Examples with PolynomialSplineFunction org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction used on opensource projects

Example 1 with PolynomialSplineFunction

use of org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction in project GDSC-SMLM by aherbert.

the class SpotAnalysis method interpolate.

private double[] interpolate(double[] xValues2, double[] yValues) {
    // Smooth the values not in the current on-frames
    double[] newX = Arrays.copyOf(xValues, xValues.length);
    double[] newY = Arrays.copyOf(yValues, yValues.length);
    for (Spot s : onFrames) {
        newX[s.frame - 1] = -1;
    }
    int c = 0;
    for (int i = 0; i < newX.length; i++) {
        if (newX[i] == -1)
            continue;
        newX[c] = newX[i];
        newY[c] = newY[i];
        c++;
    }
    newX = Arrays.copyOf(newX, c);
    newY = Arrays.copyOf(newY, c);
    double smoothing = 0.25;
    try {
        smoothing = Double.parseDouble(smoothingTextField.getText());
        if (smoothing < 0.01 || smoothing > 0.9)
            smoothing = 0.25;
    } catch (NumberFormatException e) {
    }
    LoessInterpolator loess = new LoessInterpolator(smoothing, 1);
    PolynomialSplineFunction f = loess.interpolate(newX, newY);
    // Interpolate
    double[] plotSmooth = new double[xValues.length];
    for (int i = 0; i < xValues.length; i++) {
        // Cannot interpolate outside the bounds of the input data
        if (xValues[i] < newX[0])
            plotSmooth[i] = newY[0];
        else if (xValues[i] > newX[newX.length - 1])
            plotSmooth[i] = newY[newX.length - 1];
        else
            plotSmooth[i] = f.value(xValues[i]);
    }
    return plotSmooth;
}

Example 2 with PolynomialSplineFunction

use of org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction in project GDSC-SMLM by aherbert.

the class TraceMolecules method interpolateZeroCrossingPoints.

@SuppressWarnings("unused")
private void interpolateZeroCrossingPoints() {
    double[] x = new double[zeroCrossingPoints.size()];
    double[] y = new double[zeroCrossingPoints.size()];
    for (int i = 0; i < x.length; i++) {
        double[] point = zeroCrossingPoints.get(i);
        x[i] = point[0];
        y[i] = point[1];
    }
    PolynomialSplineFunction fx = new SplineInterpolator().interpolate(x, y);
    double minX = x[0];
    double maxX = x[x.length - 1];
    double xinc = (maxX - minX) / 50;
    for (minX = minX + xinc; minX < maxX; minX += xinc) {
        zeroCrossingPoints.add(new double[] { minX, fx.value(minX) });
    }
    sortPoints();
}

Example 3 with PolynomialSplineFunction

use of org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction in project xDrip-plus by jamorham.

the class LibreAlarmReceiver method CalculateFromDataTransferObject.

