use of uk.ac.sussex.gdsc.core.utils.FastCorrelator in project GDSC-SMLM by aherbert.
the class BenchmarkSpotFilter method summariseResults.
private BenchmarkSpotFilterResult summariseResults(TIntObjectHashMap<FilterResult> filterResults, FitEngineConfiguration config, MaximaSpotFilter spotFilter, boolean batchSummary) {
final BenchmarkSpotFilterResult filterResult = new BenchmarkSpotFilterResult(simulationParameters.id, filterResults, config, spotFilter);
// Note:
// Although we can compute the TP/FP score as each additional spot is added
// using the RankedScoreCalculator this is not applicable to the PeakFit method.
// The method relies on all spot candidates being present in order to make a
// decision to fit the candidate as a multiple. So scoring the filter candidates using
// for example the top 10 may get a better score than if all candidates were scored
// and the scores accumulated for the top 10, it is not how the algorithm will use the
// candidate set. I.e. It does not use the top 10, then top 20 to refine the fit, etc.
// (the method is not iterative) .
// We require an assessment of how a subset of the scored candidates
// in ranked order contributes to the overall score, i.e. are the candidates ranked
// in the correct order, those most contributing to the match to the underlying data
// should be higher up and those least contributing will be at the end.
// TODO We could add some smart filtering of candidates before ranking. This would
// allow assessment of the candidate set handed to PeakFit. E.g. Threshold the image
// and only use candidates that are in the foreground region.
final double[][] cumul = histogramFailures(filterResult);
// Create the overall match score
final double[] total = new double[3];
final ArrayList<ScoredSpot> allSpots = new ArrayList<>();
filterResults.forEachValue(result -> {
total[0] += result.result.getTruePositives();
total[1] += result.result.getFalsePositives();
total[2] += result.result.getFalseNegatives();
allSpots.addAll(Arrays.asList(result.spots));
return true;
});
double tp = total[0];
double fp = total[1];
final double fn = total[2];
final FractionClassificationResult allResult = new FractionClassificationResult(tp, fp, 0, fn);
// The number of actual results
final double numberOfResults = (tp + fn);
final StringBuilder sb = new StringBuilder();
final double signal = (simulationParameters.minSignal + simulationParameters.maxSignal) * 0.5;
// Create the benchmark settings and the fitting settings
sb.append(imp.getStackSize()).append('\t');
final int w = border.width;
final int h = border.height;
sb.append(w).append('\t');
sb.append(h).append('\t');
sb.append(MathUtils.rounded(numberOfResults)).append('\t');
final double density = (numberOfResults / imp.getStackSize()) / (w * h) / (simulationParameters.pixelPitch * simulationParameters.pixelPitch / 1e6);
sb.append(MathUtils.rounded(density)).append('\t');
sb.append(MathUtils.rounded(signal)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.sd)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.pixelPitch)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.depth)).append('\t');
sb.append(simulationParameters.fixedDepth).append('\t');
// Camera specific
CreateData.addCameraDescription(sb, simulationParameters).append('\t');
sb.append(MathUtils.rounded(simulationParameters.background)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.noise)).append('\t');
