Examples with CoordinateStore uk.ac.sussex.gdsc.smlm.results.filter.CoordinateStore used on opensource projects

Example 1 with CoordinateStore

use of uk.ac.sussex.gdsc.smlm.results.filter.CoordinateStore in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method readResults.

private MultiPathFitResults[] readResults() {
    // Extract all the results in memory into a list per frame. This can be cached
    boolean update = false;
    Pair<Integer, TIntObjectHashMap<UniqueIdPeakResult[]>> coords = coordinateCache.get();
    if (coords.getKey() != simulationParameters.id) {
        coords = Pair.of(simulationParameters.id, getCoordinates(results));
        coordinateCache.set(coords);
        update = true;
    }
    actualCoordinates = coords.getValue();
    spotFitResults = BenchmarkSpotFit.getBenchmarkSpotFitResults();
    FitResultData localFitResultData = fitResultDataCache.get();
    final SettingsList scoreSettings = new SettingsList(settings.partialMatchDistance, settings.upperMatchDistance, settings.partialSignalFactor, settings.upperSignalFactor);
    final boolean equalScoreSettings = scoreSettings.equals(localFitResultData.scoreSettings);
    if (update || localFitResultData.fittingId != spotFitResults.id || !equalScoreSettings || localFitResultData.differentSettings(settings)) {
        IJ.showStatus("Reading results ...");
        if (localFitResultData.fittingId < 0) {
            // Copy the settings from the fitter if this is the first run.
            // This just starts the plugin with sensible settings.
            // Q. Should this be per new simulation or fitting result instead?
            final FitEngineConfiguration config = BenchmarkSpotFit.getFitEngineConfiguration();
            settings.failCount = config.getFailuresLimit();
            settings.duplicateDistance = config.getDuplicateDistance();
            settings.duplicateDistanceAbsolute = config.getDuplicateDistanceAbsolute();
            settings.residualsThreshold = (BenchmarkSpotFit.getComputeDoublets()) ? BenchmarkSpotFit.getMultiFilter().residualsThreshold : 1;
        }
        // This functionality is for choosing the optimum filter for the given scoring metric.
        if (!equalScoreSettings) {
            filterAnalysisResult.scores.clear();
        }
        localFitResultData = new FitResultData(spotFitResults.id, scoreSettings, settings);
        // @formatter:off
        // -=-=-=-
        // The scoring is designed to find the best fitter+filter combination for the given spot
        // candidates. The ideal combination would correctly fit+pick all the candidate positions
        // that are close to a localisation.
        // 
        // Use the following scoring scheme for all candidates:
        // 
        // Candidates
        // +----------------------------------------+
        // |   Actual matches                       |
        // |  +-----------+                TN       |
        // |  |  FN       |                         |
        // |  |      +----------                    |
        // |  |      | TP |    | Fitted             |
        // |  +-----------+    | spots              |
        // |         |     FP  |                    |
        // |         +---------+                    |
        // +----------------------------------------+
        // 
        // Candidates     = All the spot candidates
        // Actual matches = Any spot candidate or fitted spot candidate that matches a localisation
        // Fitted spots   = Any spot candidate that was successfully fitted
        // 
        // TP = A spot candidate that was fitted and matches a localisation and is accepted
        // FP = A spot candidate that was fitted but does not match a localisation and is accepted
        // FN = A spot candidate that failed to be fitted but matches a localisation
        // = A spot candidate that was fitted and matches a localisation and is rejected
        // TN = A spot candidate that failed to be fitted and does not match a localisation
        // = A spot candidate that was fitted and does not match a localisation and is rejected
        // 
        // When fitting only produces one result it is possible to compute the TN score.
        // Since unfitted candidates can only be TN or FN we could accumulate these scores and cache
        // them. This was the old method of benchmarking single spot fitting and allowed more scores
        // to be computed.
        // 
        // When fitting produces multiple results then we have to score each fit result against all
        // possible actual results and keep a record of the scores. These can then be assessed when
        // the specific results have been chosen by result filtering.
        // 
        // Using a distance ramped scoring function the degree of match can be varied from 0 to 1.
        // Using a signal-factor ramped scoring function the degree of fitted can be varied from 0
        // to 1. When using ramped scoring functions the fractional allocation of scores using the
        // above scheme is performed, i.e. candidates are treated as if they both match and unmatch.
        // This results in an equivalent to multiple analysis using different thresholds and averaging
        // of the scores.
        // 
        // The totals TP+FP+TN+FN must equal the number of spot candidates. This allows different
        // fitting methods to be compared since the total number of candidates is the same.
        // 
        // Precision = TP / (TP+FP)    : This is always valid as a minimum criteria score
        // Recall    = TP / (TP+FN)    : This is valid between different fitting methods since a
        // method that fits more spots will have a potentially lower FN
        // Jaccard   = TP / (TP+FN+FP) : This is valid between fitting methods
        // 
        // -=-=-=-
        // As an alternative scoring system, different fitting methods can be compared using the same
