToStats(fitResult.getMultiFitResult(), stats);
addToStats(fitResult.getDoubletFitResult(), stats);
addToStats(fitResult.getMultiDoubletFitResult(), stats);
}
// Statistics on spots that fit an actual result
for (int i = 0; i < result.match.length; i++) {
if (!result.match[i].isFitResult())
// For now just ignore the candidates that matched
continue;
FitMatch fitMatch = (FitMatch) result.match[i];
distanceStats.add(fitMatch.d * nmPerPixel);
depthStats.add(fitMatch.z * nmPerPixel);
}
}
// Store data for computing correlation
double[] i1 = new double[depthStats.getN()];
double[] i2 = new double[i1.length];
double[] is = new double[i1.length];
int ci = 0;
for (FilterCandidates result : candidates) {
for (int i = 0; i < result.match.length; i++) {
if (!result.match[i].isFitResult())
// For now just ignore the candidates that matched
continue;
FitMatch fitMatch = (FitMatch) result.match[i];
ScoredSpot spot = result.spots[fitMatch.i];
i1[ci] = fitMatch.predictedSignal;
i2[ci] = fitMatch.actualSignal;
is[ci] = spot.spot.intensity;
ci++;
}
}
// We want to compute the Jaccard against the spot metric
// Filter the results using the multi-path filter
ArrayList<MultiPathFitResults> multiPathResults = new ArrayList<MultiPathFitResults>(filterCandidates.size());
for (int i = 0; i < frames.length; i++) {
int frame = frames[i];
MultiPathFitResult[] multiPathFitResults = candidates[i].fitResult;
int totalCandidates = candidates[i].spots.length;
int nActual = actualCoordinates.get(frame).size();
multiPathResults.add(new MultiPathFitResults(frame, multiPathFitResults, totalCandidates, nActual));
}
// Score the results and count the number returned
List<FractionalAssignment[]> assignments = new ArrayList<FractionalAssignment[]>();
final TIntHashSet set = new TIntHashSet(nUniqueIDs);
FractionScoreStore scoreStore = new FractionScoreStore() {
public void add(int uniqueId) {
set.add(uniqueId);
}
};
MultiPathFitResults[] multiResults = multiPathResults.toArray(new MultiPathFitResults[multiPathResults.size()]);
// Filter with no filter
MultiPathFilter mpf = new MultiPathFilter(new SignalFilter(0), null, multiFilter.residualsThreshold);
FractionClassificationResult fractionResult = mpf.fractionScoreSubset(multiResults, Integer.MAX_VALUE, this.results.size(), assignments, scoreStore, CoordinateStoreFactory.create(imp.getWidth(), imp.getHeight(), fitConfig.getDuplicateDistance()));
double nPredicted = fractionResult.getTP() + fractionResult.getFP();
final double[][] matchScores = new double[set.size()][];
int count = 0;
for (int i = 0; i < assignments.size(); i++) {
FractionalAssignment[] a = assignments.get(i);
if (a == null)
continue;
for (int j = 0; j < a.length; j++) {
final PreprocessedPeakResult r = ((PeakFractionalAssignment) a[j]).peakResult;
set.remove(r.getUniqueId());
final double precision = Math.sqrt(r.getLocationVariance());
final double signal = r.getSignal();
final double snr = r.getSNR();
final double width = r.getXSDFactor();
final double xShift = r.getXRelativeShift2();
final double yShift = r.getYRelativeShift2();
// Since these two are combined for filtering and the max is what matters.
final double shift = (xShift > yShift) ? Math.sqrt(xShift) : Math.sqrt(yShift);
final double eshift = Math.sqrt(xShift + yShift);
final double[] score = new double[8];
score[FILTER_SIGNAL] = signal;
score[FILTER_SNR] = snr;
score[FILTER_MIN_WIDTH] = width;
score[FILTER_MAX_WIDTH] = width;
score[FILTER_SHIFT] = shift;
score[FILTER_ESHIFT] = eshift;
score[FILTER_PRECISION] = precision;
score[FILTER_PRECISION + 1] = a[j].getScore();
matchScores[count++] = score;
}
}
// Add the rest
set.forEach(new CustomTIntProcedure(count) {
public boolean execute(int uniqueId) {
// This should not be null or something has gone wrong
PreprocessedPeakResult r = preprocessedPeakResults[uniqueId];
if (r == null)
throw new RuntimeException("Missing result: " + uniqueId);
final double precision = Math.sqrt(r.getLocationVariance());
final double signal = r.getSignal();
final double snr = r.getSNR();
final double width = r.getXSDFactor();
final double xShift = r.getXRelativeShift2();
final double yShift = r.getYRelativeShift2();
// Since these two are combined for filtering and the max is what matters.