sb.append(MathUtils.rounded(signal / simulationParameters.noise)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.sd / simulationParameters.pixelPitch)).append('\t');
sb.append(config.getDataFilterType()).append('\t');
sb.append(spotFilter.getSearch()).append('\t');
sb.append(spotFilter.getBorder()).append('\t');
sb.append(MathUtils.rounded(spotFilter.getSpread())).append('\t');
sb.append(config.getDataFilterMethod(0)).append('\t');
final double param = config.getDataFilterParameterValue(0);
final boolean absolute = config.getDataFilterParameterAbsolute(0);
final double hwhmMin = config.getHwhmMin();
if (absolute) {
sb.append(MathUtils.rounded(param)).append('\t');
sb.append(MathUtils.roundUsingDecimalPlacesToBigDecimal(param / hwhmMin, 3)).append('\t');
} else {
sb.append(MathUtils.roundUsingDecimalPlacesToBigDecimal(param * hwhmMin, 3)).append('\t');
sb.append(MathUtils.rounded(param)).append('\t');
}
sb.append(spotFilter.getDescription()).append('\t');
sb.append(border.x).append('\t');
sb.append(Settings.MATCHING_METHOD[settings.matchingMethod]).append('\t');
sb.append(MathUtils.rounded(lowerMatchDistance)).append('\t');
sb.append(MathUtils.rounded(matchDistance)).append('\t');
sb.append(MathUtils.rounded(settings.lowerSignalFactor)).append('\t');
sb.append(MathUtils.rounded(settings.upperSignalFactor));
filterResult.resultPrefix = sb.toString();
// Add the results
sb.append('\t');
// Rank the scored spots by intensity
Collections.sort(allSpots, ScoredSpot::compare);
// Produce Recall, Precision, Jaccard for each cut of the spot candidates
final double[] recall = new double[allSpots.size() + 1];
final double[] precision = new double[recall.length];
final double[] jaccard = new double[recall.length];
final double[] correlation = new double[recall.length];
final double[] truePositives = new double[recall.length];
final double[] falsePositives = new double[recall.length];
final double[] intensity = new double[recall.length];
// Note: fn = n - tp
tp = fp = 0;
int index = 1;
precision[0] = 1;
final FastCorrelator corr = new FastCorrelator();
double lastCorrelation = 0;
double[] i1 = new double[recall.length];
double[] i2 = new double[recall.length];
int ci = 0;
final SimpleRegression regression = new SimpleRegression(false);
for (final ScoredSpot s : allSpots) {
if (s.match) {
// Score partial matches as part true-positive and part false-positive.
// TP can be above 1 if we are allowing multiple matches.
tp += s.getScore();
fp += s.antiScore();
// Just use a rounded intensity for now
final double spotIntensity = s.getIntensity();
final long v1 = Math.round(spotIntensity);
final long v2 = Math.round(s.intensity);
regression.addData(spotIntensity, s.intensity);
i1[ci] = spotIntensity;
i2[ci] = s.intensity;
ci++;
corr.add(v1, v2);
lastCorrelation = corr.getCorrelation();
} else {
fp++;
}
recall[index] = tp / numberOfResults;
precision[index] = tp / (tp + fp);
// (tp+fp+fn) == (fp+n) since tp+fn=n
jaccard[index] = tp / (fp + numberOfResults);
correlation[index] = lastCorrelation;
truePositives[index] = tp;
falsePositives[index] = fp;
intensity[index] = s.getIntensity();
index++;
}
i1 = Arrays.copyOf(i1, ci);
i2 = Arrays.copyOf(i2, ci);
final double slope = regression.getSlope();
sb.append(MathUtils.rounded(slope)).append('\t');
addResult(sb, allResult, correlation[correlation.length - 1]);
// Output the match results when the recall achieves the fraction of the maximum.
