From 99e5ca14506e7568e509cd794ad31c206f4e5082 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreas=20Dr=C3=A4ger?= Date: Wed, 30 Jul 2008 15:44:40 +0000 Subject: [PATCH] The metabolic models of Corynebacterium glutamicum are now part of EvA2. --- src/eva2/OptimizerFactory.java | 338 +- .../go/problems/inference/des/DESSolver.java | 54 + .../go/problems/inference/des/DESystem.java | 43 + .../go/problems/inference/des/RKSolver.java | 419 ++ .../AbstractCurveFittingProblem.java | 303 ++ .../AbstractValineCurveFittingProblem.java | 195 + .../AbstractValineSplineFittingProblem.java | 81 + .../inference/metabolic/CKMMiProblem.java | 81 + .../inference/metabolic/CKMMrProblem.java | 76 + .../inference/metabolic/GMAKiProblem.java | 65 + .../inference/metabolic/GMAKrProblem.java | 66 + .../inference/metabolic/GMMiProblem.java | 107 + .../inference/metabolic/GMMrProblem.java | 81 + .../inference/metabolic/TimeSeries.java | 243 ++ .../metabolic/odes/AbstractValineSystem.java | 3755 +++++++++++++++++ .../inference/metabolic/odes/CKMMiSystem.java | 156 + .../inference/metabolic/odes/CKMMrSystem.java | 117 + .../inference/metabolic/odes/GMAKiSystem.java | 69 + .../inference/metabolic/odes/GMAKrSystem.java | 116 + .../inference/metabolic/odes/GMMiSystem.java | 120 + .../inference/metabolic/odes/GMMrSystem.java | 87 + .../go/strategies/MonteCarloSearch.java | 12 +- 22 files changed, 6428 insertions(+), 156 deletions(-) create mode 100644 src/eva2/server/go/problems/inference/des/DESSolver.java create mode 100644 src/eva2/server/go/problems/inference/des/DESystem.java create mode 100644 src/eva2/server/go/problems/inference/des/RKSolver.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/AbstractCurveFittingProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/AbstractValineCurveFittingProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/AbstractValineSplineFittingProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/CKMMiProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/CKMMrProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/GMAKiProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/GMAKrProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/GMMiProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/GMMrProblem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/TimeSeries.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/odes/AbstractValineSystem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/odes/CKMMiSystem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/odes/CKMMrSystem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/odes/GMAKiSystem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/odes/GMAKrSystem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/odes/GMMiSystem.java create mode 100644 src/eva2/server/go/problems/inference/metabolic/odes/GMMrSystem.java diff --git a/src/eva2/OptimizerFactory.java b/src/eva2/OptimizerFactory.java index bc264dbb..2025615b 100644 --- a/src/eva2/OptimizerFactory.java +++ b/src/eva2/OptimizerFactory.java @@ -49,7 +49,6 @@ import eva2.server.go.strategies.SimulatedAnnealing; import eva2.server.go.strategies.Tribes; import eva2.server.modules.GOParameters; - /** *

* The OptimizerFactory allows quickly creating some optimizers without thinking @@ -59,9 +58,9 @@ import eva2.server.modules.GOParameters; *

*

* On the other hand this class provides an almost complete list of all - * currently available optimization procedures in EvA2. The arguments passed - * to the methods initialize the respective optimization procedure. To perform - * an optimization one has to do the following: + * currently available optimization procedures in EvA2. The arguments passed to + * the methods initialize the respective optimization procedure. To perform an + * optimization one has to do the following: * InterfaceOptimizer optimizer = OptimizerFactory.createCertainOptimizer(arguments); * EvaluationTerminator terminator = new EvaluationTerminator(); * terminator.setFitnessCalls(numOfFitnessCalls); @@ -76,33 +75,33 @@ import eva2.server.modules.GOParameters; * @date 17.04.2007 */ public class OptimizerFactory { - private static InterfaceTerminator term = null; + private static InterfaceTerminator term = null; - public final static int STD_ES = 1; + public final static int STD_ES = 1; - public final static int CMA_ES = 2; + public final static int CMA_ES = 2; - public final static int STD_GA = 3; + public final static int STD_GA = 3; - public final static int PSO = 4; + public final static int PSO = 4; - public final static int DE = 5; + public final static int DE = 5; - public final static int TRIBES = 6; + public final static int TRIBES = 6; - public final static int RANDOM = 7; + public final static int RANDOM = 7; - public final static int HILLCL = 8; + public final static int HILLCL = 8; - public final static int