Fixed fit internals

numass-main/src/main/groovy/inr/numass/scripts/FindExIonRatio.groovy

@@ -20,7 +20,7 @@ import hep.dataforge.plots.PlotFrame
 import hep.dataforge.plots.XYFunctionPlot
 import hep.dataforge.plots.jfreechart.JFreeChartFrame
 import hep.dataforge.stat.fit.ParamSet
-import inr.numass.models.LossCalculator
+import inr.numass.models.misc.LossCalculator
 import org.apache.commons.math3.analysis.UnivariateFunction
 import org.apache.commons.math3.analysis.solvers.BisectionSolver
 
@@ -54,7 +54,7 @@ double resolution = 1.5d;
 
 def X = 0.527;
 
-LossCalculator calculator = new LossCalculator();
+LossCalculator calculator = LossCalculator.INSTANCE;
 
 List<Double> lossProbs = calculator.getGunLossProbabilities(X);
 

numass-main/src/main/groovy/inr/numass/scripts/LossTailCalculation.groovy

@@ -8,7 +8,6 @@ package inr.numass.scripts
 
 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);

numass-main/src/main/groovy/inr/numass/scripts/PrintLossFunctions.groovy

@@ -6,9 +6,10 @@
 
 package inr.numass.scripts
 
-import inr.numass.models.LossCalculator
+import inr.numass.models.misc.LossCalculator
 
-LossCalculator loss = LossCalculator.instance()
+
+LossCalculator loss = LossCalculator.INSTANCE
 
 def X = 0.36
 

numass-main/src/main/groovy/inr/numass/scripts/Systematics.groovy

@@ -15,12 +15,11 @@
  */
 package inr.numass.scripts
 
-import hep.dataforge.stat.fit.FitManager
+import hep.dataforge.context.Global
-import hep.dataforge.stat.fit.FitState
+import hep.dataforge.io.FittingIOUtils
-import hep.dataforge.stat.fit.MINUITPlugin
+import hep.dataforge.meta.Meta
-import hep.dataforge.stat.fit.ParamSet
+import hep.dataforge.stat.fit.*
 import hep.dataforge.stat.models.XYModel
-import hep.dataforge.tables.ListTable
 import inr.numass.data.SpectrumAdapter
 import inr.numass.data.SpectrumGenerator
 import inr.numass.models.BetaSpectrum
@@ -30,7 +29,6 @@ import inr.numass.models.ResolutionFunction
 import inr.numass.utils.DataModelUtils
 import org.apache.commons.math3.analysis.BivariateFunction
 
-import static hep.dataforge.context.Global.out
 import static java.util.Locale.setDefault
 
 /**
@@ -41,7 +39,8 @@ import static java.util.Locale.setDefault
 setDefault(Locale.US);
 new MINUITPlugin().startGlobal();
 
-FitManager fm = new FitManager();
+FitManager fm = Global.INSTANCE.get(FitManager)
+
 
 BivariateFunction resolution = new ResolutionFunction(8.3e-5);
 
@@ -49,7 +48,7 @@ ModularSpectrum beta = new ModularSpectrum(new BetaSpectrum(), resolution, 13490
 beta.setCaching(false);
 
 NBkgSpectrum spectrum = new NBkgSpectrum(beta);
-XYModel model = new XYModel("tritium", spectrum, new SpectrumAdapter());
+XYModel model = new XYModel(Meta.empty(), new SpectrumAdapter(Meta.empty()), spectrum);
 
 ParamSet allPars = new ParamSet();
 
@@ -74,7 +73,7 @@ allPars.setPar("trap", 0, 0.01, 0d, Double.POSITIVE_INFINITY);
 
 SpectrumGenerator generator = new SpectrumGenerator(model, allPars, 12316);
 
-ListTable data = generator.generateData(DataModelUtils.getUniformSpectrumConfiguration(14000d, 18200, 1e6, 60));
+def data = generator.generateData(DataModelUtils.getUniformSpectrumConfiguration(14000d, 18200, 1e6, 60));
 
 //        data = data.filter("X", Value.of(15510.0), Value.of(18610.0));
 allPars.setParValue("U2", 0);
@@ -87,14 +86,14 @@ FitState state = new FitState(data, model, allPars);
 //    1-delta*(E-U);
 //}
 
-resolution.setTailFunction(ResolutionFunction.getRealTail())
+//resolution.setTailFunction(ResolutionFunction.getRealTail())
 
 //PlotFrame frame = JFreeChartFrame.drawFrame("Transmission function", null);
 //frame.add(new PlottableFunction("transmission",null, {U -> resolution.value(18500,U)},13500,18505,500));
 