double target = recall[recall.length - 1];
if (settings.recallFraction < 100) {
target *= settings.recallFraction / 100.0;
}
int fractionIndex = 0;
while (fractionIndex < recall.length && recall[fractionIndex] < target) {
fractionIndex++;
}
if (fractionIndex == recall.length) {
fractionIndex--;
}
sb.append('\t');
addResult(sb, new FractionClassificationResult(truePositives[fractionIndex], falsePositives[fractionIndex], 0, numberOfResults - truePositives[fractionIndex]), correlation[fractionIndex]);
// Output the match results at the maximum jaccard score
int maxIndex = 0;
for (int ii = 1; ii < recall.length; ii++) {
if (jaccard[maxIndex] < jaccard[ii]) {
maxIndex = ii;
}
}
sb.append('\t');
addResult(sb, new FractionClassificationResult(truePositives[maxIndex], falsePositives[maxIndex], 0, numberOfResults - truePositives[maxIndex]), correlation[maxIndex]);
sb.append(MathUtils.rounded(time / 1e6));
// Calculate AUC (Average precision == Area Under Precision-Recall curve)
final double auc = AucCalculator.auc(precision, recall);
// Compute the AUC using the adjusted precision curve
// which uses the maximum precision for recall >= r
final double[] maxp = new double[precision.length];
double max = 0;
for (int k = maxp.length; k-- > 0; ) {
if (max < precision[k]) {
max = precision[k];
}
maxp[k] = max;
}
final double auc2 = AucCalculator.auc(maxp, recall);
sb.append('\t').append(MathUtils.rounded(auc));
sb.append('\t').append(MathUtils.rounded(auc2));
// positives
if (cumul[0].length != 0) {
sb.append('\t').append(MathUtils.rounded(getFailures(cumul, 0.80)));
sb.append('\t').append(MathUtils.rounded(getFailures(cumul, 0.90)));
sb.append('\t').append(MathUtils.rounded(getFailures(cumul, 0.95)));
sb.append('\t').append(MathUtils.rounded(getFailures(cumul, 0.99)));
sb.append('\t').append(MathUtils.rounded(cumul[0][cumul[0].length - 1]));
} else {
sb.append("\t\t\t\t\t");
}
getTable(batchSummary).append(sb.toString());
// Store results
filterResult.auc = auc;
filterResult.auc2 = auc2;
filterResult.recall = recall;
filterResult.precision = precision;
filterResult.jaccard = jaccard;
filterResult.correlation = correlation;
filterResult.maxIndex = maxIndex;
filterResult.fractionIndex = fractionIndex;
filterResult.cumul = cumul;
filterResult.slope = slope;
filterResult.i1 = i1;
filterResult.i2 = i2;
filterResult.intensity = intensity;
filterResult.time = time;
filterResult.analysisBorder = this.border;
return filterResult;
}
use of uk.ac.sussex.gdsc.core.utils.FastCorrelator in project GDSC-SMLM by aherbert.
the class BenchmarkSpotFit method summariseResults.
private void summariseResults(BenchmarkSpotFitResult spotFitResults, long runTime, final PreprocessedPeakResult[] preprocessedPeakResults, int uniqueIdCount, CandidateData candidateData, TIntObjectHashMap<List<Coordinate>> actualCoordinates) {
// Summarise the fitting results. N fits, N failures.
// Optimal match statistics if filtering is perfect (since fitting is not perfect).
final StoredDataStatistics distanceStats = new StoredDataStatistics();
final StoredDataStatistics depthStats = new StoredDataStatistics();
// Get stats for all fitted results and those that match
// Signal, SNR, Width, xShift, yShift, Precision
createFilterCriteria();
final 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.pixelPitch;
double tp = 0;
double fp = 0;
int failCtp = 0;
int failCfp = 0;
int ctp = 0;
int cfp = 0;
final int[] singleStatus = new int[FitStatus.values().length];
final int[] multiStatus = new int[singleStatus.length];
final int[] doubletStatus = new int[singleStatus.length];
final int[] multiDoubletStatus = new int[singleStatus.length];
// Easier to materialise the values since we have a lot of non final variables to manipulate
final TIntObjectHashMap<FilterCandidates> fitResults = spotFitResults.fitResults;
final int[] frames = new int[fitResults.size()];
final FilterCandidates[] candidates = new FilterCandidates[fitResults.size()];
final int[] counter = new int[1];
fitResults.forEachEntry((frame, candidate) -> {
frames[counter[0]] = frame;
candidates[counter[0]] = candidate;
counter[0]++;
return true;
});
