Examples with Line org.apache.commons.math3.geometry.euclidean.twod.Line used on opensource projects

Example 6 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project GDSC-SMLM by aherbert.

the class PCPALMMolecules method performDistanceAnalysis.

private void performDistanceAnalysis(double[][] intraHist, int p99) {
    // We want to know the fraction of distances between molecules at the 99th percentile
    // that are intra- rather than inter-molecule.
    // Do single linkage clustering of closest pair at this distance and count the number of 
    // links that are inter and intra.
    // Convert molecules for clustering
    ArrayList<ClusterPoint> points = new ArrayList<ClusterPoint>(molecules.size());
    for (Molecule m : molecules) // Precision was used to store the molecule ID
    points.add(ClusterPoint.newClusterPoint((int) m.precision, m.x, m.y, m.photons));
    ClusteringEngine engine = new ClusteringEngine(Prefs.getThreads(), ClusteringAlgorithm.PARTICLE_SINGLE_LINKAGE, new IJTrackProgress());
    IJ.showStatus("Clustering to check inter-molecule distances");
    engine.setTrackJoins(true);
    ArrayList<Cluster> clusters = engine.findClusters(points, intraHist[0][p99]);
    IJ.showStatus("");
    if (clusters != null) {
        double[] intraIdDistances = engine.getIntraIdDistances();
        double[] interIdDistances = engine.getInterIdDistances();
        int all = interIdDistances.length + intraIdDistances.length;
        log("  * Fraction of inter-molecule particle linkage @ %s nm = %s %%", Utils.rounded(intraHist[0][p99], 4), (all > 0) ? Utils.rounded(100.0 * interIdDistances.length / all, 4) : "0");
        // Show a double cumulative histogram plot
        double[][] intraIdHist = Maths.cumulativeHistogram(intraIdDistances, false);
        double[][] interIdHist = Maths.cumulativeHistogram(interIdDistances, false);
        // Plot
        String title = TITLE + " molecule linkage distance";
        Plot2 plot = new Plot2(title, "Distance", "Frequency", intraIdHist[0], intraIdHist[1]);
        double max = (intraIdHist[1].length > 0) ? intraIdHist[1][intraIdHist[1].length - 1] : 0;
        if (interIdHist[1].length > 0)
            max = FastMath.max(max, interIdHist[1][interIdHist[1].length - 1]);
        plot.setLimits(0, intraIdHist[0][intraIdHist[0].length - 1], 0, max);
        plot.setColor(Color.blue);
        plot.addPoints(interIdHist[0], interIdHist[1], Plot2.LINE);
        plot.setColor(Color.black);
        Utils.display(title, plot);
    } else {
        log("Aborted clustering to check inter-molecule distances");
    }
}

Example 7 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project GDSC-SMLM by aherbert.

the class PCPALMClusters method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (!showDialog())
        return;
    PCPALMMolecules.logSpacer();
    Utils.log(TITLE);
    PCPALMMolecules.logSpacer();
    long start = System.currentTimeMillis();
    HistogramData histogramData;
    if (fileInput) {
        histogramData = loadHistogram(histogramFile);
    } else {
        histogramData = doClustering();
    }
    if (histogramData == null)
        return;
    float[][] hist = histogramData.histogram;
    // Create a histogram of the cluster sizes
    String title = TITLE + " Molecules/cluster";
    String xTitle = "Molecules/cluster";
    String yTitle = "Frequency";
    // Create the data required for fitting and plotting
    float[] xValues = Utils.createHistogramAxis(hist[0]);
    float[] yValues = Utils.createHistogramValues(hist[1]);
    // Plot the histogram
    float yMax = Maths.max(yValues);
    Plot2 plot = new Plot2(title, xTitle, yTitle, xValues, yValues);
    if (xValues.length > 0) {
        double xPadding = 0.05 * (xValues[xValues.length - 1] - xValues[0]);
        plot.setLimits(xValues[0] - xPadding, xValues[xValues.length - 1] + xPadding, 0, yMax * 1.05);
    }
    Utils.display(title, plot);
    HistogramData noiseData = loadNoiseHistogram(histogramData);
    if (noiseData != null) {
        if (subtractNoise(histogramData, noiseData)) {
            // Update the histogram
            title += " (noise subtracted)";
            xValues = Utils.createHistogramAxis(hist[0]);
            yValues = Utils.createHistogramValues(hist[1]);
            yMax = Maths.max(yValues);
            plot = new Plot2(title, xTitle, yTitle, xValues, yValues);
            if (xValues.length > 0) {
                double xPadding = 0.05 * (xValues[xValues.length - 1] - xValues[0]);
                plot.setLimits(xValues[0] - xPadding, xValues[xValues.length - 1] + xPadding, 0, yMax * 1.05);
            }
            Utils.display(title, plot);
            // Automatically save
            if (autoSave) {
                String newFilename = Utils.replaceExtension(histogramData.filename, ".noise.tsv");
                if (saveHistogram(histogramData, newFilename)) {
                    Utils.log("Saved noise-subtracted histogram to " + newFilename);
                }
            }
        }
    }
    // Fit the histogram
    double[] fitParameters = fitBinomial(histogramData);
    if (fitParameters != null) {
        // Add the binomial to the histogram
        int n = (int) fitParameters[0];
        double p = fitParameters[1];
        Utils.log("Optimal fit : N=%d, p=%s", n, Utils.rounded(p));
        BinomialDistribution dist = new BinomialDistribution(n, p);
        // A zero-truncated binomial was fitted.
        // pi is the adjustment factor for the probability density.
        double pi = 1 / (1 - dist.probability(0));
        if (!fileInput) {
            // Calculate the estimated number of clusters from the observed molecules:
            // Actual = (Observed / p-value) / N
            final double actual = (nMolecules / p) / n;
            Utils.log("Estimated number of clusters : (%d / %s) / %d = %s", nMolecules, Utils.rounded(p), n, Utils.rounded(actual));
        }
        double[] x = new double[n + 2];
        double[] y = new double[n + 2];
        // Scale the values to match those on the histogram
        final double normalisingFactor = count * pi;
        for (int i = 0; i <= n; i++) {
            x[i] = i + 0.5;
            y[i] = dist.probability(i) * normalisingFactor;
        }
        x[n + 1] = n + 1.5;
        y[n + 1] = 0;
        // Redraw the plot since the limits may have changed
        plot = new Plot2(title, xTitle, yTitle, xValues, yValues);
        double xPadding = 0.05 * (xValues[xValues.length - 1] - xValues[0]);
        plot.setLimits(xValues[0] - xPadding, xValues[xValues.length - 1] + xPadding, 0, Maths.maxDefault(yMax, y) * 1.05);
        plot.setColor(Color.magenta);
        plot.addPoints(x, y, Plot2.LINE);
        plot.addPoints(x, y, Plot2.CIRCLE);
        plot.setColor(Color.black);
        Utils.display(title, plot);
    }
    double seconds = (System.currentTimeMillis() - start) / 1000.0;
    String msg = TITLE + " complete : " + seconds + "s";
    IJ.showStatus(msg);
    Utils.log(msg);
    return;
}

