Examples with Statistics gdsc.core.utils.Statistics used on opensource projects

Example 6 with Statistics

use of gdsc.core.utils.Statistics in project GDSC-SMLM by aherbert.

the class BackgroundEstimator method plot.

private void plot(WindowOrganiser wo, double[] xValues, double[] data1, double[] data2, double[] data3, String title, String title1, String title2, String title3) {
    // Get limits
    double[] a = Maths.limits(xValues);
    double[] b = Maths.limits(data1);
    b = Maths.limits(b, data2);
    if (data3 != null)
        b = Maths.limits(b, data3);
    title = imp.getTitle() + " " + title;
    Plot2 plot = new Plot2(title, "Slice", title);
    double range = b[1] - b[0];
    if (range == 0)
        range = 1;
    plot.setLimits(a[0], a[1], b[0] - 0.05 * range, b[1] + 0.05 * range);
    plot.setColor(Color.blue);
    plot.addPoints(xValues, data1, Plot2.LINE);
    plot.draw();
    Statistics stats = new Statistics(data1);
    String label = String.format("%s (Blue) = %s +/- %s", title1, Utils.rounded(stats.getMean()), Utils.rounded(stats.getStandardDeviation()));
    plot.setColor(Color.red);
    plot.addPoints(xValues, data2, Plot2.LINE);
    stats = new Statistics(data2);
    label += String.format(", %s (Red) = %s +/- %s", title2, Utils.rounded(stats.getMean()), Utils.rounded(stats.getStandardDeviation()));
    if (data3 != null) {
        plot.setColor(Color.green);
        plot.addPoints(xValues, data3, Plot2.LINE);
        stats = new Statistics(data3);
        label += String.format(", %s (Green) = %s +/- %s", title3, Utils.rounded(stats.getMean()), Utils.rounded(stats.getStandardDeviation()));
    }
    plot.setColor(Color.black);
    plot.addLabel(0, 0, label);
    PlotWindow pw = Utils.display(title, plot);
    if (Utils.isNewWindow())
        wo.add(pw);
}

Example 7 with Statistics

use of gdsc.core.utils.Statistics in project GDSC-SMLM by aherbert.

the class LSQLVMGradientProcedureTest method gradientProcedureComputesSameOutputWithBias.

