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This commit is contained in:
Alexander Nozik 2016-05-25 12:18:43 +03:00
parent 091dfd8f8d
commit ec76341522
17 changed files with 372 additions and 426 deletions
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4
numass-main/src/main/groovy/inr/numass/scripts/OldTest.groovy
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@ -27,7 +27,7 @@ import hep.dataforge.likelihood.BayesianManager
import inr.numass.data.SpectrumDataAdapter
import inr.numass.models.BetaSpectrum;
import inr.numass.models.ModularSpectrum;
import inr.numass.models.ModularTritiumSpectrum
import inr.numass.models.NBkgSpectrum;
import inr.numass.models.RangedNamedSetSpectrum;
import inr.numass.models.ResolutionFunction
@ -52,7 +52,7 @@ File fssfile = new File("c:\\Users\\Darksnake\\Dropbox\\PlayGround\\FS.txt");
BivariateFunction resolution = new ResolutionFunction(2.28e-4);
//resolution.setTailFunction(ResolutionFunction.getRealTail())
ModularTritiumSpectrum sp = new ModularTritiumSpectrum(resolution, 18395d, 18580d, fssfile);
ModularSpectrum sp = new ModularSpectrum(new BetaSpectrum(fssfile), resolution, 18395d, 18580d);
sp.setCaching(false);
//RangedNamedSetSpectrum beta = new BetaSpectrum(fssfile);
//ModularSpectrum sp = new ModularSpectrum(beta, 2.28e-4, 18395d, 18580d);

5
numass-main/src/main/groovy/inr/numass/scripts/ResolutionTest.groovy
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@ -26,7 +26,8 @@ import hep.dataforge.datafitter.models.XYModel;
import hep.dataforge.exceptions.NamingException;
import inr.numass.data.SpectrumDataAdapter;
import inr.numass.data.SpectrumGenerator;
import inr.numass.models.ModularTritiumSpectrum;
import inr.numass.models.BetaSpectrum
import inr.numass.models.ModularSpectrum;
import inr.numass.models.NBkgSpectrum;
import inr.numass.utils.DataModelUtils;
import java.io.FileNotFoundException;
@ -44,7 +45,7 @@ GlobalContext global = GlobalContext.instance();
FitManager fm = new FitManager();
ModularTritiumSpectrum beta = new ModularTritiumSpectrum(9e-5, 14390d, 19001d, null);
ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(), 9e-5, 14390d, 19001d);
beta.setCaching(false);
NBkgSpectrum spectrum = new NBkgSpectrum(beta);

7
numass-main/src/main/groovy/inr/numass/scripts/SignificanceTest.groovy
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@ -19,7 +19,8 @@ import hep.dataforge.meta.MetaBuilder;
import hep.dataforge.context.GlobalContext;
import hep.dataforge.datafitter.ParamSet;
import inr.numass.data.SpectrumInformation;
import inr.numass.models.ModularTritiumSpectrum;
import inr.numass.models.ModularSpectrum;
import inr.numass.models.BetaSpectrum;
import inr.numass.models.NBkgSpectrum;
import inr.numass.models.ResolutionFunction;
import java.util.HashMap;
@ -43,8 +44,8 @@ UnivariateFunction reolutionTail = {x ->
}
};
ModularTritiumSpectrum beta = new ModularTritiumSpectrum(
new ResolutionFunction(8.3e-5, reolutionTail), 14490d, 19001d, null);
ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(),
new ResolutionFunction(8.3e-5, reolutionTail), 14490d, 19001d);
beta.setCaching(false);
NBkgSpectrum spectrum = new NBkgSpectrum(beta);

