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@ -29,11 +29,11 @@ import hep.dataforge.meta.Meta;
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import hep.dataforge.plots.PlotsPlugin;
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import hep.dataforge.plots.XYPlotFrame;
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import hep.dataforge.plots.data.PlottableData;
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import hep.dataforge.plots.data.PlottableFunction;
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import hep.dataforge.plots.data.PlottableXYFunction;
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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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import java.util.stream.StreamSupport;
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/**
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*
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@ -68,7 +68,15 @@ public class PlotFitResultAction extends OneToOneAction<FitState, FitState> {
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XYPlotFrame frame = (XYPlotFrame) PlotsPlugin
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.buildFrom(context).buildPlotFrame(getName(), name,
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metaData.getNode("plot", Meta.empty()));
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frame.add(new PlottableFunction("fit", function, data, adapter));//FIXME replace by helper
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PlottableXYFunction fit = new PlottableXYFunction("fit");
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fit.setDensity(100, false);
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fit.setSmoothing(true);
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// ensuring all data points are calculated explicitly
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StreamSupport.stream(data.spliterator(), false)
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.map(dp -> adapter.getX(dp).doubleValue()).sorted().forEach(d -> fit.calculateIn(d));
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frame.add(fit);
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frame.add(PlottableData.plot("data", adapter, data));
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@ -55,6 +55,7 @@ import inr.numass.models.TransmissionInterpolator;
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import inr.numass.models.VariableLossSpectrum;
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import org.apache.commons.math3.analysis.BivariateFunction;
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import org.apache.commons.math3.analysis.UnivariateFunction;
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import org.apache.commons.math3.util.FastMath;
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/**
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*
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@ -218,8 +219,12 @@ public class NumassPlugin extends BasicPlugin {
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}
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//Adding trapping energy dependence
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//Intercept = 4.95745, B1 = -0.36879, B2 = 0.00827
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sp.setTrappingFunction((Ei,Ef)->LossCalculator.getTrapFunction().value(Ei, Ef)*(4.95745-0.36879*Ei+0.00827*Ei*Ei));
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context.getReport().report("Using folowing trapping energy dependecy^ {}", "4.95745-0.36879*Ei+0.00827*Ei*Ei");
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//sp.setTrappingFunction((Ei,Ef)->LossCalculator.getTrapFunction().value(Ei, Ef)*(4.95745-0.36879*Ei+0.00827*Ei*Ei));
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sp.setTrappingFunction((Ei, Ef) -> {
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return 4.1e-5 * FastMath.exp(-(Ei - Ef) / 600d) + 7.2e-3 * (0.05349 - 4.36375E-6 * (Ei) + 1.09875E-10 * Ei * Ei);
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});
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context.getReport().report("Using folowing trapping formula: {}",
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"4e-5 * FastMath.exp(-(Ei - Ef) / 550d) + 7.2e-3 * (0.05349 - 4.36375E-6 * (Ei) + 1.09875E-10 * Ei**2)");
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NBkgSpectrum spectrum = new NBkgSpectrum(sp);
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return new XYModel(spectrum, getAdapter(an));
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@ -34,7 +34,7 @@ import hep.dataforge.meta.MetaBuilder;
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import hep.dataforge.plots.PlotsPlugin;
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import hep.dataforge.plots.XYPlotFrame;
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import hep.dataforge.plots.data.PlottableData;
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import hep.dataforge.plots.data.PlottableFunction;
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import hep.dataforge.plots.data.PlottableXYFunction;
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import hep.dataforge.simulation.GaussianParameterGenerator;
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import hep.dataforge.tables.ListTable;
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import hep.dataforge.tables.MapPoint;
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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", (x) -> scatterFunction.value(x), 0, 100, 1000));
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frame.add(PlottableXYFunction.plotFunction("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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@ -19,7 +19,7 @@ import hep.dataforge.functions.FunctionCaching;
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import hep.dataforge.maths.integration.GaussRuleIntegrator;
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import hep.dataforge.maths.integration.UnivariateIntegrator;
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import hep.dataforge.plots.XYPlotFrame;
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import hep.dataforge.plots.data.PlottableFunction;
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import hep.dataforge.plots.data.PlottableXYFunction;
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import hep.dataforge.values.NamedValueSet;
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import static java.lang.Math.exp;
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import java.util.ArrayList;
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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.getDouble("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", x->single.value(x), 0, 100, 1000));
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frame.add(PlottableXYFunction.plotFunction("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", x->scatter.value(x), 0, 100, 1000));
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frame.add(PlottableXYFunction.plotFunction(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", x->total.value(x), 0, 100, 1000));
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frame.add(PlottableXYFunction.plotFunction("Total loss", x->total.value(x), 0, 100, 1000));
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} else {
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frame.add(new PlottableFunction("Differential crosssection", x->scatterFunction.value(x), 0, 100, 2000));
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frame.add(PlottableXYFunction.plotFunction("Differential crosssection", x->scatterFunction.value(x), 0, 100, 2000));
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}
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}
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@ -88,6 +88,8 @@ public class ModularSpectrum extends AbstractParametricFunction {
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public void setTrappingFunction(BivariateFunction trappingFunction) {
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this.trappingFunction = trappingFunction;
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LoggerFactory.getLogger(getClass()).info("Recalculating modular spectrum cache");
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setupCache();
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}
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@ -17,7 +17,7 @@ package inr.numass.models;
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import hep.dataforge.maths.integration.GaussRuleIntegrator;
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import hep.dataforge.plots.PlotFrame;
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import hep.dataforge.plots.data.PlottableFunction;
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import hep.dataforge.plots.data.PlottableXYFunction;
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import hep.dataforge.plots.fx.FXPlotUtils;
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import org.apache.commons.math3.analysis.UnivariateFunction;
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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", 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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frame.add(PlottableXYFunction.plotFunction("old", x->oldFunction.value(x), 0, 30, 300));
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frame.add(PlottableXYFunction.plotFunction("new", x->newFunction.value(x), 0, 30, 300));
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}
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public static UnivariateFunction getSingleScatterFunction(
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@ -17,7 +17,7 @@ package inr.numass.models;
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import hep.dataforge.context.GlobalContext;
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import hep.dataforge.plots.data.PlottableData;
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import hep.dataforge.plots.data.PlottableFunction;
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import hep.dataforge.plots.data.PlottableXYFunction;
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import hep.dataforge.plots.fx.FXPlotUtils;
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import hep.dataforge.plots.jfreechart.JFreeChartFrame;
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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", x->interpolator.value(x), interpolator.getXmin(), interpolator.getXmax(), 2000));
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frame.add(PlottableXYFunction.plotFunction("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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