final double shift = (xShift > yShift) ? Math.sqrt(xShift) : Math.sqrt(yShift);
final double eshift = Math.sqrt(xShift + yShift);
final double[] score = new double[8];
score[FILTER_SIGNAL] = signal;
score[FILTER_SNR] = snr;
score[FILTER_MIN_WIDTH] = width;
score[FILTER_MAX_WIDTH] = width;
score[FILTER_SHIFT] = shift;
score[FILTER_ESHIFT] = eshift;
score[FILTER_PRECISION] = precision;
matchScores[c++] = score;
return true;
}
});
// Debug the reasons the fit failed
if (singleStatus != null) {
String name = PeakFit.getSolverName(fitConfig);
if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera())
name += " Camera";
System.out.println("Failure counts: " + name);
printFailures("Single", singleStatus);
printFailures("Multi", multiStatus);
printFailures("Doublet", doubletStatus);
printFailures("Multi doublet", multiDoubletStatus);
}
StringBuilder sb = new StringBuilder(300);
// Add information about the simulation
//(simulationParameters.minSignal + simulationParameters.maxSignal) * 0.5;
final double signal = simulationParameters.signalPerFrame;
final int n = results.size();
sb.append(imp.getStackSize()).append("\t");
final int w = imp.getWidth();
final int h = imp.getHeight();
sb.append(w).append("\t");
sb.append(h).append("\t");
sb.append(n).append("\t");
double density = ((double) n / imp.getStackSize()) / (w * h) / (simulationParameters.a * simulationParameters.a / 1e6);
sb.append(Utils.rounded(density)).append("\t");
sb.append(Utils.rounded(signal)).append("\t");
sb.append(Utils.rounded(simulationParameters.s)).append("\t");
sb.append(Utils.rounded(simulationParameters.a)).append("\t");
sb.append(Utils.rounded(simulationParameters.depth)).append("\t");
sb.append(simulationParameters.fixedDepth).append("\t");
sb.append(Utils.rounded(simulationParameters.gain)).append("\t");
sb.append(Utils.rounded(simulationParameters.readNoise)).append("\t");
sb.append(Utils.rounded(simulationParameters.b)).append("\t");
sb.append(Utils.rounded(simulationParameters.b2)).append("\t");
// Compute the noise
double noise = simulationParameters.b2;
if (simulationParameters.emCCD) {
// The b2 parameter was computed without application of the EM-CCD noise factor of 2.
//final double b2 = backgroundVariance + readVariance
// = simulationParameters.b + readVariance
// This should be applied only to the background variance.
final double readVariance = noise - simulationParameters.b;
noise = simulationParameters.b * 2 + readVariance;
}
if (simulationParameters.fullSimulation) {
// The total signal is spread over frames
}
sb.append(Utils.rounded(signal / Math.sqrt(noise))).append("\t");
sb.append(Utils.rounded(simulationParameters.s / simulationParameters.a)).append("\t");
sb.append(spotFilter.getDescription());
// nP and nN is the fractional score of the spot candidates
addCount(sb, nP + nN);
addCount(sb, nP);
addCount(sb, nN);
addCount(sb, fP);
addCount(sb, fN);
String name = PeakFit.getSolverName(fitConfig);
if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera())
name += " Camera";
add(sb, name);
add(sb, config.getFitting());
resultPrefix = sb.toString();
// Q. Should I add other fit configuration here?
// The fraction of positive and negative candidates that were included
add(sb, (100.0 * cTP) / nP);
add(sb, (100.0 * cFP) / nN);
// Score the fitting results compared to the original simulation.