CBN_ES = 9; + public final static int CBN_ES = 9; - public final static int CL_HILLCL = 10; + public final static int CL_HILLCL = 10; - public final static int defaultFitCalls = 10000; + public final static int defaultFitCalls = 10000; - public final static int randSeed = 0; + public final static int randSeed = 0; - private static OptimizerRunnable lastRunnable = null; + private static OptimizerRunnable lastRunnable = null; /** * Add an InterfaceTerminator to any new optimizer in a boolean combination. @@ -110,15 +109,16 @@ public class OptimizerFactory { * bAnd is true, and as in (TOld || TNew) if bAnd is false. * * @param newTerm - * a new InterfaceTerminator instance + * a new InterfaceTerminator instance * @param bAnd - * indicate the boolean combination + * indicate the boolean combination */ public static void addTerminator(InterfaceTerminator newTerm, boolean bAnd) { if (OptimizerFactory.term == null) OptimizerFactory.term = term; - else setTerminator(new CombinedTerminator(OptimizerFactory.term, newTerm, - bAnd)); + else + setTerminator(new CombinedTerminator(OptimizerFactory.term, + newTerm, bAnd)); } public static final GOParameters cbnES(AbstractOptimizationProblem problem) { @@ -141,7 +141,7 @@ public class OptimizerFactory { } public static final GOParameters clusteringHillClimbing( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { ClusteringHillClimbing chc = new ClusteringHillClimbing(); chc.SetProblem(problem); Population pop = new Population(); @@ -165,7 +165,7 @@ public class OptimizerFactory { AbstractEAIndividual indyTemplate = problem.getIndividualTemplate(); if ((indyTemplate != null) - && (indyTemplate instanceof InterfaceESIndividual)) { + && (indyTemplate instanceof InterfaceESIndividual)) { // Set CMA operator for mutation AbstractEAIndividual indy = (AbstractEAIndividual) indyTemplate; MutateESCovarianceMartixAdaption cmaMut = new MutateESCovarianceMartixAdaption(); @@ -174,7 +174,7 @@ public class OptimizerFactory { indy.setCrossoverOperator(new CrossoverESDefault()); } else { System.err - .println("Error, CMA-ES is implemented for ES individuals only (requires double data types)"); + .println("Error, CMA-ES is implemented for ES individuals only (requires double data types)"); return null; } @@ -190,14 +190,15 @@ public class OptimizerFactory { * @param problem * @param popsize * @param f - * @param k + * @param CR * @param lambda * @param listener * @return An optimization algorithm that performs differential evolution. */ public static final DifferentialEvolution createDifferentialEvolution( - AbstractOptimizationProblem problem, int popsize, double f, - double lambda, double k, InterfacePopulationChangedEventListener listener) { + AbstractOptimizationProblem problem, int popsize, double f, + double lambda, double CR, + InterfacePopulationChangedEventListener listener) { problem.initProblem(); @@ -212,7 +213,7 @@ public class OptimizerFactory { de.getPopulation().setPopulationSize(popsize); de.getDEType().setSelectedTag(1); de.setF(f); - de.setK(k); + de.setK(CR); de.setLambda(lambda); de.addPopulationChangedEventListener(listener); de.init(); @@ -226,6 +227,7 @@ public class OptimizerFactory { * @param mu * @param lambda * @param plus + * if true this operator uses elitism otherwise a comma strategy. * @param mutationoperator * @param pm * @param crossoveroperator @@ -236,10 +238,10 @@ public class OptimizerFactory { * @return An optimization algorithm that employes an evolution strategy. */ public static final EvolutionStrategies createEvolutionStrategy(int mu, - int lambda, boolean plus, InterfaceMutation mutationoperator, double pm, - InterfaceCrossover crossoveroperator, double pc, - InterfaceSelection selection, AbstractOptimizationProblem problem, - InterfacePopulationChangedEventListener listener) { + int lambda, boolean plus, InterfaceMutation mutationoperator, + double pm, InterfaceCrossover crossoveroperator, double pc, + InterfaceSelection selection, AbstractOptimizationProblem problem, + InterfacePopulationChangedEventListener listener) { problem.initProblem(); // RNG.setRandomSeed(100); @@ -276,10 +278,10 @@ public class OptimizerFactory { * @return An optimization algorithm that employes an genetic algorithm. */ public static final GeneticAlgorithm createGeneticAlgorithm( - InterfaceMutation mut, double pm, InterfaceCrossover cross, double pc, - InterfaceSelection select, int popsize, - AbstractOptimizationProblem problem, - InterfacePopulationChangedEventListener listener) { + InterfaceMutation mut, double pm, InterfaceCrossover cross, + double pc, InterfaceSelection select, int popsize, + AbstractOptimizationProblem problem, + InterfacePopulationChangedEventListener listener) { problem.initProblem(); @@ -299,48 +301,59 @@ public class OptimizerFactory { return ga; } - - /** - * This method creates a multi-objective EA