numass-framework/numass-main/src/main/java/inr/numass/models/EmpiricalLossSpectrum.java

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2015-12-18 16:20:47 +03:00
/*
* Copyright 2015 Alexander Nozik.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package inr.numass.models;
import hep.dataforge.exceptions.NamingException;
import hep.dataforge.exceptions.NotDefinedException;
import hep.dataforge.functions.AbstractParametricFunction;
import hep.dataforge.maths.NamedDoubleSet;
import hep.dataforge.maths.integration.GaussRuleIntegrator;
import hep.dataforge.maths.integration.UnivariateIntegrator;
import java.util.List;
import org.apache.commons.math3.analysis.BivariateFunction;
import org.apache.commons.math3.analysis.UnivariateFunction;
/**
*
* @author Darksnake
*/
public class EmpiricalLossSpectrum extends AbstractParametricFunction {
public static String[] names = {"X", "shift"};
private final UnivariateFunction transmission;
private final double eMax;
private final UnivariateIntegrator integrator;
public EmpiricalLossSpectrum(UnivariateFunction transmission, double eMax) throws NamingException {
super(names);
integrator = new GaussRuleIntegrator(300);
this.transmission = transmission;
this.eMax = eMax;
}
@Override
public double derivValue(String parName, double x, NamedDoubleSet set) {
throw new NotDefinedException();
}
@Override
public double value(double U, NamedDoubleSet set) {
if (U >= eMax) {
return 0;
}
double X = set.getValue("X");
final double shift = set.getValue("shift");
//FIXME тут толщины усреднены по длине источника, а надо брать чистого Пуассона
final List<Double> probs = LossCalculator.instance().getGunLossProbabilities(X);
final double noLossProb = probs.get(0);
final BivariateFunction lossFunction = (Ei, Ef) -> {
return LossCalculator.instance().getLossValue(probs, Ei, Ef);
};
UnivariateFunction integrand = (double x) -> transmission.value(x) * lossFunction.value(x, U - shift);
return noLossProb * transmission.value(U - shift) + integrator.integrate(integrand, U, eMax);
}
@Override
public boolean providesDeriv(String name) {
return false;
}
}