Examples with Plot2 ij.gui.Plot2 used on opensource projects

Example 31 with Plot2

use of ij.gui.Plot2 in project GDSC-SMLM by aherbert.

the class EMGainAnalysis method plotPMF.

private void plotPMF() {
    if (!showPMFDialog())
        return;
    final int gaussWidth = 5;
    int dummyBias = (int) Math.max(500, gaussWidth * _noise + 1);
    double[] pmf = pdf(0, _photons, _gain, _noise, dummyBias);
    double[] x = Utils.newArray(pmf.length, 0, 1.0);
    double yMax = Maths.max(pmf);
    // Truncate x
    int max = 0;
    double sum = 0;
    double p = 1 - tail;
    while (sum < p && max < pmf.length) {
        sum += pmf[max];
        if (sum > 0.5 && pmf[max] == 0)
            break;
        max++;
    }
    int min = pmf.length;
    sum = 0;
    p = 1 - head;
    while (sum < p && min > 0) {
        min--;
        sum += pmf[min];
        if (sum > 0.5 && pmf[min] == 0)
            break;
    }
    //int min = (int) (dummyBias - gaussWidth * _noise);
    pmf = Arrays.copyOfRange(pmf, min, max);
    x = Arrays.copyOfRange(x, min, max);
    // Get the approximation
    double[] f = new double[x.length];
    LikelihoodFunction fun;
    double myNoise = _noise;
    switch(approximation) {
        case 3:
            fun = new PoissonFunction(1.0 / _gain, true);
            break;
        case 2:
            // The mean does not matter so just use zero
            fun = PoissonGaussianFunction.createWithStandardDeviation(1.0 / _gain, 0, _noise);
            break;
        case 1:
            myNoise = 0;
        case 0:
        default:
            PoissonGammaGaussianFunction myFun = new PoissonGammaGaussianFunction(1.0 / _gain, myNoise);
            myFun.setMinimumProbability(0);
            fun = myFun;
    }
    double expected = _photons;
    if (offset != 0)
        expected += offset * expected / 100.0;
    expected *= _gain;
    //double sum2 = 0;
    for (int i = 0; i < f.length; i++) {
        // Adjust the x-values to remove the dummy bias
        x[i] -= dummyBias;
        f[i] = fun.likelihood(x[i], expected);
    //sum += pmf[i];
    //sum2 += f[i];
    }
    //System.out.printf("Approximation sum = %f : %f\n", sum ,sum2);
    if (showApproximation)
        yMax = Maths.maxDefault(yMax, f);
    String label = String.format("Gain=%s, noise=%s, photons=%s", Utils.rounded(_gain), Utils.rounded(_noise), Utils.rounded(_photons));
    Plot2 plot = new Plot2("PMF", "ADUs", "p");
    plot.setLimits(x[0], x[x.length - 1], 0, yMax);
    plot.setColor(Color.red);
    plot.addPoints(x, pmf, Plot2.LINE);
    if (showApproximation) {
        plot.setColor(Color.blue);
        plot.addPoints(x, f, Plot2.LINE);
    }
    plot.setColor(Color.magenta);
    plot.drawLine(_photons * _gain, 0, _photons * _gain, yMax);
    plot.setColor(Color.black);
    plot.addLabel(0, 0, label);
    PlotWindow win1 = Utils.display("PMF", plot);
    // Plot the difference between the actual and approximation
    double[] delta = new double[f.length];
    for (int i = 0; i < f.length; i++) {
        if (pmf[i] == 0 && f[i] == 0)
            continue;
        if (relativeDelta)
            delta[i] = DoubleEquality.relativeError(f[i], pmf[i]) * Math.signum(f[i] - pmf[i]);
        else
            delta[i] = f[i] - pmf[i];
    }
    Plot2 plot2 = new Plot2("PMF delta", "ADUs", (relativeDelta) ? "Relative delta" : "delta");
    double[] limits = Maths.limits(delta);
    plot2.setLimits(x[0], x[x.length - 1], limits[0], limits[1]);
    plot2.setColor(Color.red);
    plot2.addPoints(x, delta, Plot2.LINE);
    plot2.setColor(Color.magenta);
    plot2.drawLine(_photons * _gain, limits[0], _photons * _gain, limits[1]);
    plot2.setColor(Color.black);
    plot2.addLabel(0, 0, label + ((offset == 0) ? "" : ", expected = " + Utils.rounded(expected / _gain)));
    PlotWindow win2 = Utils.display("PMF delta", plot2);
    if (Utils.isNewWindow()) {
        Point p2 = win2.getLocation();
        p2.y += win1.getHeight();
        win2.setLocation(p2);
    }
}