optimizer. Remember to set a multi-objective - * selection method within the specific optimizer. This uses a standard archiving strategy (NSGAII) - * and InformationRetrievalInserting. - * - * @param subOpt the specific optimizer to use - * @param archiveSize maximum size of the archive - * @param problem - * @param listener - * @return An optimization algorithm that employs a multi-objective optimizer - */ - public static final MultiObjectiveEA createMultiObjectiveEA( - InterfaceOptimizer subOpt, int archiveSize, - AbstractOptimizationProblem problem, - InterfacePopulationChangedEventListener listener) { - return createMultiObjectiveEA(subOpt, new ArchivingNSGAII(), archiveSize, new InformationRetrievalInserting(), problem, listener); - } - /** - * This method creates a multi-objective EA optimizer. Remember to set a multi-objective - * selection method within the specific optimizer. + * This method creates a multi-objective EA optimizer. Remember to set a + * multi-objective selection method within the specific optimizer. This uses + * a standard archiving strategy (NSGAII) and InformationRetrievalInserting. * - * @param subOpt the specific optimizer to use - * @param archiving the archiving strategy collecting the pareto front - * @param archiveSize maximum size of the archive - * @param infoRetrieval information retrieval strategy + * @param subOpt + * the specific optimizer to use + * @param archiveSize + * maximum size of the archive * @param problem * @param listener - * @return An optimization algorithm that employs a multi-objective optimizer + * @return An optimization algorithm that employs a multi-objective + * optimizer */ public static final MultiObjectiveEA createMultiObjectiveEA( - InterfaceOptimizer subOpt, InterfaceArchiving archiving, int archiveSize, - InterfaceInformationRetrieval infoRetrieval, - AbstractOptimizationProblem problem, - InterfacePopulationChangedEventListener listener) { + InterfaceOptimizer subOpt, int archiveSize, + AbstractOptimizationProblem problem, + InterfacePopulationChangedEventListener listener) { + + return createMultiObjectiveEA(subOpt, new ArchivingNSGAII(), + archiveSize, new InformationRetrievalInserting(), problem, + listener); + } + + /** + * This method creates a multi-objective EA optimizer. Remember to set a + * multi-objective selection method within the specific optimizer. + * + * @param subOpt + * the specific optimizer to use + * @param archiving + * the archiving strategy collecting the pareto front + * @param archiveSize + * maximum size of the archive + * @param infoRetrieval + * information retrieval strategy + * @param problem + * @param listener + * @return An optimization algorithm that employs a multi-objective + * optimizer + */ + public static final MultiObjectiveEA createMultiObjectiveEA( + InterfaceOptimizer subOpt, InterfaceArchiving archiving, + int archiveSize, InterfaceInformationRetrieval infoRetrieval, + AbstractOptimizationProblem problem, + InterfacePopulationChangedEventListener listener) { problem.initProblem(); subOpt.SetProblem(problem); - return new MultiObjectiveEA(subOpt, archiving, archiveSize, infoRetrieval, problem); + return new MultiObjectiveEA(subOpt, archiving, archiveSize, + infoRetrieval, problem); } /** @@ -350,7 +363,7 @@ public class OptimizerFactory { * @return An optimization algorithm that performs gradient descent. */ public static final GradientDescentAlgorithm createGradientDescent( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { System.err.println("Currently not implemented!"); @@ -371,15 +384,15 @@ public class OptimizerFactory { * This method performs a Hill Climber algorithm. * * @param pop - * The size of the population + * The size of the population * @param problem - * The problem to be optimized + * The problem to be optimized * @param listener * @return An optimization procedure that performes hill climbing. */ public static final HillClimbing createHillClimber(int pop, - AbstractOptimizationProblem problem, - InterfacePopulationChangedEventListener listener) { + AbstractOptimizationProblem problem, + InterfacePopulationChangedEventListener listener) { problem.initProblem(); @@ -405,14 +418,15 @@ public class OptimizerFactory { * fitnesscalls. * * @param problem + * @param popsize * @param listener * @return An optimization procedure that performes the random walk. */ public static final MonteCarloSearch createMonteCarlo( - AbstractOptimizationProblem problem, - InterfacePopulationChangedEventListener listener) { - problem.initProblem(); + AbstractOptimizationProblem problem, int popsize, + InterfacePopulationChangedEventListener listener) { + problem.initProblem(); AbstractEAIndividual tmpIndi = problem.getIndividualTemplate(); tmpIndi.setMutationOperator(new NoMutation()); tmpIndi.setMutationProbability(0); @@ -420,6 +434,7 @@ public class OptimizerFactory { tmpIndi.setCrossoverProbability(0); MonteCarloSearch mc = new MonteCarloSearch(); + mc.getPopulation().setPopulationSize(popsize); mc.addPopulationChangedEventListener(listener); mc.SetProblem(problem); mc.init(); @@ -442,9 +457,10 @@ public class OptimizerFactory { * optimization. */ public static final ParticleSwarmOptimization createParticleSwarmOptimization( - AbstractOptimizationProblem problem, int popsize, double phi1, - double phi2, double k, InterfacePopulationChangedEventListener listener, - int selectedTopology) { + AbstractOptimizationProblem problem, int popsize, double phi1, + double phi2, double k, + InterfacePopulationChangedEventListener listener, + int selectedTopology) { problem.initProblem(); @@ -475,17 +491,17 @@ public class OptimizerFactory { * @param problem * @param popsize * @param alpha - * The parameter for the linear cooling + * The parameter for the linear cooling * @param temperature - * The initial temperature + * The initial temperature * @param mut * @param listener * @return Returns an optimizer that performs simulated annealing. */ public static final SimulatedAnnealing createSimulatedAnnealing( - AbstractOptimizationProblem problem, int popsize, double alpha, - double temperature, InterfaceMutation mut, - InterfacePopulationChangedEventListener listener) { + AbstractOptimizationProblem problem, int popsize, double alpha, + double temperature, InterfaceMutation mut, + InterfacePopulationChangedEventListener listener) { problem.initProblem(); @@ -508,7 +524,8 @@ public class OptimizerFactory { // /////////////////////////// Termination criteria public static InterfaceTerminator defaultTerminator() { - if (term == null) term = new EvaluationTerminator(defaultFitCalls); + if (term == null) + term = new EvaluationTerminator(defaultFitCalls); return term; } @@ -525,7 +542,7 @@ public class OptimizerFactory { // /////////////////////////// constructing a default OptimizerRunnable public static GOParameters getParams(final int optType, - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { switch (optType) { case STD_ES: return standardES(problem); @@ -548,27 +565,31 @@ public class OptimizerFactory { case CL_HILLCL: return clusteringHillClimbing(problem); default: - System.err.println("Error: optimizer type " + optType + " is unknown!"); + System.err.println("Error: optimizer type " + optType + + " is unknown!"); return null; } } public static OptimizerRunnable getOptRunnable(final int optType, - AbstractOptimizationProblem problem, int fitCalls, String outputFilePrefix) { + AbstractOptimizationProblem problem, int fitCalls, + String outputFilePrefix) { OptimizerRunnable opt = null; GOParameters params = getParams(optType, problem); if (params != null) { opt = new OptimizerRunnable(params, outputFilePrefix); if (fitCalls != defaultFitCalls) - opt.getGOParams().setTerminator(new EvaluationTerminator(fitCalls)); + opt.getGOParams().setTerminator( + new EvaluationTerminator(fitCalls)); } return opt; } // /////////////////////////// constructing a default OptimizerRunnable public static OptimizerRunnable getOptRunnable(final int optType, - AbstractOptimizationProblem problem, String outputFilePrefix) { - return getOptRunnable(optType, problem, defaultFitCalls, outputFilePrefix); + AbstractOptimizationProblem problem, String outputFilePrefix) { + return getOptRunnable(optType, problem, defaultFitCalls, + outputFilePrefix); } public static InterfaceTerminator getTerminator() { @@ -576,7 +597,7 @@ public class OptimizerFactory { } public static final GOParameters hillClimbing( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { HillClimbing hc = new HillClimbing(); hc.SetProblem(problem); Population pop = new Population(); @@ -590,8 +611,9 @@ public class OptimizerFactory { } // /////////////////////// Creating default strategies - public static GOParameters makeParams(InterfaceOptimizer opt, Population pop, - AbstractOptimizationProblem problem, long seed, InterfaceTerminator term) { + public static GOParameters makeParams(InterfaceOptimizer opt, + Population pop, AbstractOptimizationProblem problem, long seed, + InterfaceTerminator term) { GOParameters params = new GOParameters(); params.setProblem(problem); opt.SetProblem(problem); @@ -603,7 +625,7 @@ public class OptimizerFactory { } public static final GOParameters monteCarlo( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { MonteCarloSearch mc = new MonteCarloSearch(); Population pop = new Population(); pop.setPopulationSize(50); @@ -613,12 +635,13 @@ public class OptimizerFactory { // TODO hier weiter kommentieren public static OptimizerRunnable optimize(final int optType, - AbstractOptimizationProblem problem, String outputFilePrefix) { + AbstractOptimizationProblem problem, String outputFilePrefix) { return optimize(getOptRunnable(optType, problem, outputFilePrefix)); } public static