Examples with Well19937c org.apache.commons.math3.random.Well19937c used on opensource projects

Example 1 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (IJ.controlKeyDown()) {
        simpleTest();
        return;
    }
    extraOptions = Utils.isExtraOptions();
    if (!showDialog())
        return;
    lastSimulatedDataset[0] = lastSimulatedDataset[1] = "";
    lastSimulatedPrecision = 0;
    final int totalSteps = (int) Math.ceil(settings.seconds * settings.stepsPerSecond);
    conversionFactor = 1000000.0 / (settings.pixelPitch * settings.pixelPitch);
    // Diffusion rate is um^2/sec. Convert to pixels per simulation frame.
    final double diffusionRateInPixelsPerSecond = settings.diffusionRate * conversionFactor;
    final double diffusionRateInPixelsPerStep = diffusionRateInPixelsPerSecond / settings.stepsPerSecond;
    final double precisionInPixels = myPrecision / settings.pixelPitch;
    final boolean addError = myPrecision != 0;
    Utils.log(TITLE + " : D = %s um^2/sec, Precision = %s nm", Utils.rounded(settings.diffusionRate, 4), Utils.rounded(myPrecision, 4));
    Utils.log("Mean-displacement per dimension = %s nm/sec", Utils.rounded(1e3 * ImageModel.getRandomMoveDistance(settings.diffusionRate), 4));
    if (extraOptions)
        Utils.log("Step size = %s, precision = %s", Utils.rounded(ImageModel.getRandomMoveDistance(diffusionRateInPixelsPerStep)), Utils.rounded(precisionInPixels));
    // Convert diffusion co-efficient into the standard deviation for the random walk
    final double diffusionSigma = (settings.getDiffusionType() == DiffusionType.LINEAR_WALK) ? // Q. What should this be? At the moment just do 1D diffusion on a random vector
    ImageModel.getRandomMoveDistance(diffusionRateInPixelsPerStep) : ImageModel.getRandomMoveDistance(diffusionRateInPixelsPerStep);
    Utils.log("Simulation step-size = %s nm", Utils.rounded(settings.pixelPitch * diffusionSigma, 4));
    // Move the molecules and get the diffusion rate
    IJ.showStatus("Simulating ...");
    final long start = System.nanoTime();
    final long seed = System.currentTimeMillis() + System.identityHashCode(this);
    RandomGenerator[] random = new RandomGenerator[3];
    RandomGenerator[] random2 = new RandomGenerator[3];
    for (int i = 0; i < 3; i++) {
        random[i] = new Well19937c(seed + i * 12436);
        random2[i] = new Well19937c(seed + i * 678678 + 3);
    }
    Statistics[] stats2D = new Statistics[totalSteps];
    Statistics[] stats3D = new Statistics[totalSteps];
    StoredDataStatistics jumpDistances2D = new StoredDataStatistics(totalSteps);
    StoredDataStatistics jumpDistances3D = new StoredDataStatistics(totalSteps);
    for (int j = 0; j < totalSteps; j++) {
        stats2D[j] = new Statistics();
        stats3D[j] = new Statistics();
    }
    SphericalDistribution dist = new SphericalDistribution(settings.confinementRadius / settings.pixelPitch);
    Statistics asymptote = new Statistics();
    // Save results to memory
    MemoryPeakResults results = new MemoryPeakResults(totalSteps);
    Calibration cal = new Calibration(settings.pixelPitch, 1, 1000.0 / settings.stepsPerSecond);
    results.setCalibration(cal);
    results.setName(TITLE);
    int peak = 0;
    // Store raw coordinates
    ArrayList<Point> points = new ArrayList<Point>(totalSteps);
    StoredData totalJumpDistances1D = new StoredData(settings.particles);
    StoredData totalJumpDistances2D = new StoredData(settings.particles);
    StoredData totalJumpDistances3D = new StoredData(settings.particles);
    for (int i = 0; i < settings.particles; i++) {
        if (i % 16 == 0) {
            IJ.showProgress(i, settings.particles);
            if (Utils.isInterrupted())
                return;
        }
        // Increment the frame so that tracing analysis can distinguish traces
        peak++;
        double[] origin = new double[3];
        final int id = i + 1;
        MoleculeModel m = new MoleculeModel(id, origin.clone());
        if (addError)
            origin = addError(origin, precisionInPixels, random);