-FitState res = fm.runTask(state, "QOW", FitTask.TASK_RUN, "N", "bkg", "E0", "U2", "trap");
+def res = fm.runStage(state, "QOW", FitStage.TASK_RUN, "N", "bkg", "E0", "U2", "trap");
 
 
+res.printState(new PrintWriter(System.out))
 
-res.print(out());
+FittingIOUtils.printResiduals(new PrintWriter(System.out), res.optState().get())
-

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

@@ -21,6 +21,7 @@ import hep.dataforge.maths.integration.GaussRuleIntegrator;
 import hep.dataforge.maths.integration.UnivariateIntegrator;
 import hep.dataforge.stat.parametric.AbstractParametricFunction;
 import hep.dataforge.values.Values;
+import inr.numass.models.misc.LossCalculator;
 import org.apache.commons.math3.analysis.BivariateFunction;
 import org.apache.commons.math3.analysis.UnivariateFunction;
 
@@ -59,11 +60,9 @@ public class EmpiricalLossSpectrum extends AbstractParametricFunction {
         final double shift = set.getDouble("shift");
 
         //FIXME тут толщины усреднены по длине источника, а надо брать чистого Пуассона
-        final List<Double> probs = LossCalculator.instance().getGunLossProbabilities(X);
+        final List<Double> probs = LossCalculator.INSTANCE.getGunLossProbabilities(X);
         final double noLossProb = probs.get(0);
-        final BivariateFunction lossFunction = (Ei, Ef) -> {
+        final BivariateFunction lossFunction = (Ei, Ef) -> LossCalculator.INSTANCE.getLossValue(probs, 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(U, eMax, integrand);
     }