5
numass-main/src/main/groovy/inr/numass/scripts/Simulate.groovy
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@ -29,7 +29,8 @@ import hep.dataforge.exceptions.NamingException;
import hep.dataforge.exceptions.PackFormatException;
import inr.numass.data.SpectrumDataAdapter;
import inr.numass.data.SpectrumGenerator;
import inr.numass.models.ModularTritiumSpectrum;
import inr.numass.models.BetaSpectrum
import inr.numass.models.ModularSpectrum;
import inr.numass.models.NBkgSpectrum;
import inr.numass.utils.DataModelUtils;
import hep.dataforge.plotfit.PlotFitResultAction;
@ -50,7 +51,7 @@ new MINUITPlugin().startGlobal();
FitManager fm = new FitManager();
ModularTritiumSpectrum beta = new ModularTritiumSpectrum(8.3e-5, 13990d, 18600d, null);
ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(), 8.3e-5, 13990d, 18600d);
//beta.setCaching(false);
NBkgSpectrum spectrum = new NBkgSpectrum(beta);

5
numass-main/src/main/groovy/inr/numass/scripts/SystTransmission.groovy
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@ -29,7 +29,8 @@ import hep.dataforge.exceptions.NamingException;
import hep.dataforge.exceptions.PackFormatException;
import inr.numass.data.SpectrumDataAdapter;
import inr.numass.data.SpectrumGenerator;
import inr.numass.models.ModularTritiumSpectrum;
import inr.numass.models.BetaSpectrum
import inr.numass.models.ModularSpectrum
import inr.numass.models.NBkgSpectrum;
import inr.numass.models.ResolutionFunction
import inr.numass.utils.DataModelUtils;
@ -56,7 +57,7 @@ FitManager fm = new FitManager();
ResolutionFunction resolution = new ResolutionFunction(8.3e-5);
//resolution.setTailFunction(ResolutionFunction.getRealTail());
resolution.setTailFunction(ResolutionFunction.getAngledTail(0.00325));
ModularTritiumSpectrum beta = new ModularTritiumSpectrum(resolution, 18395d, 18580d, null);
ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(), resolution, 18395d, 18580d);
beta.setCaching(false);
NBkgSpectrum spectrum = new NBkgSpectrum(beta);

5
numass-main/src/main/groovy/inr/numass/scripts/Systematics.groovy
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@ -29,7 +29,8 @@ import hep.dataforge.exceptions.NamingException;
import hep.dataforge.exceptions.PackFormatException;
import inr.numass.data.SpectrumDataAdapter;
import inr.numass.data.SpectrumGenerator;
import inr.numass.models.ModularTritiumSpectrum;
import inr.numass.models.BetaSpectrum
import inr.numass.models.ModularSpectrum;
import inr.numass.models.NBkgSpectrum;
import inr.numass.models.ResolutionFunction
import inr.numass.utils.DataModelUtils;
@ -55,7 +56,7 @@ FitManager fm = new FitManager();
BivariateFunction resolution = new ResolutionFunction(8.3e-5);
ModularTritiumSpectrum beta = new ModularTritiumSpectrum(resolution, 13490d, 18575d, null);
ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(), resolution, 13490d, 18575d);
beta.setCaching(false);
NBkgSpectrum spectrum = new NBkgSpectrum(beta);

5
numass-main/src/main/groovy/inr/numass/scripts/TritiumTest.groovy
View File

@ -25,7 +25,8 @@ import hep.dataforge.datafitter.models.XYModel;
import hep.dataforge.likelihood.BayesianManager
import static hep.dataforge.maths.RandomUtils.setSeed;
import inr.numass.data.SpectrumGenerator;
import inr.numass.models.ModularTritiumSpectrum;
import inr.numass.models.BetaSpectrum
import inr.numass.models.ModularSpectrum
import inr.numass.models.NBkgSpectrum;
import static inr.numass.utils.DataModelUtils.getUniformSpectrumConfiguration;
import java.io.File;
@ -40,7 +41,7 @@ setSeed(543982);
// TritiumSpectrum beta = new TritiumSpectrum(2e-4, 13995d, 18580d);
File fssfile = new File("c:\\Users\\Darksnake\\Dropbox\\PlayGround\\FS.txt");
ModularTritiumSpectrum beta = new ModularTritiumSpectrum(8.3e-5, 14400d, 19010d, null);
ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(),8.3e-5, 14400d, 19010d);
beta.setCaching(false);
NBkgSpectrum spectrum = new NBkgSpectrum(beta);
XYModel model = new XYModel("tritium", spectrum);

