Example 16 with GlobalSettings
use of gdsc.smlm.ij.settings.GlobalSettings 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 17 with GlobalSettings
use of gdsc.smlm.ij.settings.GlobalSettings in project GDSC-SMLM by aherbert.
the class BenchmarkFit method showDialog.
private boolean showDialog() {
GenericDialog gd = new GenericDialog(TITLE);
gd.addHelp(About.HELP_URL);
final double sa = getSa();
gd.addMessage(String.format("Fits the benchmark image created by CreateData plugin.\nPSF width = %s, adjusted = %s", Utils.rounded(benchmarkParameters.s / benchmarkParameters.a), Utils.rounded(sa)));
// For each new benchmark width, reset the PSF width to the square pixel adjustment
if (lastS != benchmarkParameters.s) {
lastS = benchmarkParameters.s;
psfWidth = sa;
}
final String filename = SettingsManager.getSettingsFilename();
GlobalSettings settings = SettingsManager.loadSettings(filename);
fitConfig = settings.getFitEngineConfiguration().getFitConfiguration();
fitConfig.setNmPerPixel(benchmarkParameters.a);
gd.addSlider("Region_size", 2, 20, regionSize);
gd.addNumericField("PSF_width", psfWidth, 3);
String[] solverNames = SettingsManager.getNames((Object[]) FitSolver.values());
gd.addChoice("Fit_solver", solverNames, solverNames[fitConfig.getFitSolver().ordinal()]);
String[] functionNames = SettingsManager.getNames((Object[]) FitFunction.values());
gd.addChoice("Fit_function", functionNames, functionNames[fitConfig.getFitFunction().ordinal()]);
gd.addCheckbox("Offset_fit", offsetFitting);
gd.addNumericField("Start_offset", startOffset, 3);
gd.addCheckbox("Include_CoM_fit", comFitting);
gd.addCheckbox("Background_fitting", backgroundFitting);
gd.addMessage("Signal fitting can be disabled for " + FitFunction.FIXED.toString() + " function");
gd.addCheckbox("Signal_fitting", signalFitting);
gd.addCheckbox("Show_histograms", showHistograms);
gd.addCheckbox("Save_raw_data", saveRawData);
gd.showDialog();
if (gd.wasCanceled())
return false;
regionSize = (int) Math.abs(gd.getNextNumber());
psfWidth = Math.abs(gd.getNextNumber());
fitConfig.setFitSolver(gd.getNextChoiceIndex());
fitConfig.setFitFunction(gd.getNextChoiceIndex());
offsetFitting = gd.getNextBoolean();
startOffset = Math.abs(gd.getNextNumber());
comFitting = gd.getNextBoolean();
backgroundFitting = gd.getNextBoolean();
signalFitting = gd.getNextBoolean();
showHistograms = gd.getNextBoolean();
saveRawData = gd.getNextBoolean();
if (!comFitting && !offsetFitting) {
IJ.error(TITLE, "No initial fitting positions");
return false;
}
if (regionSize < 1)
regionSize = 1;
if (gd.invalidNumber())
return false;
// Initialise the correct calibration
Calibration calibration = settings.getCalibration();
calibration.setNmPerPixel(benchmarkParameters.a);
calibration.setGain(benchmarkParameters.gain);
calibration.setAmplification(benchmarkParameters.amplification);
calibration.setBias(benchmarkParameters.bias);
calibration.setEmCCD(benchmarkParameters.emCCD);
calibration.setReadNoise(benchmarkParameters.readNoise);
calibration.setExposureTime(1000);
if (!PeakFit.configureFitSolver(settings, filename, false))
return false;
if (showHistograms) {
gd = new GenericDialog(TITLE);
gd.addMessage("Select the histograms to display");
gd.addNumericField("Histogram_bins", histogramBins, 0);
double[] convert = getConversionFactors();
for (int i = 0; i < displayHistograms.length; i++) if (convert[i] != 0)
gd.addCheckbox(NAMES[i].replace(' ', '_'), displayHistograms[i]);
gd.showDialog();
if (gd.wasCanceled())
return false;
histogramBins = (int) Math.abs(gd.getNextNumber());
for (int i = 0; i < displayHistograms.length; i++) if (convert[i] != 0)
displayHistograms[i] = gd.getNextBoolean();
}
return true;
}
Also used :
GenericDialog(ij.gui.GenericDialog)
GlobalSettings(gdsc.smlm.ij.settings.GlobalSettings)
Calibration(gdsc.smlm.results.Calibration)
Example 18 with GlobalSettings
use of gdsc.smlm.ij.settings.GlobalSettings in project GDSC-SMLM by aherbert.
the class BenchmarkFilterAnalysis method saveTemplate.
