Example 11 with TIntHashSet
use of gnu.trove.set.hash.TIntHashSet 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);
}
}
Also used :
ArrayList(java.util.ArrayList)
TIntHashSet(gnu.trove.set.hash.TIntHashSet)
MultiPathFitResult(gdsc.smlm.results.filter.MultiPathFitResult)
FractionalAssignment(gdsc.core.match.FractionalAssignment)
PeakFractionalAssignment(gdsc.smlm.results.filter.PeakFractionalAssignment)
ImmutableFractionalAssignment(gdsc.core.match.ImmutableFractionalAssignment)
FractionClassificationResult(gdsc.core.match.FractionClassificationResult)
BasePreprocessedPeakResult(gdsc.smlm.results.filter.BasePreprocessedPeakResult)
PreprocessedPeakResult(gdsc.smlm.results.filter.PreprocessedPeakResult)
SignalFilter(gdsc.smlm.results.filter.SignalFilter)
FilterSettings(gdsc.smlm.ij.settings.FilterSettings)
ScoredSpot(gdsc.smlm.ij.plugins.BenchmarkSpotFilter.ScoredSpot)
FastCorrelator(gdsc.core.utils.FastCorrelator)
Plot(ij.gui.Plot)
StoredDataStatistics(gdsc.core.utils.StoredDataStatistics)
PlotWindow(ij.gui.PlotWindow)
GlobalSettings(gdsc.smlm.ij.settings.GlobalSettings)
WindowOrganiser(ij.plugin.WindowOrganiser)
PeakResultPoint(gdsc.smlm.ij.plugins.ResultsMatchCalculator.PeakResultPoint)
BasePoint(gdsc.core.match.BasePoint)
PeakFractionalAssignment(gdsc.smlm.results.filter.PeakFractionalAssignment)
FractionScoreStore(gdsc.smlm.results.filter.MultiPathFilter.FractionScoreStore)
SimpleRegression(org.apache.commons.math3.stat.regression.SimpleRegression)
SignalFilter(gdsc.smlm.results.filter.SignalFilter)
DirectFilter(gdsc.smlm.results.filter.DirectFilter)
ShiftFilter(gdsc.smlm.results.filter.ShiftFilter)
PrecisionFilter(gdsc.smlm.results.filter.PrecisionFilter)
Filter(gdsc.smlm.results.filter.Filter)
EShiftFilter(gdsc.smlm.results.filter.EShiftFilter)
WidthFilter(gdsc.smlm.results.filter.WidthFilter)
SNRFilter(gdsc.smlm.results.filter.SNRFilter)
MultiPathFilter(gdsc.smlm.results.filter.MultiPathFilter)
MaximaSpotFilter(gdsc.smlm.filters.MaximaSpotFilter)
MultiFilter2(gdsc.smlm.results.filter.MultiFilter2)
MultiPathFitResults(gdsc.smlm.results.filter.MultiPathFitResults)
MultiPathFilter(gdsc.smlm.results.filter.MultiPathFilter)
Example 12 with TIntHashSet
use of gnu.trove.set.hash.TIntHashSet in project GDSC-SMLM by aherbert.
the class BenchmarkFilterAnalysis method showOverlay.
/**
* Show overlay.
*
* @param allAssignments
* The assignments generated from running the filter (or null)
* @param filter
* the filter
* @return The results from running the filter (or null)
*/
private PreprocessedPeakResult[] showOverlay(ArrayList<FractionalAssignment[]> allAssignments, DirectFilter filter) {
ImagePlus imp = CreateData.getImage();
if (imp == null)
return null;
// Run the filter manually to get the results that pass.