        if (useConfinement) {
            // Note: When using confinement the average displacement should asymptote
            // at the average distance of a point from the centre of a ball. This is 3r/4.
            // See: http://answers.yahoo.com/question/index?qid=20090131162630AAMTUfM
            // The equivalent in 2D is 2r/3. However although we are plotting 2D distance
            // this is a projection of the 3D position onto the plane and so the particles
            // will not be evenly spread (there will be clustering at centre caused by the
            // poles)
            final double[] axis = (settings.getDiffusionType() == DiffusionType.LINEAR_WALK) ? nextVector() : null;
            for (int j = 0; j < totalSteps; j++) {
                double[] xyz = m.getCoordinates();
                double[] originalXyz = xyz.clone();
                for (int n = confinementAttempts; n-- > 0; ) {
                    if (settings.getDiffusionType() == DiffusionType.GRID_WALK)
                        m.walk(diffusionSigma, random);
                    else if (settings.getDiffusionType() == DiffusionType.LINEAR_WALK)
                        m.slide(diffusionSigma, axis, random[0]);
                    else
                        m.move(diffusionSigma, random);
                    if (!dist.isWithin(m.getCoordinates())) {
                        // Reset position
                        for (int k = 0; k < 3; k++) xyz[k] = originalXyz[k];
                    } else {
                        // The move was allowed
                        break;
                    }
                }
                points.add(new Point(id, xyz));
                if (addError)
                    xyz = addError(xyz, precisionInPixels, random2);
                peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
            }
            asymptote.add(distance(m.getCoordinates()));
        } else {
            if (settings.getDiffusionType() == DiffusionType.GRID_WALK) {
                for (int j = 0; j < totalSteps; j++) {
                    m.walk(diffusionSigma, random);
                    double[] xyz = m.getCoordinates();
                    points.add(new Point(id, xyz));
                    if (addError)
                        xyz = addError(xyz, precisionInPixels, random2);
                    peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
                }
            } else if (settings.getDiffusionType() == DiffusionType.LINEAR_WALK) {
                final double[] axis = nextVector();
                for (int j = 0; j < totalSteps; j++) {
                    m.slide(diffusionSigma, axis, random[0]);
                    double[] xyz = m.getCoordinates();
                    points.add(new Point(id, xyz));
                    if (addError)
                        xyz = addError(xyz, precisionInPixels, random2);
                    peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
                }
            } else {
                for (int j = 0; j < totalSteps; j++) {
                    m.move(diffusionSigma, random);
                    double[] xyz = m.getCoordinates();
                    points.add(new Point(id, xyz));
                    if (addError)
                        xyz = addError(xyz, precisionInPixels, random2);
                    peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
                }
            }
        }
        // Debug: record all the particles so they can be analysed
        // System.out.printf("%f %f %f\n", m.getX(), m.getY(), m.getZ());
        final double[] xyz = m.getCoordinates();
        double d2 = 0;
        totalJumpDistances1D.add(d2 = xyz[0] * xyz[0]);
        totalJumpDistances2D.add(d2 += xyz[1] * xyz[1]);
        totalJumpDistances3D.add(d2 += xyz[2] * xyz[2]);
    }
    final double time = (System.nanoTime() - start) / 1000000.0;
    IJ.showProgress(1);
    MemoryPeakResults.addResults(results);
    lastSimulatedDataset[0] = results.getName();
    lastSimulatedPrecision = myPrecision;
