use of org.apache.commons.math3.stat.descriptive.moment.Variance 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 org.apache.commons.math3.stat.descriptive.moment.Variance in project GDSC-SMLM by aherbert.
the class BenchmarkSpotFilter method summariseResults.
private BenchmarkFilterResult summariseResults(TIntObjectHashMap<FilterResult> filterResults, FitEngineConfiguration config, MaximaSpotFilter spotFilter, boolean relativeDistances, boolean batchSummary) {
BenchmarkFilterResult filterResult = new BenchmarkFilterResult(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.
double[][] cumul = histogramFailures(filterResult);
// Create the overall match score
final double[] total = new double[3];
final ArrayList<ScoredSpot> allSpots = new ArrayList<BenchmarkSpotFilter.ScoredSpot>();
filterResults.forEachValue(new TObjectProcedure<FilterResult>() {
public boolean execute(FilterResult result) {
total[0] += result.result.getTP();
total[1] += result.result.getFP();
total[2] += result.result.getFN();
allSpots.addAll(Arrays.asList(result.spots));
return true;
}
});
double tp = total[0], fp = total[1], fn = total[2];
FractionClassificationResult allResult = new FractionClassificationResult(tp, fp, 0, fn);
// The number of actual results
final double n = (tp + fn);
StringBuilder sb = new StringBuilder();
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 = lastAnalysisBorder.width;
final int h = lastAnalysisBorder.height;
sb.append(w).append("\t");
sb.append(h).append("\t");
sb.append(Utils.rounded(n)).append("\t");
double density = (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;
}
sb.append(Utils.rounded(signal / Math.sqrt(noise))).append("\t");
sb.append(Utils.rounded(simulationParameters.s / simulationParameters.a)).append("\t");
sb.append(config.getDataFilterType()).append("\t");
//sb.append(spotFilter.getName()).append("\t");
sb.append(spotFilter.getSearch()).append("\t");
sb.append(spotFilter.getBorder()).append("\t");
sb.append(Utils.rounded(spotFilter.getSpread())).append("\t");
sb.append(config.getDataFilter(0)).append("\t");
final double param = config.getSmooth(0);
final double hwhmMin = config.getHWHMMin();
if (relativeDistances) {
sb.append(Utils.rounded(param * hwhmMin)).append("\t");
sb.append(Utils.rounded(param)).append("\t");
} else {
sb.append(Utils.rounded(param)).append("\t");
sb.append(Utils.rounded(param / hwhmMin)).append("\t");
}
sb.append(spotFilter.getDescription()).append("\t");
sb.append(lastAnalysisBorder.x).append("\t");
sb.append(MATCHING_METHOD[matchingMethod]).append("\t");
sb.append(Utils.rounded(lowerMatchDistance)).append("\t");
sb.append(Utils.rounded(matchDistance)).append("\t");
sb.append(Utils.rounded(lowerSignalFactor)).append("\t");
sb.append(Utils.rounded(upperSignalFactor));
resultPrefix = sb.toString();
// Add the results
sb.append("\t");
// Rank the scored spots by intensity
Collections.sort(allSpots);
// Produce Recall, Precision, Jaccard for each cut of the spot candidates
double[] r = new double[allSpots.size() + 1];
double[] p = new double[r.length];
double[] j = new double[r.length];
double[] c = new double[r.length];
double[] truePositives = new double[r.length];
double[] falsePositives = new double[r.length];
double[] intensity = new double[r.length];
// Note: fn = n - tp
tp = fp = 0;
int i = 1;
p[0] = 1;
FastCorrelator corr = new FastCorrelator();
double lastC = 0;
double[] i1 = new double[r.length];
double[] i2 = new double[r.length];
int ci = 0;
SimpleRegression regression = new SimpleRegression(false);
for (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 = (long) Math.round(spotIntensity);
final long v2 = (long) Math.round(s.intensity);
regression.addData(spotIntensity, s.intensity);
i1[ci] = spotIntensity;
i2[ci] = s.intensity;
ci++;
corr.add(v1, v2);
lastC = corr.getCorrelation();
} else
fp++;
r[i] = (double) tp / n;
p[i] = (double) tp / (tp + fp);
// (tp+fp+fn) == (fp+n) since tp+fn=n;
j[i] = (double) tp / (fp + n);
c[i] = lastC;
truePositives[i] = tp;
falsePositives[i] = fp;
intensity[i] = s.getIntensity();
i++;
}
i1 = Arrays.copyOf(i1, ci);
i2 = Arrays.copyOf(i2, ci);
final double slope = regression.getSlope();
sb.append(Utils.rounded(slope)).append("\t");
addResult(sb, allResult, c[c.length - 1]);
// Output the match results when the recall achieves the fraction of the maximum.