// Score the candidate selection:
add(sb, cTP + cFP);
add(sb, cTP);
add(sb, cFP);
// TP are all candidates that can be matched to a spot
// FP are all candidates that cannot be matched to a spot
// FN = The number of missed spots
FractionClassificationResult m = new FractionClassificationResult(cTP, cFP, 0, simulationParameters.molecules - cTP);
add(sb, m.getRecall());
add(sb, m.getPrecision());
add(sb, m.getF1Score());
add(sb, m.getJaccard());
// Score the fitting results:
add(sb, failcTP);
add(sb, failcFP);
// TP are all fit results that can be matched to a spot
// FP are all fit results that cannot be matched to a spot
// FN = The number of missed spots
add(sb, tp);
add(sb, fp);
m = new FractionClassificationResult(tp, fp, 0, simulationParameters.molecules - tp);
add(sb, m.getRecall());
add(sb, m.getPrecision());
add(sb, m.getF1Score());
add(sb, m.getJaccard());
// Do it again but pretend we can perfectly filter all the false positives
//add(sb, tp);
m = new FractionClassificationResult(tp, 0, 0, simulationParameters.molecules - tp);
// Recall is unchanged
// Precision will be 100%
add(sb, m.getF1Score());
add(sb, m.getJaccard());
// The mean may be subject to extreme outliers so use the median
double median = distanceStats.getMedian();
add(sb, median);
WindowOrganiser wo = new WindowOrganiser();
String label = String.format("Recall = %s. n = %d. Median = %s nm. SD = %s nm", Utils.rounded(m.getRecall()), distanceStats.getN(), Utils.rounded(median), Utils.rounded(distanceStats.getStandardDeviation()));
int id = Utils.showHistogram(TITLE, distanceStats, "Match Distance (nm)", 0, 0, 0, label);
if (Utils.isNewWindow())
wo.add(id);
median = depthStats.getMedian();
add(sb, median);
// Sort by spot intensity and produce correlation
int[] indices = Utils.newArray(i1.length, 0, 1);
if (showCorrelation)
Sort.sort(indices, is, rankByIntensity);
double[] r = (showCorrelation) ? new double[i1.length] : null;
double[] sr = (showCorrelation) ? new double[i1.length] : null;
double[] rank = (showCorrelation) ? new double[i1.length] : null;
ci = 0;
FastCorrelator fastCorrelator = new FastCorrelator();
ArrayList<Ranking> pc1 = new ArrayList<Ranking>();
ArrayList<Ranking> pc2 = new ArrayList<Ranking>();
for (int ci2 : indices) {
fastCorrelator.add((long) Math.round(i1[ci2]), (long) Math.round(i2[ci2]));
pc1.add(new Ranking(i1[ci2], ci));
pc2.add(new Ranking(i2[ci2], ci));
if (showCorrelation) {
r[ci] = fastCorrelator.getCorrelation();
sr[ci] = Correlator.correlation(rank(pc1), rank(pc2));
if (rankByIntensity)
rank[ci] = is[0] - is[ci];
else
rank[ci] = ci;
}
ci++;
}
final double pearsonCorr = fastCorrelator.getCorrelation();
final double rankedCorr = Correlator.correlation(rank(pc1), rank(pc2));
// Get the regression
SimpleRegression regression = new SimpleRegression(false);
for (int i = 0; i < pc1.size(); i++) regression.addData(pc1.get(i).value, pc2.get(i).value);
//final double intercept = regression.getIntercept();
final double slope = regression.getSlope();
if (showCorrelation) {
String title = TITLE + " Intensity";
Plot plot = new Plot(title, "Candidate", "Spot");
double[] limits1 = Maths.limits(i1);
double[] limits2 = Maths.limits(i2);
plot.setLimits(limits1[0], limits1[1], limits2[0], limits2[1]);