6
numass-main/src/main/java/inr/numass/NumassPlugin.java
View File

@ -36,6 +36,7 @@ import inr.numass.actions.MonitorCorrectAction;
import inr.numass.actions.PrepareDataAction;
import inr.numass.actions.ReadNumassDataAction;
import inr.numass.actions.ReadNumassStorageAction;
import inr.numass.actions.ShowEnergySpectrumAction;
import inr.numass.actions.ShowLossSpectrumAction;
import inr.numass.actions.SlicingAction;
import inr.numass.actions.SummaryAction;
@ -45,6 +46,7 @@ import inr.numass.models.EmpiricalLossSpectrum;
import inr.numass.models.ExperimentalVariableLossSpectrum;
import inr.numass.models.GaussSourceSpectrum;
import inr.numass.models.GunSpectrum;
import inr.numass.models.LossCalculator;
import inr.numass.models.ModularSpectrum;
import inr.numass.models.NBkgSpectrum;
import inr.numass.models.RangedNamedSetSpectrum;
@ -83,6 +85,7 @@ public class NumassPlugin extends BasicPlugin {
actions.registerAction(ShowLossSpectrumAction.class);
actions.registerAction(AdjustErrorsAction.class);
actions.registerAction(ReadNumassStorageAction.class);
actions.registerAction(ShowEnergySpectrumAction.class);
}
@Override
@ -211,6 +214,9 @@ public class NumassPlugin extends BasicPlugin {
if (!an.getBoolean("caching", false)) {
sp.setCaching(false);
}
//Adding trapping energy dependence
//Intercept = 4.95745, B1 = -0.36879, B2 = 0.00827
sp.setTrappingFunction((Ei,Ef)->LossCalculator.getTrapFunction().value(Ei, Ef)*(4.95745-0.36879*Ei+0.00827*Ei*Ei));
NBkgSpectrum spectrum = new NBkgSpectrum(sp);
return new XYModel("tritium", spectrum, getAdapter(an));