Example 32 with Plot2

use of ij.gui.Plot2 in project GDSC-SMLM by aherbert.

the class DoubletAnalysis method showHistogram.

/**
	 * Show a cumulative histogram of the data.
	 *
	 * @param values
	 *            the values
	 * @param xTitle
	 *            The name of plotted statistic
	 */
public void showHistogram(double[] values, String xTitle) {
    double[][] h = Maths.cumulativeHistogram(values, false);
    String title = TITLE + " " + xTitle + " Cumulative";
    Plot2 plot = new Plot2(title, xTitle, "Frequency");
    double xMax = h[0][h[0].length - 1];
    double yMax = h[1][h[1].length - 1];
    plot.setLimits(0, xMax, 0, 1.05 * yMax);
    plot.setColor(Color.blue);
    plot.addPoints(h[0], h[1], Plot2.BAR);
    display(title, plot);
}

Example 33 with Plot2

use of ij.gui.Plot2 in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method showExample.

private void showExample(int totalSteps, double diffusionSigma, RandomGenerator[] random) {
    MoleculeModel m = new MoleculeModel(0, new double[3]);
    float[] xValues = new float[totalSteps];
    float[] x = new float[totalSteps];
    float[] y = new float[totalSteps];
    final double[] axis = (settings.getDiffusionType() == DiffusionType.LINEAR_WALK) ? nextVector() : null;
    for (int j = 0; j < totalSteps; j++) {
        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);
        x[j] = (float) (m.getX());
        y[j] = (float) (m.getY());
        xValues[j] = (float) ((j + 1) / settings.stepsPerSecond);
    }
    // Plot x and y coords on a timeline
    String title = TITLE + " example coordinates";
    Plot2 plot = new Plot2(title, "Time (seconds)", "Distance (um)");
    float[] xUm = convertToUm(x);
    float[] yUm = convertToUm(y);
    float[] limits = Maths.limits(xUm);
    limits = Maths.limits(limits, yUm);
    plot.setLimits(0, totalSteps / settings.stepsPerSecond, limits[0], limits[1]);
    plot.setColor(Color.red);
    plot.addPoints(xValues, xUm, Plot2.LINE);
    plot.setColor(Color.blue);
    plot.addPoints(xValues, yUm, Plot2.LINE);
    Utils.display(title, plot);
    // Scale up and draw 2D position
    for (int j = 0; j < totalSteps; j++) {
        x[j] *= magnification;
        y[j] *= magnification;
    }
    float[] limitsx = getLimits(x);
    float[] limitsy = getLimits(y);
    int width = (int) (limitsx[1] - limitsx[0]);
    int height = (int) (limitsy[1] - limitsy[0]);
    // Ensure we draw something, even it is a simple dot at the centre for no diffusion
    if (width == 0) {
        width = (int) (32 * magnification);
        limitsx[0] = -width / 2;
    }
    if (height == 0) {
        height = (int) (32 * magnification);
        limitsy[0] = -height / 2;
    }
    ImageProcessor ip = new ByteProcessor(width, height);
    // Adjust x and y using the minimum to centre
    x[0] -= limitsx[0];
    y[0] -= limitsy[0];
    for (int j = 1; j < totalSteps; j++) {
        // Adjust x and y using the minimum to centre
        x[j] -= limitsx[0];
        y[j] -= limitsy[0];
        // Draw a line
        ip.setColor(32 + (223 * j) / (totalSteps - 1));
        ip.drawLine(round(x[j - 1]), round(y[j - 1]), round(x[j]), round(y[j]));
    }
    // Draw the final position
    ip.putPixel((int) round(x[totalSteps - 1]), (int) round(y[totalSteps - 1]), 255);
    ImagePlus imp = Utils.display(TITLE + " example", ip);
    // Apply the fire lookup table
    WindowManager.setTempCurrentImage(imp);
    LutLoader lut = new LutLoader();
    lut.run("fire");
    WindowManager.setTempCurrentImage(null);
}

Example 34 with Plot2

use of ij.gui.Plot2 in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method plotJumpDistances.