if (allAssignments == null)
allAssignments = getAssignments(filter);
final Overlay o = new Overlay();
// Do TP
final TIntHashSet actual = new TIntHashSet();
final TIntHashSet predicted = new TIntHashSet();
//int tp = 0, fp = 0, fn = 0;
for (FractionalAssignment[] assignments : allAssignments) {
if (assignments == null || assignments.length == 0)
continue;
float[] tx = null, ty = null;
int t = 0;
//tp += assignments.length;
if (showTP) {
tx = new float[assignments.length];
ty = new float[assignments.length];
}
int frame = 0;
for (int i = 0; i < assignments.length; i++) {
CustomFractionalAssignment c = (CustomFractionalAssignment) assignments[i];
IdPeakResult peak = (IdPeakResult) c.peak;
BasePreprocessedPeakResult spot = (BasePreprocessedPeakResult) c.peakResult;
actual.add(peak.uniqueId);
predicted.add(spot.getUniqueId());
frame = spot.getFrame();
if (showTP) {
tx[t] = spot.getX();
ty[t++] = spot.getY();
}
}
if (showTP)
SpotFinderPreview.addRoi(frame, o, tx, ty, t, Color.green);
}
float[] x = new float[10];
float[] y = new float[x.length];
float[] x2 = new float[10];
float[] y2 = new float[x2.length];
// Do FP (all remaining results that are not a TP)
PreprocessedPeakResult[] filterResults = null;
if (showFP) {
final MultiPathFilter multiPathFilter = createMPF(filter, minimalFilter);
//multiPathFilter.setDebugFile("/tmp/filter.txt");
filterResults = filterResults(multiPathFilter);
int frame = 0;
int c = 0;
int c2 = 0;
for (int i = 0; i < filterResults.length; i++) {
if (frame != filterResults[i].getFrame()) {
if (c != 0)
SpotFinderPreview.addRoi(frame, o, x, y, c, Color.red);
if (c2 != 0)
SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.magenta);
c = c2 = 0;
}
frame = filterResults[i].getFrame();
if (predicted.contains(filterResults[i].getUniqueId()))
continue;
if (filterResults[i].ignore()) {
if (x2.length == c2) {
x2 = Arrays.copyOf(x2, c2 * 2);
y2 = Arrays.copyOf(y2, c2 * 2);
}
x2[c2] = filterResults[i].getX();
y2[c2++] = filterResults[i].getY();
} else {
if (x.length == c) {
x = Arrays.copyOf(x, c * 2);
y = Arrays.copyOf(y, c * 2);
}
x[c] = filterResults[i].getX();
y[c++] = filterResults[i].getY();
}
}
//fp += c;
if (c != 0)
SpotFinderPreview.addRoi(frame, o, x, y, c, Color.red);
if (c2 != 0)
SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.magenta);
}
// Do TN (all remaining peaks that have not been matched)
if (showFN) {
final boolean checkBorder = (BenchmarkSpotFilter.lastAnalysisBorder != null && BenchmarkSpotFilter.lastAnalysisBorder.x != 0);
final float border, xlimit, ylimit;
if (checkBorder) {
final Rectangle lastAnalysisBorder = BenchmarkSpotFilter.lastAnalysisBorder;
border = lastAnalysisBorder.x;
xlimit = lastAnalysisBorder.x + lastAnalysisBorder.width;
ylimit = lastAnalysisBorder.y + lastAnalysisBorder.height;
} else
border = xlimit = ylimit = 0;
// Add the results to the lists
actualCoordinates.forEachEntry(new CustomTIntObjectProcedure(x, y, x2, y2) {
public boolean execute(int frame, IdPeakResult[] results) {
int c = 0, c2 = 0;
if (x.length <= results.length) {
x = new float[results.length];
y = new float[results.length];
}
if (x2.length <= results.length) {
x2 = new float[results.length];
y2 = new float[results.length];
}
for (int i = 0; i < results.length; i++) {
// Ignore those that were matched by TP
if (actual.contains(results[i].uniqueId))
continue;
if (checkBorder && outsideBorder(results[i], border, xlimit, ylimit)) {
x2[c2] = results[i].getXPosition();
y2[c2++] = results[i].getYPosition();
} else {
x[c] = results[i].getXPosition();
y[c++] = results[i].getYPosition();
}
}
//fn += c;
if (c != 0)
SpotFinderPreview.addRoi(frame, o, x, y, c, Color.yellow);
if (c2 != 0)
SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.orange);
return true;
}
});
}
//System.out.printf("TP=%d, FP=%d, FN=%d, N=%d (%d)\n", tp, fp, fn, tp + fn, results.size());
imp.setOverlay(o);
return filterResults;
}
Also used :
BasePreprocessedPeakResult(gdsc.smlm.results.filter.BasePreprocessedPeakResult)
Rectangle(java.awt.Rectangle)
ImagePlus(ij.ImagePlus)
TIntHashSet(gnu.trove.set.hash.TIntHashSet)
FractionalAssignment(gdsc.core.match.FractionalAssignment)
PeakFractionalAssignment(gdsc.smlm.results.filter.PeakFractionalAssignment)
BasePreprocessedPeakResult(gdsc.smlm.results.filter.BasePreprocessedPeakResult)
PreprocessedPeakResult(gdsc.smlm.results.filter.PreprocessedPeakResult)
MultiPathFilter(gdsc.smlm.results.filter.MultiPathFilter)
Overlay(ij.gui.Overlay)
Example 13 with TIntHashSet
use of gnu.trove.set.hash.TIntHashSet in project GDSC-SMLM by aherbert.
the class CreateData method removeFilteredFluorophores.