130
numass-main/src/main/java/inr/numass/actions/ShowEnergySpectrumAction.java Normal file
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@ -0,0 +1,130 @@
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
package inr.numass.actions;
import hep.dataforge.actions.OneToOneAction;
import hep.dataforge.context.Context;
import hep.dataforge.description.TypedActionDef;
import hep.dataforge.io.ColumnedDataWriter;
import hep.dataforge.io.reports.Reportable;
import hep.dataforge.meta.Laminate;
import hep.dataforge.meta.Meta;
import hep.dataforge.meta.MetaBuilder;
import hep.dataforge.plots.PlotsPlugin;
import hep.dataforge.plots.XYPlotFrame;
import hep.dataforge.plots.XYPlottable;
import hep.dataforge.plots.data.PlottableData;
import hep.dataforge.tables.DataPoint;
import hep.dataforge.tables.ListTable;
import hep.dataforge.tables.MapPoint;
import hep.dataforge.tables.Table;
import hep.dataforge.tables.TableFormatBuilder;
import hep.dataforge.tables.XYAdapter;
import hep.dataforge.values.ValueType;
import inr.numass.storage.NMPoint;
import inr.numass.storage.NumassData;
import java.io.OutputStream;
import java.util.ArrayList;
import java.util.Collection;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
/**
*
* @author Alexander Nozik
*/
@TypedActionDef(inputType = NumassData.class, outputType = Table.class, name = "energySpectrum", info = "Generate output table and optionally plot for detector energy spectra")
public class ShowEnergySpectrumAction extends OneToOneAction<NumassData, Table> {
@Override
protected Table execute(Context context, Reportable log, String name, Laminate inputMeta, NumassData input) {
int binning = inputMeta.getInt("binning", 20);
boolean normalize = inputMeta.getBoolean("normalize", true);
List<NMPoint> points = input.getNMPoints();
if (points.isEmpty()) {
logger().error("Empty data");
return null;
}
//build header
List<String> names = new ArrayList<>();
for (int i = 0; i < points.size(); i++) {
names.add(String.format("%d: %.2f", i, points.get(i).getUset()));
}
LinkedHashMap<String, Map<Double, Double>> valueMap = points.stream()
.collect(Collectors.toMap(
p -> names.get(points.indexOf(p)),
p -> p.getMapWithBinning(binning, normalize),
(v1, v2) -> v1,
() -> new LinkedHashMap<>()
));
Collection<Double> rows = valueMap.values().stream().findAny().get().keySet();
//Building table format
TableFormatBuilder formatBuilder = new TableFormatBuilder();
formatBuilder.addColumn("channel",ValueType.NUMBER);
names.stream().forEach((columnName) -> {
formatBuilder.addColumn(columnName, ValueType.NUMBER);
});
ListTable.Builder builder = new ListTable.Builder(formatBuilder.build());
rows.stream().forEachOrdered((Double channel) -> {
MapPoint.Builder mb = new MapPoint.Builder();
mb.putValue("channel", channel);
valueMap.entrySet().forEach((Map.Entry<String, Map<Double, Double>> entry) -> {
mb.putValue(entry.getKey(), entry.getValue().get(channel));
});
builder.addRow(mb.build());
});
OutputStream out = buildActionOutput(context, name);
Table table = builder.build();
ColumnedDataWriter.writeDataSet(out, table, inputMeta.toString());
if (inputMeta.hasNode("plot") || inputMeta.getBoolean("plot", false)) {
XYPlotFrame frame = (XYPlotFrame) PlotsPlugin
.buildFrom(context).buildPlotFrame(getName(), name,
inputMeta.getNode("plot", Meta.empty()));
fillDetectorData(valueMap).forEach(frame::add);
}
return table;
}
private List<XYPlottable> fillDetectorData(LinkedHashMap<String, Map<Double, Double>> map) {
List<XYPlottable> plottables = new ArrayList<>();
Meta plottableConfig = new MetaBuilder("plot")
.setValue("connectionType", "step")
.setValue("thickness", 2)
.setValue("showLine", true)
.setValue("showSymbol", false)
.setValue("showErrors", false)
.build();
int index = 0;
for (Map.Entry<String, Map<Double, Double>> entry : map.entrySet()) {
index++;
String seriesName = String.format("%d: %s", index, entry.getKey());
String[] nameList = {"x", "y"};
List<DataPoint> data = entry.getValue().entrySet().stream()
.map(e -> new MapPoint(nameList, e.getKey(), e.getValue()))
.collect(Collectors.toList());
PlottableData datum = PlottableData.plot(seriesName, new XYAdapter(), data);
datum.configure(plottableConfig);
plottables.add(datum);
}
return plottables;
}
}

2
numass-main/src/main/java/inr/numass/actions/ShowLossSpectrumAction.java
View File

@ -228,7 +228,7 @@ public class ShowLossSpectrumAction extends OneToOneAction<FitState, FitState> {
new MetaBuilder("plot").setValue("plotTitle", "Ion ratio Distribution for " + name)
);
// XYPlotFrame frame = JFreeChartFrame.drawFrame("Ion ratio Distribution for " + name, null);
frame.add(PlottableData.plot("ionRatio", hist, new XYAdapter("binCenter", "count")));
frame.add(PlottableData.plot("ionRatio", new XYAdapter("binCenter", "count"), hist));
return new DescriptiveStatistics(res).getStandardDeviation();
}

2
numass-main/src/main/java/inr/numass/models/LossCalculator.java
View File

@ -126,7 +126,7 @@ public class LossCalculator {
return getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
}
static BivariateFunction getTrapFunction() {
public static BivariateFunction getTrapFunction() {
return (double Ei, double Ef) -> {
double eps = Ei - Ef;
if (eps > 10) {