OptimizerRunnable optimize(OptimizerRunnable runnable) { - if (runnable == null) return null; + if (runnable == null) + return null; new Thread(runnable).run(); lastRunnable = runnable; return runnable; @@ -636,25 +659,27 @@ public class OptimizerFactory { * @return */ public static OptimizerRunnable optimizeInThread(final int optType, - AbstractOptimizationProblem problem, String outputFilePrefix) { + AbstractOptimizationProblem problem, String outputFilePrefix) { OptimizerRunnable runnable = getOptRunnable(optType, problem, - outputFilePrefix); - if (runnable != null) new Thread(runnable).start(); + outputFilePrefix); + if (runnable != null) + new Thread(runnable).start(); return runnable; } // ///////////////////////////// Optimize a given parameter instance public static BitSet optimizeToBinary(GOParameters params, - String outputFilePrefix) { + String outputFilePrefix) { OptimizerRunnable runnable = optimize(new OptimizerRunnable(params, - outputFilePrefix)); + outputFilePrefix)); return runnable.getBinarySolution(); } // ///////////////////////////// Optimize using a default strategy public static BitSet optimizeToBinary(final int optType, - AbstractOptimizationProblem problem, String outputFilePrefix) { - OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); + AbstractOptimizationProblem problem, String outputFilePrefix) { + OptimizerRunnable runnable = optimize(optType, problem, + outputFilePrefix); return (runnable != null) ? runnable.getBinarySolution() : null; } @@ -665,15 +690,16 @@ public class OptimizerFactory { } public static double[] optimizeToDouble(GOParameters params, - String outputFilePrefix) { + String outputFilePrefix) { OptimizerRunnable runnable = optimize(new OptimizerRunnable(params, - outputFilePrefix)); + outputFilePrefix)); return runnable.getDoubleSolution(); } public static double[] optimizeToDouble(final int optType, - AbstractOptimizationProblem problem, String outputFilePrefix) { - OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); + AbstractOptimizationProblem problem, String outputFilePrefix) { + OptimizerRunnable runnable = optimize(optType, problem, + outputFilePrefix); return (runnable != null) ? runnable.getDoubleSolution() : null; } @@ -683,15 +709,16 @@ public class OptimizerFactory { } public static IndividualInterface optimizeToInd(GOParameters params, - String outputFilePrefix) { + String outputFilePrefix) { OptimizerRunnable runnable = optimize(new OptimizerRunnable(params, - outputFilePrefix)); + outputFilePrefix)); return runnable.getResult(); } public static IndividualInterface optimizeToInd(final int optType, - AbstractOptimizationProblem problem, String outputFilePrefix) { - OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); + AbstractOptimizationProblem problem, String outputFilePrefix) { + OptimizerRunnable runnable = optimize(optType, problem, + outputFilePrefix); return (runnable != null) ? runnable.getResult() : null; } @@ -701,15 +728,16 @@ public class OptimizerFactory { } public static Population optimizeToPop(GOParameters params, - String outputFilePrefix) { + String outputFilePrefix) { OptimizerRunnable runnable = optimize(new OptimizerRunnable(params, - outputFilePrefix)); + outputFilePrefix)); return runnable.getSolutionSet(); } public static Population optimizeToPop(final int optType, - AbstractOptimizationProblem problem, String outputFilePrefix) { - OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); + AbstractOptimizationProblem problem, String outputFilePrefix) { + OptimizerRunnable runnable = optimize(optType, problem, + outputFilePrefix); return (runnable != null) ? runnable.getSolutionSet() : null; } @@ -720,7 +748,7 @@ public class OptimizerFactory { public static Population postProcess(int steps, double sigma, int nBest) { return (lastRunnable == null) ? null : postProcess(lastRunnable, - new PostProcessParams(steps, sigma, nBest)); + new PostProcessParams(steps, sigma, nBest)); } public static Population postProcess(InterfacePostProcessParams ppp) { @@ -728,39 +756,42 @@ public class OptimizerFactory { } public static Population postProcess(OptimizerRunnable runnable, int steps, - double sigma, int nBest) { + double sigma, int nBest) { PostProcessParams ppp = new PostProcessParams(steps, sigma, nBest); return postProcess(runnable, ppp); } public static Population postProcess(OptimizerRunnable runnable, - InterfacePostProcessParams ppp) { + InterfacePostProcessParams ppp) { runnable.setDoRestart(true); runnable.setDoPostProcessOnly(true); runnable.setPostProcessingParams(ppp); - runnable.run(); // this run will not set the lastRunnable - postProcessing + runnable.run(); // this run will not set the lastRunnable - + // postProcessing // starts always anew return runnable.getSolutionSet(); } public static Vector postProcessBinVec(int steps, double sigma, - int nBest) { + int