double target = r[r.length - 1];
if (recallFraction < 100)
target *= recallFraction / 100.0;
int fractionIndex = 0;
while (fractionIndex < r.length && r[fractionIndex] < target) {
fractionIndex++;
}
if (fractionIndex == r.length)
fractionIndex--;
addResult(sb, new FractionClassificationResult(truePositives[fractionIndex], falsePositives[fractionIndex], 0, n - truePositives[fractionIndex]), c[fractionIndex]);
// Output the match results at the maximum jaccard score
int maxIndex = 0;
for (int ii = 1; ii < r.length; ii++) {
if (j[maxIndex] < j[ii])
maxIndex = ii;
}
addResult(sb, new FractionClassificationResult(truePositives[maxIndex], falsePositives[maxIndex], 0, n - truePositives[maxIndex]), c[maxIndex]);
sb.append(Utils.rounded(time / 1e6));
// Calculate AUC (Average precision == Area Under Precision-Recall curve)
final double auc = AUCCalculator.auc(p, r);
// Compute the AUC using the adjusted precision curve
// which uses the maximum precision for recall >= r
final double[] maxp = new double[p.length];
double max = 0;
for (int k = maxp.length; k-- > 0; ) {
if (max < p[k])
max = p[k];
maxp[k] = max;
}
final double auc2 = AUCCalculator.auc(maxp, r);
sb.append("\t").append(Utils.rounded(auc));
sb.append("\t").append(Utils.rounded(auc2));
// Output the number of fit failures that must be processed to capture fractions of the true positives
if (cumul[0].length != 0) {
sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.80)));
sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.90)));
sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.95)));
sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.99)));
sb.append("\t").append(Utils.rounded(cumul[0][cumul[0].length - 1]));
} else
sb.append("\t\t\t\t\t");
BufferedTextWindow resultsTable = getTable(batchSummary);
resultsTable.append(sb.toString());
// Store results
filterResult.auc = auc;
filterResult.auc2 = auc2;
filterResult.r = r;
filterResult.p = p;
filterResult.j = j;
filterResult.c = c;
filterResult.maxIndex = maxIndex;
filterResult.fractionIndex = fractionIndex;
filterResult.cumul = cumul;
filterResult.slope = slope;
filterResult.i1 = i1;
filterResult.i2 = i2;
filterResult.intensity = intensity;
filterResult.relativeDistances = relativeDistances;
filterResult.time = time;
return filterResult;
}
use of org.apache.commons.math3.stat.descriptive.moment.Variance in project GDSC-SMLM by aherbert.
the class MeanVarianceTest method run.