    // Convert pixels^2/step to um^2/sec
    final double msd2D = (jumpDistances2D.getMean() / conversionFactor) / (results.getCalibration().getExposureTime() / 1000);
    final double msd3D = (jumpDistances3D.getMean() / conversionFactor) / (results.getCalibration().getExposureTime() / 1000);
    Utils.log("Raw data D=%s um^2/s, Precision = %s nm, N=%d, step=%s s, mean2D=%s um^2, MSD 2D = %s um^2/s, mean3D=%s um^2, MSD 3D = %s um^2/s", Utils.rounded(settings.diffusionRate), Utils.rounded(myPrecision), jumpDistances2D.getN(), Utils.rounded(results.getCalibration().getExposureTime() / 1000), Utils.rounded(jumpDistances2D.getMean() / conversionFactor), Utils.rounded(msd2D), Utils.rounded(jumpDistances3D.getMean() / conversionFactor), Utils.rounded(msd3D));
    aggregateIntoFrames(points, addError, precisionInPixels, random2);
    IJ.showStatus("Analysing results ...");
    if (showDiffusionExample) {
        showExample(totalSteps, diffusionSigma, random);
    }
    // Plot a graph of mean squared distance
    double[] xValues = new double[stats2D.length];
    double[] yValues2D = new double[stats2D.length];
    double[] yValues3D = new double[stats3D.length];
    double[] upper2D = new double[stats2D.length];
    double[] lower2D = new double[stats2D.length];
    double[] upper3D = new double[stats3D.length];
    double[] lower3D = new double[stats3D.length];
    SimpleRegression r2D = new SimpleRegression(false);
    SimpleRegression r3D = new SimpleRegression(false);
    final int firstN = (useConfinement) ? fitN : totalSteps;
    for (int j = 0; j < totalSteps; j++) {
        // Convert steps to seconds
        xValues[j] = (double) (j + 1) / settings.stepsPerSecond;
        // Convert values in pixels^2 to um^2
        final double mean2D = stats2D[j].getMean() / conversionFactor;
        final double mean3D = stats3D[j].getMean() / conversionFactor;
        final double sd2D = stats2D[j].getStandardDeviation() / conversionFactor;
        final double sd3D = stats3D[j].getStandardDeviation() / conversionFactor;
        yValues2D[j] = mean2D;
        yValues3D[j] = mean3D;
        upper2D[j] = mean2D + sd2D;
        lower2D[j] = mean2D - sd2D;
        upper3D[j] = mean3D + sd3D;
        lower3D[j] = mean3D - sd3D;
        if (j < firstN) {
            r2D.addData(xValues[j], yValues2D[j]);
            r3D.addData(xValues[j], yValues3D[j]);
        }
    }
    // TODO - Fit using the equation for 2D confined diffusion:
    // MSD = 4s^2 + R^2 (1 - 0.99e^(-1.84^2 Dt / R^2)
    // s = localisation precision
    // R = confinement radius
    // D = 2D diffusion coefficient
    // t = time
    final PolynomialFunction fitted2D, fitted3D;
    if (r2D.getN() > 0) {
        // Do linear regression to get diffusion rate
        final double[] best2D = new double[] { r2D.getIntercept(), r2D.getSlope() };
        fitted2D = new PolynomialFunction(best2D);
        final double[] best3D = new double[] { r3D.getIntercept(), r3D.getSlope() };
        fitted3D = new PolynomialFunction(best3D);
        // For 2D diffusion: d^2 = 4D
        // where: d^2 = mean-square displacement
        double D = best2D[1] / 4.0;
        String msg = "2D Diffusion rate = " + Utils.rounded(D, 4) + " um^2 / sec (" + Utils.timeToString(time) + ")";
        IJ.showStatus(msg);
        Utils.log(msg);
        D = best3D[1] / 6.0;
        Utils.log("3D Diffusion rate = " + Utils.rounded(D, 4) + " um^2 / sec (" + Utils.timeToString(time) + ")");
    } else {
        fitted2D = fitted3D = null;
    }
    // Create plots
    plotMSD(totalSteps, xValues, yValues2D, lower2D, upper2D, fitted2D, 2);
    plotMSD(totalSteps, xValues, yValues3D, lower3D, upper3D, fitted3D, 3);
    plotJumpDistances(TITLE, jumpDistances2D, 2, 1);
    plotJumpDistances(TITLE, jumpDistances3D, 3, 1);
    if (idCount > 0)
        new WindowOrganiser().tileWindows(idList);
    if (useConfinement)
        Utils.log("3D asymptote distance = %s nm (expected %.2f)", Utils.rounded(asymptote.getMean() * settings.pixelPitch, 4), 3 * settings.confinementRadius / 4);
}