@Test
public void gradientProcedureComputesSameOutputWithBias() {
    ErfGaussian2DFunction func = new SingleFreeCircularErfGaussian2DFunction(blockWidth, blockWidth);
    int nparams = func.getNumberOfGradients();
    int iter = 100;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    ArrayList<double[]> yList = new ArrayList<double[]>(iter);
    ArrayList<double[]> alphaList = new ArrayList<double[]>(iter);
    ArrayList<double[]> betaList = new ArrayList<double[]>(iter);
    ArrayList<double[]> xList = new ArrayList<double[]>(iter);
    // Manipulate the background
    double defaultBackground = Background;
    try {
        Background = 1e-2;
        createData(1, iter, paramsList, yList, true);
        EJMLLinearSolver solver = new EJMLLinearSolver(1e-5, 1e-6);
        for (int i = 0; i < paramsList.size(); i++) {
            double[] y = yList.get(i);
            double[] a = paramsList.get(i);
            BaseLSQLVMGradientProcedure p = LSQLVMGradientProcedureFactory.create(y, func);
            p.gradient(a);
            double[] beta = p.beta;
            alphaList.add(p.getAlphaLinear());
            betaList.add(beta.clone());
            for (int j = 0; j < nparams; j++) {
                if (Math.abs(beta[j]) < 1e-6)
                    System.out.printf("[%d] Tiny beta %s %g\n", i, func.getName(j), beta[j]);
            }
            // Solve
            if (!solver.solve(p.getAlphaMatrix(), beta))
                throw new AssertionError();
            xList.add(beta);
        //System.out.println(Arrays.toString(beta));
        }
        //for (int b = 1; b < 1000; b *= 2)
        for (double b : new double[] { -500, -100, -10, -1, -0.1, 0, 0.1, 1, 10, 100, 500 }) {
            Statistics[] rel = new Statistics[nparams];
            Statistics[] abs = new Statistics[nparams];
            for (int i = 0; i < nparams; i++) {
                rel[i] = new Statistics();
                abs[i] = new Statistics();
            }
            for (int i = 0; i < paramsList.size(); i++) {
                double[] y = add(yList.get(i), b);
                double[] a = paramsList.get(i).clone();
                a[0] += b;
                BaseLSQLVMGradientProcedure p = LSQLVMGradientProcedureFactory.create(y, func);
                p.gradient(a);
                double[] beta = p.beta;
                double[] alpha2 = alphaList.get(i);
                double[] beta2 = betaList.get(i);
                double[] x2 = xList.get(i);
                Assert.assertArrayEquals("Beta", beta2, beta, 1e-10);
                Assert.assertArrayEquals("Alpha", alpha2, p.getAlphaLinear(), 1e-10);
                // Solve
                solver.solve(p.getAlphaMatrix(), beta);
                Assert.assertArrayEquals("X", x2, beta, 1e-10);
                for (int j = 0; j < nparams; j++) {
                    rel[j].add(DoubleEquality.relativeError(x2[j], beta[j]));
                    abs[j].add(Math.abs(x2[j] - beta[j]));
                }
            }
            for (int i = 0; i < nparams; i++) System.out.printf("Bias = %.2f : %s : Rel %g +/- %g: Abs %g +/- %g\n", b, func.getName(i), rel[i].getMean(), rel[i].getStandardDeviation(), abs[i].getMean(), abs[i].getStandardDeviation());
        }
    } finally {
        Background = defaultBackground;
    }
}

Example 8 with Statistics

use of gdsc.core.utils.Statistics in project GDSC-SMLM by aherbert.

the class GradientCalculatorSpeedTest method gradientCalculatorComputesSameOutputWithBias.

@Test