/**
* Save PeakFit configuration template using the current benchmark settings.
*
* @param topFilterSummary
*/
private void saveTemplate(String topFilterSummary) {
FitEngineConfiguration config = new FitEngineConfiguration(new FitConfiguration());
if (!updateAllConfiguration(config, true)) {
IJ.log("Unable to create the template configuration");
return;
}
// Remove the PSF width to make the template generic
config.getFitConfiguration().setInitialPeakStdDev(0);
String filename = getFilename("Template_File", templateFilename);
if (filename != null) {
templateFilename = filename;
Prefs.set(KEY_TEMPLATE_FILENAME, filename);
GlobalSettings settings = new GlobalSettings();
settings.setNotes(getNotes(topFilterSummary));
settings.setFitEngineConfiguration(config);
if (!SettingsManager.saveSettings(settings, filename, true)) {
IJ.log("Unable to save the template configuration");
return;
}
// Save some random frames from the test image data
ImagePlus imp = CreateData.getImage();
if (imp == null)
return;
// Get the number of frames
final ImageStack stack = imp.getImageStack();
if (sampler == null || sampler.getResults() != results) {
sampler = new ResultsImageSampler(results, stack, 32);
sampler.analyse();
}
if (!sampler.isValid())
return;
// Iteratively show the example until the user is happy.
// Yes = OK, No = Repeat, Cancel = Do not save
String keyNo = "nNo";
String keyLow = "nLower";
String keyHigh = "nHigher";
if (Utils.isMacro()) {
// Collect the options if running in a macro
String options = Macro.getOptions();
nNo = Integer.parseInt(Macro.getValue(options, keyNo, Integer.toString(nNo)));
nLow = Integer.parseInt(Macro.getValue(options, keyLow, Integer.toString(nLow)));
nHigh = Integer.parseInt(Macro.getValue(options, keyHigh, Integer.toString(nHigh)));
} else {
if (nLow + nHigh == 0)
nLow = nHigh = 1;
}
final ImagePlus[] out = new ImagePlus[1];
out[0] = sampler.getSample(nNo, nLow, nHigh);
if (!Utils.isMacro()) {
// Show the template results
final ConfigurationTemplate configTemplate = new ConfigurationTemplate();
// Interactively show the sample image data
final boolean[] close = new boolean[1];
final ImagePlus[] outImp = new ImagePlus[1];
if (out[0] != null) {
outImp[0] = display(out[0]);
if (Utils.isNewWindow()) {
close[0] = true;
// Zoom a bit
ImageWindow iw = outImp[0].getWindow();
for (int i = 7; i-- > 0 && Math.max(iw.getWidth(), iw.getHeight()) < 512; ) {
iw.getCanvas().zoomIn(0, 0);
}
}
configTemplate.createResults(outImp[0]);
}
// TODO - fix this when a second sample is made as the results are not updated.
ImageListener listener = new ImageListener() {
public void imageOpened(ImagePlus imp) {
}
public void imageClosed(ImagePlus imp) {
}
public void imageUpdated(ImagePlus imp) {
if (imp != null && imp == outImp[0]) {
configTemplate.updateResults(imp.getCurrentSlice());
}
}
};
ImagePlus.addImageListener(listener);
// For the dialog
String msg = String.format("Showing image data for the template example.\n \nSample Frames:\nEmpty = %d\nLower density = %d\nHigher density = %d\n", sampler.getNumberOfEmptySamples(), sampler.getNumberOfLowDensitySamples(), sampler.getNumberOfHighDensitySamples());
// Turn off the recorder when the dialog is showing
boolean record = Recorder.record;
Recorder.record = false;
NonBlockingGenericDialog gd = new NonBlockingGenericDialog(TITLE);
gd.addMessage(msg);
//gd.enableYesNoCancel(" Save ", " Resample ");
gd.addSlider(keyNo, 0, 10, nNo);
gd.addSlider(keyLow, 0, 10, nLow);
gd.addSlider(keyHigh, 0, 10, nHigh);
gd.addDialogListener(new DialogListener() {
public boolean dialogItemChanged(GenericDialog gd, AWTEvent e) {
// image the user has not seen.