/**
	 * Plot a cumulative histogram and standard histogram of the jump distances.
	 *
	 * @param title
	 *            the title
	 * @param jumpDistances
	 *            the jump distances
	 * @param dimensions
	 *            the number of dimensions for the jumps
	 * @param steps
	 *            the steps
	 */
private void plotJumpDistances(String title, DoubleData jumpDistances, int dimensions) {
    // Cumulative histogram
    // --------------------
    double[] values = jumpDistances.values();
    String title2 = title + " Cumulative Jump Distance " + dimensions + "D";
    double[][] jdHistogram = JumpDistanceAnalysis.cumulativeHistogram(values);
    Plot2 jdPlot = new Plot2(title2, "Distance (um^2)", "Cumulative Probability", jdHistogram[0], jdHistogram[1]);
    PlotWindow pw2 = Utils.display(title2, jdPlot);
    if (Utils.isNewWindow())
        idList[idCount++] = pw2.getImagePlus().getID();
    // Plot the expected function
    // This is the Chi-squared distribution: The sum of the squares of k independent
    // standard normal random variables with k = dimensions. It is a special case of
    // the gamma distribution. If the normals have non-unit variance the distribution 
    // is scaled.
    // Chi       ~ Gamma(k/2, 2)      // using the scale parameterisation of the gamma
    // s^2 * Chi ~ Gamma(k/2, 2*s^2)
    // So if s^2 = 2D:
    // 2D * Chi  ~ Gamma(k/2, 4D)
    double estimatedD = simpleD * simpleSteps;
    double max = Maths.max(values);
    double[] x = Utils.newArray(1000, 0, max / 1000);
    double k = dimensions / 2.0;
    double mean = 4 * estimatedD;
    GammaDistribution dist = new GammaDistribution(k, mean);
    double[] y = new double[x.length];
    for (int i = 0; i < x.length; i++) y[i] = dist.cumulativeProbability(x[i]);
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    Utils.display(title2, jdPlot);
    // Histogram
    // ---------
    title2 = title + " Jump " + dimensions + "D";
    int plotId = Utils.showHistogram(title2, jumpDistances, "Distance (um^2)", 0, 0, Math.max(20, values.length / 1000));
    if (Utils.isNewWindow())
        idList[idCount++] = plotId;
    // Recompute the expected function
    for (int i = 0; i < x.length; i++) y[i] = dist.density(x[i]);
    // Scale to have the same area
    if (Utils.xValues.length > 1) {
        final double area1 = jumpDistances.size() * (Utils.xValues[1] - Utils.xValues[0]);
        final double area2 = dist.cumulativeProbability(x[x.length - 1]);
        final double scaleFactor = area1 / area2;
        for (int i = 0; i < y.length; i++) y[i] *= scaleFactor;
    }
    jdPlot = Utils.plot;
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    Utils.display(WindowManager.getImage(plotId).getTitle(), jdPlot);
}

Example 35 with Plot2

use of ij.gui.Plot2 in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFit method showDoubleHistogram.

private double[] showDoubleHistogram(StoredDataStatistics[][] stats, final int i, WindowOrganiser wo, double[][] matchScores, double nPredicted) {
    String xLabel = filterCriteria[i].name;
    LowerLimit lower = filterCriteria[i].lower;
    UpperLimit upper = filterCriteria[i].upper;
    double[] j = null;
    double[] metric = null;
    double maxJ = 0;
    if (i <= FILTER_PRECISION && (showFilterScoreHistograms || upper.requiresJaccard || lower.requiresJaccard)) {
        // Jaccard score verses the range of the metric
        Arrays.sort(matchScores, new Comparator<double[]>() {