Example 8 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project triplea by triplea-game.

the class MapRouteDrawer method drawCurvedPath.

/**
 * Draws a smooth curve through the given array of points
 *
 * <p>
 * This algorithm is called Spline-Interpolation
 * because the Apache-commons-math library we are using here does not accept
 * values but {@code f(x)=y} with x having to increase all the time
 * the idea behind this is to use a parameter array - the so called index
 * as x array and splitting the points into a x and y coordinates array.
 * </p>
 *
 * <p>
 * Finally those 2 interpolated arrays get unified into a single {@linkplain Point2D} array and drawn to the Map
 * </p>
 *
 * @param graphics The {@linkplain Graphics2D} Object to be drawn on
 * @param points The Knot Points for the Spline-Interpolator aka the joints
 */
private void drawCurvedPath(final Graphics2D graphics, final Point2D[] points) {
    final double[] index = createParameterizedIndex(points);
    final PolynomialSplineFunction xcurve = splineInterpolator.interpolate(index, getValues(points, Point2D::getX));
    final double[] xcoords = getCoords(xcurve, index);
    final PolynomialSplineFunction ycurve = splineInterpolator.interpolate(index, getValues(points, Point2D::getY));
    final double[] ycoords = getCoords(ycurve, index);
    final List<Path2D> paths = routeCalculator.getAllNormalizedLines(xcoords, ycoords);
    for (final Path2D path : paths) {
        drawTransformedShape(graphics, path);
    }
    // draws the Line to the Cursor on every possible screen, so that the line ends at the cursor no matter what...
    final List<Point2D[]> finishingPoints = routeCalculator.getAllPoints(new Point2D.Double(xcoords[xcoords.length - 1], ycoords[ycoords.length - 1]), points[points.length - 1]);
    final boolean hasArrowEnoughSpace = points[points.length - 2].distance(points[points.length - 1]) > ARROW_LENGTH;
    for (final Point2D[] finishingPointArray : finishingPoints) {
        drawTransformedShape(graphics, new Line2D.Double(finishingPointArray[0], finishingPointArray[1]));
        if (hasArrowEnoughSpace) {
            drawArrow(graphics, finishingPointArray[0], finishingPointArray[1]);
        }
    }
}

Example 9 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project chordatlas by twak.

the class Slice method findSupportPoints.

public void findSupportPoints() {
    supportPoints.clear();
    supportMax = 0;
    // Map<Line, Double> totalLineSupport = new HashedMap();
    double distSigma = 0.2;
    Gaussian distanceG = new Gaussian(0, distSigma), angleG = new Gaussian(0, 0.3);
    for (Line l : gis.allLines()) {
        double length = l.length();
        int noPoints = (int) Math.max(2, length / 0.1);
        for (int n = 0; n <= noPoints; n++) {
            SupportPoint sp = new SupportPoint(l, l.fromPPram(n / (double) noPoints));
            // Double.MAX_VALUE;
            sp.support = 0;
            for (Line nearL : slice.getNear(sp.pt, distSigma * 3)) {
                double angle = nearL.absAngle(sp.line);
                // Math.min  (sp.pt.distance(line.start), sp.pt.distance(line.end)) ;
                double dist = nearL.distance(sp.pt, true);
                sp.support += distanceG.value(dist) * angleG.value(angle) * nearL.length();
            }
            supportPoints.add(sp);
        }
    }
    foundLines = null;
    repaint();
}

