use of org.apache.commons.math3.analysis.interpolation.LinearInterpolator 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 };
}
use of org.apache.commons.math3.analysis.interpolation.LinearInterpolator in project GDSC-SMLM by aherbert.
the class DriftCalculator method interpolate.
private void interpolate(double[] dx, double[] dy, double[] originalDriftTimePoints) {
// Interpolator can only create missing values within the range provided by the input values.
// The two ends have to be extrapolated.
// TODO: Perform extrapolation. Currently the end values are used.
// Find end points
int startT = 0;
while (originalDriftTimePoints[startT] == 0) startT++;
int endT = originalDriftTimePoints.length - 1;
while (originalDriftTimePoints[endT] == 0) endT--;
// Extrapolate using a constant value
for (int t = startT; t-- > 0; ) {
dx[t] = dx[startT];
dy[t] = dy[startT];
}
for (int t = endT; ++t < dx.length; ) {
dx[t] = dx[endT];
dy[t] = dy[endT];
}
double[][] values = extractValues(originalDriftTimePoints, startT, endT, dx, dy);
PolynomialSplineFunction fx, fy;
if (values[0].length < 3) {
fx = new LinearInterpolator().interpolate(values[0], values[1]);
fy = new LinearInterpolator().interpolate(values[0], values[2]);
} else {
fx = new SplineInterpolator().interpolate(values[0], values[1]);
fy = new SplineInterpolator().interpolate(values[0], values[2]);
}
for (int t = startT; t <= endT; t++) {
if (originalDriftTimePoints[t] == 0) {
dx[t] = fx.value(t);
dy[t] = fy.value(t);
}
}
this.interpolationStart = startT;
this.interpolationEnd = endT;
}
use of org.apache.commons.math3.analysis.interpolation.LinearInterpolator 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 };
}
use of org.apache.commons.math3.analysis.interpolation.LinearInterpolator in project GDSC-SMLM by aherbert.
the class BenchmarkFilterAnalysis method depthAnalysis.
/**
* Depth analysis.
*
* @param allAssignments
* The assignments generated from running the filter (or null)
* @param filter
* the filter
* @return the assignments
*/
private ArrayList<FractionalAssignment[]> depthAnalysis(ArrayList<FractionalAssignment[]> allAssignments, DirectFilter filter) {
if (!depthRecallAnalysis || simulationParameters.fixedDepth)
return null;
// Build a histogram of the number of spots at different depths
final double[] depths = depthStats.getValues();
double[] limits = Maths.limits(depths);
//final int bins = Math.max(10, nActual / 100);
//final int bins = Utils.getBinsSturges(depths.length);
final int bins = Utils.getBinsSqrt(depths.length);
double[][] h1 = Utils.calcHistogram(depths, limits[0], limits[1], bins);
double[][] h2 = Utils.calcHistogram(depthFitStats.getValues(), limits[0], limits[1], bins);
// manually to get the results that pass.
if (allAssignments == null)
allAssignments = getAssignments(filter);
double[] depths2 = new double[results.size()];
int count = 0;
for (FractionalAssignment[] assignments : allAssignments) {
if (assignments == null)
continue;
for (int i = 0; i < assignments.length; i++) {
final CustomFractionalAssignment c = (CustomFractionalAssignment) assignments[i];
depths2[count++] = c.peak.error;
}
}
depths2 = Arrays.copyOf(depths2, count);
// Build a histogram using the same limits
double[][] h3 = Utils.calcHistogram(depths2, limits[0], limits[1], bins);
// Convert pixel depth to nm
for (int i = 0; i < h1[0].length; i++) h1[0][i] *= simulationParameters.a;
limits[0] *= simulationParameters.a;
limits[1] *= simulationParameters.a;
// Produce a histogram of the number of spots at each depth
String title1 = TITLE + " Depth Histogram";
Plot2 plot1 = new Plot2(title1, "Depth (nm)", "Frequency");
plot1.setLimits(limits[0], limits[1], 0, Maths.max(h1[1]));
plot1.setColor(Color.black);
plot1.addPoints(h1[0], h1[1], Plot2.BAR);
plot1.addLabel(0, 0, "Black = Spots; Blue = Fitted; Red = Filtered");
plot1.setColor(Color.blue);
plot1.addPoints(h1[0], h2[1], Plot2.BAR);
plot1.setColor(Color.red);
plot1.addPoints(h1[0], h3[1], Plot2.BAR);
plot1.setColor(Color.magenta);
PlotWindow pw1 = Utils.display(title1, plot1);
if (Utils.isNewWindow())
wo.add(pw1);
// Interpolate
final double halfBinWidth = (h1[0][1] - h1[0][0]) * 0.5;
// Remove final value of the histogram as this is at the upper limit of the range (i.e. count zero)
h1[0] = Arrays.copyOf(h1[0], h1[0].length - 1);
h1[1] = Arrays.copyOf(h1[1], h1[0].length);
h2[1] = Arrays.copyOf(h2[1], h1[0].length);
h3[1] = Arrays.copyOf(h3[1], h1[0].length);
// TODO : Fix the smoothing since LOESS sometimes does not work.