27
numass-main/src/main/java/inr/numass/models/ModularSpectrum.java
View File

@ -35,9 +35,10 @@ public class ModularSpectrum extends AbstractParametricFunction {
private static final String[] list = {"X", "trap"};
private LossCalculator calculator;
List<NamedSpectrumCaching> cacheList;
NamedSpectrumCaching trapping;
NamedSpectrumCaching trappingCache;
BivariateFunction resolution;
RangedNamedSetSpectrum sourceSpectrum;
BivariateFunction trappingFunction;
boolean caching = true;
double cacheMin;
double cacheMax;
@ -84,6 +85,12 @@ public class ModularSpectrum extends AbstractParametricFunction {
this(source, new ResolutionFunction(resA));
}
public void setTrappingFunction(BivariateFunction trappingFunction) {
this.trappingFunction = trappingFunction;
}
/**
* Отдельный метод нужен на случай, если бета-спектр(FSS) или разрешение
* будут меняться в процессе
@ -91,7 +98,8 @@ public class ModularSpectrum extends AbstractParametricFunction {
private void setupCache() {
//обновляем кэши для трэппинга и упругого прохождения
BivariateFunction trapFunc = LossCalculator.getTrapFunction();
//Using external trappingCache function if provided
BivariateFunction trapFunc = trappingFunction != null ? trappingFunction : LossCalculator.getTrapFunction();
BivariateFunction trapRes = new LossResConvolution(trapFunc, resolution);
ParametricFunction elasticSpectrum = new TransmissionConvolution(sourceSpectrum, resolution, sourceSpectrum);
@ -105,8 +113,8 @@ public class ModularSpectrum extends AbstractParametricFunction {
TritiumSpectrumCaching elasticCache = new TritiumSpectrumCaching(elasticSpectrum, cacheMin, cacheMax);
elasticCache.setCachingEnabled(caching);
cacheList.add(elasticCache);
this.trapping = new TritiumSpectrumCaching(trapSpectrum, cacheMin, cacheMax);
this.trapping.setCachingEnabled(caching);
this.trappingCache = new TritiumSpectrumCaching(trapSpectrum, cacheMin, cacheMax);
this.trappingCache.setCachingEnabled(caching);
}
/**
@ -150,7 +158,7 @@ public class ModularSpectrum extends AbstractParametricFunction {
return derivSum;
case "trap":
return this.trapping.value(U, set);
return this.trappingCache.value(U, set);
default:
if (sourceSpectrum.names().contains(parName)) {
List<Double> probs = calculator.getLossProbabilities(X);
@ -161,7 +169,7 @@ public class ModularSpectrum extends AbstractParametricFunction {
sum += probs.get(i) * cacheList.get(i).derivValue(parName, U, set);
}
sum += this.getTrap(set) * this.trapping.derivValue(parName, U, set);
sum += this.getTrap(set) * this.trappingCache.derivValue(parName, U, set);
return sum;
} else {
return 0;
@ -184,6 +192,7 @@ public class ModularSpectrum extends AbstractParametricFunction {
/**
* Set the boundaries and recalculate cache
*
* @param cacheMin
* @param cacheMax
*/
@ -199,14 +208,14 @@ public class ModularSpectrum extends AbstractParametricFunction {
}
this.caching = caching;
this.trapping.setCachingEnabled(caching);
this.trappingCache.setCachingEnabled(caching);
for (NamedSpectrumCaching sp : this.cacheList) {
sp.setCachingEnabled(caching);
}
}
public void setSuppressWarnings(boolean suppress) {
this.trapping.setSuppressWarnings(suppress);
this.trappingCache.setSuppressWarnings(suppress);
for (NamedSpectrumCaching sp : this.cacheList) {
sp.setSuppressWarnings(suppress);
@ -228,7 +237,7 @@ public class ModularSpectrum extends AbstractParametricFunction {
res += probs.get(i) * cacheList.get(i).value(U, set);
}
res += this.getTrap(set) * this.trapping.value(U, set);
res += this.getTrap(set) * this.trappingCache.value(U, set);
return res;
}
}