/**
* Remove all fluorophores which were not drawn
*
* @param fluorophores
* @param localisations
* @return
*/
private List<? extends FluorophoreSequenceModel> removeFilteredFluorophores(List<? extends FluorophoreSequenceModel> fluorophores, List<LocalisationModel> localisations) {
if (fluorophores == null)
return null;
// movingMolecules will be created with an initial capacity to hold all the unique IDs
TIntHashSet idSet = new TIntHashSet((movingMolecules != null) ? movingMolecules.capacity() : 0);
for (LocalisationModel l : localisations) idSet.add(l.getId());
List<FluorophoreSequenceModel> newFluorophores = new ArrayList<FluorophoreSequenceModel>(idSet.size());
for (FluorophoreSequenceModel f : fluorophores) {
if (idSet.contains(f.getId()))
newFluorophores.add(f);
}
return newFluorophores;
}
Also used :
LocalisationModel(gdsc.smlm.model.LocalisationModel)
FluorophoreSequenceModel(gdsc.smlm.model.FluorophoreSequenceModel)
ArrayList(java.util.ArrayList)
TIntHashSet(gnu.trove.set.hash.TIntHashSet)
Example 14 with TIntHashSet
use of gnu.trove.set.hash.TIntHashSet in project GDSC-SMLM by aherbert.
the class ResultsMatchCalculator method compareCoordinates.
private void compareCoordinates(MemoryPeakResults results1, MemoryPeakResults results2, double dThreshold, int increments, double delta) {
boolean requirePairs = showPairs || saveClassifications;
FilePeakResults fileResults = createFilePeakResults(results2);
List<PointPair> allMatches = new LinkedList<PointPair>();
List<PointPair> pairs = (requirePairs) ? new LinkedList<PointPair>() : null;
List<PeakResult> actualPoints = results1.getResults();
List<PeakResult> predictedPoints = results2.getResults();
double maxDistance = dThreshold + increments * delta;
// Old implementation
//// Process each time point
//for (Integer t : getTimepoints(actualPoints, predictedPoints))
//{
// Coordinate[] actual = getCoordinates(actualPoints, t);
// Coordinate[] predicted = getCoordinates(predictedPoints, t);
// Divide the results into time points
TIntObjectHashMap<ArrayList<Coordinate>> actualCoordinates = getCoordinates(actualPoints);
TIntObjectHashMap<ArrayList<Coordinate>> predictedCoordinates = getCoordinates(predictedPoints);
int n1 = 0;
int n2 = 0;
// Process each time point
for (Integer t : getTimepoints(actualCoordinates, predictedCoordinates)) {
Coordinate[] actual = getCoordinates(actualCoordinates, t);
Coordinate[] predicted = getCoordinates(predictedCoordinates, t);
List<Coordinate> TP = null;
List<Coordinate> FP = null;
List<Coordinate> FN = null;
List<PointPair> matches = new LinkedList<PointPair>();
if (requirePairs) {
FP = new LinkedList<Coordinate>();
FN = new LinkedList<Coordinate>();
}
MatchCalculator.analyseResults2D(actual, predicted, maxDistance, TP, FP, FN, matches);
// Aggregate
n1 += actual.length;
n2 += predicted.length;
allMatches.addAll(matches);
if (showPairs) {
pairs.addAll(matches);
for (Coordinate c : FN) pairs.add(new PointPair(c, null));
for (Coordinate c : FP) pairs.add(new PointPair(null, c));
}
if (fileResults != null) {
// Matches are marked in the original value with 1 for true, 0 for false
for (PointPair pair : matches) {