for (final 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;
}
final FitMatch fitMatch = (FitMatch) result.match[i];
distanceStats.add(fitMatch.distance * nmPerPixel);
depthStats.add(fitMatch.zdepth * nmPerPixel);
}
}
if (tp == 0) {
IJ.error(TITLE, "No fit results matched the simulation actual results");
return;
}
// Store data for computing correlation
final double[] i1 = new double[depthStats.getN()];
final double[] i2 = new double[i1.length];
final double[] is = new double[i1.length];
int ci = 0;
for (final 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;
}
final FitMatch fitMatch = (FitMatch) result.match[i];
final ScoredSpot spot = result.spots[fitMatch.index];
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
final ArrayList<MultiPathFitResults> multiPathResults = new ArrayList<>(fitResults.size());
for (int i = 0; i < frames.length; i++) {
final int frame = frames[i];
final MultiPathFitResult[] multiPathFitResults = candidates[i].fitResult;
final int totalCandidates = candidates[i].spots.length;
final List<Coordinate> list = actualCoordinates.get(frame);
final int nActual = (list == null) ? 0 : list.size();
multiPathResults.add(new MultiPathFitResults(frame, multiPathFitResults, totalCandidates, nActual));
}
// Score the results and count the number returned
final List<FractionalAssignment[]> assignments = new ArrayList<>();
final TIntHashSet set = new TIntHashSet(uniqueIdCount);
final FractionScoreStore scoreStore = set::add;
final MultiPathFitResults[] multiResults = multiPathResults.toArray(new MultiPathFitResults[0]);
// Filter with no filter
final MultiPathFilter mpf = new MultiPathFilter(new SignalFilter(0), null, multiFilter.residualsThreshold);
mpf.fractionScoreSubset(multiResults, NullFailCounter.INSTANCE, this.results.size(), assignments, scoreStore, CoordinateStoreFactory.create(0, 0, imp.getWidth(), imp.getHeight(), config.convertUsingHwhMax(config.getDuplicateDistanceParameter())));
final double[][] matchScores = new double[set.size()][];
int count = 0;
for (int i = 0; i < assignments.size(); i++) {
final 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) {
@Override
public boolean execute(int uniqueId) {
// This should not be null or something has gone wrong
final PreprocessedPeakResult r = preprocessedPeakResults[uniqueId];
if (r == null) {
throw new IllegalArgumentException("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[count++] = score;
return true;
}
});
final FitConfiguration fitConfig = config.getFitConfiguration();
// Debug the reasons the fit failed
if (singleStatus != null) {
String name = PeakFit.getSolverName(fitConfig);
if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera()) {
name += " Camera";
}
IJ.log("Failure counts: " + name);
printFailures("Single", singleStatus);
printFailures("Multi", multiStatus);
printFailures("Doublet", doubletStatus);
printFailures("Multi doublet", multiDoubletStatus);
}
final StringBuilder sb = new StringBuilder(300);
// Add information about the simulation
final double signal = simulationParameters.averageSignal;
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');
final double density = ((double) n / imp.getStackSize()) / (w * h) / (simulationParameters.pixelPitch * simulationParameters.pixelPitch / 1e6);
sb.append(MathUtils.rounded(density)).append('\t');
sb.append(MathUtils.rounded(signal)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.sd)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.pixelPitch)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.depth)).append('\t');
sb.append(simulationParameters.fixedDepth).append('\t');
sb.append(MathUtils.rounded(simulationParameters.gain)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.readNoise)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.background)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.noise)).append('\t');
if (simulationParameters.fullSimulation) {
// The total signal is spread over frames
}
sb.append(MathUtils.rounded(signal / simulationParameters.noise)).append('\t');
sb.append(MathUtils.rounded(simulationParameters.sd / simulationParameters.pixelPitch)).append('\t');
sb.append(spotFilter.getDescription());