216
numass-main/src/main/java/inr/numass/models/ModularTritiumSpectrum.java
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@ -1,216 +0,0 @@
/*
* 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.functions.AbstractParametricFunction;
import hep.dataforge.functions.ParametricFunction;
import hep.dataforge.maths.NamedDoubleSet;
import java.io.File;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.analysis.BivariateFunction;
import org.slf4j.LoggerFactory;
/**
* Modular tritium spectrum with separate calculation for different transmission
* components
*
* @author Darksnake
*/
public class ModularTritiumSpectrum extends AbstractParametricFunction {
private static final String[] list = {"U2", "E0", "mnu2", "msterile2", "X", "trap"};
ParametricFunction bareBeta;
boolean caching;
private LossCalculator calculator;
double elow;
double endpoint;
File fssfile;
BivariateFunction resolution;
List<NamedSpectrumCaching> scatterList;
NamedSpectrumCaching trapping;
// NamedSpectrumCaching elastic;
// NamedSpectrumCaching inelastic;
// NamedSpectrumCaching inelastic2;
/**
*
* @param resolution
* @param elow - нижняя граница кэширования. Должна быть с небольшим запасом
* по отношению к данным
* @param endpoint - верхняя граница кэширования, может быть без запаса.
* @param fssFile
*/
public ModularTritiumSpectrum(BivariateFunction resolution, double elow, double endpoint, File fssFile) {
super(list);
assert (endpoint > elow);
this.elow = elow;
this.endpoint = endpoint;
this.fssfile = fssFile;
this.resolution = resolution;
this.calculator = LossCalculator.instance();
setupCache();
}
/**
*
* @param resA - относительная ширина разрешения
* @param elow - нижняя граница кэширования. Должна быть с небольшим запасом
* по отношению к данным
* @param endpoint - верхняя граница кэширования, может быть без запаса.
* @param fssFile
*/
public ModularTritiumSpectrum(double resA, double elow, double endpoint, File fssFile) {
this(new ResolutionFunction(resA), elow, endpoint, fssFile);
}
@Override
public double derivValue(String parName, double U, NamedDoubleSet set) {
if (U >= endpoint) {
return 0;
}
double X = this.getX(set);
switch (parName) {
case "U2":
case "E0":
case "mnu2":
case "msterile2":
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) * scatterList.get(i).derivValue(parName, U, set);
}
return sum + this.getTrap(set) * this.trapping.derivValue(parName, U, set);
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) * scatterList.get(i).value(U, set);
}
return derivSum;
// return (X / 3 - 0.5) * this.elastic.value(x, set)
// + (0.5 - 2 * X / 3) * this.inelastic.value(x, set)
// + (X / 3) * this.inelastic2.value(x, set);
case "trap":
return this.trapping.value(U, set);
default:
return 0;
}
}
private double getTrap(NamedDoubleSet set) {
return set.getValue("trap");
}
private double getX(NamedDoubleSet set) {
return set.getValue("X");
}
@Override
public boolean providesDeriv(String name) {
return true;
}
public void setCaching(boolean caching) {
this.caching = caching;
this.trapping.setCachingEnabled(caching);
for (NamedSpectrumCaching sp : this.scatterList) {
sp.setCachingEnabled(caching);
}
}
public void setSuppressWarnings(boolean suppress) {
this.trapping.setSuppressWarnings(suppress);
for (NamedSpectrumCaching sp : this.scatterList) {
sp.setSuppressWarnings(suppress);
}
}
/**
* Отдельный метод нужен на случай, если бета-спектр(FSS) или разрешение
* будут меняться в процессе
*/
private void setupCache() {
if (fssfile == null) {
bareBeta = new BetaSpectrum();
} else {
bareBeta = new BetaSpectrum(fssfile);
}
//обновляем кэши для трэппинга и упругого прохождения
BivariateFunction trapFunc = LossCalculator.getTrapFunction();
BivariateFunction trapRes = new LossResConvolution(trapFunc, resolution);
ParametricFunction elasticSpectrum = new TransmissionConvolution(bareBeta, resolution, endpoint);
ParametricFunction trapSpectrum = new TransmissionConvolution(bareBeta, trapRes, endpoint);
scatterList = new ArrayList<>();
//добавляем нулевой порядок - упругое рассеяние
scatterList.add(new TritiumSpectrumCaching(elasticSpectrum, elow, endpoint));
this.trapping = new TritiumSpectrumCaching(trapSpectrum, elow, endpoint);
/**
* обнуляем кэш рассеяния
*/
}
/**
* Обновляем кэш рассеяния если требуемый порядок выше, чем тот, что есть
*
* @param order
*/
private void updateScatterCache(int order) {
if (order >= scatterList.size()) {
LoggerFactory.getLogger(getClass())
.debug("Updating scatter cache up to order of '{}'", order);
// здесь можно сэкономить вызовы, начиная с scatterList.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(bareBeta, lossRes, endpoint);
TritiumSpectrumCaching spCatch = new TritiumSpectrumCaching(inelasticSpectrum, elow, endpoint);
spCatch.setCachingEnabled(caching);
//TODO сделать пороверку
scatterList.add(i, spCatch);
}
}
}
@Override
public double value(double x, NamedDoubleSet set) {
if (x >= endpoint) {
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) * scatterList.get(i).value(x, set);
}
return res + this.getTrap(set) * this.trapping.value(x, set);
}
}

