Examples with Min org.apache.commons.math3.stat.descriptive.rank.Min used on opensource projects

Example 41 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min in project GDSC-SMLM by aherbert.

the class SpotInspector method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    if (!showDialog())
        return;
    // Load the results
    results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        IJ.showStatus("");
        return;
    }
    // Check if the original image is open
    ImageSource source = results.getSource();
    if (source == null) {
        IJ.error(TITLE, "Unknown original source image");
        return;
    }
    source = source.getOriginal();
    if (!source.open()) {
        IJ.error(TITLE, "Cannot open original source image: " + source.toString());
        return;
    }
    final float stdDevMax = getStandardDeviation(results);
    if (stdDevMax < 0) {
        // TODO - Add dialog to get the initial peak width
        IJ.error(TITLE, "Fitting configuration (for initial peak width) is not available");
        return;
    }
    // Rank spots
    rankedResults = new ArrayList<PeakResultRank>(results.size());
    final double a = results.getNmPerPixel();
    final double gain = results.getGain();
    final boolean emCCD = results.isEMCCD();
    for (PeakResult r : results.getResults()) {
        float[] score = getScore(r, a, gain, emCCD, stdDevMax);
        rankedResults.add(new PeakResultRank(r, score[0], score[1]));
    }
    Collections.sort(rankedResults);
    // Prepare results table. Get bias if necessary
    if (showCalibratedValues) {
        // Get a bias if required
        Calibration calibration = results.getCalibration();
        if (calibration.getBias() == 0) {
            ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
            gd.addMessage("Calibrated results requires a camera bias");
            gd.addNumericField("Camera_bias (ADUs)", calibration.getBias(), 2);
            gd.showDialog();
            if (!gd.wasCanceled()) {
                calibration.setBias(Math.abs(gd.getNextNumber()));
            }
        }
    }
    IJTablePeakResults table = new IJTablePeakResults(false, results.getName(), true);
    table.copySettings(results);
    table.setTableTitle(TITLE);
    table.setAddCounter(true);
    table.setShowCalibratedValues(showCalibratedValues);
    table.begin();
    // Add a mouse listener to jump to the frame for the clicked line
    textPanel = table.getResultsWindow().getTextPanel();
    // We must ignore old instances of this class from the mouse listeners
    id = ++currentId;
    textPanel.addMouseListener(this);
    // Add results to the table
    int n = 0;
    for (PeakResultRank rank : rankedResults) {
        rank.rank = n++;
        PeakResult r = rank.peakResult;
        table.add(r.getFrame(), r.origX, r.origY, r.origValue, r.error, r.noise, r.params, r.paramsStdDev);
    }
    table.end();
    if (plotScore || plotHistogram) {
        // Get values for the plots
        float[] xValues = null, yValues = null;
        double yMin, yMax;
        int spotNumber = 0;
        xValues = new float[rankedResults.size()];
        yValues = new float[xValues.length];
        for (PeakResultRank rank : rankedResults) {
            xValues[spotNumber] = spotNumber + 1;
            yValues[spotNumber++] = recoverScore(rank.score);
        }
        // Set the min and max y-values using 1.5 x IQR 
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (float v : yValues) stats.addValue(v);
        if (removeOutliers) {
            double lower = stats.getPercentile(25);
            double upper = stats.getPercentile(75);
            double iqr = upper - lower;
            yMin = FastMath.max(lower - iqr, stats.getMin());
            yMax = FastMath.min(upper + iqr, stats.getMax());
            IJ.log(String.format("Data range: %f - %f. Plotting 1.5x IQR: %f - %f", stats.getMin(), stats.getMax(), yMin, yMax));
        } else {
            yMin = stats.getMin();
            yMax = stats.getMax();
            IJ.log(String.format("Data range: %f - %f", yMin, yMax));
        }
        plotScore(xValues, yValues, yMin, yMax);
        plotHistogram(yValues, yMin, yMax);
    }
    // Extract spots into a stack
    final int w = source.getWidth();
    final int h = source.getHeight();
    final int size = 2 * radius + 1;
    ImageStack spots = new ImageStack(size, size, rankedResults.size());
    // To assist the extraction of data from the image source, process them in time order to allow 
    // frame caching. Then set the appropriate slice in the result stack
    Collections.sort(rankedResults, new Comparator<PeakResultRank>() {