/*
* (non-Javadoc)
*
* @see ij.plugin.PlugIn#run(java.lang.String)
*/
public void run(String arg) {
SMLMUsageTracker.recordPlugin(this.getClass(), arg);
if (Utils.isExtraOptions()) {
ImagePlus imp = WindowManager.getCurrentImage();
if (imp.getStackSize() > 1) {
GenericDialog gd = new GenericDialog(TITLE);
gd.addMessage("Perform single image analysis on the current image?");
gd.addNumericField("Bias", _bias, 0);
gd.showDialog();
if (gd.wasCanceled())
return;
singleImage = true;
_bias = Math.abs(gd.getNextNumber());
} else {
IJ.error(TITLE, "Single-image mode requires a stack");
return;
}
}
List<ImageSample> images;
String inputDirectory = "";
if (singleImage) {
IJ.showStatus("Loading images...");
images = getImages();
if (images.size() == 0) {
IJ.error(TITLE, "Not enough images for analysis");
return;
}
} else {
inputDirectory = IJ.getDirectory("Select image series ...");
if (inputDirectory == null)
return;
SeriesOpener series = new SeriesOpener(inputDirectory, false, 0);
series.setVariableSize(true);
if (series.getNumberOfImages() < 3) {
IJ.error(TITLE, "Not enough images in the selected directory");
return;
}
if (!IJ.showMessageWithCancel(TITLE, String.format("Analyse %d images, first image:\n%s", series.getNumberOfImages(), series.getImageList()[0]))) {
return;
}
IJ.showStatus("Loading images");
images = getImages(series);
if (images.size() < 3) {
IJ.error(TITLE, "Not enough images for analysis");
return;
}
if (images.get(0).exposure != 0) {
IJ.error(TITLE, "First image in series must have exposure 0 (Bias image)");
return;
}
}
boolean emMode = (arg != null && arg.contains("em"));
GenericDialog gd = new GenericDialog(TITLE);
gd.addMessage("Set the output options:");
gd.addCheckbox("Show_table", showTable);
gd.addCheckbox("Show_charts", showCharts);
if (emMode) {
// Ask the user for the camera gain ...
gd.addMessage("Estimating the EM-gain requires the camera gain without EM readout enabled");
gd.addNumericField("Camera_gain (ADU/e-)", cameraGain, 4);
}
gd.showDialog();
if (gd.wasCanceled())
return;
showTable = gd.getNextBoolean();
showCharts = gd.getNextBoolean();
if (emMode) {
cameraGain = gd.getNextNumber();
}
IJ.showStatus("Computing mean & variance");
final double nImages = images.size();
for (int i = 0; i < images.size(); i++) {
IJ.showStatus(String.format("Computing mean & variance %d/%d", i + 1, images.size()));
images.get(i).compute(singleImage, i / nImages, (i + 1) / nImages);
}
IJ.showProgress(1);
IJ.showStatus("Computing results");
// Allow user to input multiple bias images
int start = 0;
Statistics biasStats = new Statistics();
Statistics noiseStats = new Statistics();
final double bias;
if (singleImage) {
bias = _bias;
} else {
while (start < images.size()) {
ImageSample sample = images.get(start);
if (sample.exposure == 0) {
biasStats.add(sample.means);
for (PairSample pair : sample.samples) {
noiseStats.add(pair.variance);
}
start++;
} else
break;
}
bias = biasStats.getMean();
}
// Get the mean-variance data
int total = 0;
for (int i = start; i < images.size(); i++) total += images.get(i).samples.size();
if (showTable && total > 2000) {
gd = new GenericDialog(TITLE);
gd.addMessage("Table output requires " + total + " entries.\n \nYou may want to disable the table.");
gd.addCheckbox("Show_table", showTable);