if (e == null)
return true;
nNo = (int) gd.getNextNumber();
nLow = (int) gd.getNextNumber();
nHigh = (int) gd.getNextNumber();
out[0] = sampler.getSample(nNo, nLow, nHigh);
if (out[0] != null) {
outImp[0] = display(out[0]);
if (Utils.isNewWindow()) {
close[0] = true;
// Zoom a bit
ImageWindow iw = outImp[0].getWindow();
for (int i = 7; i-- > 0 && Math.max(iw.getWidth(), iw.getHeight()) < 512; ) {
iw.getCanvas().zoomIn(0, 0);
}
}
configTemplate.createResults(outImp[0]);
}
return true;
}
});
gd.showDialog();
if (gd.wasCanceled()) {
out[0] = null;
// For the recorder
nNo = nLow = nHigh = 0;
}
if (close[0]) {
// Because closing the image sets the stack pixels array to null
if (out[0] != null)
out[0] = out[0].duplicate();
outImp[0].close();
}
configTemplate.closeResults();
ImagePlus.removeImageListener(listener);
if (record) {
Recorder.record = true;
Recorder.recordOption(keyNo, Integer.toString(nNo));
Recorder.recordOption(keyLow, Integer.toString(nLow));
Recorder.recordOption(keyHigh, Integer.toString(nHigh));
}
}
if (out[0] == null)
return;
ImagePlus example = out[0];
filename = Utils.replaceExtension(filename, ".tif");
IJ.save(example, filename);
}
}
Also used :
ResultsImageSampler(gdsc.smlm.ij.results.ResultsImageSampler)
ImageWindow(ij.gui.ImageWindow)
ImageStack(ij.ImageStack)
ImageListener(ij.ImageListener)
FitEngineConfiguration(gdsc.smlm.engine.FitEngineConfiguration)
GlobalSettings(gdsc.smlm.ij.settings.GlobalSettings)
NonBlockingGenericDialog(ij.gui.NonBlockingGenericDialog)
ImagePlus(ij.ImagePlus)
FitConfiguration(gdsc.smlm.fitting.FitConfiguration)
DialogListener(ij.gui.DialogListener)
GenericDialog(ij.gui.GenericDialog)
NonBlockingGenericDialog(ij.gui.NonBlockingGenericDialog)
AWTEvent(java.awt.AWTEvent)
Example 19 with GlobalSettings
use of gdsc.smlm.ij.settings.GlobalSettings in project GDSC-SMLM by aherbert.
the class PSFCreator method fitPSF.
/**
* Fit the new PSF image and show a graph of the amplitude/width
*
* @param psf
* @param loess
* @param averageRange
* @param fitCom
* @return The width of the PSF in the z-centre
*/
private double fitPSF(ImageStack psf, LoessInterpolator loess, int cz, double averageRange, double[][] fitCom) {
IJ.showStatus("Fitting final PSF");
// is not appropriate for a normalised PSF.
if (fitConfig.getFitSolver() == FitSolver.MLE) {
Utils.log(" Maximum Likelihood Estimation (MLE) is not appropriate for final PSF fitting.");
Utils.log(" Switching to Least Square Estimation");
fitConfig.setFitSolver(FitSolver.LVM);
if (interactiveMode) {
GlobalSettings settings = new GlobalSettings();
settings.setFitEngineConfiguration(config);
PeakFit.configureFitSolver(settings, null, false, false);
}
}
// Update the box radius since this is used in the fitSpot method.
boxRadius = psf.getWidth() / 2;
int x = boxRadius, y = boxRadius;
FitConfiguration fitConfig = config.getFitConfiguration();
final double shift = fitConfig.getCoordinateShiftFactor();
fitConfig.setInitialPeakStdDev0(fitConfig.getInitialPeakStdDev0() * magnification);
fitConfig.setInitialPeakStdDev1(fitConfig.getInitialPeakStdDev1() * magnification);
// Need to be updated after the widths have been set
fitConfig.setCoordinateShiftFactor(shift);
fitConfig.setBackgroundFitting(false);
// Since the PSF will be normalised
fitConfig.setMinPhotons(0);
//fitConfig.setLog(new IJLogger());