nBest) { return (lastRunnable != null) ? postProcessBinVec(lastRunnable, - new PostProcessParams(steps, sigma, nBest)) : null; + new PostProcessParams(steps, sigma, nBest)) : null; } - public static Vector postProcessBinVec(InterfacePostProcessParams ppp) { - return (lastRunnable != null) ? postProcessBinVec(lastRunnable, ppp) : null; + public static Vector postProcessBinVec( + InterfacePostProcessParams ppp) { + return (lastRunnable != null) ? postProcessBinVec(lastRunnable, ppp) + : null; } public static Vector postProcessBinVec(OptimizerRunnable runnable, - int steps, double sigma, int nBest) { + int steps, double sigma, int nBest) { return postProcessBinVec(runnable, new PostProcessParams(steps, sigma, - nBest)); + nBest)); } public static Vector postProcessBinVec(OptimizerRunnable runnable, - InterfacePostProcessParams ppp) { + InterfacePostProcessParams ppp) { Population resPop = postProcess(runnable, ppp); Vector ret = new Vector(resPop.size()); for (Object o : resPop) { @@ -773,24 +804,25 @@ public class OptimizerFactory { } public static Vector postProcessDblVec(int steps, double sigma, - int nBest) { + int nBest) { return (lastRunnable == null) ? null : postProcessDblVec(lastRunnable, - new PostProcessParams(steps, sigma, nBest)); + new PostProcessParams(steps, sigma, nBest)); } public static Vector postProcessDblVec( - InterfacePostProcessParams ppp) { - return (lastRunnable != null) ? postProcessDblVec(lastRunnable, ppp) : null; + InterfacePostProcessParams ppp) { + return (lastRunnable != null) ? postProcessDblVec(lastRunnable, ppp) + : null; } - public static Vector postProcessDblVec(OptimizerRunnable runnable, - int steps, double sigma, int nBest) { + public static Vector postProcessDblVec( + OptimizerRunnable runnable, int steps, double sigma, int nBest) { return postProcessDblVec(runnable, new PostProcessParams(steps, sigma, - nBest)); + nBest)); } - public static Vector postProcessDblVec(OptimizerRunnable runnable, - InterfacePostProcessParams ppp) { + public static Vector postProcessDblVec( + OptimizerRunnable runnable, InterfacePostProcessParams ppp) { Population resPop = postProcess(runnable, ppp); Vector ret = new Vector(resPop.size()); for (Object o : resPop) { @@ -803,28 +835,29 @@ public class OptimizerFactory { } public static Vector postProcessIndVec(int steps, - double sigma, int nBest) { + double sigma, int nBest) { return (lastRunnable != null) ? postProcessIndVec(lastRunnable, - new PostProcessParams(steps, sigma, nBest)) : null; + new PostProcessParams(steps, sigma, nBest)) : null; } public static Vector postProcessIndVec( - InterfacePostProcessParams ppp) { - return (lastRunnable != null) ? postProcessIndVec(lastRunnable, ppp) : null; + InterfacePostProcessParams ppp) { + return (lastRunnable != null) ? postProcessIndVec(lastRunnable, ppp) + : null; } // /////////////////////////// post processing public static Vector postProcessIndVec( - OptimizerRunnable runnable, int steps, double sigma, int nBest) { + OptimizerRunnable runnable, int steps, double sigma, int nBest) { return postProcessIndVec(runnable, new PostProcessParams(steps, sigma, - nBest)); + nBest)); } public static Vector postProcessIndVec( - OptimizerRunnable runnable, InterfacePostProcessParams ppp) { + OptimizerRunnable runnable, InterfacePostProcessParams ppp) { Population resPop = postProcess(runnable, ppp); - Vector ret = new Vector(resPop - .size()); + Vector ret = new Vector( + resPop.size()); for (Object o : resPop) { if (o instanceof AbstractEAIndividual) { AbstractEAIndividual indy = (AbstractEAIndividual) o; @@ -839,7 +872,8 @@ public class OptimizerFactory { } public static void setFitnessConvergenceTerminator(double fitThresh) { - setTerminator(new FitnessConvergenceTerminator(fitThresh, 100, true, true)); + setTerminator(new FitnessConvergenceTerminator(fitThresh, 100, true, + true)); } public static void setTerminator(InterfaceTerminator term) { @@ -854,11 +888,11 @@ public class OptimizerFactory { */ public static String showOptimizers() { return "1: Standard ES \n2: CMA-ES \n3: GA \n4: PSO \n5: DE \n6: Tribes \n7: Random (Monte Carlo) " - + "\n8: Hill-Climbing \n9: Cluster-based niching ES \n10: Clustering Hill-Climbing"; + + "\n8: Hill-Climbing \n9: Cluster-based niching ES \n10: Clustering Hill-Climbing"; } public static final GOParameters standardDE( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { DifferentialEvolution de = new DifferentialEvolution(); Population pop = new Population(); pop.setPopulationSize(50); @@ -873,7 +907,7 @@ public class OptimizerFactory { } public static final GOParameters standardES( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { EvolutionStrategies es = new EvolutionStrategies(); es.setMu(15); es.setLambda(50); @@ -887,7 +921,7 @@ public class OptimizerFactory { indy.setCrossoverOperator(new CrossoverESDefault()); } else { System.err - .println("Error, standard ES is implemented for ES individuals only (requires double data types)"); + .println("Error, standard ES is implemented for ES individuals only (requires double data types)"); return null; } @@ -898,7 +932,7 @@ public class OptimizerFactory { } public static final GOParameters standardGA( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { GeneticAlgorithm ga = new GeneticAlgorithm(); Population pop = new Population(); pop.setPopulationSize(100); @@ -909,7 +943,7 @@ public class OptimizerFactory { } public static final GOParameters standardPSO( - AbstractOptimizationProblem problem) { + AbstractOptimizationProblem problem) { ParticleSwarmOptimization pso = new ParticleSwarmOptimization(); Population pop = new Population(); pop.setPopulationSize(30); diff --git a/src/eva2/server/go/problems/inference/des/DESSolver.java b/src/eva2/server/go/problems/inference/des/DESSolver.java new file mode 100644 index 00000000..d3cc9709 --- /dev/null +++ b/src/eva2/server/go/problems/inference/des/DESSolver.java @@ -0,0 +1,54 @@ +/** + * Title: JAVA-EVA Description: Copyright: Copyright (c) 2002 Company: + * University of Tübingen, Computer Architecture + * + * @author + * @version 1.0 + */ + +package eva2.server.go.problems.inference.des; + +import java.io.Serializable; + +/** + * + * TODO: comment missing + * + * @since 2.0 + * @version + * Copyright (c) ZBiT, University of Tübingen, Germany + * Compiler: JDK 1.6.0 + * @date Sep 10, 2007 + * + */ +public interface DESSolver extends Serializable { + + /** + * put your documentation comment here + * + * @param DES + * @param initalvalue + * @param x + * @param h + * @param steps + * @return + */ + public double[][] solve(DESystem DES, double[] initalvalue, double x, + double h, int steps); + + public double[][] solveAtTimePoints(DESystem DES, double[] initialvalue, + double[] timepoints); + + public double[][] solveAtTimePointsWithInitialConditions(DESystem DES, + double[][] initconditions, double[] timepoints); + + public boolean isUnstable(); + + /* + * public double[][] solveatTimepointsSSystem (double[] param, int order, + * double[] initialvalue, double[] timepoints); public boolean + * lastDESystemInvalid(); public void plotY(); public double[][] + * solveatTimepointsSSystemSeparated (double[] param, int order, double[] + * initialvalue, double[] timepoints, GEdata gedata, int tooptimize); + */ +} diff --git a/src/eva2/server/go/problems/inference/des/DESystem.java b/src/eva2/server/go/problems/inference/des/DESystem.java new file mode 100644 index 00000000..fcd4af8c --- /dev/null +++ b/src/eva2/server/go/problems/inference/des/DESystem.java @@ -0,0 +1,43 @@ +package eva2.server.go.problems.inference.des; + +import java.io.Serializable; + +/** + * Title: JAVA-EVA Description: Copyright: Copyright (c) 2002 Company: + * University of Tübingen, Computer Architecture + * + * @author Hannes Planatscher + * @version 1.0 + */ + +public interface DESystem extends Serializable { + + /** + * Returns the number of dimensions of this ODE system. + * + * @return Returns the number of dimensions of this ODE system. + */ + public int getDESystemDimension(); + + /** + * Returns the value of the ODE system at the time t given the current + * values of Y + * + * @param t + * @param Y + * @return + * @deprecated use getValue(double t, double[] Y, double[] res) to avoid + * array reallocations and gain speed + */ + public double[] getValue(double t, double[] Y); + + /** + * Returns the value of the ODE system at the time t given the current + * values of Y within resultVector. + * + * @param t + * @param Y + * @param resultVector + */ + public void getValue(double t, double[] Y, double[] resultVector); +} diff --git a/src/eva2/server/go/problems/inference/des/RKSolver.java b/src/eva2/server/go/problems/inference/des/RKSolver.java new file mode 100644 index 00000000..1228a9ef --- /dev/null +++ b/src/eva2/server/go/problems/inference/des/RKSolver.java @@ -0,0 +1,419 @@ +package eva2.server.go.problems.inference.des; + +import eva2.tools.Mathematics; + +/** + * Title: JAVA-EVA Description: Runge-Kutta Method Copyright: Copyright (c) 2002 + * Company: University of Tübingen, Computer Architecture + * + * @author Hannes Planatscher + * @author Andreas Dräger + * @author Marcel Kronfeld + * @version 1.0 Status: works, but numerical inaccurate + */ +public class RKSolver implements DESSolver, java.io.Serializable { + double stepSize = 0.01; + boolean nonnegative = true; + boolean unstableFlag; + transient protected double[][] kVals = null; + transient protected double[] k0tmp, k1tmp, k2tmp; + private static boolean useLinearCalc = true; + + /** + * + */ + public RKSolver() { + } + + /** + * A constructor. + * + * @param withLinearCalc set whether the linear or old calculation method will be used. + */ + public RKSolver(boolean