public static void CalculateFromDataTransferObject(ReadingData.TransferObject object, boolean use_raw) {
    // insert any recent data we can
    final List<GlucoseData> mTrend = object.data.trend;
    if (mTrend != null) {
        Collections.sort(mTrend);
        final long thisSensorAge = mTrend.get(mTrend.size() - 1).sensorTime;
        sensorAge = Pref.getInt("nfc_sensor_age", 0);
        if (thisSensorAge > sensorAge) {
            sensorAge = thisSensorAge;
            Pref.setInt("nfc_sensor_age", (int) sensorAge);
            Pref.setBoolean("nfc_age_problem", false);
            Log.d(TAG, "Sensor age advanced to: " + thisSensorAge);
        } else if (thisSensorAge == sensorAge) {
            Log.wtf(TAG, "Sensor age has not advanced: " + sensorAge);
            JoH.static_toast_long("Sensor clock has not advanced!");
            Pref.setBoolean("nfc_age_problem", true);
            // do not try to insert again
            return;
        } else {
            Log.wtf(TAG, "Sensor age has gone backwards!!! " + sensorAge);
            JoH.static_toast_long("Sensor age has gone backwards!!");
            sensorAge = thisSensorAge;
            Pref.setInt("nfc_sensor_age", (int) sensorAge);
            Pref.setBoolean("nfc_age_problem", true);
        }
        if (d)
            Log.d(TAG, "Oldest cmp: " + JoH.dateTimeText(oldest_cmp) + " Newest cmp: " + JoH.dateTimeText(newest_cmp));
        long shiftx = 0;
        if (mTrend.size() > 0) {
            shiftx = getTimeShift(mTrend);
            if (shiftx != 0)
                Log.d(TAG, "Lag Timeshift: " + shiftx);
            for (GlucoseData gd : mTrend) {
                if (d)
                    Log.d(TAG, "DEBUG: sensor time: " + gd.sensorTime);
                if ((timeShiftNearest > 0) && ((timeShiftNearest - gd.realDate) < segmentation_timeslice) && (timeShiftNearest - gd.realDate != 0)) {
                    if (d)
                        Log.d(TAG, "Skipping record due to closeness: " + JoH.dateTimeText(gd.realDate));
                    continue;
                }
                if (use_raw) {
                    // not quick for recent
                    createBGfromGD(gd, false);
                } else {
                    BgReading.bgReadingInsertFromInt(gd.glucoseLevel, gd.realDate, true);
                }
            }
        } else {
            Log.e(TAG, "Trend data was empty!");
        }
        // munge and insert the history data if any is missing
        final List<GlucoseData> mHistory = object.data.history;
        if ((mHistory != null) && (mHistory.size() > 1)) {
            Collections.sort(mHistory);
            // applyTimeShift(mTrend, shiftx);
            final List<Double> polyxList = new ArrayList<Double>();
            final List<Double> polyyList = new ArrayList<Double>();
            for (GlucoseData gd : mHistory) {
                if (d)
                    Log.d(TAG, "history : " + JoH.dateTimeText(gd.realDate) + " " + gd.glucose(false));
                polyxList.add((double) gd.realDate);
                if (use_raw) {
                    polyyList.add((double) gd.glucoseLevelRaw);
                    createBGfromGD(gd, true);
                } else {
                    polyyList.add((double) gd.glucoseLevel);
                    // add in the actual value
                    BgReading.bgReadingInsertFromInt(gd.glucoseLevel, gd.realDate, false);
                }
            }
            // ConstrainedSplineInterpolator splineInterp = new ConstrainedSplineInterpolator();
            final SplineInterpolator splineInterp = new SplineInterpolator();
            try {
                PolynomialSplineFunction polySplineF = splineInterp.interpolate(Forecast.PolyTrendLine.toPrimitiveFromList(polyxList), Forecast.PolyTrendLine.toPrimitiveFromList(polyyList));
                final long startTime = mHistory.get(0).realDate;
                final long endTime = mHistory.get(mHistory.size() - 1).realDate;
                for (long ptime = startTime; ptime <= endTime; ptime += 300000) {
                    if (d)
                        Log.d(TAG, "Spline: " + JoH.dateTimeText((long) ptime) + " value: " + (int) polySplineF.value(ptime));
                    if (use_raw) {
                        createBGfromGD(new GlucoseData((int) polySplineF.value(ptime), ptime), true);
                    } else {
                        BgReading.bgReadingInsertFromInt((int) polySplineF.value(ptime), ptime, false);
                    }
                }
            } catch (org.apache.commons.math3.exception.NonMonotonicSequenceException e) {
                Log.e(TAG, "NonMonotonicSequenceException: " + e);
            }
        } else {
            Log.e(TAG, "no librealarm history data");
        }
    } else {
        Log.d(TAG, "Trend data is null!");
    }
}

Example 4 with PolynomialSplineFunction

use of org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction 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 5 with PolynomialSplineFunction

use of org.apache.commons.math3.analysis.polynomials.PolynomialSplineFunction in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFit method showDoubleHistogram.