Example 2 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method simpleTest.

/**
	 * Perform a simple diffusion test. This can be used to understand the distributions that are generated during
	 * 3D diffusion.
	 */
private void simpleTest() {
    if (!showSimpleDialog())
        return;
    StoredDataStatistics[] stats2 = new StoredDataStatistics[3];
    StoredDataStatistics[] stats = new StoredDataStatistics[3];
    RandomGenerator[] random = new RandomGenerator[3];
    final long seed = System.currentTimeMillis() + System.identityHashCode(this);
    for (int i = 0; i < 3; i++) {
        stats2[i] = new StoredDataStatistics(simpleParticles);
        stats[i] = new StoredDataStatistics(simpleParticles);
        random[i] = new Well19937c(seed + i);
    }
    final double scale = Math.sqrt(2 * simpleD);
    final int report = Math.max(1, simpleParticles / 200);
    for (int particle = 0; particle < simpleParticles; particle++) {
        if (particle % report == 0)
            IJ.showProgress(particle, simpleParticles);
        double[] xyz = new double[3];
        if (linearDiffusion) {
            double[] dir = nextVector();
            for (int step = 0; step < simpleSteps; step++) {
                final double d = ((random[1].nextDouble() > 0.5) ? -1 : 1) * random[0].nextGaussian();
                for (int i = 0; i < 3; i++) {
                    xyz[i] += dir[i] * d;
                }
            }
        } else {
            for (int step = 0; step < simpleSteps; step++) {
                for (int i = 0; i < 3; i++) {
                    xyz[i] += random[i].nextGaussian();
                }
            }
        }
        for (int i = 0; i < 3; i++) xyz[i] *= scale;
        double msd = 0;
        for (int i = 0; i < 3; i++) {
            msd += xyz[i] * xyz[i];
            stats2[i].add(msd);
            // Store the actual distances
            stats[i].add(xyz[i]);
        }
    }
    IJ.showProgress(1);
    for (int i = 0; i < 3; i++) {
        plotJumpDistances(TITLE, stats2[i], i + 1);
        // Save stats to file for fitting
        save(stats2[i], i + 1, "msd");
        save(stats[i], i + 1, "d");
    }
    if (idCount > 0)
        new WindowOrganiser().tileWindows(idList);
}

Example 3 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method msdAnalysis.

/**
	 * Tabulate the observed MSD for different jump distances
	 * 
	 * @param points
	 */
private void msdAnalysis(ArrayList<Point> points) {
    if (myMsdAnalysisSteps == 0)
        return;
    IJ.showStatus("MSD analysis ...");
    IJ.showProgress(1, myMsdAnalysisSteps);
    // This will only be fast if the list is an array
    Point[] list = points.toArray(new Point[points.size()]);
    // Compute the base MSD
    Point origin = new Point(0, 0, 0);
    double sum = origin.distance2(list[0]);
    int count = 1;
    for (int i = 1; i < list.length; i++) {
        Point last = list[i - 1];
        Point current = list[i];
        if (last.id == current.id) {
            sum += last.distance2(current);
        } else {
            sum += origin.distance2(current);
        }
        count++;
    }
    createMsdTable((sum / count) * settings.stepsPerSecond / conversionFactor);
    // Create a new set of points that have coordinates that 
    // are the rolling average over the number of aggregate steps
    RollingArray x = new RollingArray(aggregateSteps);
    RollingArray y = new RollingArray(aggregateSteps);
    int id = 0;
    int length = 0;
    for (Point p : points) {
        if (p.id != id) {
            x.reset();
            y.reset();
        }
        id = p.id;
        x.add(p.x);
        y.add(p.y);
        // Only create a point if the full aggregation size is reached
        if (x.isFull()) {
            list[length++] = new Point(id, x.getAverage(), y.getAverage());
        }
    }
    // Q - is this useful?
    final double p = myPrecision / settings.pixelPitch;
    final long seed = System.currentTimeMillis() + System.identityHashCode(this);
    RandomGenerator rand = new Well19937c(seed);
    final int totalSteps = (int) Math.ceil(settings.seconds * settings.stepsPerSecond - aggregateSteps);
    final int limit = Math.min(totalSteps, myMsdAnalysisSteps);
    final int interval = Utils.getProgressInterval(limit);
    final ArrayList<String> results = new ArrayList<String>(totalSteps);
    for (int step = 1; step <= myMsdAnalysisSteps; step++) {
        if (step % interval == 0)
            IJ.showProgress(step, limit);
        sum = 0;
        count = 0;
        for (int i = step; i < length; i++) {
            Point last = list[i - step];
            Point current = list[i];
            if (last.id == current.id) {
                if (p == 0) {
                    sum += last.distance2(current);
                    count++;
                } else {
                    // is the same if enough samples are present
                    for (int ii = 1; ii-- > 0; ) {
                        sum += last.distance2(current, p, rand);
                        count++;
                    }
                }
            }
        }
        if (count == 0)
            break;
        results.add(addResult(step, sum, count));
        // Flush to auto-space the columns
        if (step == 9) {
            msdTable.getTextPanel().append(results);
            results.clear();
        }
    }
    msdTable.getTextPanel().append(results);
    IJ.showProgress(1);
}