PeakResult p = ((PeakResultPoint) pair.getPoint2()).peakResult;
fileResults.add(p.getFrame(), p.origX, p.origY, 1, p.error, p.noise, p.params, null);
}
for (Coordinate c : FP) {
PeakResult p = ((PeakResultPoint) c).peakResult;
fileResults.add(p.getFrame(), p.origX, p.origY, 0, p.error, p.noise, p.params, null);
}
}
}
if (fileResults != null)
fileResults.end();
// XXX : DEBUGGING : Output for signal correlation and fitting analysis
/*
* try
* {
* OutputStreamWriter o = new OutputStreamWriter(new FileOutputStream("/tmp/ResultsMatchCalculator.txt"));
* FilePeakResults r1 = new FilePeakResults("/tmp/" + results1.getName() + ".txt", false);
* FilePeakResults r2 = new FilePeakResults("/tmp/" + results2.getName() + ".txt", false);
* r1.begin();
* r2.begin();
* //OutputStreamWriter o2 = new OutputStreamWriter(new FileOutputStream("/tmp/"+results1.getName()+".txt"));
* //OutputStreamWriter o3 = new OutputStreamWriter(new FileOutputStream("/tmp/"+results2.getName()+".txt"));
* for (PointPair pair : allMatches)
* {
* PeakResult p1 = ((PeakResultPoint) pair.getPoint1()).peakResult;
* PeakResult p2 = ((PeakResultPoint) pair.getPoint2()).peakResult;
* r1.add(p1);
* r2.add(p2);
* o.write(Float.toString(p1.getSignal()));
* o.write('\t');
* o.write(Float.toString(p2.getSignal()));
* o.write('\n');
* }
* o.close();
* r1.end();
* r2.end();
* }
* catch (Exception e)
* {
* e.printStackTrace();
* }
*/
boolean doIdAnalysis1 = (idAnalysis) ? haveIds(results1) : false;
boolean doIdAnalysis2 = (idAnalysis) ? haveIds(results2) : false;
boolean doIdAnalysis = doIdAnalysis1 || doIdAnalysis2;
// Create output
if (!java.awt.GraphicsEnvironment.isHeadless()) {
String header = createResultsHeader(doIdAnalysis);
Utils.refreshHeadings(resultsWindow, header, true);
if (showTable && (resultsWindow == null || !resultsWindow.isShowing())) {
resultsWindow = new TextWindow(TITLE + " Results", header, "", 900, 300);
}
if (showPairs) {
if (pairsWindow == null || !pairsWindow.isShowing()) {
pairsWindow = new TextWindow(TITLE + " Pairs", createPairsHeader(pairs), "", 900, 300);
if (resultsWindow != null) {
Point p = resultsWindow.getLocation();
p.y += resultsWindow.getHeight();
pairsWindow.setLocation(p);
}
pairPainter = new ImageROIPainter(pairsWindow.getTextPanel(), "", this);
}
pairsWindow.getTextPanel().clear();
String title = "Results 1";
if (results1.getSource() != null && results1.getSource().getOriginal().getName().length() > 0)
title = results1.getSource().getOriginal().getName();
pairPainter.setTitle(title);
IJ.showStatus("Writing pairs table");
IJ.showProgress(0);
int c = 0;
final int total = pairs.size();
final int step = Utils.getProgressInterval(total);
final ArrayList<String> list = new ArrayList<String>(total);
boolean flush = true;
for (PointPair pair : pairs) {
if (++c % step == 0)
IJ.showProgress(c, total);
list.add(addPairResult(pair));
if (flush && c == 9) {
pairsWindow.getTextPanel().append(list);
list.clear();
flush = false;
}
}
pairsWindow.getTextPanel().append(list);
IJ.showProgress(1);
}
} else {
if (writeHeader && showTable) {
writeHeader = false;
IJ.log(createResultsHeader(idAnalysis));
}
}
if (!showTable)
return;
// We have the results for the largest distance.