label = String.format("Correlation=%s; Ranked=%s; Slope=%s", Utils.rounded(pearsonCorr), Utils.rounded(rankedCorr), Utils.rounded(slope));
plot.addLabel(0, 0, label);
plot.setColor(Color.red);
plot.addPoints(i1, i2, Plot.DOT);
if (slope > 1)
plot.drawLine(limits1[0], limits1[0] * slope, limits1[1], limits1[1] * slope);
else
plot.drawLine(limits2[0] / slope, limits2[0], limits2[1] / slope, limits2[1]);
PlotWindow pw = Utils.display(title, plot);
if (Utils.isNewWindow())
wo.add(pw);
title = TITLE + " Correlation";
plot = new Plot(title, "Spot Rank", "Correlation");
double[] xlimits = Maths.limits(rank);
double[] ylimits = Maths.limits(r);
ylimits = Maths.limits(ylimits, sr);
plot.setLimits(xlimits[0], xlimits[1], ylimits[0], ylimits[1]);
plot.setColor(Color.red);
plot.addPoints(rank, r, Plot.LINE);
plot.setColor(Color.blue);
plot.addPoints(rank, sr, Plot.LINE);
plot.setColor(Color.black);
plot.addLabel(0, 0, label);
pw = Utils.display(title, plot);
if (Utils.isNewWindow())
wo.add(pw);
}
add(sb, pearsonCorr);
add(sb, rankedCorr);
add(sb, slope);
label = String.format("n = %d. Median = %s nm", depthStats.getN(), Utils.rounded(median));
id = Utils.showHistogram(TITLE, depthStats, "Match Depth (nm)", 0, 1, 0, label);
if (Utils.isNewWindow())
wo.add(id);
// Plot histograms of the stats on the same window
double[] lower = new double[filterCriteria.length];
double[] upper = new double[lower.length];
min = new double[lower.length];
max = new double[lower.length];
for (int i = 0; i < stats[0].length; i++) {
double[] limits = showDoubleHistogram(stats, i, wo, matchScores, nPredicted);
lower[i] = limits[0];
upper[i] = limits[1];
min[i] = limits[2];
max[i] = limits[3];
}
// Reconfigure some of the range limits
// Make this a bit bigger
upper[FILTER_SIGNAL] *= 2;
// Make this a bit bigger
upper[FILTER_SNR] *= 2;
double factor = 0.25;
if (lower[FILTER_MIN_WIDTH] != 0)
// (assuming lower is less than 1)
upper[FILTER_MIN_WIDTH] = 1 - Math.max(0, factor * (1 - lower[FILTER_MIN_WIDTH]));
if (upper[FILTER_MIN_WIDTH] != 0)
// (assuming upper is more than 1)
lower[FILTER_MAX_WIDTH] = 1 + Math.max(0, factor * (upper[FILTER_MAX_WIDTH] - 1));
// Round the ranges
final double[] interval = new double[stats[0].length];
interval[FILTER_SIGNAL] = SignalFilter.DEFAULT_INCREMENT;
interval[FILTER_SNR] = SNRFilter.DEFAULT_INCREMENT;
interval[FILTER_MIN_WIDTH] = WidthFilter2.DEFAULT_MIN_INCREMENT;
interval[FILTER_MAX_WIDTH] = WidthFilter.DEFAULT_INCREMENT;
interval[FILTER_SHIFT] = ShiftFilter.DEFAULT_INCREMENT;
interval[FILTER_ESHIFT] = EShiftFilter.DEFAULT_INCREMENT;
interval[FILTER_PRECISION] = PrecisionFilter.DEFAULT_INCREMENT;
interval[FILTER_ITERATIONS] = 0.1;
interval[FILTER_EVALUATIONS] = 0.1;
// Create a range increment
double[] increment = new double[lower.length];
for (int i = 0; i < increment.length; i++) {
lower[i] = Maths.floor(lower[i], interval[i]);
upper[i] = Maths.ceil(upper[i], interval[i]);
double range = upper[i] - lower[i];
// Allow clipping if the range is small compared to the min increment
double multiples = range / interval[i];
// Use 8 multiples for the equivalent of +/- 4 steps around the centre
if (multiples < 8) {
multiples = Math.ceil(multiples);
} else
multiples = 8;
increment[i] = Maths.ceil(range / multiples, interval[i]);
if (i == FILTER_MIN_WIDTH)
// Requires clipping based on the upper limit