public void gradientCalculatorComputesSameOutputWithBias() {
    Gaussian2DFunction func = new SingleEllipticalGaussian2DFunction(blockWidth, blockWidth);
    int nparams = func.getNumberOfGradients();
    GradientCalculator calc = new GradientCalculator(nparams);
    int n = func.size();
    int iter = 100;
    rdg = new RandomDataGenerator(new Well19937c(30051977));
    ArrayList<double[]> paramsList = new ArrayList<double[]>(iter);
    ArrayList<double[]> yList = new ArrayList<double[]>(iter);
    ArrayList<double[][]> alphaList = new ArrayList<double[][]>(iter);
    ArrayList<double[]> betaList = new ArrayList<double[]>(iter);
    ArrayList<double[]> xList = new ArrayList<double[]>(iter);
    // Manipulate the background
    double defaultBackground = Background;
    try {
        Background = 1e-2;
        createData(1, iter, paramsList, yList, true);
        EJMLLinearSolver solver = new EJMLLinearSolver(1e-5, 1e-6);
        for (int i = 0; i < paramsList.size(); i++) {
            double[] y = yList.get(i);
            double[] a = paramsList.get(i);
            double[][] alpha = new double[nparams][nparams];
            double[] beta = new double[nparams];
            calc.findLinearised(n, y, a, alpha, beta, func);
            alphaList.add(alpha);
            betaList.add(beta.clone());
            for (int j = 0; j < nparams; j++) {
                if (Math.abs(beta[j]) < 1e-6)
                    System.out.printf("[%d] Tiny beta %s %g\n", i, func.getName(j), beta[j]);
            }
            // Solve
            if (!solver.solve(alpha, beta))
                throw new AssertionError();
            xList.add(beta);
        //System.out.println(Arrays.toString(beta));
        }
        double[][] alpha = new double[nparams][nparams];
        double[] beta = new double[nparams];
        //for (int b = 1; b < 1000; b *= 2)
        for (double b : new double[] { -500, -100, -10, -1, -0.1, 0, 0.1, 1, 10, 100, 500 }) {
            Statistics[] rel = new Statistics[nparams];
            Statistics[] abs = new Statistics[nparams];
            for (int i = 0; i < nparams; i++) {
                rel[i] = new Statistics();
                abs[i] = new Statistics();
            }
            for (int i = 0; i < paramsList.size(); i++) {
                double[] y = add(yList.get(i), b);
                double[] a = paramsList.get(i).clone();
                a[0] += b;
                calc.findLinearised(n, y, a, alpha, beta, func);
                double[][] alpha2 = alphaList.get(i);
                double[] beta2 = betaList.get(i);
                double[] x2 = xList.get(i);
                Assert.assertArrayEquals("Beta", beta2, beta, 1e-10);
                for (int j = 0; j < nparams; j++) {
                    Assert.assertArrayEquals("Alpha", alpha2[j], alpha[j], 1e-10);
                }
                // Solve
                solver.solve(alpha, beta);
                Assert.assertArrayEquals("X", x2, beta, 1e-10);
                for (int j = 0; j < nparams; j++) {
                    rel[j].add(DoubleEquality.relativeError(x2[j], beta[j]));
                    abs[j].add(Math.abs(x2[j] - beta[j]));
                }
            }
            for (int i = 0; i < nparams; i++) System.out.printf("Bias = %.2f : %s : Rel %g +/- %g: Abs %g +/- %g\n", b, func.getName(i), rel[i].getMean(), rel[i].getStandardDeviation(), abs[i].getMean(), abs[i].getStandardDeviation());
        }
    } finally {
        Background = defaultBackground;
    }
}