// Now reduce the distance threshold and recalculate the results
double[] distanceThresholds = getDistances(dThreshold, increments, delta);
double[] pairDistances = getPairDistances(allMatches);
// Re-use storage for the ID analysis
TIntHashSet id1 = null, id2 = null, matchId1 = null, matchId2 = null;
if (doIdAnalysis) {
if (doIdAnalysis1) {
id1 = getIds(results1);
matchId1 = new TIntHashSet(id1.size());
}
if (doIdAnalysis2) {
id2 = getIds(results2);
matchId2 = new TIntHashSet(id2.size());
}
}
for (double distanceThreshold : distanceThresholds) {
double rms = 0;
int tp2 = 0;
final double d2 = distanceThreshold * distanceThreshold;
for (double d : pairDistances) {
if (d <= d2) {
rms += d;
tp2++;
}
}
// All non-true positives must be added to the false totals.
int fp2 = n2 - tp2;
int fn2 = n1 - tp2;
MatchResult result = new MatchResult(tp2, fp2, fn2, (tp2 > 0) ? Math.sqrt(rms / tp2) : 0);
MatchResult idResult1 = null, idResult2 = null;
if (doIdAnalysis) {
if (doIdAnalysis1)
matchId1.clear();
if (doIdAnalysis2)
matchId2.clear();
int i = 0;
for (PointPair pair : allMatches) {
if (pairDistances[i++] <= d2) {
if (doIdAnalysis1)
matchId1.add(((PeakResultPoint) pair.getPoint1()).peakResult.getId());
if (doIdAnalysis2)
matchId2.add(((PeakResultPoint) pair.getPoint2()).peakResult.getId());
}
}
// => Only the recall will be valid: tp / (tp + fn)
if (doIdAnalysis1)
idResult1 = new MatchResult(matchId1.size(), 0, id1.size() - matchId1.size(), 0);
if (doIdAnalysis2)
idResult2 = new MatchResult(matchId2.size(), 0, id2.size() - matchId2.size(), 0);
}
addResult(inputOption1, inputOption2, distanceThreshold, result, idResult1, idResult2);
}
}
Also used :
ArrayList(java.util.ArrayList)
MatchResult(gdsc.core.match.MatchResult)
PeakResult(gdsc.smlm.results.PeakResult)
TIntHashSet(gnu.trove.set.hash.TIntHashSet)
PointPair(gdsc.core.match.PointPair)
ImageROIPainter(gdsc.smlm.ij.utils.ImageROIPainter)
Point(java.awt.Point)
BasePoint(gdsc.core.match.BasePoint)
LinkedList(java.util.LinkedList)
Point(java.awt.Point)
BasePoint(gdsc.core.match.BasePoint)
FilePeakResults(gdsc.smlm.results.FilePeakResults)
TextWindow(ij.text.TextWindow)
Coordinate(gdsc.core.match.Coordinate)
Example 15 with TIntHashSet
use of gnu.trove.set.hash.TIntHashSet in project GDSC-SMLM by aherbert.
the class BlinkEstimatorTest method estimateBlinking.
private TIntHashSet estimateBlinking(double nBlinks, double tOn, double tOff, int particles, double fixedFraction, boolean timeAtLowerBound, boolean doAssert) {
SpatialIllumination activationIllumination = new UniformIllumination(100);
int totalSteps = 100;
double eAct = totalSteps * 0.3 * activationIllumination.getAveragePhotons();
ImageModel imageModel = new ActivationEnergyImageModel(eAct, activationIllumination, tOn, 0, tOff, 0, nBlinks);
imageModel.setRandomGenerator(rand);
double[] max = new double[] { 256, 256, 32 };
double[] min = new double[3];
SpatialDistribution distribution = new UniformDistribution(min, max, rand.nextInt());
List<CompoundMoleculeModel> compounds = new ArrayList<CompoundMoleculeModel>(1);
CompoundMoleculeModel c = new CompoundMoleculeModel(1, 0, 0, 0, Arrays.asList(new MoleculeModel(0, 0, 0, 0)));
c.setDiffusionRate(diffusionRate);
c.setDiffusionType(DiffusionType.RANDOM_WALK);
compounds.add(c);
List<CompoundMoleculeModel> molecules = imageModel.createMolecules(compounds, particles, distribution, false);
// Activate fluorophores
List<? extends FluorophoreSequenceModel> fluorophores = imageModel.createFluorophores(molecules, totalSteps);
totalSteps = checkTotalSteps(totalSteps, fluorophores);
List<LocalisationModel> localisations = imageModel.createImage(molecules, fixedFraction, totalSteps, photons, 0.5, false);
// // Remove localisations to simulate missed counts.
// List<LocalisationModel> newLocalisations = new ArrayList<LocalisationModel>(localisations.size());
// boolean[] id = new boolean[fluorophores.size() + 1];
// Statistics photonStats = new Statistics();
// for (LocalisationModel l : localisations)
// {
// photonStats.add(l.getIntensity());
// // Remove by intensity threshold and optionally at random.