Example 4 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class DensityImage method computeRipleysPlot.

/**
	 * Compute the Ripley's L-function for user selected radii and show it on a plot.
	 * 
	 * @param results
	 */
private void computeRipleysPlot(MemoryPeakResults results) {
    ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
    gd.addMessage("Compute Ripley's L(r) - r plot");
    gd.addNumericField("Min_radius", minR, 2);
    gd.addNumericField("Max_radius", maxR, 2);
    gd.addNumericField("Increment", incrementR, 2);
    gd.addCheckbox("Confidence_intervals", confidenceIntervals);
    gd.showDialog();
    if (gd.wasCanceled())
        return;
    minR = gd.getNextNumber();
    maxR = gd.getNextNumber();
    incrementR = gd.getNextNumber();
    confidenceIntervals = gd.getNextBoolean();
    if (minR > maxR || incrementR < 0 || gd.invalidNumber()) {
        IJ.error(TITLE, "Invalid radius parameters");
        return;
    }
    DensityManager dm = createDensityManager(results);
    double[][] values = calculateLScores(dm);
    // 99% confidence intervals
    final int iterations = (confidenceIntervals) ? 99 : 0;
    double[] upper = null;
    double[] lower = null;
    Rectangle bounds = results.getBounds();
    // Use a uniform distribution for the coordinates
    HaltonSequenceGenerator dist = new HaltonSequenceGenerator(2);
    dist.skipTo(new Well19937c(System.currentTimeMillis() + System.identityHashCode(this)).nextInt());
    for (int i = 0; i < iterations; i++) {
        IJ.showProgress(i, iterations);
        IJ.showStatus(String.format("L-score confidence interval %d / %d", i + 1, iterations));
        // Randomise coordinates
        float[] x = new float[results.size()];
        float[] y = new float[x.length];
        for (int j = x.length; j-- > 0; ) {
            final double[] d = dist.nextVector();
            x[j] = (float) (d[0] * bounds.width);
            y[j] = (float) (d[1] * bounds.height);
        }
        double[][] values2 = calculateLScores(new DensityManager(x, y, bounds));
        if (upper == null) {
            upper = values2[1];
            lower = new double[upper.length];
            System.arraycopy(upper, 0, lower, 0, upper.length);
        } else {
            for (int m = upper.length; m-- > 0; ) {
                if (upper[m] < values2[1][m])
                    upper[m] = values2[1][m];
                if (lower[m] > values2[1][m])
                    lower[m] = values2[1][m];
            }
        }
    }
    String title = results.getName() + " Ripley's (L(r) - r) / r";
    Plot2 plot = new Plot2(title, "Radius", "(L(r) - r) / r", values[0], values[1]);
    // Get the limits
    double yMin = min(0, values[1]);
    double yMax = max(0, values[1]);
    if (iterations > 0) {
        yMin = min(yMin, lower);
        yMax = max(yMax, upper);
    }
    plot.setLimits(0, values[0][values[0].length - 1], yMin, yMax);
    if (iterations > 0) {
        plot.setColor(Color.BLUE);
        plot.addPoints(values[0], upper, 1);
        plot.setColor(Color.RED);
        plot.addPoints(values[0], lower, 1);
        plot.setColor(Color.BLACK);
    }
    Utils.display(title, plot);
}

