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Alexander Nozik 2017-08-02 14:54:19 +03:00
parent a3120d72e7
commit ae18bb66ec
3 changed files with 49 additions and 36 deletions
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21
numass-main/src/main/groovy/inr/numass/scripts/LossTailCalculation.groovy
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@ -6,25 +6,26 @@
package inr.numass.scripts
import hep.dataforge.maths.integration.GaussRuleIntegrator;
import hep.dataforge.maths.integration.UnivariateIntegrator;
import inr.numass.models.LossCalculator;
import hep.dataforge.maths.integration.GaussRuleIntegrator
import hep.dataforge.maths.integration.UnivariateIntegrator
import inr.numass.models.LossCalculator
import org.apache.commons.math3.analysis.UnivariateFunction
UnivariateIntegrator integrator = new GaussRuleIntegrator(400);
def exPos = 12.878;
def ionPos = 13.86;
def exW = 1.32;
def ionW = 12.47;
def exIonRatio = 3.96;
def cutoff = 25d
def exPos = 12.587;
def ionPos = 11.11;
def exW = 1.20;
def ionW = 11.02;
def exIonRatio = 2.43;
def cutoff = 20d
UnivariateFunction loss = LossCalculator.getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
println integrator.integrate(loss,0,600);
println integrator.integrate(loss,0,cutoff);
println integrator.integrate(loss,0, cutoff);
println integrator.integrate(loss,cutoff,600d);
println (integrator.integrate(loss,0,cutoff) + integrator.integrate(loss,cutoff,3000d));

52
numass-main/src/main/groovy/inr/numass/scripts/PrintLossFunctions.groovy
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@ -8,39 +8,53 @@ package inr.numass.scripts
import inr.numass.models.LossCalculator
LossCalculator loss = new LossCalculator();
LossCalculator loss = LossCalculator.instance()
def X = 0.6
def X = 0.34
def lossProbs = loss.getGunLossProbabilities(X);
printf("%8s\t%8s\t%8s\t%8s\t%n",
"eps",
"p1",
"p2",
"p3"
"eps",
"p1",
"p2",
"p3"
)
def singleScatter = loss.getSingleScatterFunction();
/*
'exPos' = 12.587 ± 0.049
'ionPos' = 11.11 ± 0.50
'exW' = 1.20 ± 0.12
'ionW' = 11.02 ± 0.68
'exIonRatio' = 2.43 ± 0.42
*/
for(double d = 0; d < 30; d += 0.3){
def singleScatter = loss.getSingleScatterFunction(
12.587,
11.11,
1.20,
11.02,
2.43
);
for (double d = 0; d < 30; d += 0.3) {
double ei = 18500;
double ef = ei-d;
double ef = ei - d;
printf("%8f\t%8f\t%8f\t%8f\t%n",
d,
lossProbs[1]*loss.getLossValue(1,ei,ef),
lossProbs[2]*loss.getLossValue(2,ei,ef),
lossProbs[3]*loss.getLossValue(3,ei,ef)
d,
lossProbs[1] * singleScatter.value(ei - ef),
lossProbs[2] * loss.getLossValue(2, ei, ef),
lossProbs[3] * loss.getLossValue(3, ei, ef)
)
}
for(double d = 30; d < 100; d += 1){
for (double d = 30; d < 100; d += 1) {
double ei = 18500;
double ef = ei-d;
double ef = ei - d;
printf("%8f\t%8f\t%8f\t%8f\t%n",
d,
lossProbs[1]*loss.getLossValue(1,ei,ef),
lossProbs[2]*loss.getLossValue(2,ei,ef),
lossProbs[3]*loss.getLossValue(3,ei,ef)
d,
lossProbs[1] * singleScatter.value(ei - ef),
lossProbs[2] * loss.getLossValue(2, ei, ef),
lossProbs[3] * loss.getLossValue(3, ei, ef)
)
}

12
numass-main/src/main/java/inr/numass/models/LossCalculator.java
View File

@ -178,12 +178,12 @@ public class LossCalculator {
final LossCalculator loss = LossCalculator.instance;
final List<Double> probs = loss.getGunLossProbabilities(set.getDouble("X"));
UnivariateFunction single = (double e) -> probs.get(1) * scatterFunction.value(e);
frame.add(PlottableXYFunction.plotFunction("Single scattering", x -> single.value(x), 0, 100, 1000));
frame.add(PlottableXYFunction.plotFunction("Single scattering", single::value, 0, 100, 1000));
for (int i = 2; i < probs.size(); i++) {
final int j = i;
UnivariateFunction scatter = (double e) -> probs.get(j) * loss.getLossValue(j, e, 0d);
frame.add(PlottableXYFunction.plotFunction(j + " scattering", x -> scatter.value(x), 0, 100, 1000));
frame.add(PlottableXYFunction.plotFunction(j + " scattering", scatter::value, 0, 100, 1000));
}
UnivariateFunction total = (eps) -> {
@ -197,11 +197,11 @@ public class LossCalculator {
return sum;
};
frame.add(PlottableXYFunction.plotFunction("Total loss", x -> total.value(x), 0, 100, 1000));
frame.add(PlottableXYFunction.plotFunction("Total loss", total::value, 0, 100, 1000));
} else {
frame.add(PlottableXYFunction.plotFunction("Differential crosssection", x -> scatterFunction.value(x), 0, 100, 2000));
frame.add(PlottableXYFunction.plotFunction("Differential crosssection", scatterFunction::value, 0, 100, 2000));
}
}
@ -261,9 +261,7 @@ public class LossCalculator {
public BivariateFunction getLossFunction(int order) {
assert order > 0;
return (double Ei, double Ef) -> {
return getLossValue(order, Ei, Ef);
};
return (double Ei, double Ef) -> getLossValue(order, Ei, Ef);
}
public List<Double> getLossProbDerivs(double X) {