private double[] showDoubleHistogram(StoredDataStatistics[][] stats, final int index, WindowOrganiser wo, double[][] matchScores) {
    final String xLabel = filterCriteria[index].name;
    LowerLimit lower = filterCriteria[index].lower;
    UpperLimit upper = filterCriteria[index].upper;
    double[] jaccard = null;
    double[] metric = null;
    double maxJaccard = 0;
    if (index <= FILTER_PRECISION && (settings.showFilterScoreHistograms || upper.requiresJaccard || lower.requiresJaccard)) {
        // Jaccard score verses the range of the metric
        for (final double[] d : matchScores) {
            if (!Double.isFinite(d[index])) {
                System.out.printf("Error in fit data [%d]: %s%n", index, d[index]);
            }
        }
        // Do not use Double.compare(double, double) so we get exceptions in the sort for inf/nan
        Arrays.sort(matchScores, (o1, o2) -> {
            if (o1[index] < o2[index]) {
                return -1;
            }
            if (o1[index] > o2[index]) {
                return 1;
            }
            return 0;
        });
        final int scoreIndex = FILTER_PRECISION + 1;
        final int n = results.size();
        double tp = 0;
        double fp = 0;
        jaccard = new double[matchScores.length + 1];
        metric = new double[jaccard.length];
        for (int k = 0; k < matchScores.length; k++) {
            final double score = matchScores[k][scoreIndex];
            tp += score;
            fp += (1 - score);
            jaccard[k + 1] = tp / (fp + n);
            metric[k + 1] = matchScores[k][index];
        }
        metric[0] = metric[1];
        maxJaccard = MathUtils.max(jaccard);
        if (settings.showFilterScoreHistograms) {
            final String title = TITLE + " Jaccard " + xLabel;
            final Plot plot = new Plot(title, xLabel, "Jaccard");
            plot.addPoints(metric, jaccard, Plot.LINE);
            // Remove outliers
            final double[] limitsx = MathUtils.limits(metric);
            final Percentile p = new Percentile();
            final double l = p.evaluate(metric, 25);
            final double u = p.evaluate(metric, 75);
            final double iqr = 1.5 * (u - l);
            limitsx[1] = Math.min(limitsx[1], u + iqr);
            plot.setLimits(limitsx[0], limitsx[1], 0, MathUtils.max(jaccard));
            ImageJUtils.display(title, plot, wo);
        }
    }
    // [0] is all
    // [1] is matches
    // [2] is no match
    final StoredDataStatistics s1 = stats[0][index];
    final StoredDataStatistics s2 = stats[1][index];
    final StoredDataStatistics s3 = stats[2][index];
    if (s1.getN() == 0) {
        return new double[4];
    }
    final DescriptiveStatistics d = s1.getStatistics();
    double median = 0;
    Plot plot = null;
    String title = null;
    if (settings.showFilterScoreHistograms) {
        median = d.getPercentile(50);
        final String label = String.format("n = %d. Median = %s nm", s1.getN(), MathUtils.rounded(median));
        final HistogramPlot histogramPlot = new HistogramPlotBuilder(TITLE, s1, xLabel).setMinBinWidth(filterCriteria[index].minBinWidth).setRemoveOutliersOption((filterCriteria[index].restrictRange) ? 1 : 0).setPlotLabel(label).build();
        final PlotWindow plotWindow = histogramPlot.show(wo);
        if (plotWindow == null) {
            IJ.log("Failed to show the histogram: " + xLabel);
            return new double[4];
        }
        title = plotWindow.getTitle();
        // Reverse engineer the histogram settings
        plot = histogramPlot.getPlot();
        final double[] xvalues = histogramPlot.getPlotXValues();
        final int bins = xvalues.length;
        final double yMin = xvalues[0];
        final double binSize = xvalues[1] - xvalues[0];
        final double yMax = xvalues[0] + (bins - 1) * binSize;
        if (s2.getN() > 0) {
            final double[] values = s2.getValues();
            final double[][] hist = HistogramPlot.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.red);
                plot.addPoints(hist[0], hist[1], Plot.BAR);
                ImageJUtils.display(title, plot);
            }
        }
        if (s3.getN() > 0) {
            final double[] values = s3.getValues();
            final double[][] hist = HistogramPlot.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.blue);
                plot.addPoints(hist[0], hist[1], Plot.BAR);
                ImageJUtils.display(title, plot);
            }