numass-main/src/main/java/inr/numass/models/LossCalculator.java

@@ -1,442 +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.maths.functions.FunctionCaching;
-import hep.dataforge.maths.integration.GaussRuleIntegrator;
-import hep.dataforge.maths.integration.UnivariateIntegrator;
-import hep.dataforge.plots.PlotFrame;
-import hep.dataforge.plots.XYFunctionPlot;
-import hep.dataforge.utils.Misc;
-import hep.dataforge.values.Values;
-import org.apache.commons.math3.analysis.BivariateFunction;
-import org.apache.commons.math3.analysis.UnivariateFunction;
-import org.apache.commons.math3.exception.OutOfRangeException;
-import org.slf4j.LoggerFactory;
-
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-import static java.lang.Math.exp;
-
-/**
- * Вычисление произвольного порядка функции рассеяния. Не учитывается
- * зависимость сечения от энергии электрона
- *
- * @author Darksnake
- */
-public class LossCalculator {
-
-    /**
-     * порог по вероятности, до которого вычисляются компоненты функции потерь
-     */
-    public final static double SCATTERING_PROBABILITY_THRESHOLD = 1e-3;
-
-    private static final LossCalculator instance = new LossCalculator();
-    private static final UnivariateIntegrator integrator = new GaussRuleIntegrator(100);
-    private static final Map<Double, List<Double>> lossProbCache = Misc.getLRUCache(100);
-    private final Map<Integer, UnivariateFunction> cache = new HashMap<>();
-
-
-
-
-    private LossCalculator() {
-        cache.put(1, getSingleScatterFunction());
-//        immutable.put(2, getDoubleScatterFunction());
-    }
-
-    public static UnivariateFunction getSingleScatterFunction() {
-        final double A1 = 0.204;
-        final double A2 = 0.0556;
-        final double b = 14.0;
-        final double pos1 = 12.6;
-        final double pos2 = 14.3;
-        final double w1 = 1.85;
-        final double w2 = 12.5;
-
-        return (double eps) -> {
-            if (eps <= 0) {
-                return 0;
-            }
-            if (eps <= b) {
-                double z = eps - pos1;
-                return A1 * exp(-2 * z * z / w1 / w1);
-            } else {
-                double z = 4 * (eps - pos2) * (eps - pos2);
-                return A2 / (1 + z / w2 / w2);
-            }
-        };
-    }
-
-    /**
-     * A generic loss function for numass experiment in "Lobashev"
-     * parameterization
-     *
-     * @param exPos
-     * @param ionPos
-     * @param exW
-     * @param ionW
-     * @param exIonRatio
-     * @return
-     */
-    public static UnivariateFunction getSingleScatterFunction(
-            final double exPos,
-            final double ionPos,
-            final double exW,
-            final double ionW,
-            final double exIonRatio) {
-        UnivariateFunction func = (double eps) -> {
-            if (eps <= 0) {
-                return 0;
-            }
-            double z1 = eps - exPos;
-            double ex = exIonRatio * exp(-2 * z1 * z1 / exW / exW);
-
-            double z = 4 * (eps - ionPos) * (eps - ionPos);
-            double ion = 1 / (1 + z / ionW / ionW);
-
-            double res;
-            if (eps < exPos) {
-                res = ex;
-            } else {
-                res = Math.max(ex, ion);
-            }
-
-            return res;
-        };
-
-        double cutoff = 25d;
-        //caclulating lorentz integral analythically
-        double tailNorm = (Math.atan((ionPos - cutoff) * 2d / ionW) + 0.5 * Math.PI) * ionW / 2d;
-        final double norm = integrator.integrate(0d, cutoff, func) + tailNorm;
-        return (e) -> func.value(e) / norm;
-    }
-
-    public static UnivariateFunction getSingleScatterFunction(Values set) {
-
-        final double exPos = set.getDouble("exPos");
-        final double ionPos = set.getDouble("ionPos");
-        final double exW = set.getDouble("exW");
-        final double ionW = set.getDouble("ionW");
-        final double exIonRatio = set.getDouble("exIonRatio");
-
-        return getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
-    }
-
-    public static BivariateFunction getTrapFunction() {
-        return (double Ei, double Ef) -> {
-            double eps = Ei - Ef;
-            if (eps > 10) {
-                return 1.86e-04 * exp(-eps / 25.0) + 5.5e-05;
-            } else {
-                return 0;
-            }
-        };
-    }
-
-    /**
-     * Синглетон, так как кэши функций потреь можно вычислять один раз
-     *
-     * @return
-     */
-    public static LossCalculator instance() {
-        return instance;
-    }
-
-    public static void plotScatter(PlotFrame frame, Values set) {
-        //"X", "shift", "exPos", "ionPos", "exW", "ionW", "exIonRatio"
-
-//        JFreeChartFrame frame = JFreeChartFrame.drawFrame("Differential scattering crosssection", null);
-        double X = set.getDouble("X");
-
-        final double exPos = set.getDouble("exPos");
-
-        final double ionPos = set.getDouble("ionPos");
-
-        final double exW = set.getDouble("exW");
-
-        final double ionW = set.getDouble("ionW");
-
-        final double exIonRatio = set.getDouble("exIonRatio");
-
-        UnivariateFunction scatterFunction = getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
-
-        if (set.getNames().contains("X")) {
-            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(XYFunctionPlot.Companion.plot("Single scattering", 0, 100, 1000, single::value));
-
-            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(XYFunctionPlot.Companion.plot(j + " scattering", 0, 100, 1000, scatter::value));
-            }
-
-            UnivariateFunction total = (eps) -> {
-                if (probs.size() == 1) {
-                    return 0;
-                }
-                double sum = probs.get(1) * scatterFunction.value(eps);
-                for (int i = 2; i < probs.size(); i++) {
-                    sum += probs.get(i) * loss.getLossValue(i, eps, 0);
-                }
-                return sum;
-            };
-
-            frame.add(XYFunctionPlot.Companion.plot("Total loss", 0, 100, 1000, total::value));
-
-        } else {
-
-            frame.add(XYFunctionPlot.Companion.plot("Differential crosssection", 0, 100, 2000, scatterFunction::value));
-        }
-
-    }
-
-    public List<Double> getGunLossProbabilities(double X) {
-        List<Double> res = new ArrayList<>();
-        double prob;
