New plot serialization. Minor dependency optimization.
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@ -32,6 +32,7 @@ import hep.dataforge.plots.data.PlottableData;
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import hep.dataforge.plots.data.PlottableFunction;
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import hep.dataforge.tables.PointSource;
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import hep.dataforge.tables.XYAdapter;
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import java.util.function.Function;
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import org.apache.commons.math3.analysis.UnivariateFunction;
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/**
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@ -62,7 +63,7 @@ public class PlotFitResultAction extends OneToOneAction<FitState, FitState> {
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throw new ContentException("No adapter defined for data interpretation");
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}
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UnivariateFunction function = (double x) -> model.getSpectrum().value(x, input.getParameters());
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Function<Double, Double> function = (x) -> model.getSpectrum().value(x, input.getParameters());
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XYPlotFrame frame = (XYPlotFrame) PlotsPlugin
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.buildFrom(context).buildPlotFrame(getName(), name,
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@ -90,7 +90,7 @@ public class ShowLossSpectrumAction extends OneToOneAction<FitState, FitState> {
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case "scatter-empiric-experimental":
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scatterFunction = new ExperimentalVariableLossSpectrum.Loss(0.3).total(pars);
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frame.add(new PlottableFunction("Cross-section", scatterFunction, 0, 100, 1000));
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frame.add(new PlottableFunction("Cross-section", (x) -> scatterFunction.value(x), 0, 100, 1000));
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break;
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default:
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throw new RuntimeException("Can work only with variable loss spectra");
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@ -168,12 +168,12 @@ public class LossCalculator {
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final LossCalculator loss = LossCalculator.instance;
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final List<Double> probs = loss.getGunLossProbabilities(set.getValue("X"));
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UnivariateFunction single = (double e) -> probs.get(1) * scatterFunction.value(e);
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frame.add(new PlottableFunction("Single scattering", single, 0, 100, 1000));
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frame.add(new PlottableFunction("Single scattering", x->single.value(x), 0, 100, 1000));
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for (int i = 2; i < probs.size(); i++) {
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final int j = i;
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UnivariateFunction scatter = (double e) -> probs.get(j) * loss.getLossValue(j, e, 0d);
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frame.add(new PlottableFunction(j + " scattering", scatter, 0, 100, 1000));
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frame.add(new PlottableFunction(j + " scattering", x->scatter.value(x), 0, 100, 1000));
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}
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UnivariateFunction total = (eps) -> {
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@ -187,11 +187,11 @@ public class LossCalculator {
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return sum;
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};
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frame.add(new PlottableFunction("Total loss", total, 0, 100, 1000));
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frame.add(new PlottableFunction("Total loss", x->total.value(x), 0, 100, 1000));
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} else {
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frame.add(new PlottableFunction("Differential crosssection", scatterFunction, 0, 100, 2000));
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frame.add(new PlottableFunction("Differential crosssection", x->scatterFunction.value(x), 0, 100, 2000));
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}
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}
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@ -40,8 +40,8 @@ public class TestNeLossParametrisation {
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System.out.println(norm);
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frame.add(new PlottableFunction("old", oldFunction, 0, 30, 300));
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frame.add(new PlottableFunction("new", newFunction, 0, 30, 300));
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frame.add(new PlottableFunction("old", x->oldFunction.value(x), 0, 30, 300));
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frame.add(new PlottableFunction("new", x->newFunction.value(x), 0, 30, 300));
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}
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public static UnivariateFunction getSingleScatterFunction(
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@ -33,7 +33,7 @@ public class TransmissionInterpolatorTest {
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TransmissionInterpolator interpolator = TransmissionInterpolator.fromFile(GlobalContext.instance(),
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"d:\\sterile-new\\loss2014-11\\.dataforge\\merge\\empty_sum.out", "Uset", "CR", 15, 0.8, 19002d);
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frame.add(PlottableData.plot("data", interpolator.getX(), interpolator.getY()));
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frame.add(new PlottableFunction("interpolated", interpolator, interpolator.getXmin(), interpolator.getXmax(), 2000));
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frame.add(new PlottableFunction("interpolated", x->interpolator.value(x), interpolator.getXmin(), interpolator.getXmax(), 2000));
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// PrintFunction.printFuntionSimple(new PrintWriter(System.out), interpolator, interpolator.getXmin(), interpolator.getXmax(), 500);
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}
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