            public int compare(double[] o1, double[] o2) {
                if (o1[i] < o2[i])
                    return -1;
                if (o1[i] > o2[i])
                    return 1;
                return 0;
            }
        });
        final int scoreIndex = FILTER_PRECISION + 1;
        int n = results.size();
        double tp = 0;
        double fp = 0;
        j = new double[matchScores.length + 1];
        metric = new double[j.length];
        for (int k = 0; k < matchScores.length; k++) {
            final double score = matchScores[k][scoreIndex];
            tp += score;
            fp += (1 - score);
            j[k + 1] = tp / (fp + n);
            metric[k + 1] = matchScores[k][i];
        }
        metric[0] = metric[1];
        maxJ = Maths.max(j);
        if (showFilterScoreHistograms) {
            String title = TITLE + " Jaccard " + xLabel;
            Plot plot = new Plot(title, xLabel, "Jaccard", metric, j);
            // Remove outliers
            double[] limitsx = Maths.limits(metric);
            Percentile p = new Percentile();
            double l = p.evaluate(metric, 25);
            double u = p.evaluate(metric, 75);
            double iqr = 1.5 * (u - l);
            limitsx[1] = Math.min(limitsx[1], u + iqr);
            plot.setLimits(limitsx[0], limitsx[1], 0, Maths.max(j));
            PlotWindow pw = Utils.display(title, plot);
            if (Utils.isNewWindow())
                wo.add(pw);
        }
    }
    // [0] is all
    // [1] is matches
    // [2] is no match
    StoredDataStatistics s1 = stats[0][i];
    StoredDataStatistics s2 = stats[1][i];
    StoredDataStatistics s3 = stats[2][i];
    if (s1.getN() == 0)
        return new double[4];
    DescriptiveStatistics d = s1.getStatistics();
    double median = 0;
    Plot2 plot = null;
    String title = null;
    if (showFilterScoreHistograms) {
        median = d.getPercentile(50);
        String label = String.format("n = %d. Median = %s nm", s1.getN(), Utils.rounded(median));
        int id = Utils.showHistogram(TITLE, s1, xLabel, filterCriteria[i].minBinWidth, (filterCriteria[i].restrictRange) ? 1 : 0, 0, label);
        if (id == 0) {
            IJ.log("Failed to show the histogram: " + xLabel);
            return new double[4];
        }
        if (Utils.isNewWindow())
            wo.add(id);
        title = WindowManager.getImage(id).getTitle();
        // Reverse engineer the histogram settings
        plot = Utils.plot;
        double[] xValues = Utils.xValues;
        int bins = xValues.length;
        double yMin = xValues[0];
        double binSize = xValues[1] - xValues[0];
        double yMax = xValues[0] + (bins - 1) * binSize;
        if (s2.getN() > 0) {
            double[] values = s2.getValues();
            double[][] hist = Utils.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.red);
                plot.addPoints(hist[0], hist[1], Plot2.BAR);
                Utils.display(title, plot);
            }
        }
        if (s3.getN() > 0) {
            double[] values = s3.getValues();
            double[][] hist = Utils.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.blue);
                plot.addPoints(hist[0], hist[1], Plot2.BAR);
                Utils.display(title, plot);
            }
        }
    }
    // Do cumulative histogram
    double[][] h1 = Maths.cumulativeHistogram(s1.getValues(), true);
    double[][] h2 = Maths.cumulativeHistogram(s2.getValues(), true);
    double[][] h3 = Maths.cumulativeHistogram(s3.getValues(), true);
    if (showFilterScoreHistograms) {
        title = TITLE + " Cumul " + xLabel;
        plot = new Plot2(title, xLabel, "Frequency");
        // Find limits
        double[] xlimit = Maths.limits(h1[0]);
        xlimit = Maths.limits(xlimit, h2[0]);
        xlimit = Maths.limits(xlimit, h3[0]);
        // Restrict using the inter-quartile range 
        if (filterCriteria[i].restrictRange) {
            double q1 = d.getPercentile(25);
            double q2 = d.getPercentile(75);
            double iqr = (q2 - q1) * 2.5;
            xlimit[0] = Maths.max(xlimit[0], median - iqr);