-        if (X > 0) {
-            prob = Math.exp(-X);
-        } else {
-            // если x ==0, то выживает только нулевой член, первый равен 1
-            res.add(1d);
-            return res;
-        }
-        res.add(prob);
-
-        int n = 0;
-        while (prob > SCATTERING_PROBABILITY_THRESHOLD) {
-            /*
-            * prob(n) = prob(n-1)*X/n;
-             */
-            n++;
-            prob *= X / n;
-            res.add(prob);
-        }
-
-        return res;
-    }
-
-    public double getGunZeroLossProb(double X) {
-        return Math.exp(-X);
-    }
-
-    /**
-     * Ленивое рекурсивное вычисление функции потерь через предыдущие
-     *
-     * @param order
-     * @return
-     */
-    private UnivariateFunction getLoss(int order) {
-        if (order <= 0) {
-            throw new IllegalArgumentException();
-        }
-        if (cache.containsKey(order)) {
-            return cache.get(order);
-        } else {
-            synchronized (this) {
-                cache.computeIfAbsent(order, (i) -> {
-                    LoggerFactory.getLogger(getClass())
-                            .debug("Scatter immutable of order {} not found. Updating", i);
-                    return getNextLoss(getMargin(i), getLoss(i - 1));
-                });
-                return cache.get(order);
-            }
-        }
-    }
-
-    public BivariateFunction getLossFunction(int order) {
-        assert order > 0;
-        return (double Ei, double Ef) -> getLossValue(order, Ei, Ef);
-    }
-
-    public List<Double> getLossProbDerivs(double X) {
-        List<Double> res = new ArrayList<>();
-        List<Double> probs = getLossProbabilities(X);
-
-        double delta = Math.exp(-X);
-        res.add((delta - probs.get(0)) / X);
-        for (int i = 1; i < probs.size(); i++) {
-            delta *= X / i;
-            res.add((delta - probs.get(i)) / X);
-        }
-
-        return res;
-    }
-
-    /**
-     * рекурсивно вычисляем все вероятности, котрорые выше порога
-     * <p>
-     * дисер, стр.48
-     * </p>
-     * @param X
-     * @return
-     */
-    public List<Double> getLossProbabilities(double X) {
-        return lossProbCache.computeIfAbsent(X, x -> {
-            List<Double> res = new ArrayList<>();
-            double prob;
-            if (x > 0) {
-                prob = 1 / x * (1 - Math.exp(-x));
-            } else {
-                // если x ==0, то выживает только нулевой член, первый равен нулю
-                res.add(1d);
-                return res;
-            }
-            res.add(prob);
-
-            while (prob > SCATTERING_PROBABILITY_THRESHOLD) {
-            /*
-            * prob(n) = prob(n-1)-1/n! * X^n * exp(-X);
-             */
-                double delta = Math.exp(-x);
-                for (int i = 1; i < res.size() + 1; i++) {
-                    delta *= x / i;
-                }
-                prob -= delta / x;
-                res.add(prob);
-            }
-
-            return res;
-        });
-    }
-
-    public double getLossProbability(int order, double X) {
-        if (order == 0) {
-            if (X > 0) {
-                return 1 / X * (1 - Math.exp(-X));
-            } else {
-                return 1d;
-            }
-        }
-        List<Double> probs = getLossProbabilities(X);
-        if (order >= probs.size()) {
-            return 0;
-        } else {
-            return probs.get(order);
-        }
-    }
-
-    public double getLossValue(int order, double Ei, double Ef) {
-        if (Ei - Ef < 5d) {
-            return 0;
-        } else if (Ei - Ef >= getMargin(order)) {
-            return 0;
-        } else {
-            return getLoss(order).value(Ei - Ef);
-        }
-    }
-
-    /**
-     * функция потерь с произвольными вероятностями рассеяния
-     *
-     * @param probs
-     * @param Ei
-     * @param Ef
-     * @return
-     */
-    public double getLossValue(List<Double> probs, double Ei, double Ef) {
-        double sum = 0;
-        for (int i = 1; i < probs.size(); i++) {
-            sum += probs.get(i) * getLossValue(i, Ei, Ef);
-        }
-        return sum;
-    }
-
-    /**
-     * граница интегрирования
-     *
-     * @param order
-     * @return
-     */
-    private double getMargin(int order) {
-        return 80 + order * 50d;
-    }
-
-    /**
-     * генерирует кэшированную функцию свертки loss со спектром однократных
-     * потерь
-     *
-     * @param loss
-     * @return
-     */
-    private UnivariateFunction getNextLoss(double margin, UnivariateFunction loss) {
-        UnivariateFunction res = (final double x) -> {
-            UnivariateFunction integrand = (double y) -> {
-                try {
-                    return loss.value(x - y) * getSingleScatterFunction().value(y);
-                } catch (OutOfRangeException ex) {
-                    return 0;
-                }
-            };
-            return integrator.integrate(5d, margin, integrand);
-        };
-
-        return FunctionCaching.cacheUnivariateFunction(0, margin, 200, res);
-
-    }
-
-    public BivariateFunction getTotalLossBivariateFunction(final double X) {
-        return (double Ei, double Ef) -> getTotalLossValue(X, Ei, Ef);
-    }
-
-    /**
-     * Значение полной производной функции потерь с учетом всех неисчезающих
-     * порядков
-     *
-     * @param X
-     * @param Ei
-     * @param Ef
-     * @return
-     */
-    public double getTotalLossDeriv(double X, double Ei, double Ef) {
-        List<Double> probs = getLossProbDerivs(X);
-
-        double sum = 0;
-        for (int i = 1; i < probs.size(); i++) {
-            sum += probs.get(i) * getLossValue(i, Ei, Ef);
-        }
-        return sum;
-    }
-
-    public BivariateFunction getTotalLossDerivBivariateFunction(final double X) {
-        return (double Ei, double Ef) -> getTotalLossDeriv(X, Ei, Ef);
-    }
-
-    /**
-     * Значение полной функции потерь с учетом всех неисчезающих порядков
-     *
-     * @param X
-     * @param Ei
-     * @param Ef
-     * @return
-     */
-    public double getTotalLossValue(double X, double Ei, double Ef) {
-        if (X == 0) {
-            return 0;
-        }
-        List<Double> probs = getLossProbabilities(X);
-
-        double sum = 0;
-        for (int i = 1; i < probs.size(); i++) {
-            sum += probs.get(i) * getLossValue(i, Ei, Ef);
-        }
-        return sum;
-    }
-}