// nP and nN is the fractional score of the spot candidates
addCount(sb, (double) candidateData.countPositive + candidateData.countNegative);
addCount(sb, candidateData.countPositive);
addCount(sb, candidateData.countNegative);
addCount(sb, candidateData.fractionPositive);
addCount(sb, candidateData.fractionNegative);
String name = PeakFit.getSolverName(fitConfig);
if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera()) {
name += " Camera";
}
add(sb, name);
add(sb, config.getFitting());
spotFitResults.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) / candidateData.countPositive);
add(sb, (100.0 * cfp) / candidateData.countNegative);
// 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 match = new FractionClassificationResult(ctp, cfp, 0, simulationParameters.molecules - ctp);
add(sb, match.getRecall());
add(sb, match.getPrecision());
add(sb, match.getF1Score());
add(sb, match.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);
match = new FractionClassificationResult(tp, fp, 0, simulationParameters.molecules - tp);
add(sb, match.getRecall());
add(sb, match.getPrecision());
add(sb, match.getF1Score());
add(sb, match.getJaccard());
// Do it again but pretend we can perfectly filter all the false positives
// add(sb, tp);
match = new FractionClassificationResult(tp, 0, 0, simulationParameters.molecules - tp);
// Recall is unchanged
// Precision will be 100%
add(sb, match.getF1Score());
add(sb, match.getJaccard());
// The mean may be subject to extreme outliers so use the median
double median = distanceStats.getMedian();
add(sb, median);
final WindowOrganiser wo = new WindowOrganiser();
String label = String.format("Recall = %s. n = %d. Median = %s nm. SD = %s nm", MathUtils.rounded(match.getRecall()), distanceStats.getN(), MathUtils.rounded(median), MathUtils.rounded(distanceStats.getStandardDeviation()));
new HistogramPlotBuilder(TITLE, distanceStats, "Match Distance (nm)").setPlotLabel(label).show(wo);
median = depthStats.getMedian();
add(sb, median);
// Sort by spot intensity and produce correlation
double[] correlation = null;
double[] rankCorrelation = null;
double[] rank = null;
final FastCorrelator fastCorrelator = new FastCorrelator();
final ArrayList<Ranking> pc1 = new ArrayList<>();
final ArrayList<Ranking> pc2 = new ArrayList<>();
ci = 0;
if (settings.showCorrelation) {
final int[] indices = SimpleArrayUtils.natural(i1.length);
SortUtils.sortData(indices, is, settings.rankByIntensity, true);
correlation = new double[i1.length];
rankCorrelation = new double[i1.length];
rank = new double[i1.length];
for (final int ci2 : indices) {
fastCorrelator.add(Math.round(i1[ci2]), Math.round(i2[ci2]));
pc1.add(new Ranking(i1[ci2], ci));
pc2.add(new Ranking(i2[ci2], ci));
correlation[ci] = fastCorrelator.getCorrelation();
rankCorrelation[ci] = Correlator.correlation(rank(pc1), rank(pc2));
if (settings.rankByIntensity) {
rank[ci] = is[0] - is[ci];
} else {
rank[ci] = ci;
}
ci++;
}
} else {
for (int i = 0; i < i1.length; i++) {
fastCorrelator.add(Math.round(i1[i]), Math.round(i2[i]));
pc1.add(new Ranking(i1[i], i));
pc2.add(new Ranking(i2[i], i));
}
}
final double pearsonCorr = fastCorrelator.getCorrelation();
final double rankedCorr = Correlator.correlation(rank(pc1), rank(pc2));
// Get the regression
final 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 (settings.showCorrelation) {
String title = TITLE + " Intensity";
Plot plot = new Plot(title, "Candidate", "Spot");
final double[] limits1 = MathUtils.limits(i1);
final double[] limits2 = MathUtils.limits(i2);
plot.setLimits(limits1[0], limits1[1], limits2[0], limits2[1]);
label = String.format("Correlation=%s; Ranked=%s; Slope=%s", MathUtils.rounded(pearsonCorr), MathUtils.rounded(rankedCorr), MathUtils.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]);
}
ImageJUtils.display(title, plot, wo);
title = TITLE + " Correlation";
plot = new Plot(title, "Spot Rank", "Correlation");
final double[] xlimits = MathUtils.limits(rank);
double[] ylimits = MathUtils.limits(correlation);
ylimits = MathUtils.limits(ylimits, rankCorrelation);
plot.setLimits(xlimits[0], xlimits[1], ylimits[0], ylimits[1]);