6
numass-main/src/main/java/inr/numass/workbench/Workbench.java
View File

@ -13,7 +13,7 @@ import javafx.fxml.FXMLLoader;
import javafx.scene.Parent;
import javafx.scene.Scene;
import javafx.stage.Stage;
import javafx.stage.WindowEvent;
/**
*
@ -34,6 +34,10 @@ public class Workbench extends Application {
primaryStage.setTitle("Numass workbench");
primaryStage.setScene(scene);
primaryStage.show();
scene.getWindow().setOnCloseRequest((WindowEvent event) -> {
controller.getContext().processManager().getRootProcess().cancel(true);
});
}
/**

5
numass-main/src/test/java/inr/numass/run/NumassSpectrumTest.java
View File

@ -19,7 +19,8 @@ import hep.dataforge.context.GlobalContext;
import hep.dataforge.datafitter.MINUITPlugin;
import hep.dataforge.datafitter.ParamSet;
import hep.dataforge.exceptions.NamingException;
import inr.numass.models.ModularTritiumSpectrum;
import inr.numass.models.BetaSpectrum;
import inr.numass.models.ModularSpectrum;
import java.io.File;
import java.io.FileNotFoundException;
import java.util.Locale;
@ -64,7 +65,7 @@ public class NumassSpectrumTest {
allPars.setParError("trap", 0.01d);
allPars.setParDomain("trap", 0d, Double.POSITIVE_INFINITY);
ModularTritiumSpectrum betaNew = new ModularTritiumSpectrum(1e-4, 14390d, 19001d, new File("d:\\PlayGround\\FS.txt"));
ModularSpectrum betaNew = new ModularSpectrum(new BetaSpectrum(new File("d:\\PlayGround\\FS.txt")), 1e-4, 14390d, 19001d);
betaNew.setCaching(false);
System.out.println(betaNew.value(17000d, allPars));

8
numass-storage/src/main/java/inr/numass/storage/NumassDataLoader.java
View File

@ -28,6 +28,7 @@ import hep.dataforge.storage.api.Storage;
import hep.dataforge.storage.loaders.AbstractLoader;
import hep.dataforge.tables.Table;
import hep.dataforge.values.Value;
import static inr.numass.storage.RawNMPoint.MAX_EVENTS_PER_POINT;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
@ -193,6 +194,13 @@ public class NumassDataLoader extends AbstractLoader implements ObjectLoader<Env
} else {
pointTime = envelope.meta().getValue("acquisition_time").doubleValue();
}
//Check if the point is composite
boolean segmented = envelope.meta().hasValue("events") && envelope.meta().getValue("events").isList();
if (!segmented && events.size() > MAX_EVENTS_PER_POINT) {
pointTime = events.get(events.size() - 1).getTime() - events.get(0).getTime();
}
RawNMPoint raw = new RawNMPoint(u, u,
events,
pointTime,

4
numass-storage/src/main/java/inr/numass/storage/RawNMPoint.java
View File

@ -105,9 +105,7 @@ public class RawNMPoint implements Cloneable {
if (Double.isNaN(length)) {
throw new Error();
}
if(events.size()>MAX_EVENTS_PER_POINT){
return events.get(events.size()-1).getTime()-events.get(0).getTime();
}
return length;
}