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

@@ -20,6 +20,7 @@ import hep.dataforge.stat.parametric.AbstractParametricFunction;
 import hep.dataforge.stat.parametric.ParametricFunction;
 import hep.dataforge.values.ValueProvider;
 import hep.dataforge.values.Values;
+import inr.numass.models.misc.LossCalculator;
 import org.apache.commons.math3.analysis.BivariateFunction;
 import org.slf4j.LoggerFactory;
 
@@ -35,7 +36,7 @@ import java.util.List;
 public class ModularSpectrum extends AbstractParametricFunction {
 
     private static final String[] list = {"X", "trap"};
-    private LossCalculator calculator;
+    private LossCalculator calculator = LossCalculator.INSTANCE;
     List<NamedSpectrumCaching> cacheList;
     NamedSpectrumCaching trappingCache;
     BivariateFunction resolution;
@@ -61,7 +62,6 @@ public class ModularSpectrum extends AbstractParametricFunction {
         this.cacheMin = cacheMin;
         this.cacheMax = cacheMax;
         this.resolution = resolution;
-        this.calculator = LossCalculator.instance();
         this.sourceSpectrum = source;
         setupCache();
     }
@@ -102,7 +102,7 @@ public class ModularSpectrum extends AbstractParametricFunction {
 
         //обновляем кэши для трэппинга и упругого прохождения
         //Using external trappingCache function if provided
-        BivariateFunction trapFunc = trappingFunction != null ? trappingFunction : LossCalculator.getTrapFunction();
+        BivariateFunction trapFunc = trappingFunction != null ? trappingFunction : calculator.getTrapFunction();
         BivariateFunction trapRes = new LossResConvolution(trapFunc, resolution);
 
         ParametricFunction elasticSpectrum = new TransmissionConvolution(sourceSpectrum, resolution, sourceSpectrum);

numass-main/src/main/java/inr/numass/models/VariableLossSpectrum.java

@@ -21,6 +21,7 @@ import hep.dataforge.stat.parametric.AbstractParametricFunction;
 import hep.dataforge.stat.parametric.ParametricFunction;
 import hep.dataforge.values.ValueProvider;
 import hep.dataforge.values.Values;
+import inr.numass.models.misc.LossCalculator;
 import inr.numass.utils.NumassIntegrator;
 import org.apache.commons.math3.analysis.BivariateFunction;
 import org.apache.commons.math3.analysis.UnivariateFunction;
@@ -40,7 +41,7 @@ public class VariableLossSpectrum extends AbstractParametricFunction {
     }
 
     public static VariableLossSpectrum withData(final UnivariateFunction transmission, double eMax) {
-        return new VariableLossSpectrum(new AbstractParametricFunction(new String[0]) {
+        return new VariableLossSpectrum(new AbstractParametricFunction() {
 
             @Override
             public double derivValue(String parName, double x, Values set) {
@@ -82,7 +83,7 @@ public class VariableLossSpectrum extends AbstractParametricFunction {
         double X = set.getDouble("X");
         final double shift = set.getDouble("shift");
 
-        final LossCalculator loss = LossCalculator.instance();
+        final LossCalculator loss = LossCalculator.INSTANCE;
 
         final List<Double> probs = loss.getGunLossProbabilities(X);
         final double noLossProb = probs.get(0);
@@ -126,7 +127,7 @@ public class VariableLossSpectrum extends AbstractParametricFunction {
             final double exW,
             final double ionW,
             final double exIonRatio) {
-        return LossCalculator.getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
+        return LossCalculator.INSTANCE.getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio);
     }
 
     @Override

numass-main/src/main/kotlin/inr/numass/NumassPlugin.kt

@@ -23,7 +23,7 @@ import hep.dataforge.meta.Meta
 import hep.dataforge.plots.jfreechart.JFreeChartFrame
 import hep.dataforge.providers.Provides
 import hep.dataforge.providers.ProvidesNames
-import hep.dataforge.stat.models.ModelManager
+import hep.dataforge.stat.models.ModelLibrary
 import hep.dataforge.stat.models.WeightedXYModel
 import hep.dataforge.stat.models.XYModel
 import hep.dataforge.tables.Adapters
@@ -53,7 +53,7 @@ class NumassPlugin : BasicPlugin() {
         //        StorageManager.buildFrom(context);
         super.attach(context)
         //TODO Replace by local providers
-        loadModels(context[ModelManager::class.java])
+        loadModels(context[ModelLibrary::class.java])
         loadMath(FunctionLibrary.buildFrom(context))
     }
 