Example 10 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project chordatlas by twak.

the class Concarnie method apply.

private List<Problem> apply(Problem problem, int count) {
    if (depth > 10 || problem.soup.isEmpty()) {
        problem.addPortal();
        return Collections.emptyList();
    }
    Set<Line> portals = new HashSet<>();
    Set<Line> in = new HashSet<>(problem.soup);
    for (Line sl : problem.hull) {
        RangeMerge<Line> rm = new RangeMerge<>(P.CON_TOL, P.CON_TOL);
        LinearForm lf = new LinearForm(sl);
        for (Line l : problem.soup) {
            if (onHull(sl, l)) {
                double pps = lf.findPParam(l.start), ppe = lf.findPParam(l.end);
                rm.add(pps, ppe, l);
                in.remove(l);
            }
        }
        List<Double> rmGet = rm.get();
        if (rmGet.isEmpty()) {
            if (!sl.start.equals(sl.end)) {
                if (Double.isNaN(sl.start.x))
                    System.out.println("help!");
                // whole thing is portal
                portals.add(sl);
            }
        } else {
            List<Point2d> occupied = new ArrayList();
            {
                double lf1 = lf.findPParam(sl.start), lf2 = lf.findPParam(sl.end);
                if (lf1 > lf2) {
                    double tmp = lf1;
                    lf1 = lf2;
                    lf2 = tmp;
                }
                for (double d : rmGet) {
                    d = Mathz.clamp(d, lf1, lf2);
                    occupied.add(lf.fromPParam(d));
                }
            }
            boolean onHull = false;
            {
                Point2d snapE = occupied.get(0), snapS = occupied.get(occupied.size() - 1);
                if (snapS.distance(sl.start) < P.CON_TOL)
                    snapS.set(sl.start);
                else {
                    occupied.add(new Point2d(sl.start));
                }
                if (snapE.distance(sl.end) < P.CON_TOL)
                    snapE.set(sl.end);
                else {
                    occupied.add(0, new Point2d(sl.end));
                    onHull = true;
                }
            }
            for (Pair<Point2d, Point2d> pair : new ConsecutiveItPairs<Point2d>(occupied)) {
                onHull = !onHull;
                if (pair.first().equals(pair.second()))
                    continue;
                Line line = new Line(pair.first(), pair.second());
                if (onHull) {
                    if (depth % 2 == 0)
                        line = line.reverse();
                    graph.add(line);
                } else {
                    portals.add(line.reverse());
                }
            }
        }
    }
    if (in.size() == problem.soup.size()) {
        // we didn't do anything! remove something, anything...
        List<Line> d = new ArrayList(in);
        Collections.sort(d, (a, b) -> Double.compare(a.length(), b.length()));
        for (// remove the shortest 1/3 lines
        int i = 0; // remove the shortest 1/3 lines
        i < Math.max(1, in.size() * 0.33); // remove the shortest 1/3 lines
        i++) in.remove(d.get(i));
    }
    List<Portal> mergedPortals = mergeConsecutive(portals);
    // assign each member of in to a portal
    MultiMapSet<Portal, Line> subproblems = new MultiMapSet();
    if (!mergedPortals.isEmpty()) {
        // O(n^3) closest-clique assignment
        MultiMapSet<Portal, Line> sub2 = new MultiMapSet();
        for (Portal p : mergedPortals) sub2.putAll(p, p.lines);
        while (!in.isEmpty()) {
            double bestDist = Double.MAX_VALUE;
            Portal bestP = null;
            Line bestL = null;
            for (Line l : in) for (Portal p : sub2.keySet()) for (Line sl : sub2.get(p)) {
                double dlsl = l.distance(sl);
                if (// ignore lines a long way away
                dlsl > Math.max(P.CON_TOL * 3, p.length * 0.5))
                    continue;
                double dist = dlsl + 0.1 * l.distance(p.summary);
                if (dist < bestDist) {
                    bestP = p;
                    bestDist = dist;
                    bestL = l;
                }
            }
            if (bestL == null)
                break;
            in.remove(bestL);
            double lenBestL = bestL.length();
            if (lenBestL > P.CON_TOL && lenBestL > 0.5 * bestP.summary.length()) {
                in.add(new Line(bestL.start, bestL.fromPPram(0.5)));
                in.add(new Line(bestL.fromPPram(0.5), bestL.end));
            } else {
                subproblems.put(bestP, bestL);
                sub2.put(bestP, bestL);
            }
        }
    } else {
        mergedPortals.add(null);
        subproblems.putAll(null, in);
    }
    return mergedPortals.stream().map(x -> new Problem(x == null ? Collections.emptyList() : x.lines, subproblems.get(x))).collect(Collectors.toList());
}