        public int compare(PeakResultRank o1, PeakResultRank o2) {
            if (o1.peakResult.getFrame() < o2.peakResult.getFrame())
                return -1;
            if (o1.peakResult.getFrame() > o2.peakResult.getFrame())
                return 1;
            return 0;
        }
    });
    for (PeakResultRank rank : rankedResults) {
        PeakResult r = rank.peakResult;
        // Extract image
        // Note that the coordinates are relative to the middle of the pixel (0.5 offset)
        // so do not round but simply convert to int
        final int x = (int) (r.params[Gaussian2DFunction.X_POSITION]);
        final int y = (int) (r.params[Gaussian2DFunction.Y_POSITION]);
        // Extract a region but crop to the image bounds
        int minX = x - radius;
        int minY = y - radius;
        int maxX = FastMath.min(x + radius + 1, w);
        int maxY = FastMath.min(y + radius + 1, h);
        int padX = 0, padY = 0;
        if (minX < 0) {
            padX = -minX;
            minX = 0;
        }
        if (minY < 0) {
            padY = -minY;
            minY = 0;
        }
        int sizeX = maxX - minX;
        int sizeY = maxY - minY;
        float[] data = source.get(r.getFrame(), new Rectangle(minX, minY, sizeX, sizeY));
        // Prevent errors with missing data
        if (data == null)
            data = new float[sizeX * sizeY];
        ImageProcessor spotIp = new FloatProcessor(sizeX, sizeY, data, null);
        // Pad if necessary, i.e. the crop is too small for the stack
        if (padX > 0 || padY > 0 || sizeX < size || sizeY < size) {
            ImageProcessor spotIp2 = spotIp.createProcessor(size, size);
            spotIp2.insert(spotIp, padX, padY);
            spotIp = spotIp2;
        }
        int slice = rank.rank + 1;
        spots.setPixels(spotIp.getPixels(), slice);
        spots.setSliceLabel(Utils.rounded(rank.originalScore), slice);
    }
    source.close();
    ImagePlus imp = Utils.display(TITLE, spots);
    imp.setRoi((PointRoi) null);
    // Make bigger		
    for (int i = 10; i-- > 0; ) imp.getWindow().getCanvas().zoomIn(imp.getWidth() / 2, imp.getHeight() / 2);
}

Example 42 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min in project gatk by broadinstitute.

the class OptimizationUtils method argmax.

public static double argmax(final Function<Double, Double> function, final double min, final double max, final double guess, final double relativeTolerance, final double absoluteTolerance, final int maxEvaluations) {
    final BrentOptimizer optimizer = new BrentOptimizer(relativeTolerance, absoluteTolerance);
    final SearchInterval interval = new SearchInterval(min, max, guess);
    return optimizer.optimize(new UnivariateObjectiveFunction(function::apply), GoalType.MAXIMIZE, interval, new MaxEval(maxEvaluations)).getPoint();
}

Example 43 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min in project gatk-protected by broadinstitute.

the class OptimizationUtils method argmax.

public static double argmax(final Function<Double, Double> function, final double min, final double max, final double guess, final double relativeTolerance, final double absoluteTolerance, final int maxEvaluations) {
    final BrentOptimizer optimizer = new BrentOptimizer(relativeTolerance, absoluteTolerance);
    final SearchInterval interval = new SearchInterval(min, max, guess);
    return optimizer.optimize(new UnivariateObjectiveFunction(function::apply), GoalType.MAXIMIZE, interval, new MaxEval(maxEvaluations)).getPoint();
}

Example 44 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min in project gatk by broadinstitute.

the class PosteriorSummaryUtils method calculatePosteriorMode.