@@ -116,9 +116,9 @@ class NumassPlugin : BasicPlugin() {
     /**
      * Load all numass model factories
      *
-     * @param manager
+     * @param library
      */
-    private fun loadModels(manager: ModelManager) {
+    private fun loadModels(library: ModelLibrary) {
 
         //        manager.addModel("modularbeta", (context, an) -> {
         //            double A = an.getDouble("resolution", 8.3e-5);//8.3e-5
@@ -131,7 +131,7 @@ class NumassPlugin : BasicPlugin() {
         //            return new XYModel(spectrum, getAdapter(an));
         //        });
 
-        manager.addModel("scatter") { context, meta ->
+        library.addModel("scatter") { context, meta ->
             val A = meta.getDouble("resolution", 8.3e-5)//8.3e-5
             val from = meta.getDouble("from", 0.0)
             val to = meta.getDouble("to", 0.0)
@@ -148,7 +148,7 @@ class NumassPlugin : BasicPlugin() {
             XYModel(meta, getAdapter(meta), spectrum)
         }
 
-        manager.addModel("scatter-empiric") { context, meta ->
+        library.addModel("scatter-empiric") { context, meta ->
             val eGun = meta.getDouble("eGun", 19005.0)
 
             val interpolator = buildInterpolator(context, meta, eGun)
@@ -161,7 +161,7 @@ class NumassPlugin : BasicPlugin() {
             WeightedXYModel(meta, getAdapter(meta), spectrum) { dp -> weightReductionFactor }
         }
 
-        manager.addModel("scatter-empiric-variable") { context, meta ->
+        library.addModel("scatter-empiric-variable") { context, meta ->
             val eGun = meta.getDouble("eGun", 19005.0)
 
             //builder transmisssion with given data, annotation and smoothing
@@ -183,7 +183,7 @@ class NumassPlugin : BasicPlugin() {
             WeightedXYModel(meta, getAdapter(meta), spectrum) { dp -> weightReductionFactor }
         }
 
-        manager.addModel("scatter-analytic-variable") { context, meta ->
+        library.addModel("scatter-analytic-variable") { context, meta ->
             val eGun = meta.getDouble("eGun", 19005.0)
 
             val loss = VariableLossSpectrum.withGun(eGun + 5)
@@ -200,7 +200,7 @@ class NumassPlugin : BasicPlugin() {
             XYModel(meta, getAdapter(meta), spectrum)
         }
 
-        manager.addModel("scatter-empiric-experimental") { context, meta ->
+        library.addModel("scatter-empiric-experimental") { context, meta ->
             val eGun = meta.getDouble("eGun", 19005.0)
 
             //builder transmisssion with given data, annotation and smoothing
@@ -217,14 +217,14 @@ class NumassPlugin : BasicPlugin() {
             WeightedXYModel(meta, getAdapter(meta), spectrum) { dp -> weightReductionFactor }
         }
 
-        manager.addModel("sterile") { context, meta ->
+        library.addModel("sterile") { context, meta ->
             val sp = SterileNeutrinoSpectrum(context, meta)
             val spectrum = NBkgSpectrum(sp)
 
             XYModel(meta, getAdapter(meta), spectrum)
         }
 
-        manager.addModel("gun") { context, meta ->
+        library.addModel("gun") { context, meta ->
             val gsp = GunSpectrum()
 
             val tritiumBackground = meta.getDouble("tritiumBkg", 0.0)

numass-main/src/main/kotlin/inr/numass/models/NumassModels.kt

@@ -3,7 +3,6 @@ package inr.numass.models
 import hep.dataforge.maths.integration.UnivariateIntegrator
 import hep.dataforge.names.Names
 import hep.dataforge.stat.models.Model
-import hep.dataforge.stat.models.ModelDescriptor
 import hep.dataforge.stat.models.ModelFactory
 import hep.dataforge.stat.parametric.AbstractParametricFunction
 import hep.dataforge.stat.parametric.ParametricFunction
@@ -14,8 +13,8 @@ import inr.numass.utils.NumassIntegrator
 import java.util.stream.Stream
 
 
-fun model(name: String, descriptor: ModelDescriptor? = null, factory: ContextMetaFactory<Model>): ModelFactory {
+fun model(name: String,factory: ContextMetaFactory<Model>): ModelFactory {
-    return ModelFactory.build(name, descriptor, factory);
+    return ModelFactory.build(name, factory);
 }
 