MemoryPeakResults results = fitSpot(psf, psf.getWidth(), psf.getHeight(), x, y);
if (results.size() < 5) {
Utils.log(" Final PSF: Not enough fit results %d", results.size());
return 0;
}
// Get the results for the spot centre and width
double[] z = new double[results.size()];
double[] xCoord = new double[z.length];
double[] yCoord = new double[z.length];
double[] sd = new double[z.length];
double[] a = new double[z.length];
int i = 0;
// Set limits for the fit
final float maxWidth = (float) (FastMath.max(fitConfig.getInitialPeakStdDev0(), fitConfig.getInitialPeakStdDev1()) * magnification * 4);
// PSF is normalised to 1
final float maxSignal = 2;
for (PeakResult peak : results.getResults()) {
// Remove bad fits where the width/signal is above the expected
final float w = FastMath.max(peak.getXSD(), peak.getYSD());
if (peak.getSignal() > maxSignal || w > maxWidth)
continue;
z[i] = peak.getFrame();
fitCom[0][peak.getFrame() - 1] = xCoord[i] = peak.getXPosition() - x;
fitCom[1][peak.getFrame() - 1] = yCoord[i] = peak.getYPosition() - y;
sd[i] = w;
a[i] = peak.getAmplitude();
i++;
}
// Truncate
z = Arrays.copyOf(z, i);
xCoord = Arrays.copyOf(xCoord, i);
yCoord = Arrays.copyOf(yCoord, i);
sd = Arrays.copyOf(sd, i);
a = Arrays.copyOf(a, i);
// Extract the average smoothed range from the individual fits
int r = (int) Math.ceil(averageRange / 2);
int start = 0, stop = z.length - 1;
for (int j = 0; j < z.length; j++) {
if (z[j] > cz - r) {
start = j;
break;
}
}
for (int j = z.length; j-- > 0; ) {
if (z[j] < cz + r) {
stop = j;
break;
}
}
// Extract xy centre coords and smooth
double[] smoothX = new double[stop - start + 1];
double[] smoothY = new double[smoothX.length];
double[] smoothSd = new double[smoothX.length];
double[] smoothA = new double[smoothX.length];
double[] newZ = new double[smoothX.length];
int smoothCzIndex = 0;
for (int j = start, k = 0; j <= stop; j++, k++) {
smoothX[k] = xCoord[j];
smoothY[k] = yCoord[j];
smoothSd[k] = sd[j];
smoothA[k] = a[j];
newZ[k] = z[j];
if (newZ[k] == cz)
smoothCzIndex = k;
}
smoothX = loess.smooth(newZ, smoothX);
smoothY = loess.smooth(newZ, smoothY);
smoothSd = loess.smooth(newZ, smoothSd);
smoothA = loess.smooth(newZ, smoothA);
// Update the widths and positions using the magnification
final double scale = 1.0 / magnification;
for (int j = 0; j < xCoord.length; j++) {
xCoord[j] *= scale;
yCoord[j] *= scale;
sd[j] *= scale;
}
for (int j = 0; j < smoothX.length; j++) {
smoothX[j] *= scale;
smoothY[j] *= scale;
smoothSd[j] *= scale;
}
showPlots(z, a, newZ, smoothA, xCoord, yCoord, sd, newZ, smoothX, smoothY, smoothSd, cz);
// Store the data for replotting
this.z = z;
this.a = a;
this.smoothAz = newZ;
this.smoothA = smoothA;
this.xCoord = xCoord;
this.yCoord = yCoord;
this.sd = sd;
this.newZ = newZ;
this.smoothX = smoothX;
this.smoothY = smoothY;
this.smoothSd = smoothSd;
//maximumIndex = findMinimumIndex(smoothSd, maximumIndex - start);
return smoothSd[smoothCzIndex];
}
Also used :
FitConfiguration(gdsc.smlm.fitting.FitConfiguration)
GlobalSettings(gdsc.smlm.ij.settings.GlobalSettings)
MemoryPeakResults(gdsc.smlm.results.MemoryPeakResults)
Point(java.awt.Point)
BasePoint(gdsc.core.match.BasePoint)
PeakResult(gdsc.smlm.results.PeakResult)
Example 20 with GlobalSettings
use of gdsc.smlm.ij.settings.GlobalSettings in project GDSC-SMLM by aherbert.
the class PSFDrift method run.