            xlimit[1] = Maths.min(xlimit[1], median + iqr);
        }
        plot.setLimits(xlimit[0], xlimit[1], 0, 1.05);
        plot.addPoints(h1[0], h1[1], Plot.LINE);
        plot.setColor(Color.red);
        plot.addPoints(h2[0], h2[1], Plot.LINE);
        plot.setColor(Color.blue);
        plot.addPoints(h3[0], h3[1], Plot.LINE);
    }
    // Determine the maximum difference between the TP and FP
    double maxx1 = 0;
    double maxx2 = 0;
    double max1 = 0;
    double max2 = 0;
    // We cannot compute the delta histogram, or use percentiles
    if (s2.getN() == 0) {
        upper = UpperLimit.ZERO;
        lower = LowerLimit.ZERO;
    }
    final boolean requireLabel = (showFilterScoreHistograms && filterCriteria[i].requireLabel);
    if (requireLabel || upper.requiresDeltaHistogram() || lower.requiresDeltaHistogram()) {
        if (s2.getN() != 0 && s3.getN() != 0) {
            LinearInterpolator li = new LinearInterpolator();
            PolynomialSplineFunction f1 = li.interpolate(h2[0], h2[1]);
            PolynomialSplineFunction f2 = li.interpolate(h3[0], h3[1]);
            for (double x : h1[0]) {
                if (x < h2[0][0] || x < h3[0][0])
                    continue;
                try {
                    double v1 = f1.value(x);
                    double v2 = f2.value(x);
                    double diff = v2 - v1;
                    if (diff > 0) {
                        if (max1 < diff) {
                            max1 = diff;
                            maxx1 = x;
                        }
                    } else {
                        if (max2 > diff) {
                            max2 = diff;
                            maxx2 = x;
                        }
                    }
                } catch (OutOfRangeException e) {
                    // Because we reached the end
                    break;
                }
            }
        } else {
            // Switch to percentiles if we have no delta histogram
            if (upper.requiresDeltaHistogram())
                upper = UpperLimit.NINETY_NINE_PERCENT;
            if (lower.requiresDeltaHistogram())
                lower = LowerLimit.ONE_PERCENT;
        }
    //			System.out.printf("Bounds %s : %s, pos %s, neg %s, %s\n", xLabel, Utils.rounded(getPercentile(h2, 0.01)),
    //					Utils.rounded(maxx1), Utils.rounded(maxx2), Utils.rounded(getPercentile(h1, 0.99)));
    }
    if (showFilterScoreHistograms) {
        // We use bins=1 on charts where we do not need a label
        if (requireLabel) {
            String label = String.format("Max+ %s @ %s, Max- %s @ %s", Utils.rounded(max1), Utils.rounded(maxx1), Utils.rounded(max2), Utils.rounded(maxx2));
            plot.setColor(Color.black);
            plot.addLabel(0, 0, label);
        }
        PlotWindow pw = Utils.display(title, plot);
        if (Utils.isNewWindow())
            wo.add(pw.getImagePlus().getID());
    }
    // Now compute the bounds using the desired limit
    double l, u;
    switch(lower) {
        case ONE_PERCENT:
            l = getPercentile(h2, 0.01);
            break;
        case MAX_NEGATIVE_CUMUL_DELTA:
            l = maxx2;
            break;
        case ZERO:
            l = 0;
            break;
        case HALF_MAX_JACCARD_VALUE:
            l = getValue(metric, j, maxJ * 0.5);
            break;
        default:
            throw new RuntimeException("Missing lower limit method");
    }
    switch(upper) {
        case MAX_POSITIVE_CUMUL_DELTA:
            u = maxx1;
            break;
        case NINETY_NINE_PERCENT:
            u = getPercentile(h2, 0.99);
            break;
        case NINETY_NINE_NINE_PERCENT:
            u = getPercentile(h2, 0.999);
            break;
        case ZERO:
            u = 0;
            break;
        case MAX_JACCARD2:
            u = getValue(metric, j, maxJ) * 2;
            //System.out.printf("MaxJ = %.4f @ %.3f\n", maxJ, u / 2);
            break;
        default:
            throw new RuntimeException("Missing upper limit method");
    }
    double min = getPercentile(h1, 0);
    double max = getPercentile(h1, 1);
    return new double[] { l, u, min, max };
}