site update
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@ -6,25 +6,26 @@
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package inr.numass.scripts
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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 inr.numass.models.LossCalculator;
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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 inr.numass.models.LossCalculator
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import org.apache.commons.math3.analysis.UnivariateFunction
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UnivariateIntegrator integrator = new GaussRuleIntegrator(400);
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def exPos = 12.878;
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def ionPos = 13.86;
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def exW = 1.32;
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def ionW = 12.47;
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def exIonRatio = 3.96;
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def cutoff = 25d
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def exPos = 12.587;
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def ionPos = 11.11;
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def exW = 1.20;
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def ionW = 11.02;
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def exIonRatio = 2.43;
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def cutoff = 20d
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UnivariateFunction loss = LossCalculator.getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
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println integrator.integrate(loss,0,600);
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println integrator.integrate(loss,0,cutoff);
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println integrator.integrate(loss,0, cutoff);
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println integrator.integrate(loss,cutoff,600d);
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println (integrator.integrate(loss,0,cutoff) + integrator.integrate(loss,cutoff,3000d));
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@ -8,39 +8,53 @@ package inr.numass.scripts
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import inr.numass.models.LossCalculator
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LossCalculator loss = new LossCalculator();
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LossCalculator loss = LossCalculator.instance()
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def X = 0.6
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def X = 0.34
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def lossProbs = loss.getGunLossProbabilities(X);
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printf("%8s\t%8s\t%8s\t%8s\t%n",
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"eps",
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"p1",
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"p2",
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"p3"
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"eps",
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"p1",
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"p2",
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"p3"
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)
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def singleScatter = loss.getSingleScatterFunction();
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/*
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'exPos' = 12.587 ± 0.049
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'ionPos' = 11.11 ± 0.50
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'exW' = 1.20 ± 0.12
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'ionW' = 11.02 ± 0.68
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'exIonRatio' = 2.43 ± 0.42
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*/
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for(double d = 0; d < 30; d += 0.3){
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def singleScatter = loss.getSingleScatterFunction(
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12.587,
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11.11,
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1.20,
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11.02,
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2.43
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);
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for (double d = 0; d < 30; d += 0.3) {
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double ei = 18500;
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double ef = ei-d;
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double ef = ei - d;
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printf("%8f\t%8f\t%8f\t%8f\t%n",
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d,
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lossProbs[1]*loss.getLossValue(1,ei,ef),
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lossProbs[2]*loss.getLossValue(2,ei,ef),
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lossProbs[3]*loss.getLossValue(3,ei,ef)
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d,
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lossProbs[1] * singleScatter.value(ei - ef),
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lossProbs[2] * loss.getLossValue(2, ei, ef),
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lossProbs[3] * loss.getLossValue(3, ei, ef)
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)
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}
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for(double d = 30; d < 100; d += 1){
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for (double d = 30; d < 100; d += 1) {
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double ei = 18500;
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double ef = ei-d;
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double ef = ei - d;
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printf("%8f\t%8f\t%8f\t%8f\t%n",
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d,
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lossProbs[1]*loss.getLossValue(1,ei,ef),
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lossProbs[2]*loss.getLossValue(2,ei,ef),
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lossProbs[3]*loss.getLossValue(3,ei,ef)
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d,
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lossProbs[1] * singleScatter.value(ei - ef),
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lossProbs[2] * loss.getLossValue(2, ei, ef),
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lossProbs[3] * loss.getLossValue(3, ei, ef)
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)
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}
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@ -178,12 +178,12 @@ 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(PlottableXYFunction.plotFunction("Single scattering", x -> single.value(x), 0, 100, 1000));
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frame.add(PlottableXYFunction.plotFunction("Single scattering", single::value, 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(PlottableXYFunction.plotFunction(j + " scattering", x -> scatter.value(x), 0, 100, 1000));
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frame.add(PlottableXYFunction.plotFunction(j + " scattering", scatter::value, 0, 100, 1000));
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}
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UnivariateFunction total = (eps) -> {
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@ -197,11 +197,11 @@ public class LossCalculator {
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return sum;
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};
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frame.add(PlottableXYFunction.plotFunction("Total loss", x -> total.value(x), 0, 100, 1000));
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frame.add(PlottableXYFunction.plotFunction("Total loss", total::value, 0, 100, 1000));
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} else {
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frame.add(PlottableXYFunction.plotFunction("Differential crosssection", x -> scatterFunction.value(x), 0, 100, 2000));
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frame.add(PlottableXYFunction.plotFunction("Differential crosssection", scatterFunction::value, 0, 100, 2000));
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}
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}
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@ -261,9 +261,7 @@ public class LossCalculator {
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public BivariateFunction getLossFunction(int order) {
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assert order > 0;
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return (double Ei, double Ef) -> {
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return getLossValue(order, Ei, Ef);
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};
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return (double Ei, double Ef) -> getLossValue(order, Ei, Ef);
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}
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public List<Double> getLossProbDerivs(double X) {
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