/*
* (non-Javadoc)
*
* @see ij.plugin.PlugIn#run(java.lang.String)
*/
public void run(String arg) {
SMLMUsageTracker.recordPlugin(this.getClass(), arg);
// Build a list of suitable images
List<String> titles = createImageList();
if (titles.isEmpty()) {
IJ.error(TITLE, "No suitable PSF images");
return;
}
if ("hwhm".equals(arg)) {
showHWHM(titles);
return;
}
GenericDialog gd = new GenericDialog(TITLE);
gd.addMessage("Select the input PSF image");
gd.addChoice("PSF", titles.toArray(new String[titles.size()]), title);
gd.addCheckbox("Use_offset", useOffset);
gd.addNumericField("Scale", scale, 2);
gd.addNumericField("z_depth", zDepth, 2, 6, "nm");
gd.addNumericField("Grid_size", gridSize, 0);
gd.addSlider("Recall_limit", 0.01, 1, recallLimit);
gd.addSlider("Region_size", 2, 20, regionSize);
gd.addCheckbox("Background_fitting", backgroundFitting);
String[] solverNames = SettingsManager.getNames((Object[]) FitSolver.values());
gd.addChoice("Fit_solver", solverNames, solverNames[fitConfig.getFitSolver().ordinal()]);
String[] functionNames = SettingsManager.getNames((Object[]) FitFunction.values());
gd.addChoice("Fit_function", functionNames, functionNames[fitConfig.getFitFunction().ordinal()]);
// We need these to set bounds for any bounded fitters
gd.addSlider("Min_width_factor", 0, 0.99, fitConfig.getMinWidthFactor());
gd.addSlider("Width_factor", 1.01, 5, fitConfig.getWidthFactor());
gd.addCheckbox("Offset_fit", offsetFitting);
gd.addNumericField("Start_offset", startOffset, 3);
gd.addCheckbox("Include_CoM_fit", comFitting);
gd.addCheckbox("Use_sampling", useSampling);
gd.addNumericField("Photons", photons, 0);
gd.addSlider("Photon_limit", 0, 1, photonLimit);
gd.addSlider("Smoothing", 0, 0.5, smoothing);
gd.showDialog();
if (gd.wasCanceled())
return;
title = gd.getNextChoice();
useOffset = gd.getNextBoolean();
scale = gd.getNextNumber();
zDepth = gd.getNextNumber();
gridSize = (int) gd.getNextNumber();
recallLimit = gd.getNextNumber();
regionSize = (int) Math.abs(gd.getNextNumber());
backgroundFitting = gd.getNextBoolean();
fitConfig.setFitSolver(gd.getNextChoiceIndex());
fitConfig.setFitFunction(gd.getNextChoiceIndex());
fitConfig.setMinWidthFactor(gd.getNextNumber());
fitConfig.setWidthFactor(gd.getNextNumber());
offsetFitting = gd.getNextBoolean();
startOffset = Math.abs(gd.getNextNumber());
comFitting = gd.getNextBoolean();
useSampling = gd.getNextBoolean();
photons = Math.abs(gd.getNextNumber());
photonLimit = Math.abs(gd.getNextNumber());
smoothing = Math.abs(gd.getNextNumber());
if (!comFitting && !offsetFitting) {
IJ.error(TITLE, "No initial fitting positions");
return;
}
if (regionSize < 1)
regionSize = 1;
if (gd.invalidNumber())
return;
GlobalSettings settings = new GlobalSettings();
settings.setFitEngineConfiguration(new FitEngineConfiguration(fitConfig));
if (!PeakFit.configureFitSolver(settings, null, false, true))
return;
imp = WindowManager.getImage(title);
if (imp == null) {
IJ.error(TITLE, "No PSF image for image: " + title);
return;
}
psfSettings = getPSFSettings(imp);
if (psfSettings == null) {
IJ.error(TITLE, "No PSF settings for image: " + title);
return;
}
computeDrift();
}
Also used :
FitEngineConfiguration(gdsc.smlm.engine.FitEngineConfiguration)
GenericDialog(ij.gui.GenericDialog)
GlobalSettings(gdsc.smlm.ij.settings.GlobalSettings)
Aggregations
GlobalSettings (gdsc.smlm.ij.settings.GlobalSettings)34
FitEngineConfiguration (gdsc.smlm.engine.FitEngineConfiguration)9
GenericDialog (ij.gui.GenericDialog)9
FitConfiguration (gdsc.smlm.fitting.FitConfiguration)8
Checkbox (java.awt.Checkbox)7
ExtendedGenericDialog (ij.gui.ExtendedGenericDialog)6
Choice (java.awt.Choice)6
BasePoint (gdsc.core.match.BasePoint)5
FilterSettings (gdsc.smlm.ij.settings.FilterSettings)5
PeakResultPoint (gdsc.smlm.ij.plugins.ResultsMatchCalculator.PeakResultPoint)4
Calibration (gdsc.smlm.results.Calibration)4
Point (java.awt.Point)4
TextField (java.awt.TextField)4
Vector (java.util.Vector)4
MemoryPeakResults (gdsc.smlm.results.MemoryPeakResults)3
DirectFilter (gdsc.smlm.results.filter.DirectFilter)3
MultiPathFilter (gdsc.smlm.results.filter.MultiPathFilter)3
ImagePlus (ij.ImagePlus)3
OpenDialog (ij.io.OpenDialog)3
File (java.io.File)3