lower[i] = upper[i] - increment[i] * multiples;
else
upper[i] = lower[i] + increment[i] * multiples;
}
for (int i = 0; i < stats[0].length; i++) {
lower[i] = Maths.round(lower[i]);
upper[i] = Maths.round(upper[i]);
min[i] = Maths.round(min[i]);
max[i] = Maths.round(max[i]);
increment[i] = Maths.round(increment[i]);
sb.append("\t").append(min[i]).append(':').append(lower[i]).append('-').append(upper[i]).append(':').append(max[i]);
}
// Disable some filters
increment[FILTER_SIGNAL] = Double.POSITIVE_INFINITY;
//increment[FILTER_SHIFT] = Double.POSITIVE_INFINITY;
increment[FILTER_ESHIFT] = Double.POSITIVE_INFINITY;
wo.tile();
sb.append("\t").append(Utils.timeToString(runTime / 1000000.0));
summaryTable.append(sb.toString());
if (saveFilterRange) {
GlobalSettings gs = SettingsManager.loadSettings();
FilterSettings filterSettings = gs.getFilterSettings();
String filename = (silent) ? filterSettings.filterSetFilename : Utils.getFilename("Filter_range_file", filterSettings.filterSetFilename);
if (filename == null)
return;
// Remove extension to store the filename
filename = Utils.replaceExtension(filename, ".xml");
filterSettings.filterSetFilename = filename;
// Create a filter set using the ranges
ArrayList<Filter> filters = new ArrayList<Filter>(3);
filters.add(new MultiFilter2(lower[0], (float) lower[1], lower[2], lower[3], lower[4], lower[5], lower[6]));
filters.add(new MultiFilter2(upper[0], (float) upper[1], upper[2], upper[3], upper[4], upper[5], upper[6]));
filters.add(new MultiFilter2(increment[0], (float) increment[1], increment[2], increment[3], increment[4], increment[5], increment[6]));
if (saveFilters(filename, filters))
SettingsManager.saveSettings(gs);
// Create a filter set using the min/max and the initial bounds.
// Set sensible limits
min[FILTER_SIGNAL] = Math.max(min[FILTER_SIGNAL], 30);
max[FILTER_PRECISION] = Math.min(max[FILTER_PRECISION], 100);
// Commented this out so that the 4-set filters are the same as the 3-set filters.
// The difference leads to differences when optimising.
// // Use half the initial bounds (hoping this is a good starting guess for the optimum)
// final boolean[] limitToLower = new boolean[min.length];
// limitToLower[FILTER_SIGNAL] = true;
// limitToLower[FILTER_SNR] = true;
// limitToLower[FILTER_MIN_WIDTH] = true;
// limitToLower[FILTER_MAX_WIDTH] = false;
// limitToLower[FILTER_SHIFT] = false;
// limitToLower[FILTER_ESHIFT] = false;
// limitToLower[FILTER_PRECISION] = true;
// for (int i = 0; i < limitToLower.length; i++)
// {
// final double range = (upper[i] - lower[i]) / 2;
// if (limitToLower[i])
// upper[i] = lower[i] + range;
// else
// lower[i] = upper[i] - range;
// }
filters = new ArrayList<Filter>(4);
filters.add(new MultiFilter2(min[0], (float) min[1], min[2], min[3], min[4], min[5], min[6]));
filters.add(new MultiFilter2(lower[0], (float) lower[1], lower[2], lower[3], lower[4], lower[5], lower[6]));
filters.add(new MultiFilter2(upper[0], (float) upper[1], upper[2], upper[3], upper[4], upper[5], upper[6]));
filters.add(new MultiFilter2(max[0], (float) max[1], max[2], max[3], max[4], max[5], max[6]));
saveFilters(Utils.replaceExtension(filename, ".4.xml"), filters);
}
}
use of ij.gui.PlotWindow in project GDSC-SMLM by aherbert.
the class BenchmarkSpotFit method showDoubleHistogram.
