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

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/*
* 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;
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import hep.dataforge.stat.parametric.AbstractParametricFunction;
import hep.dataforge.stat.parametric.ParametricFunction;
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import hep.dataforge.names.NamedUtils;
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import hep.dataforge.values.NamedValueSet;
import hep.dataforge.values.ValueProvider;
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import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.analysis.BivariateFunction;
import org.slf4j.LoggerFactory;
/**
* Modular spectrum for any source spectrum with separate calculation for
* different transmission components
*
* @author Darksnake
*/
public class ModularSpectrum extends AbstractParametricFunction {
private static final String[] list = {"X", "trap"};
private LossCalculator calculator;
List<NamedSpectrumCaching> cacheList;
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NamedSpectrumCaching trappingCache;
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BivariateFunction resolution;
RangedNamedSetSpectrum sourceSpectrum;
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BivariateFunction trappingFunction;
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boolean caching = false;
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double cacheMin;
double cacheMax;
/**
*
* @param source
* @param resolution
* @param cacheMin - нижняя граница кэширования. Должна быть с небольшим
* запасом по отношению к данным
* @param cacheMax - верхняя граница кэширования.
*/
public ModularSpectrum(RangedNamedSetSpectrum source, BivariateFunction resolution, double cacheMin, double cacheMax) {
super(NamedUtils.combineNamesWithEquals(list, source.namesAsArray()));
if (cacheMin >= cacheMax) {
throw new IllegalArgumentException();
}
this.cacheMin = cacheMin;
this.cacheMax = cacheMax;
this.resolution = resolution;
this.calculator = LossCalculator.instance();
this.sourceSpectrum = source;
setupCache();
}
public ModularSpectrum(RangedNamedSetSpectrum source, BivariateFunction resolution) {
this(source, resolution, Double.NaN, Double.NaN);
}
/**
*
* @param source
* @param resA - относительная ширина разрешения
* @param cacheMin - нижняя граница кэширования. Должна быть с небольшим
* запасом по отношению к данным
* @param cacheMax - верхняя граница кэширования, может быть без запаса.
*/
public ModularSpectrum(RangedNamedSetSpectrum source, double resA, double cacheMin, double cacheMax) {
this(source, new ResolutionFunction(resA), cacheMin, cacheMax);
}
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public ModularSpectrum(RangedNamedSetSpectrum source, double resA) {
this(source, new ResolutionFunction(resA));
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}
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public void setTrappingFunction(BivariateFunction trappingFunction) {
this.trappingFunction = trappingFunction;
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LoggerFactory.getLogger(getClass()).info("Recalculating modular spectrum cache");
setupCache();
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}
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/**
* Отдельный метод нужен на случай, если бета-спектр(FSS) или разрешение
* будут меняться в процессе
*/
private void setupCache() {
//обновляем кэши для трэппинга и упругого прохождения
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//Using external trappingCache function if provided
BivariateFunction trapFunc = trappingFunction != null ? trappingFunction : LossCalculator.getTrapFunction();
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BivariateFunction trapRes = new LossResConvolution(trapFunc, resolution);
ParametricFunction elasticSpectrum = new TransmissionConvolution(sourceSpectrum, resolution, sourceSpectrum);
ParametricFunction trapSpectrum = new TransmissionConvolution(sourceSpectrum, trapRes, sourceSpectrum);
/**
* обнуляем кэш рассеяния
*/
cacheList = new ArrayList<>();
//добавляем нулевой порядок - упругое рассеяние
TritiumSpectrumCaching elasticCache = new TritiumSpectrumCaching(elasticSpectrum, cacheMin, cacheMax);
elasticCache.setCachingEnabled(caching);
cacheList.add(elasticCache);
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this.trappingCache = new TritiumSpectrumCaching(trapSpectrum, cacheMin, cacheMax);
this.trappingCache.setCachingEnabled(caching);
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}
/**
* Обновляем кэш рассеяния если требуемый порядок выше, чем тот, что есть
*
* @param order
*/
private void updateScatterCache(int order) {
if (order >= cacheList.size()) {
LoggerFactory.getLogger(getClass())
.debug("Updating scatter cache up to order of '{}'", order);
// здесь можно сэкономить вызовы, начиная с cacheList.size(), но надо это?
for (int i = 1; i < order + 1; i++) {
BivariateFunction loss = calculator.getLossFunction(i);
BivariateFunction lossRes = new LossResConvolution(loss, resolution);
ParametricFunction inelasticSpectrum = new TransmissionConvolution(sourceSpectrum, lossRes, sourceSpectrum);
TritiumSpectrumCaching spCatch = new TritiumSpectrumCaching(inelasticSpectrum, cacheMin, cacheMax);
spCatch.setCachingEnabled(caching);
spCatch.setSuppressWarnings(true);
//TODO сделать пороверку
cacheList.add(i, spCatch);
}
}
}
@Override
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public double derivValue(String parName, double U, NamedValueSet set) {
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if (U >= sourceSpectrum.max(set)) {
return 0;
}
double X = this.getX(set);
switch (parName) {
case "X":
List<Double> probDerivs = calculator.getLossProbDerivs(X);
updateScatterCache(probDerivs.size() - 1);
double derivSum = 0;
for (int i = 0; i < probDerivs.size(); i++) {
derivSum += probDerivs.get(i) * cacheList.get(i).value(U, set);
}
return derivSum;
case "trap":
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return this.trappingCache.value(U, set);
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default:
if (sourceSpectrum.names().contains(parName)) {
List<Double> probs = calculator.getLossProbabilities(X);
updateScatterCache(probs.size() - 1);
double sum = 0;
for (int i = 0; i < probs.size(); i++) {
sum += probs.get(i) * cacheList.get(i).derivValue(parName, U, set);
}
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sum += this.getTrap(set) * this.trappingCache.derivValue(parName, U, set);
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return sum;
} else {
return 0;
}
}
}
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private double getTrap(ValueProvider set) {
return set.getDouble("trap");
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}
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private double getX(ValueProvider set) {
return set.getDouble("X");
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}
@Override
public boolean providesDeriv(String name) {
return sourceSpectrum.providesDeriv(name);
}
/**
* Set the boundaries and recalculate cache
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*
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* @param cacheMin
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* @param cacheMax
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*/
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public void setCachingBoundaries(double cacheMin, double cacheMax) {
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this.cacheMin = cacheMin;
this.cacheMax = cacheMax;
setupCache();
}
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public final void setCaching(boolean caching) {
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if (caching && (cacheMin == Double.NaN || cacheMax == Double.NaN)) {
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throw new IllegalStateException("Cahing boundaries are not defined");
}
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this.caching = caching;
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this.trappingCache.setCachingEnabled(caching);
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this.cacheList.stream().forEach((sp) -> {
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sp.setCachingEnabled(caching);
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});
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}
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/**
* Suppress warnings about cache recalculation
* @param suppress
*/
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public void setSuppressWarnings(boolean suppress) {
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this.trappingCache.setSuppressWarnings(suppress);
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this.cacheList.stream().forEach((sp) -> {
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sp.setSuppressWarnings(suppress);
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});
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}
@Override
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public double value(double U, NamedValueSet set) {
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if (U >= sourceSpectrum.max(set)) {
return 0;
}
double X = this.getX(set);
List<Double> probs = calculator.getLossProbabilities(X);
updateScatterCache(probs.size() - 1);
double res = 0;
for (int i = 0; i < probs.size(); i++) {
res += probs.get(i) * cacheList.get(i).value(U, set);
}
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res += this.getTrap(set) * this.trappingCache.value(U, set);
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return res;
}
}