        // TP value but calculating FN = localisations - TP and FP as Positives - TP. This creates a
        // score against the original number of simulated molecules using everything that was passed
        // through the filter (Positives). This score is comparable when a different spot candidate
        // filter has been used and the total number of candidates is different, e.g. Mean filtering
        // vs. Gaussian filtering
        // -=-=-=-
        // @formatter:on
        final RampedScore distanceScore = RampedScore.of(spotFitResults.distanceInPixels * settings.upperMatchDistance / 100.0, spotFitResults.distanceInPixels * settings.partialMatchDistance / 100.0, false);
        localFitResultData.lowerDistanceInPixels = distanceScore.edge1;
        localFitResultData.distanceInPixels = distanceScore.edge0;
        final double matchDistance = MathUtils.pow2(localFitResultData.distanceInPixels);
        localFitResultData.resultsPrefix3 = "\t" + MathUtils.rounded(distanceScore.edge1 * simulationParameters.pixelPitch) + "\t" + MathUtils.rounded(distanceScore.edge0 * simulationParameters.pixelPitch);
        localFitResultData.limitRange = ", d=" + MathUtils.rounded(distanceScore.edge1 * simulationParameters.pixelPitch) + "-" + MathUtils.rounded(distanceScore.edge0 * simulationParameters.pixelPitch);
        // Signal factor must be greater than 1
        final RampedScore signalScore;
        final double spotSignalFactor = BenchmarkSpotFit.getSignalFactor();
        if (spotSignalFactor > 0 && settings.upperSignalFactor > 0) {
            signalScore = RampedScore.of(spotSignalFactor * settings.upperSignalFactor / 100.0, spotSignalFactor * settings.partialSignalFactor / 100.0, false);
            localFitResultData.lowerSignalFactor = signalScore.edge1;
            localFitResultData.signalFactor = signalScore.edge0;
            localFitResultData.resultsPrefix3 += "\t" + MathUtils.rounded(signalScore.edge1) + "\t" + MathUtils.rounded(signalScore.edge0);
            localFitResultData.limitRange += ", s=" + MathUtils.rounded(signalScore.edge1) + "-" + MathUtils.rounded(signalScore.edge0);
        } else {
            signalScore = null;
            localFitResultData.resultsPrefix3 += "\t0\t0";
            localFitResultData.lowerSignalFactor = localFitResultData.signalFactor = 0;
        }
        // Store all the results
        final ArrayList<MultiPathFitResults> multiPathFitResults = new ArrayList<>(spotFitResults.fitResults.size());
        final List<MultiPathFitResults> syncResults = Collections.synchronizedList(multiPathFitResults);
        // This could be multi-threaded ...
        final int nThreads = getThreads(spotFitResults.fitResults.size());
        final BlockingQueue<Job> jobs = new ArrayBlockingQueue<>(nThreads * 2);
        final List<FitResultsWorker> workers = new LinkedList<>();
        final List<Thread> threads = new LinkedList<>();
        final AtomicInteger uniqueId = new AtomicInteger();
        final CoordinateStore localCoordinateStore = createCoordinateStore();
        final Ticker ticker = ImageJUtils.createTicker(spotFitResults.fitResults.size(), nThreads, null);
        for (int i = 0; i < nThreads; i++) {
            final FitResultsWorker worker = new FitResultsWorker(jobs, syncResults, matchDistance, distanceScore, signalScore, uniqueId, localCoordinateStore.newInstance(), ticker, actualCoordinates);
            final Thread t = new Thread(worker);
            workers.add(worker);
            threads.add(t);
            t.start();
        }
        spotFitResults.fitResults.forEachEntry((frame, candidates) -> {
            put(jobs, new Job(frame, candidates));
            return true;
        });
        // Finish all the worker threads by passing in a null job
        for (int i = 0; i < threads.size(); i++) {
            put(jobs, new Job(0, null));
        }
        // Wait for all to finish
        for (int i = 0; i < threads.size(); i++) {
            try {
                threads.get(i).join();
                final FitResultsWorker worker = workers.get(i);
                localFitResultData.matches += worker.matches;
                localFitResultData.fittedResults += worker.included;
                localFitResultData.totalResults += worker.total;
                localFitResultData.notDuplicateCount += worker.notDuplicateCount;
                localFitResultData.newResultCount += worker.newResultCount;
                localFitResultData.countActual += worker.includedActual;
                if (i == 0) {
                    localFitResultData.depthStats = worker.depthStats;
                    localFitResultData.depthFitStats = worker.depthFitStats;
                    localFitResultData.signalFactorStats = worker.signalFactorStats;
                    localFitResultData.distanceStats = worker.distanceStats;
                } else {
                    localFitResultData.depthStats.add(worker.depthStats);
                    localFitResultData.depthFitStats.add(worker.depthFitStats);
                    localFitResultData.signalFactorStats.add(worker.signalFactorStats);
                    localFitResultData.distanceStats.add(worker.distanceStats);
                }
            } catch (final InterruptedException ex) {
                Thread.currentThread().interrupt();
                throw new ConcurrentRuntimeException("Unexpected interrupt", ex);
            }
        }
        threads.clear();
        ImageJUtils.finished();
        localFitResultData.maxUniqueId = uniqueId.get();
        localFitResultData.resultsList = multiPathFitResults.toArray(new MultiPathFitResults[0]);
        Arrays.sort(localFitResultData.resultsList, (o1, o2) -> Integer.compare(o1.getFrame(), o2.getFrame()));
        MultiPathFilter.resetValidationFlag(localFitResultData.resultsList);
        fitResultDataCache.set(localFitResultData);
    }
    fitResultData = localFitResultData;
    return localFitResultData.resultsList;
}