private double[] showDoubleHistogram(StoredDataStatistics[][] stats, final int i, WindowOrganiser wo, double[][] matchScores, double nPredicted) {
String xLabel = filterCriteria[i].name;
LowerLimit lower = filterCriteria[i].lower;
UpperLimit upper = filterCriteria[i].upper;
double[] j = null;
double[] metric = null;
double maxJ = 0;
if (i <= FILTER_PRECISION && (showFilterScoreHistograms || upper.requiresJaccard || lower.requiresJaccard)) {
// Jaccard score verses the range of the metric
Arrays.sort(matchScores, new Comparator<double[]>() {
public int compare(double[] o1, double[] o2) {
if (o1[i] < o2[i])
return -1;
if (o1[i] > o2[i])
return 1;
return 0;
}
});
final int scoreIndex = FILTER_PRECISION + 1;
int n = results.size();
double tp = 0;
double fp = 0;
j = new double[matchScores.length + 1];
metric = new double[j.length];
for (int k = 0; k < matchScores.length; k++) {
final double score = matchScores[k][scoreIndex];
tp += score;
fp += (1 - score);
j[k + 1] = tp / (fp + n);
metric[k + 1] = matchScores[k][i];
}
metric[0] = metric[1];
maxJ = Maths.max(j);
if (showFilterScoreHistograms) {
String title = TITLE + " Jaccard " + xLabel;
Plot plot = new Plot(title, xLabel, "Jaccard", metric, j);
// Remove outliers
double[] limitsx = Maths.limits(metric);
Percentile p = new Percentile();
double l = p.evaluate(metric, 25);
double u = p.evaluate(metric, 75);
double iqr = 1.5 * (u - l);
limitsx[1] = Math.min(limitsx[1], u + iqr);
plot.setLimits(limitsx[0], limitsx[1], 0, Maths.max(j));
PlotWindow pw = Utils.display(title, plot);
if (Utils.isNewWindow())
wo.add(pw);
}
}
// [0] is all
// [1] is matches
// [2] is no match
StoredDataStatistics s1 = stats[0][i];
StoredDataStatistics s2 = stats[1][i];
StoredDataStatistics s3 = stats[2][i];
if (s1.getN() == 0)
return new double[4];
DescriptiveStatistics d = s1.getStatistics();
double median = 0;
Plot2 plot = null;
String title = null;
if (showFilterScoreHistograms) {
median = d.getPercentile(50);
String label = String.format("n = %d. Median = %s nm", s1.getN(), Utils.rounded(median));
int id = Utils.showHistogram(TITLE, s1, xLabel, filterCriteria[i].minBinWidth, (filterCriteria[i].restrictRange) ? 1 : 0, 0, label);
if (id == 0) {
IJ.log("Failed to show the histogram: " + xLabel);
return new double[4];
}
if (Utils.isNewWindow())
wo.add(id);
title = WindowManager.getImage(id).getTitle();
// Reverse engineer the histogram settings
plot = Utils.plot;
double[] xValues = Utils.xValues;
int bins = xValues.length;
double yMin = xValues[0];
double binSize = xValues[1] - xValues[0];
double yMax = xValues[0] + (bins - 1) * binSize;
if (s2.getN() > 0) {
double[] values = s2.getValues();
double[][] hist = Utils.calcHistogram(values, yMin, yMax, bins);
if (hist[0].length > 0) {
plot.setColor(Color.red);
plot.addPoints(hist[0], hist[1], Plot2.BAR);
Utils.display(title, plot);
}
}
if (s3.getN() > 0) {
double[] values = s3.getValues();
double[][] hist = Utils.calcHistogram(values, yMin, yMax, bins);
if (hist[0].length > 0) {
plot.setColor(Color.blue);
plot.addPoints(hist[0], hist[1], Plot2.BAR);
Utils.display(title, plot);
}
}
}
// Do cumulative histogram
double[][] h1 = Maths.cumulativeHistogram(s1.getValues(), true);
double[][] h2 = Maths.cumulativeHistogram(s2.getValues(), true);
double[][] h3 = Maths.cumulativeHistogram(s3.getValues(), true);
if (showFilterScoreHistograms) {
title = TITLE + " Cumul " + xLabel;
