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