// Perhaps allow configuration of the number of histogram bins and the smoothing bandwidth.
// Use minimum of 3 points for smoothing
// Ensure we use at least x% of data
double bandwidth = Math.max(3.0 / h1[0].length, 0.15);
LoessInterpolator loess = new LoessInterpolator(bandwidth, 1);
PolynomialSplineFunction spline1 = loess.interpolate(h1[0], h1[1]);
PolynomialSplineFunction spline2 = loess.interpolate(h1[0], h2[1]);
PolynomialSplineFunction spline3 = loess.interpolate(h1[0], h3[1]);
// Use a second interpolator in case the LOESS fails
LinearInterpolator lin = new LinearInterpolator();
PolynomialSplineFunction spline1b = lin.interpolate(h1[0], h1[1]);
PolynomialSplineFunction spline2b = lin.interpolate(h1[0], h2[1]);
PolynomialSplineFunction spline3b = lin.interpolate(h1[0], h3[1]);
// Increase the number of points to show a smooth curve
double[] points = new double[bins * 5];
limits = Maths.limits(h1[0]);
final double interval = (limits[1] - limits[0]) / (points.length - 1);
double[] v = new double[points.length];
double[] v2 = new double[points.length];
double[] v3 = new double[points.length];
for (int i = 0; i < points.length - 1; i++) {
points[i] = limits[0] + i * interval;
v[i] = getSplineValue(spline1, spline1b, points[i]);
v2[i] = getSplineValue(spline2, spline2b, points[i]);
v3[i] = getSplineValue(spline3, spline3b, points[i]);
points[i] += halfBinWidth;
}
// Final point on the limit of the spline range
int ii = points.length - 1;
v[ii] = getSplineValue(spline1, spline1b, limits[1]);
v2[ii] = getSplineValue(spline2, spline2b, limits[1]);
v3[ii] = getSplineValue(spline3, spline3b, limits[1]);
points[ii] = limits[1] + halfBinWidth;
// Calculate recall
for (int i = 0; i < v.length; i++) {
v2[i] = v2[i] / v[i];
v3[i] = v3[i] / v[i];
}
final double halfSummaryDepth = summaryDepth * 0.5;
String title2 = TITLE + " Depth Histogram (normalised)";
Plot2 plot2 = new Plot2(title2, "Depth (nm)", "Recall");
plot2.setLimits(limits[0] + halfBinWidth, limits[1] + halfBinWidth, 0, Maths.min(1, Maths.max(v2)));
plot2.setColor(Color.black);
plot2.addLabel(0, 0, "Blue = Fitted; Red = Filtered");
plot2.setColor(Color.blue);
plot2.addPoints(points, v2, Plot2.LINE);
plot2.setColor(Color.red);
plot2.addPoints(points, v3, Plot2.LINE);
plot2.setColor(Color.magenta);
if (-halfSummaryDepth - halfBinWidth >= limits[0]) {
plot2.drawLine(-halfSummaryDepth, 0, -halfSummaryDepth, getSplineValue(spline3, spline3b, -halfSummaryDepth - halfBinWidth) / getSplineValue(spline1, spline1b, -halfSummaryDepth - halfBinWidth));
}
if (halfSummaryDepth - halfBinWidth <= limits[1]) {
plot2.drawLine(halfSummaryDepth, 0, halfSummaryDepth, getSplineValue(spline3, spline3b, halfSummaryDepth - halfBinWidth) / getSplineValue(spline1, spline1b, halfSummaryDepth - halfBinWidth));
}
PlotWindow pw2 = Utils.display(title2, plot2);
if (Utils.isNewWindow())
wo.add(pw2);
return allAssignments;
}
use of org.apache.commons.math3.analysis.interpolation.LinearInterpolator in project mafscaling by vimsh.
the class BilinearInterpolator method interpolate.
/**
* Method returns an interpolated/extrapolated value, based on
* @param x, array of x values
* @param y, array of y values
* @param xi, is x value you want to interpolate at
* @param type, interpolation method type
* @return interpolated value
* @throws Exception
*/
public static double interpolate(double[] x, double[] y, double xi, InterpolatorType type) throws Exception {
UnivariateInterpolator interpolator = null;
switch(type) {
case AkimaCubicSpline:
interpolator = new AkimaSplineInterpolator();
break;
case Linear:
interpolator = new LinearInterpolator();
break;
case Regression:
interpolator = new LoessInterpolator();
break;
case CubicSpline:
interpolator = new SplineInterpolator();
break;
default:
throw new Exception("Invalid interpolator type for this function");
}
UnivariateFunction function = interpolator.interpolate(x, y);
PolynomialFunction[] polynomials = ((PolynomialSplineFunction) function).getPolynomials();
if (xi > x[x.length - 1])
return polynomials[polynomials.length - 1].value(xi - x[x.length - 2]);
if (xi < x[0])
return polynomials[0].value(xi - x[0]);
return function.value(xi);
}
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