// if (l.getIntensity() < minPhotons || rand.nextDouble() < pDelete)
// continue;
// newLocalisations.add(l);
// id[l.getId()] = true;
// }
// localisations = newLocalisations;
// System.out.printf("Photons = %f\n", photonStats.getMean());
//
// List<FluorophoreSequenceModel> newFluorophores = new ArrayList<FluorophoreSequenceModel>(fluorophores.size());
// for (FluorophoreSequenceModel f : fluorophores)
// {
// if (id[f.getId()])
// newFluorophores.add(f);
// }
// fluorophores = newFluorophores;
MemoryPeakResults results = new MemoryPeakResults();
results.setCalibration(new Calibration(pixelPitch, 1, msPerFrame));
for (LocalisationModel l : localisations) {
// Remove by intensity threshold and optionally at random.
if (l.getIntensity() < minPhotons || rand.nextDouble() < pDelete)
continue;
float[] params = new float[7];
params[Gaussian2DFunction.X_POSITION] = (float) l.getX();
params[Gaussian2DFunction.Y_POSITION] = (float) l.getY();
params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = psfWidth;
params[Gaussian2DFunction.SIGNAL] = (float) (l.getIntensity());
results.addf(l.getTime(), 0, 0, 0, 0, 0, params, null);
}
// Add random localisations
for (int i = (int) (localisations.size() * pAdd); i-- > 0; ) {
float[] params = new float[7];
params[Gaussian2DFunction.X_POSITION] = (float) (rand.nextDouble() * max[0]);
params[Gaussian2DFunction.Y_POSITION] = (float) (rand.nextDouble() * max[1]);
params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = psfWidth;
// Intensity doesn't matter at the moment for tracing
params[Gaussian2DFunction.SIGNAL] = (float) (photons);
results.addf(1 + rand.nextInt(totalSteps), 0, 0, 0, 0, 0, params, null);
}
// Get actual simulated stats ...
Statistics statsNBlinks = new Statistics();
Statistics statsTOn = new Statistics();
Statistics statsTOff = new Statistics();
Statistics statsSampledNBlinks = new Statistics();
Statistics statsSampledTOn = new Statistics();
StoredDataStatistics statsSampledTOff = new StoredDataStatistics();
for (FluorophoreSequenceModel f : fluorophores) {
statsNBlinks.add(f.getNumberOfBlinks());
statsTOn.add(f.getOnTimes());
statsTOff.add(f.getOffTimes());
int[] on = f.getSampledOnTimes();
statsSampledNBlinks.add(on.length);
statsSampledTOn.add(on);
statsSampledTOff.add(f.getSampledOffTimes());
}
System.out.printf("N = %d (%d), N-blinks = %f, tOn = %f, tOff = %f, Fixed = %f\n", fluorophores.size(), localisations.size(), nBlinks, tOn, tOff, fixedFraction);
System.out.printf("Actual N-blinks = %f (%f), tOn = %f (%f), tOff = %f (%f), 95%% = %f, max = %f\n", statsNBlinks.getMean(), statsSampledNBlinks.getMean(), statsTOn.getMean(), statsSampledTOn.getMean(), statsTOff.getMean(), statsSampledTOff.getMean(), statsSampledTOff.getStatistics().getPercentile(95), statsSampledTOff.getStatistics().getMax());
System.out.printf("-=-=--=-\n");
BlinkEstimator be = new BlinkEstimator();
be.maxDarkTime = (int) (tOff * 10);
be.msPerFrame = msPerFrame;
be.relativeDistance = false;
double d = ImageModel.getRandomMoveDistance(diffusionRate);
be.searchDistance = (fixedFraction < 1) ? Math.sqrt(2 * d * d) * 3 : 0;
be.timeAtLowerBound = timeAtLowerBound;
be.showPlots = false;
//Assert.assertTrue("Max dark time must exceed the dark time of the data (otherwise no plateau)",
// be.maxDarkTime > statsSampledTOff.getStatistics().getMax());
int nMolecules = fluorophores.size();
if (usePopulationStatistics) {
nBlinks = statsNBlinks.getMean();
tOff = statsTOff.getMean();