Example 9 with Statistics

use of gdsc.core.utils.Statistics in project GDSC-SMLM by aherbert.

the class PSFCreator method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.filter.PlugInFilter#run(ij.process.ImageProcessor)
	 */
public void run(ImageProcessor ip) {
    loadConfiguration();
    BasePoint[] spots = getSpots();
    if (spots.length == 0) {
        IJ.error(TITLE, "No spots without neighbours within " + (boxRadius * 2) + "px");
        return;
    }
    ImageStack stack = getImageStack();
    final int width = imp.getWidth();
    final int height = imp.getHeight();
    final int currentSlice = imp.getSlice();
    // Adjust settings for a single maxima
    config.setIncludeNeighbours(false);
    fitConfig.setDuplicateDistance(0);
    ArrayList<double[]> centres = new ArrayList<double[]>(spots.length);
    int iterations = 1;
    LoessInterpolator loess = new LoessInterpolator(smoothing, iterations);
    // TODO - The fitting routine may not produce many points. In this instance the LOESS interpolator
    // fails to smooth the data very well. A higher bandwidth helps this but perhaps 
    // try a different smoothing method.
    // For each spot
    Utils.log(TITLE + ": " + imp.getTitle());
    Utils.log("Finding spot locations...");
    Utils.log("  %d spot%s without neighbours within %dpx", spots.length, ((spots.length == 1) ? "" : "s"), (boxRadius * 2));
    StoredDataStatistics averageSd = new StoredDataStatistics();
    StoredDataStatistics averageA = new StoredDataStatistics();
    Statistics averageRange = new Statistics();
    MemoryPeakResults allResults = new MemoryPeakResults();
    allResults.setName(TITLE);
    allResults.setBounds(new Rectangle(0, 0, width, height));
    MemoryPeakResults.addResults(allResults);
    for (int n = 1; n <= spots.length; n++) {
        BasePoint spot = spots[n - 1];
        final int x = (int) spot.getX();
        final int y = (int) spot.getY();
        MemoryPeakResults results = fitSpot(stack, width, height, x, y);
        allResults.addAllf(results.getResults());
        if (results.size() < 5) {
            Utils.log("  Spot %d: Not enough fit results %d", n, results.size());
            continue;
        }
        // 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;
        for (PeakResult peak : results.getResults()) {
            z[i] = peak.getFrame();
            xCoord[i] = peak.getXPosition() - x;
            yCoord[i] = peak.getYPosition() - y;
            sd[i] = FastMath.max(peak.getXSD(), peak.getYSD());
            a[i] = peak.getAmplitude();
            i++;
        }
        // Smooth the amplitude plot
        double[] smoothA = loess.smooth(z, a);
        // Find the maximum amplitude
        int maximumIndex = findMaximumIndex(smoothA);
        // Find the range at a fraction of the max. This is smoothed to find the X/Y centre
        int start = 0, stop = smoothA.length - 1;
        double limit = smoothA[maximumIndex] * amplitudeFraction;
        for (int j = 0; j < smoothA.length; j++) {
            if (smoothA[j] > limit) {
                start = j;
                break;
            }
        }
        for (int j = smoothA.length; j-- > 0; ) {
            if (smoothA[j] > limit) {
                stop = j;
                break;
            }
        }
        averageRange.add(stop - start + 1);
        // 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[] newZ = new double[smoothX.length];
        for (int j = start, k = 0; j <= stop; j++, k++) {
            smoothX[k] = xCoord[j];
            smoothY[k] = yCoord[j];
            smoothSd[k] = sd[j];
            newZ[k] = z[j];
        }
        smoothX = loess.smooth(newZ, smoothX);
        smoothY = loess.smooth(newZ, smoothY);
        smoothSd = loess.smooth(newZ, smoothSd);
        // Since the amplitude is not very consistent move from this peak to the 
        // lowest width which is the in-focus spot.
        maximumIndex = findMinimumIndex(smoothSd, maximumIndex - start);
        // Find the centre at the amplitude peak
        double cx = smoothX[maximumIndex] + x;
        double cy = smoothY[maximumIndex] + y;
        int cz = (int) newZ[maximumIndex];
        double csd = smoothSd[maximumIndex];
        double ca = smoothA[maximumIndex + start];
        // The average should weight the SD using the signal for each spot
        averageSd.add(smoothSd[maximumIndex]);
        averageA.add(ca);
        if (ignoreSpot(n, z, a, smoothA, xCoord, yCoord, sd, newZ, smoothX, smoothY, smoothSd, cx, cy, cz, csd)) {
            Utils.log("  Spot %d was ignored", n);
            continue;
        }
        // Store result - it may have been moved interactively
        maximumIndex += this.slice - cz;
        cz = (int) newZ[maximumIndex];
        csd = smoothSd[maximumIndex];
        ca = smoothA[maximumIndex + start];
        Utils.log("  Spot %d => x=%.2f, y=%.2f, z=%d, sd=%.2f, A=%.2f\n", n, cx, cy, cz, csd, ca);
        centres.add(new double[] { cx, cy, cz, csd, n });
    }