gd.showDialog();
if (gd.wasCanceled())
return;
showTable = gd.getNextBoolean();
}
TextWindow results = (showTable) ? createResultsWindow() : null;
double[] mean = new double[total];
double[] variance = new double[mean.length];
Statistics gainStats = (singleImage) ? new StoredDataStatistics(total) : new Statistics();
final WeightedObservedPoints obs = new WeightedObservedPoints();
for (int i = (singleImage) ? 0 : start, j = 0; i < images.size(); i++) {
StringBuilder sb = (showTable) ? new StringBuilder() : null;
ImageSample sample = images.get(i);
for (PairSample pair : sample.samples) {
if (j % 16 == 0)
IJ.showProgress(j, total);
mean[j] = pair.getMean();
variance[j] = pair.variance;
// Gain is in ADU / e
double gain = variance[j] / (mean[j] - bias);
gainStats.add(gain);
obs.add(mean[j], variance[j]);
if (emMode) {
gain /= (2 * cameraGain);
}
if (showTable) {
sb.append(sample.title).append("\t");
sb.append(sample.exposure).append("\t");
sb.append(pair.slice1).append("\t");
sb.append(pair.slice2).append("\t");
sb.append(IJ.d2s(pair.mean1, 2)).append("\t");
sb.append(IJ.d2s(pair.mean2, 2)).append("\t");
sb.append(IJ.d2s(mean[j], 2)).append("\t");
sb.append(IJ.d2s(variance[j], 2)).append("\t");
sb.append(Utils.rounded(gain, 4)).append("\n");
}
j++;
}
if (showTable)
results.append(sb.toString());
}
IJ.showProgress(1);
if (singleImage) {
StoredDataStatistics stats = (StoredDataStatistics) gainStats;
Utils.log(TITLE);
if (emMode) {
double[] values = stats.getValues();
MathArrays.scaleInPlace(0.5, values);
stats = new StoredDataStatistics(values);
}
if (showCharts) {
// Plot the gain over time
String title = TITLE + " Gain vs Frame";
Plot2 plot = new Plot2(title, "Slice", "Gain", Utils.newArray(gainStats.getN(), 1, 1.0), stats.getValues());
PlotWindow pw = Utils.display(title, plot);
// Show a histogram
String label = String.format("Mean = %s, Median = %s", Utils.rounded(stats.getMean()), Utils.rounded(stats.getMedian()));
int id = Utils.showHistogram(TITLE, stats, "Gain", 0, 1, 100, true, label);
if (Utils.isNewWindow()) {
Point point = pw.getLocation();
point.x = pw.getLocation().x;
point.y += pw.getHeight();
WindowManager.getImage(id).getWindow().setLocation(point);
}
}
Utils.log("Single-image mode: %s camera", (emMode) ? "EM-CCD" : "Standard");
final double gain = stats.getMedian();
if (emMode) {
final double totalGain = gain;
final double emGain = totalGain / cameraGain;
Utils.log(" Gain = 1 / %s (ADU/e-)", Utils.rounded(cameraGain, 4));
Utils.log(" EM-Gain = %s", Utils.rounded(emGain, 4));
Utils.log(" Total Gain = %s (ADU/e-)", Utils.rounded(totalGain, 4));
} else {
cameraGain = gain;
Utils.log(" Gain = 1 / %s (ADU/e-)", Utils.rounded(cameraGain, 4));
}
} else {
IJ.showStatus("Computing fit");
// Sort
int[] indices = rank(mean);
mean = reorder(mean, indices);
variance = reorder(variance, indices);
// Compute optimal coefficients.
// a - b x
final double[] init = { 0, 1 / gainStats.getMean() };
final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2).withStartPoint(init);
final double[] best = fitter.fit(obs.toList());
// Construct the polynomial that best fits the data.
final PolynomialFunction fitted = new PolynomialFunction(best);
if (showCharts) {
// Plot mean verses variance. Gradient is gain in ADU/e.