Example 5 with Well19937c

use of org.apache.commons.math3.random.Well19937c in project GDSC-SMLM by aherbert.

the class FilterTest method directCompareMultiFilterIsFaster.

@Test
public void directCompareMultiFilterIsFaster() {
    RandomGenerator randomGenerator = new Well19937c(System.currentTimeMillis() + System.identityHashCode(this));
    final MultiFilter f1 = new MultiFilter(0, 0, 0, 0, 0, 0, 0);
    final MultiFilter2 f2 = new MultiFilter2(0, 0, 0, 0, 0, 0, 0);
    final double[][][] data = new double[1000][][];
    for (int i = data.length; i-- > 0; ) {
        data[i] = new double[][] { random(f1.getNumberOfParameters(), randomGenerator), random(f1.getNumberOfParameters(), randomGenerator) };
    }
    TimingService ts = new TimingService();
    ts.execute(new TimingTask() {

        public Object getData(int i) {
            return new MultiFilter[] { (MultiFilter) f1.create(data[i][0]), (MultiFilter) f1.create(data[i][1]) };
        }

        public Object run(Object data) {
            MultiFilter f1 = ((MultiFilter[]) data)[0];
            MultiFilter f2 = ((MultiFilter[]) data)[1];
            f1.weakest((Filter) f2);
            return null;
        }

        public void check(int i, Object result) {
        }

        public int getSize() {
            return data.length;
        }

        public String getName() {
            return "MultiFilter";
        }
    });
    ts.execute(new TimingTask() {

        public Object getData(int i) {
            return new MultiFilter[] { (MultiFilter) f1.create(data[i][0]), (MultiFilter) f1.create(data[i][1]) };
        }

        public Object run(Object data) {
            MultiFilter f1 = ((MultiFilter[]) data)[0];
            MultiFilter f2 = ((MultiFilter[]) data)[1];
            f1.weakest(f2);
            return null;
        }

        public void check(int i, Object result) {
        }

        public int getSize() {
            return data.length;
        }

        public String getName() {
            return "MultiFilter direct";
        }
    });
    ts.execute(new TimingTask() {

        public Object getData(int i) {
            return new MultiFilter2[] { (MultiFilter2) f2.create(data[i][0]), (MultiFilter2) f2.create(data[i][1]) };
        }

        public Object run(Object data) {
            MultiFilter2 f1 = ((MultiFilter2[]) data)[0];
            MultiFilter2 f2 = ((MultiFilter2[]) data)[1];
            f1.weakest((Filter) f2);
            return null;
        }

        public void check(int i, Object result) {
        }

        public int getSize() {
            return data.length;
        }

        public String getName() {
            return "MultiFilter2";
        }
    });
    ts.execute(new TimingTask() {

        public Object getData(int i) {
            return new MultiFilter2[] { (MultiFilter2) f2.create(data[i][0]), (MultiFilter2) f2.create(data[i][1]) };
        }

        public Object run(Object data) {
            MultiFilter2 f1 = ((MultiFilter2[]) data)[0];
            MultiFilter2 f2 = ((MultiFilter2[]) data)[1];
            f1.weakest(f2);
            return null;
        }

        public void check(int i, Object result) {
        }

        public int getSize() {
            return data.length;
        }

        public String getName() {
            return "MultiFilter2 direct";
        }
    });
    ts.check();
    int size = ts.repeat();
    ts.repeat(size);
    ts.report();
    for (int i = 0; i < ts.getSize(); i += 2) {
        TimingResult slow = ts.get(i);
        TimingResult fast = ts.get(i + 1);
        Assert.assertTrue(slow.getMin() > fast.getMin());
    }
}