        }
    }
    // Do cumulative histogram
    final double[][] h1 = MathUtils.cumulativeHistogram(s1.getValues(), true);
    final double[][] h2 = MathUtils.cumulativeHistogram(s2.getValues(), true);
    final double[][] h3 = MathUtils.cumulativeHistogram(s3.getValues(), true);
    if (settings.showFilterScoreHistograms) {
        title = TITLE + " Cumul " + xLabel;
        plot = new Plot(title, xLabel, "Frequency");
        // Find limits
        double[] xlimit = MathUtils.limits(h1[0]);
        xlimit = MathUtils.limits(xlimit, h2[0]);
        xlimit = MathUtils.limits(xlimit, h3[0]);
        // Restrict using the inter-quartile range
        if (filterCriteria[index].restrictRange) {
            final double q1 = d.getPercentile(25);
            final double q2 = d.getPercentile(75);
            final double iqr = (q2 - q1) * 2.5;
            xlimit[0] = MathUtils.max(xlimit[0], median - iqr);
            xlimit[1] = MathUtils.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 = (settings.showFilterScoreHistograms && filterCriteria[index].requireLabel);
    if (requireLabel || upper.requiresDeltaHistogram() || lower.requiresDeltaHistogram()) {
        if (s2.getN() != 0 && s3.getN() != 0) {
            final LinearInterpolator li = new LinearInterpolator();
            final PolynomialSplineFunction f1 = li.interpolate(h2[0], h2[1]);
            final PolynomialSplineFunction f2 = li.interpolate(h3[0], h3[1]);
            for (final double x : h1[0]) {
                if (x < h2[0][0] || x < h3[0][0]) {
                    continue;
                }
                try {
                    final double v1 = f1.value(x);
                    final double v2 = f2.value(x);
                    final double diff = v2 - v1;
                    if (diff > 0) {
                        if (max1 < diff) {
                            max1 = diff;
                            maxx1 = x;
                        }
                    } else if (max2 > diff) {
                        max2 = diff;
                        maxx2 = x;
                    }
                } catch (final OutOfRangeException ex) {
                    // Because we reached the end
                    break;
                }
            }
        }
    }
    if (plot != null) {
        // We use bins=1 on charts where we do not need a label
        if (requireLabel) {
            final String label = String.format("Max+ %s @ %s, Max- %s @ %s", MathUtils.rounded(max1), MathUtils.rounded(maxx1), MathUtils.rounded(max2), MathUtils.rounded(maxx2));
            plot.setColor(Color.black);
            plot.addLabel(0, 0, label);
        }
        ImageJUtils.display(title, plot, wo);
    }
    // Now compute the bounds using the desired limit
    double lowerBound;
    double upperBound;
    switch(lower) {
        case MAX_NEGATIVE_CUMUL_DELTA:
            // Switch to percentiles if we have no delta histogram
            if (maxx2 < 0) {
                lowerBound = maxx2;
                break;
            }
        // fall-through
        case ONE_PERCENT:
            lowerBound = getPercentile(h2, 0.01);
            break;
        case MIN:
            lowerBound = getPercentile(h2, 0.0);
            break;
        case ZERO:
            lowerBound = 0;
            break;
        case HALF_MAX_JACCARD_VALUE:
            lowerBound = getXValue(metric, jaccard, maxJaccard * 0.5);
            break;
        default:
            throw new IllegalStateException("Missing lower limit method");
    }
    switch(upper) {
        case MAX_POSITIVE_CUMUL_DELTA:
            // Switch to percentiles if we have no delta histogram
            if (maxx1 > 0) {
                upperBound = maxx1;
                break;
            }
        // fall-through
        case NINETY_NINE_PERCENT:
            upperBound = getPercentile(h2, 0.99);
            break;
        case NINETY_NINE_NINE_PERCENT:
            upperBound = getPercentile(h2, 0.999);
            break;
        case ZERO:
            upperBound = 0;
            break;
        case MAX_JACCARD2:
            upperBound = getXValue(metric, jaccard, maxJaccard) * 2;
            // System.out.printf("MaxJ = %.4f @ %.3f\n", maxJ, u / 2);
            break;
        default:
            throw new IllegalStateException("Missing upper limit method");
    }
    final double min = getPercentile(h1, 0);
    final double max = getPercentile(h1, 1);
    return new double[] { lowerBound, upperBound, min, max };
}