    if (interactiveMode) {
        imp.setSlice(currentSlice);
        imp.setOverlay(null);
        // Hide the amplitude and spot plots
        Utils.hide(TITLE_AMPLITUDE);
        Utils.hide(TITLE_PSF_PARAMETERS);
    }
    if (centres.isEmpty()) {
        String msg = "No suitable spots could be identified centres";
        Utils.log(msg);
        IJ.error(TITLE, msg);
        return;
    }
    // Find the limits of the z-centre
    int minz = (int) centres.get(0)[2];
    int maxz = minz;
    for (double[] centre : centres) {
        if (minz > centre[2])
            minz = (int) centre[2];
        else if (maxz < centre[2])
            maxz = (int) centre[2];
    }
    IJ.showStatus("Creating PSF image");
    // Create a stack that can hold all the data.
    ImageStack psf = createStack(stack, minz, maxz, magnification);
    // For each spot
    Statistics stats = new Statistics();
    boolean ok = true;
    for (int i = 0; ok && i < centres.size(); i++) {
        double progress = (double) i / centres.size();
        final double increment = 1.0 / (stack.getSize() * centres.size());
        IJ.showProgress(progress);
        double[] centre = centres.get(i);
        // Extract the spot
        float[][] spot = new float[stack.getSize()][];
        Rectangle regionBounds = null;
        for (int slice = 1; slice <= stack.getSize(); slice++) {
            ImageExtractor ie = new ImageExtractor((float[]) stack.getPixels(slice), width, height);
            if (regionBounds == null)
                regionBounds = ie.getBoxRegionBounds((int) centre[0], (int) centre[1], boxRadius);
            spot[slice - 1] = ie.crop(regionBounds);
        }
        int n = (int) centre[4];
        final float b = getBackground(n, spot);
        if (!subtractBackgroundAndWindow(spot, b, regionBounds.width, regionBounds.height, centre, loess)) {
            Utils.log("  Spot %d was ignored", n);
            continue;
        }
        stats.add(b);
        // Adjust the centre using the crop
        centre[0] -= regionBounds.x;
        centre[1] -= regionBounds.y;
        // This takes a long time so this should track progress
        ok = addToPSF(maxz, magnification, psf, centre, spot, regionBounds, progress, increment, centreEachSlice);
    }
    if (interactiveMode) {
        Utils.hide(TITLE_INTENSITY);
    }
    IJ.showProgress(1);
    if (threadPool != null) {
        threadPool.shutdownNow();
        threadPool = null;
    }
    if (!ok || stats.getN() == 0)
        return;
    final double avSd = getAverage(averageSd, averageA, 2);
    Utils.log("  Average background = %.2f, Av. SD = %s px", stats.getMean(), Utils.rounded(avSd, 4));
    normalise(psf, maxz, avSd * magnification, false);
    IJ.showProgress(1);
    psfImp = Utils.display("PSF", psf);
    psfImp.setSlice(maxz);
    psfImp.resetDisplayRange();
    psfImp.updateAndDraw();
    double[][] fitCom = new double[2][psf.getSize()];
    Arrays.fill(fitCom[0], Double.NaN);
    Arrays.fill(fitCom[1], Double.NaN);
    double fittedSd = fitPSF(psf, loess, maxz, averageRange.getMean(), fitCom);
    // Compute the drift in the PSF:
    // - Use fitted centre if available; otherwise find CoM for each frame
    // - express relative to the average centre
    double[][] com = calculateCentreOfMass(psf, fitCom, nmPerPixel / magnification);
    double[] slice = Utils.newArray(psf.getSize(), 1, 1.0);
    String title = TITLE + " CoM Drift";
    Plot2 plot = new Plot2(title, "Slice", "Drift (nm)");
    plot.addLabel(0, 0, "Red = X; Blue = Y");
    //double[] limitsX = Maths.limits(com[0]);
    //double[] limitsY = Maths.limits(com[1]);
    double[] limitsX = getLimits(com[0]);
    double[] limitsY = getLimits(com[1]);
    plot.setLimits(1, psf.getSize(), Math.min(limitsX[0], limitsY[0]), Math.max(limitsX[1], limitsY[1]));
    plot.setColor(Color.red);
    plot.addPoints(slice, com[0], Plot.DOT);
    plot.addPoints(slice, loess.smooth(slice, com[0]), Plot.LINE);
    plot.setColor(Color.blue);
    plot.addPoints(slice, com[1], Plot.DOT);
    plot.addPoints(slice, loess.smooth(slice, com[1]), Plot.LINE);
    Utils.display(title, plot);
    // TODO - Redraw the PSF with drift correction applied. 
    // This means that the final image should have no drift.
    // This is relevant when combining PSF images. It doesn't matter too much for simulations 
    // unless the drift is large.
    // Add Image properties containing the PSF details
    final double fwhm = getFWHM(psf, maxz);
    psfImp.setProperty("Info", XmlUtils.toXML(new PSFSettings(maxz, nmPerPixel / magnification, nmPerSlice, stats.getN(), fwhm, createNote())));
    Utils.log("%s : z-centre = %d, nm/Pixel = %s, nm/Slice = %s, %d images, PSF SD = %s nm, FWHM = %s px\n", psfImp.getTitle(), maxz, Utils.rounded(nmPerPixel / magnification, 3), Utils.rounded(nmPerSlice, 3), stats.getN(), Utils.rounded(fittedSd * nmPerPixel, 4), Utils.rounded(fwhm));
    createInteractivePlots(psf, maxz, nmPerPixel / magnification, fittedSd * nmPerPixel);
    IJ.showStatus("");
}