} else {
nBlinks = statsSampledNBlinks.getMean();
tOff = statsSampledTOff.getMean();
}
// See if any fitting regime gets a correct answer
TIntHashSet ok = new TIntHashSet();
for (int nFittedPoints = MIN_FITTED_POINTS; nFittedPoints <= MAX_FITTED_POINTS; nFittedPoints++) {
be.nFittedPoints = nFittedPoints;
be.computeBlinkingRate(results, true);
double moleculesError = DoubleEquality.relativeError(nMolecules, be.getNMolecules());
double blinksError = DoubleEquality.relativeError(nBlinks, be.getNBlinks());
double offError = DoubleEquality.relativeError(tOff * msPerFrame, be.getTOff());
System.out.printf("Error %d: N = %f, blinks = %f, tOff = %f : %f\n", nFittedPoints, moleculesError, blinksError, offError, (moleculesError + blinksError + offError) / 3);
if (moleculesError < relativeError && blinksError < relativeError && offError < relativeError) {
ok.add(nFittedPoints);
System.out.printf("-=-=--=-\n");
System.out.printf("*** Correct at %d fitted points ***\n", nFittedPoints);
if (doAssert)
break;
}
//if (!be.isIncreaseNFittedPoints())
// break;
}
System.out.printf("-=-=--=-\n");
if (doAssert)
Assert.assertFalse(ok.isEmpty());
//Assert.assertEquals("Invalid t-off", tOff * msPerFrame, be.getTOff(), tOff * msPerFrame * relativeError);
return ok;
}
Also used :
ActivationEnergyImageModel(gdsc.smlm.model.ActivationEnergyImageModel)
CompoundMoleculeModel(gdsc.smlm.model.CompoundMoleculeModel)
ArrayList(java.util.ArrayList)
TIntHashSet(gnu.trove.set.hash.TIntHashSet)
CompoundMoleculeModel(gdsc.smlm.model.CompoundMoleculeModel)
MoleculeModel(gdsc.smlm.model.MoleculeModel)
SpatialIllumination(gdsc.smlm.model.SpatialIllumination)
MemoryPeakResults(gdsc.smlm.results.MemoryPeakResults)
SpatialDistribution(gdsc.smlm.model.SpatialDistribution)
UniformDistribution(gdsc.smlm.model.UniformDistribution)
StoredDataStatistics(gdsc.core.utils.StoredDataStatistics)
Calibration(gdsc.smlm.results.Calibration)
StoredDataStatistics(gdsc.core.utils.StoredDataStatistics)
Statistics(gdsc.core.utils.Statistics)
UniformIllumination(gdsc.smlm.model.UniformIllumination)
LocalisationModel(gdsc.smlm.model.LocalisationModel)
FluorophoreSequenceModel(gdsc.smlm.model.FluorophoreSequenceModel)
ImageModel(gdsc.smlm.model.ImageModel)
ActivationEnergyImageModel(gdsc.smlm.model.ActivationEnergyImageModel)
Aggregations
TIntHashSet (gnu.trove.set.hash.TIntHashSet)15
ArrayList (java.util.ArrayList)6
LocalisationModel (gdsc.smlm.model.LocalisationModel)3
BasePoint (gdsc.core.match.BasePoint)2
FractionalAssignment (gdsc.core.match.FractionalAssignment)2
StoredDataStatistics (gdsc.core.utils.StoredDataStatistics)2
FluorophoreSequenceModel (gdsc.smlm.model.FluorophoreSequenceModel)2
PeakResult (gdsc.smlm.results.PeakResult)2
BasePreprocessedPeakResult (gdsc.smlm.results.filter.BasePreprocessedPeakResult)2
MultiPathFilter (gdsc.smlm.results.filter.MultiPathFilter)2
PeakFractionalAssignment (gdsc.smlm.results.filter.PeakFractionalAssignment)2
PreprocessedPeakResult (gdsc.smlm.results.filter.PreprocessedPeakResult)2
TIntIterator (gnu.trove.iterator.TIntIterator)2
TIntProcedure (gnu.trove.procedure.TIntProcedure)2
MutableBlockPos (net.minecraft.util.math.BlockPos.MutableBlockPos)2
Coordinate (gdsc.core.match.Coordinate)1
FractionClassificationResult (gdsc.core.match.FractionClassificationResult)1
ImmutableFractionalAssignment (gdsc.core.match.ImmutableFractionalAssignment)1
MatchResult (gdsc.core.match.MatchResult)1
PointPair (gdsc.core.match.PointPair)1