plot = new Plot2(title, xLabel, "Frequency");
// Find limits
double[] xlimit = Maths.limits(h1[0]);
xlimit = Maths.limits(xlimit, h2[0]);
xlimit = Maths.limits(xlimit, h3[0]);
// Restrict using the inter-quartile range
if (filterCriteria[i].restrictRange) {
double q1 = d.getPercentile(25);
double q2 = d.getPercentile(75);
double iqr = (q2 - q1) * 2.5;
xlimit[0] = Maths.max(xlimit[0], median - iqr);
xlimit[1] = Maths.min(xlimit[1], median + iqr);
}
plot.setLimits(xlimit[0], xlimit[1], 0, 1.05);
plot.addPoints(h1[0], h1[1], Plot.LINE);
plot.setColor(Color.red);
plot.addPoints(h2[0], h2[1], Plot.LINE);
plot.setColor(Color.blue);
plot.addPoints(h3[0], h3[1], Plot.LINE);
}
// Determine the maximum difference between the TP and FP
double maxx1 = 0;
double maxx2 = 0;
double max1 = 0;
double max2 = 0;
// We cannot compute the delta histogram, or use percentiles
if (s2.getN() == 0) {
upper = UpperLimit.ZERO;
lower = LowerLimit.ZERO;
}
final boolean requireLabel = (showFilterScoreHistograms && filterCriteria[i].requireLabel);
if (requireLabel || upper.requiresDeltaHistogram() || lower.requiresDeltaHistogram()) {
if (s2.getN() != 0 && s3.getN() != 0) {
LinearInterpolator li = new LinearInterpolator();
PolynomialSplineFunction f1 = li.interpolate(h2[0], h2[1]);
PolynomialSplineFunction f2 = li.interpolate(h3[0], h3[1]);
for (double x : h1[0]) {
if (x < h2[0][0] || x < h3[0][0])
continue;
try {
double v1 = f1.value(x);
double v2 = f2.value(x);
double diff = v2 - v1;
if (diff > 0) {
if (max1 < diff) {
max1 = diff;
maxx1 = x;
}
} else {
if (max2 > diff) {
max2 = diff;
maxx2 = x;
}
}
} catch (OutOfRangeException e) {
// Because we reached the end
break;
}
}
} else {
// Switch to percentiles if we have no delta histogram
if (upper.requiresDeltaHistogram())
upper = UpperLimit.NINETY_NINE_PERCENT;
if (lower.requiresDeltaHistogram())
lower = LowerLimit.ONE_PERCENT;
}
// System.out.printf("Bounds %s : %s, pos %s, neg %s, %s\n", xLabel, Utils.rounded(getPercentile(h2, 0.01)),
// Utils.rounded(maxx1), Utils.rounded(maxx2), Utils.rounded(getPercentile(h1, 0.99)));
}
if (showFilterScoreHistograms) {
// We use bins=1 on charts where we do not need a label
if (requireLabel) {
String label = String.format("Max+ %s @ %s, Max- %s @ %s", Utils.rounded(max1), Utils.rounded(maxx1), Utils.rounded(max2), Utils.rounded(maxx2));
plot.setColor(Color.black);
plot.addLabel(0, 0, label);
}
PlotWindow pw = Utils.display(title, plot);
if (Utils.isNewWindow())
wo.add(pw.getImagePlus().getID());
}
// Now compute the bounds using the desired limit
double l, u;
switch(lower) {
case ONE_PERCENT:
l = getPercentile(h2, 0.01);
break;
case MAX_NEGATIVE_CUMUL_DELTA:
l = maxx2;
break;
case ZERO:
l = 0;
break;
case HALF_MAX_JACCARD_VALUE:
l = getValue(metric, j, maxJ * 0.5);
break;
default:
throw new RuntimeException("Missing lower limit method");
}
switch(upper) {
case MAX_POSITIVE_CUMUL_DELTA:
u = maxx1;
break;
case NINETY_NINE_PERCENT:
u = getPercentile(h2, 0.99);
break;
case NINETY_NINE_NINE_PERCENT:
u = getPercentile(h2, 0.999);
break;
case ZERO:
u = 0;
break;
case MAX_JACCARD2:
u = getValue(metric, j, maxJ) * 2;
//System.out.printf("MaxJ = %.4f @ %.3f\n", maxJ, u / 2);
break;
default:
throw new RuntimeException("Missing upper limit method");
}
double min = getPercentile(h1, 0);
double max = getPercentile(h1, 1);
return new double[] { l, u, min, max };
}
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