 // spectra operations

numass-main/src/main/kotlin/inr/numass/models/misc/LossCalculator.kt

@@ -21,6 +21,10 @@ import hep.dataforge.plots.PlotFrame
 import hep.dataforge.plots.XYFunctionPlot
 import hep.dataforge.utils.Misc
 import hep.dataforge.values.Values
+import kotlinx.coroutines.experimental.CompletableDeferred
+import kotlinx.coroutines.experimental.Deferred
+import kotlinx.coroutines.experimental.async
+import kotlinx.coroutines.experimental.runBlocking
 import org.apache.commons.math3.analysis.BivariateFunction
 import org.apache.commons.math3.analysis.UnivariateFunction
 import org.apache.commons.math3.exception.OutOfRangeException
@@ -35,9 +39,7 @@ import java.util.*
  * @author Darksnake
  */
 object LossCalculator {
-    private val cache = HashMap<Int, UnivariateFunction>().also {
+    private val cache = HashMap<Int, Deferred<UnivariateFunction>>()
-        it[1] = singleScatterFunction
-    }
 
 
     fun getGunLossProbabilities(X: Double): List<Double> {
@@ -65,8 +67,23 @@ object LossCalculator {
         return res
     }
 
-    fun getGunZeroLossProb(X: Double): Double {
+    fun getGunZeroLossProb(x: Double): Double {
-        return Math.exp(-X)
+        return Math.exp(-x)
+    }
+
+
+    private fun getCachedSpectrum(order: Int): Deferred<UnivariateFunction> {
+        return when {
+            order <= 0 -> error("Non-positive loss cache order")
+            order == 1 -> CompletableDeferred(singleScatterFunction)
+            else -> cache.getOrPut(order) {
+                async {
+                    LoggerFactory.getLogger(javaClass)
+                            .debug("Scatter cache of order {} not found. Updating", order)
+                    getNextLoss(getMargin(order), getCachedSpectrum(order - 1).await())
+                }
+            }
+        }
     }
 
     /**
@@ -75,22 +92,8 @@ object LossCalculator {
      * @param order
      * @return
      */
-    private fun getLoss(order: Int): UnivariateFunction? {
+    private fun getLoss(order: Int): UnivariateFunction {
-        if (order <= 0) {
+        return runBlocking { getCachedSpectrum(order).await() }
-            throw IllegalArgumentException()
-        }
-        return if (cache.containsKey(order)) {
-            cache[order]
-        } else {
-            synchronized(this) {
-                cache.getOrPut(order) {
-                    LoggerFactory.getLogger(javaClass)
-                            .debug("Scatter cache of order {} not found. Updating", order)
-                    getNextLoss(getMargin(order), getLoss(order - 1))
-                }
-                return cache[order]
-            }
-        }
     }
 
     fun getLossFunction(order: Int): BivariateFunction {
@@ -122,7 +125,6 @@ object LossCalculator {
      * @return
      */
     fun getLossProbabilities(x: Double): List<Double> {
-        return (lossProbCache).getOrPut(x) {
         val res = ArrayList<Double>()
         var prob: Double
         if (x > 0) {
@@ -130,7 +132,7 @@ object LossCalculator {
         } else {
             // если x ==0, то выживает только нулевой член, первый равен нулю
             res.add(1.0)
-                return@getOrPut res
+            return res
         }
         res.add(prob)
 
@@ -146,8 +148,7 @@ object LossCalculator {
             res.add(prob)
         }
 
-            res
+        return res
-        }
     }
 
     fun getLossProbability(order: Int, X: Double): Double {
@@ -167,12 +168,10 @@ object LossCalculator {
     }
 
     fun getLossValue(order: Int, Ei: Double, Ef: Double): Double {
-        return if (Ei - Ef < 5.0) {
+        return when {
-            0.0
+            Ei - Ef < 5.0 -> 0.0
-        } else if (Ei - Ef >= getMargin(order)) {
+            Ei - Ef >= getMargin(order) -> 0.0
-            0.0
+            else -> getLoss(order).value(Ei - Ef)
-        } else {
-            getLoss(order)!!.value(Ei - Ef)
         }
     }
 
@@ -261,11 +260,11 @@ object LossCalculator {
      * @return
      */
     fun getTotalLossValue(x: Double, Ei: Double, Ef: Double): Double {
-        if (x == 0.0) {
+        return if (x == 0.0) {
-            return 0.0
+            0.0
         } else {
             val probs = getLossProbabilities(x)
-            return (0 until probs.size).sumByDouble { i ->
+            (1 until probs.size).sumByDouble { i ->
                 probs[i] * getLossValue(i, Ei, Ef)
             }
         }

numass-main/src/main/kotlin/inr/numass/models/sterile/NumassTransmission.kt

@@ -10,7 +10,7 @@ import hep.dataforge.maths.functions.FunctionLibrary
 import hep.dataforge.meta.Meta
 import hep.dataforge.stat.parametric.AbstractParametricBiFunction
 import hep.dataforge.values.Values
-import inr.numass.models.LossCalculator
+import inr.numass.models.misc.LossCalculator
 import inr.numass.utils.ExpressionUtils
 import org.apache.commons.math3.analysis.BivariateFunction
 import org.slf4j.LoggerFactory
@@ -20,7 +20,6 @@ import java.util.*
  * @author [Alexander Nozik](mailto:altavir@gmail.com)
  */
 class NumassTransmission(context: Context, meta: Meta) : AbstractParametricBiFunction(list) {
-    private val calculator: LossCalculator = LossCalculator.instance()
     private val trapFunc: BivariateFunction
     //private val lossCache = HashMap<Double, UnivariateFunction>()
 