Example 10 with Statistics

use of gdsc.core.utils.Statistics in project GDSC-SMLM by aherbert.

the class Noise method drawPlot.

/**
	 * Build a plot of the noise estimate from the current frame.
	 * Limit the preview to 100 frames.
	 */
private void drawPlot() {
    NoiseEstimator.Method method1 = NoiseEstimator.Method.values()[algorithm];
    NoiseEstimator.Method method2 = NoiseEstimator.Method.values()[algorithm2];
    IJ.showStatus("Estimating noise ...");
    boolean twoMethods = method1 != method2;
    boolean preserveResiduals = method1.name().contains("Residuals") && method2.name().contains("Residuals") && twoMethods;
    int start = imp.getCurrentSlice();
    int end = FastMath.min(imp.getStackSize(), start + 100);
    int size = end - start + 1;
    double[] xValues = new double[size];
    double[] yValues1 = new double[size];
    double[] yValues2 = (twoMethods) ? new double[size] : null;
    ImageStack stack = imp.getImageStack();
    Rectangle bounds = imp.getProcessor().getRoi();
    float[] buffer = null;
    for (int slice = start, i = 0; slice <= end; slice++, i++) {
        IJ.showProgress(i, size);
        final ImageProcessor ip = stack.getProcessor(slice);
        buffer = ImageConverter.getData(ip.getPixels(), ip.getWidth(), ip.getHeight(), bounds, buffer);
        final NoiseEstimator ne = new NoiseEstimator(buffer, bounds.width, bounds.height);
        ne.preserveResiduals = preserveResiduals;
        ne.setRange(lowestPixelsRange);
        xValues[i] = slice;
        yValues1[i] = ne.getNoise(method1);
        if (twoMethods)
            yValues2[i] = ne.getNoise(method2);
    }
    IJ.showProgress(1);
    IJ.showStatus("Plotting noise ...");
    // Get limits
    double[] a = Tools.getMinMax(xValues);
    double[] b1 = Tools.getMinMax(yValues1);
    if (twoMethods) {
        double[] b2 = Tools.getMinMax(yValues2);
        b1[0] = FastMath.min(b1[0], b2[0]);
        b1[1] = FastMath.max(b1[1], b2[1]);
    }
    String title = imp.getTitle() + " Noise";
    Plot2 plot = new Plot2(title, "Slice", "Noise", xValues, yValues1);
    double range = b1[1] - b1[0];
    if (range == 0)
        range = 1;
    plot.setLimits(a[0], a[1], b1[0] - 0.05 * range, b1[1] + 0.05 * range);
    plot.setColor(Color.blue);
    plot.draw();
    String label = String.format("Blue = %s", Utils.rounded(new Statistics(yValues1).getMean()));
    if (twoMethods) {
        plot.setColor(Color.red);
        plot.addPoints(xValues, yValues2, Plot2.LINE);
        label += String.format(", Red = %s", Utils.rounded(new Statistics(yValues2).getMean()));
    }
    plot.addLabel(0, 0, label);
    Utils.display(title, plot);
    IJ.showStatus("");
}