/**
     * Given a list of posterior samples, returns an estimate of the posterior mode (using
     * mllib kernel density estimation in {@link KernelDensity} and {@link BrentOptimizer}).
     * Note that estimate may be poor if number of samples is small (resulting in poor kernel density estimation),
     * or if posterior is not unimodal (or is sufficiently pathological otherwise). If the samples contain
     * {@link Double#NaN}, {@link Double#NaN} will be returned.
     * @param samples   posterior samples, cannot be {@code null} and number of samples must be greater than 0
     * @param ctx       {@link JavaSparkContext} used by {@link KernelDensity} for mllib kernel density estimation
     */
public static double calculatePosteriorMode(final List<Double> samples, final JavaSparkContext ctx) {
    Utils.nonNull(samples);
    Utils.validateArg(samples.size() > 0, "Number of samples must be greater than zero.");
    //calculate sample min, max, mean, and standard deviation
    final double sampleMin = Collections.min(samples);
    final double sampleMax = Collections.max(samples);
    final double sampleMean = new Mean().evaluate(Doubles.toArray(samples));
    final double sampleStandardDeviation = new StandardDeviation().evaluate(Doubles.toArray(samples));
    //if samples are all the same or contain NaN, can simply return mean
    if (sampleStandardDeviation == 0. || Double.isNaN(sampleMean)) {
        return sampleMean;
    }
    //use Silverman's rule to set bandwidth for kernel density estimation from sample standard deviation
    //see https://en.wikipedia.org/wiki/Kernel_density_estimation#Practical_estimation_of_the_bandwidth
    final double bandwidth = SILVERMANS_RULE_CONSTANT * sampleStandardDeviation * Math.pow(samples.size(), SILVERMANS_RULE_EXPONENT);
    //use kernel density estimation to approximate posterior from samples
    final KernelDensity pdf = new KernelDensity().setSample(ctx.parallelize(samples, 1)).setBandwidth(bandwidth);
    //use Brent optimization to find mode (i.e., maximum) of kernel-density-estimated posterior
    final BrentOptimizer optimizer = new BrentOptimizer(RELATIVE_TOLERANCE, RELATIVE_TOLERANCE * (sampleMax - sampleMin));
    final UnivariateObjectiveFunction objective = new UnivariateObjectiveFunction(f -> pdf.estimate(new double[] { f })[0]);
    //search for mode within sample range, start near sample mean
    final SearchInterval searchInterval = new SearchInterval(sampleMin, sampleMax, sampleMean);
    return optimizer.optimize(objective, GoalType.MAXIMIZE, searchInterval, BRENT_MAX_EVAL).getPoint();
}

Example 45 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min in project dhis2-core by dhis2.

the class DefaultChartService method getJFreeChartHistory.

@Override
public JFreeChart getJFreeChartHistory(DataElement dataElement, DataElementCategoryOptionCombo categoryOptionCombo, DataElementCategoryOptionCombo attributeOptionCombo, Period lastPeriod, OrganisationUnit organisationUnit, int historyLength, I18nFormat format) {
    lastPeriod = periodService.reloadPeriod(lastPeriod);
    List<Period> periods = periodService.getPeriods(lastPeriod, historyLength);
    MinMaxDataElement minMax = minMaxDataElementService.getMinMaxDataElement(organisationUnit, dataElement, categoryOptionCombo);
    UnivariateInterpolator interpolator = new SplineInterpolator();
    Integer periodCount = 0;
    List<Double> x = new ArrayList<>();
    List<Double> y = new ArrayList<>();
    // ---------------------------------------------------------------------
    // DataValue, MinValue and MaxValue DataSets
    // ---------------------------------------------------------------------
    DefaultCategoryDataset dataValueDataSet = new DefaultCategoryDataset();
    DefaultCategoryDataset metaDataSet = new DefaultCategoryDataset();
    for (Period period : periods) {
        ++periodCount;
        period.setName(format.formatPeriod(period));
        DataValue dataValue = dataValueService.getDataValue(dataElement, period, organisationUnit, categoryOptionCombo, attributeOptionCombo);
        double value = 0;
        if (dataValue != null && dataValue.getValue() != null && MathUtils.isNumeric(dataValue.getValue())) {
            value = Double.parseDouble(dataValue.getValue());
            x.add(periodCount.doubleValue());
            y.add(value);
        }
        dataValueDataSet.addValue(value, dataElement.getShortName(), period.getName());
        if (minMax != null) {
            metaDataSet.addValue(minMax.getMin(), "Min value", period.getName());
            metaDataSet.addValue(minMax.getMax(), "Max value", period.getName());
        }
    }
    if (// minimum 3 points required for interpolation
    x.size() >= 3) {
        periodCount = 0;
        double[] xa = getArray(x);
        int min = MathUtils.getMin(xa).intValue();
        int max = MathUtils.getMax(xa).intValue();
        try {
            UnivariateFunction function = interpolator.interpolate(xa, getArray(y));
            for (Period period : periods) {
                if (++periodCount >= min && periodCount <= max) {
                    metaDataSet.addValue(function.value(periodCount), "Regression value", period.getName());
                }
            }
        } catch (MathRuntimeException ex) {
            throw new RuntimeException("Failed to interpolate", ex);
        }
    }
    // ---------------------------------------------------------------------
    // Plots
    // ---------------------------------------------------------------------
    CategoryPlot plot = getCategoryPlot(dataValueDataSet, getBarRenderer(), PlotOrientation.VERTICAL, CategoryLabelPositions.UP_45);
    plot.setDataset(1, metaDataSet);
    plot.setRenderer(1, getLineRenderer());
    JFreeChart jFreeChart = getBasicJFreeChart(plot);
    return jFreeChart;
}