@@ -55,13 +54,13 @@ class NumassTransmission(context: Context, meta: Meta) : AbstractParametricBiFun
     }
 
     fun p0(eIn: Double, set: Values): Double {
-        return LossCalculator.instance().getLossProbability(0, getX(eIn, set))
+        return LossCalculator.getLossProbability(0, getX(eIn, set))
     }
 
     override fun derivValue(parName: String, eIn: Double, eOut: Double, set: Values): Double {
         return when (parName) {
             "trap" -> trapFunc.value(eIn, eOut)
-            "X" -> calculator.getTotalLossDeriv(getX(eIn, set), eIn, eOut)
+            "X" -> LossCalculator.getTotalLossDeriv(getX(eIn, set), eIn, eOut)
             else -> super.derivValue(parName, eIn, eOut, set)
         }
     }
@@ -74,7 +73,7 @@ class NumassTransmission(context: Context, meta: Meta) : AbstractParametricBiFun
         //calculate X taking into account its energy dependence
         val X = getX(eIn, set)
         // loss part
-        val loss = calculator.getTotalLossValue(X, eIn, eOut)
+        val loss = LossCalculator.getTotalLossValue(X, eIn, eOut)
         //        double loss;
         //
         //        if(eIn-eOut >= 300){

numass-main/src/main/kotlin/inr/numass/scripts/models/IntegralSpectrum.kt

@@ -31,10 +31,11 @@ import hep.dataforge.stat.fit.FitStage
 import hep.dataforge.stat.fit.FitState
 import hep.dataforge.stat.fit.ParamSet
 import hep.dataforge.stat.models.XYModel
-import hep.dataforge.tables.Adapters
+import hep.dataforge.tables.Adapters.X_AXIS
-import hep.dataforge.tables.ListTable
+import hep.dataforge.values.ValueMap
 import inr.numass.NumassPlugin
 import inr.numass.data.SpectrumAdapter
+import inr.numass.data.SpectrumGenerator
 import inr.numass.models.NBkgSpectrum
 import inr.numass.models.sterile.SterileNeutrinoSpectrum
 import kotlinx.coroutines.experimental.launch
@@ -98,14 +99,20 @@ fun main(args: Array<String>) {
 
         val x = (14000.0..18400.0).step(100.0).toList()
 
-        val table = ListTable.Builder(Adapters.getFormat(adapter)).apply {
-            x.forEach { u ->
-                row(adapter.buildSpectrumDataPoint(u, t * spectrum.value(u, params).toLong(), t.toDouble()))
-            }
-        }.build()
-
         val model = XYModel(Meta.empty(), adapter, spectrum)
-        val state = FitState(table, model, params)
+
+        val generator = SpectrumGenerator(model, paramsMod, 12316);
+        val configuration = x.map { ValueMap.ofPairs(X_AXIS to it, "time" to t) }
+        val data  = generator.generateData(configuration);
+
+//        val table = ListTable.Builder(Adapters.getFormat(adapter)).apply {
+//            x.forEach { u ->
+//                row(adapter.buildSpectrumDataPoint(u, t * spectrum.value(u, paramsMod).toLong(), t.toDouble()))
+//            }
+//        }.build()
+
+
+        val state = FitState(data, model, params)
         val res = fm.runStage(state, "QOW", FitStage.TASK_RUN, "N", "E0","bkg")
 
         res.printState(PrintWriter(System.out))

numass-main/src/main/kotlin/inr/numass/scripts/tristan/AnalyzeTristan.kt

@@ -12,14 +12,14 @@ fun main(args: Array<String>) {
     val point = ProtoNumassPoint.readFile(file)
     point.plotAmplitudeSpectrum()
 
-    point.blocks.filter { it.channel == 0 }.firstOrNull()?.let {
+    point.blocks.firstOrNull { it.channel == 0 }?.let {
         it.plotAmplitudeSpectrum(plotName = "0") {
             "title" to "pixel 0"
             "binning" to 50
         }
     }
 
-    point.blocks.filter { it.channel == 4 }.firstOrNull()?.let {
+    point.blocks.firstOrNull { it.channel == 4 }?.let {
         it.plotAmplitudeSpectrum(plotName = "4") {
             "title" to "pixel 4"
             "binning" to 50