plot.setColor(Color.red);
plot.addPoints(rank, correlation, Plot.LINE);
plot.setColor(Color.blue);
plot.addPoints(rank, rankCorrelation, Plot.LINE);
plot.setColor(Color.black);
plot.addLabel(0, 0, label);
ImageJUtils.display(title, plot, wo);
}
add(sb, pearsonCorr);
add(sb, rankedCorr);
add(sb, slope);
label = String.format("n = %d. Median = %s nm", depthStats.getN(), MathUtils.rounded(median));
new HistogramPlotBuilder(TITLE, depthStats, "Match Depth (nm)").setRemoveOutliersOption(1).setPlotLabel(label).show(wo);
// Plot histograms of the stats on the same window
final double[] lower = new double[filterCriteria.length];
final double[] upper = new double[lower.length];
final double[] min = new double[lower.length];
final double[] max = new double[lower.length];
for (int i = 0; i < stats[0].length; i++) {
final double[] limits = showDoubleHistogram(stats, i, wo, matchScores);
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;
final 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
final double[] increment = new double[lower.length];
for (int i = 0; i < increment.length; i++) {
lower[i] = MathUtils.floor(lower[i], interval[i]);
upper[i] = MathUtils.ceil(upper[i], interval[i]);
final 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] = MathUtils.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] = MathUtils.round(lower[i]);
upper[i] = MathUtils.round(upper[i]);
min[i] = MathUtils.round(min[i]);
max[i] = MathUtils.round(max[i]);
increment[i] = MathUtils.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(TextUtils.nanosToString(runTime));
createTable().append(sb.toString());
if (settings.saveFilterRange) {
GUIFilterSettings filterSettings = SettingsManager.readGuiFilterSettings(0);
String filename = (silent) ? filterSettings.getFilterSetFilename() : ImageJUtils.getFilename("Filter_range_file", filterSettings.getFilterSetFilename());
if (filename == null) {
return;
}
// Remove extension to store the filename
filename = FileUtils.replaceExtension(filename, ".xml");
filterSettings = filterSettings.toBuilder().setFilterSetFilename(filename).build();
// Create a filter set using the ranges
final ArrayList<Filter> filters = new ArrayList<>(4);
// Create the multi-filter using the same precision type as that used during fitting.
// Currently no support for z-filter as 3D astigmatism fitting is experimental.
final PrecisionMethod precisionMethod = getPrecisionMethod((DirectFilter) multiFilter.getFilter());
Function<double[], Filter> generator;
if (precisionMethod == PrecisionMethod.POISSON_CRLB) {
generator = parameters -> new MultiFilterCrlb(parameters[FILTER_SIGNAL], (float) parameters[FILTER_SNR], parameters[FILTER_MIN_WIDTH], parameters[FILTER_MAX_WIDTH], parameters[FILTER_SHIFT], parameters[FILTER_ESHIFT], parameters[FILTER_PRECISION], 0f, 0f);
} else if (precisionMethod == PrecisionMethod.MORTENSEN) {
generator = parameters -> new MultiFilter(parameters[FILTER_SIGNAL], (float) parameters[FILTER_SNR], parameters[FILTER_MIN_WIDTH], parameters[FILTER_MAX_WIDTH], parameters[FILTER_SHIFT], parameters[FILTER_ESHIFT], parameters[FILTER_PRECISION], 0f, 0f);
} else {
// Default
generator = parameters -> new MultiFilter2(parameters[FILTER_SIGNAL], (float) parameters[FILTER_SNR], parameters[FILTER_MIN_WIDTH], parameters[FILTER_MAX_WIDTH], parameters[FILTER_SHIFT], parameters[FILTER_ESHIFT], parameters[FILTER_PRECISION], 0f, 0f);
}
filters.add(generator.apply(lower));
filters.add(generator.apply(upper));
filters.add(generator.apply(increment));
if (saveFilters(filename, filters)) {
SettingsManager.writeSettings(filterSettings);
}
// 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_SNR] = Math.min(max[FILTER_SNR], 10000);
max[FILTER_PRECISION] = Math.min(max[FILTER_PRECISION], 100);
// Make the 4-set filters the same as the 3-set filters.
filters.clear();
filters.add(generator.apply(min));
filters.add(generator.apply(lower));
filters.add(generator.apply(upper));
filters.add(generator.apply(max));
saveFilters(FileUtils.replaceExtension(filename, ".4.xml"), filters);
}
spotFitResults.min = min;
spotFitResults.max = max;
}
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