String title = TITLE + " results";
Plot2 plot = new Plot2(title, "Mean", "Variance");
double[] xlimits = Maths.limits(mean);
double[] ylimits = Maths.limits(variance);
double xrange = (xlimits[1] - xlimits[0]) * 0.05;
if (xrange == 0)
xrange = 0.05;
double yrange = (ylimits[1] - ylimits[0]) * 0.05;
if (yrange == 0)
yrange = 0.05;
plot.setLimits(xlimits[0] - xrange, xlimits[1] + xrange, ylimits[0] - yrange, ylimits[1] + yrange);
plot.setColor(Color.blue);
plot.addPoints(mean, variance, Plot2.CROSS);
plot.setColor(Color.red);
plot.addPoints(new double[] { mean[0], mean[mean.length - 1] }, new double[] { fitted.value(mean[0]), fitted.value(mean[mean.length - 1]) }, Plot2.LINE);
Utils.display(title, plot);
}
final double avBiasNoise = Math.sqrt(noiseStats.getMean());
Utils.log(TITLE);
Utils.log(" Directory = %s", inputDirectory);
Utils.log(" Bias = %s +/- %s (ADU)", Utils.rounded(bias, 4), Utils.rounded(avBiasNoise, 4));
Utils.log(" Variance = %s + %s * mean", Utils.rounded(best[0], 4), Utils.rounded(best[1], 4));
if (emMode) {
final double emGain = best[1] / (2 * cameraGain);
// Noise is standard deviation of the bias image divided by the total gain (in ADU/e-)
final double totalGain = emGain * cameraGain;
Utils.log(" Read Noise = %s (e-) [%s (ADU)]", Utils.rounded(avBiasNoise / totalGain, 4), Utils.rounded(avBiasNoise, 4));
Utils.log(" Gain = 1 / %s (ADU/e-)", Utils.rounded(1 / cameraGain, 4));
Utils.log(" EM-Gain = %s", Utils.rounded(emGain, 4));
Utils.log(" Total Gain = %s (ADU/e-)", Utils.rounded(totalGain, 4));
} else {
// Noise is standard deviation of the bias image divided by the gain (in ADU/e-)
cameraGain = best[1];
final double readNoise = avBiasNoise / cameraGain;
Utils.log(" Read Noise = %s (e-) [%s (ADU)]", Utils.rounded(readNoise, 4), Utils.rounded(readNoise * cameraGain, 4));
Utils.log(" Gain = 1 / %s (ADU/e-)", Utils.rounded(1 / cameraGain, 4));
}
}
IJ.showStatus("");
}
use of org.apache.commons.math3.stat.descriptive.moment.Variance in project gatk by broadinstitute.
the class CoverageModelCopyRatioEmissionProbabilityCalculator method logLikelihoodPoisson.
/**
* Calculate emission log probability directly using Poisson distribution
*
* TODO github/gatk-protected issue #854
*
* @implNote This is a naive implementation where the variance of log bias ($\Psi$) is
* ignored altogether. This routine must be improved by performing a few-point numerical
* integration of:
*
* \int_{-\infty}^{+\infty} db Poisson(n | \lambda = d*c*exp(b) + eps*d)
* * Normal(b | \mu, \Psi)
*
* @param emissionData an instance of {@link CoverageModelCopyRatioEmissionData}
* @param copyRatio copy ratio on which the emission probability is conditioned on
* @return a double value
*/
private double logLikelihoodPoisson(@Nonnull CoverageModelCopyRatioEmissionData emissionData, double copyRatio) {
final double multBias = FastMath.exp(emissionData.getMu());
final double readDepth = emissionData.getCopyRatioCallingMetadata().getSampleCoverageDepth();
final double err = emissionData.getMappingErrorProbability();
final double poissonMean = readDepth * ((1 - err) * copyRatio * multBias + err);
return new PoissonDistribution(null, poissonMean, PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS).logProbability(emissionData.getReadCount());
}
use of org.apache.commons.math3.stat.descriptive.moment.Variance in project gatk by broadinstitute.
the class MatrixSummaryUtils method getRowVariances.
/**
* Return an array containing the variance for each row in the given matrix.
* @param m Not {@code null}. Size MxN. If any entry is NaN, the corresponding rows will have a
* variance of NaN.
* @return array of size M. Never {@code null} IF there is only one column (or only one entry
*/
public static double[] getRowVariances(final RealMatrix m) {
Utils.nonNull(m, "Cannot calculate medians on a null matrix.");
final StandardDeviation std = new StandardDeviation();
return IntStream.range(0, m.getRowDimension()).boxed().mapToDouble(i -> Math.pow(std.evaluate(m.getRow(i)), 2)).toArray();
}
Aggregations