withLinearCalc) { + useLinearCalc = withLinearCalc; + } + + + /** + * put your documentation comment here + */ + public RKSolver(double stepSize) { + this.stepSize = stepSize; + } + + /** + * Set whether the linear or old calculation method will be used. + * + * @param withLinearCalc + */ + public void setWithLinearCalc(boolean withLinearCalc) { + useLinearCalc = withLinearCalc; + } + + /** + * put your documentation comment here + * + * @param DES + * @param initialValues + * @param x + * @param h + * @param steps + * @return + */ + public double[][] solve(DESystem DES, double[] initialValues, double x, + double h, int steps) { + double[] timeVector = new double[steps]; + for (int i = 0; i < steps; i++) + timeVector[i] = x + i * h; + return solveAtTimePoints(DES, initialValues, timeVector); + } + + /** + * @param DES + * @param initialValues + * @param timeBegin + * @param timeEnd + * @return + */ + public double[][] solveByStepSize(DESystem DES, double[] initialValues, + double timeBegin, double timeEnd) { + return solveByStepSize(DES, initialValues, timeBegin, timeEnd, false); + } + + /** + * @param DES + * @param initialValues + * @param timeBegin + * @param timeEnd + * @return + */ + public double[][] solveByStepSizeIncludingTime(DESystem DES, + double[] initialValues, double timeBegin, double timeEnd) { + return solveByStepSize(DES, initialValues, timeBegin, timeEnd, true); + } + + /** + * @param DES + * @param initialValues + * @param timeBegin + * @param timeEnd + * @return + */ + private double[][] solveByStepSize(DESystem DES, double[] initialValues, + double timeBegin, double timeEnd, boolean time) { + int numsteps = (int) Math.round(((timeEnd - timeBegin) / stepSize) + 1); + unstableFlag = false; + // System.out.println(numsteps); + int order = DES.getDESystemDimension(), i; + double[][] result; + if (time) { + result = new double[numsteps][order + 1]; + result[0][0] = timeBegin; + for (i = 0; i < order; i++) + result[0][i + 1] = initialValues[i]; + } else { + result = new double[numsteps][order]; + for (i = 0; i < order; i++) + result[0][i] = initialValues[i]; + } + double x = timeBegin; + for (i = 1; i < numsteps; i++) { + double h = stepSize, change[] = null, Ytemp[] = null; + if (time) { + double tmp[] = new double[result[i - 1].length - 1]; + System.arraycopy(result[i - 1], 1, tmp, 0, result[i - 1].length - 1); + change = rkTerm(DES, h, x, tmp); + Ytemp = Mathematics.vvAdd(tmp, change); + } else { + change = rkTerm(DES, h, x, result[i - 1]); + Ytemp = Mathematics.vvAdd(result[i - 1], change); + } + if (this.nonnegative) { + for (int k = 0; k < Ytemp.length; k++) { + if (Ytemp[k] < 0) Ytemp[k] = 0; + } + } + x += h; + if (time) { + System.arraycopy(Ytemp, 0, result[i], 1, Ytemp.length); + result[i][0] = x; + } else result[i] = Ytemp; + } + + return result; + } + + public double[][] solveAtTimePoints(DESystem DES, double[] initialValues, + double[] timePoints) { + return solveAtTimePoints(DES, initialValues, timePoints, false); + } + + /** + * This method returns a matrix in which the first column includes all time + * points. Every row is composed as time and all values at this time point. It + * uses the same integration method than the regular + * solveatTimepoints method. + * + * @param DES + * @param initialValues + * @param timePoints + * @return + */ + public double[][] solveAtTimePointsIncludingTime(DESystem DES, + double[] initialValues, double[] timePoints) { + return solveAtTimePoints(DES, initialValues, timePoints, true); + } + + /** + * When set to TRUE, includeTimes will make the + * solver to return a matrix with the first column containing the times. By + * default the result of the ODE solver just returns the values for Y. + * + * @param includeTimes + */ + private double[][] solveAtTimePoints(DESystem DES, double[] initialValues, + double[] timePoints, boolean includeTimes) { + // sorted timepoints!!!!!!!!!!!!!!!!!!!!! + int order = DES.getDESystemDimension(); + double result[][], x = timePoints[0]; + if (includeTimes) { + result = new double[timePoints.length][order + 1]; + result[0][0] = timePoints[0]; + for (int i = 1; i <= order; i++) + result[0][i] = initialValues[i - 1]; + } else { + result = new double[timePoints.length][order]; + for (int i = 0; i < order; i++) + result[0][i] = initialValues[i]; + } + // System.out.println("JavaCalled"); + unstableFlag = false; + + double h = stepSize; + double change[] = new double[order]; + double[] Ytemp = new double[order]; + + for (int i = 1; i < timePoints.length; i++) { + h = stepSize; + +// int inbetweensteps = (int) Math.round((timePoints[i] - timePoints[i - +// 1]) / h + 1); + int inbetweensteps = (int) Math.floor((timePoints[i] - timePoints[i - 1]) + / h); + + //System.out.println("inbetweensteps at " + i + ": " + inbetweensteps); + if (includeTimes) + System.arraycopy(result[i - 1], 1, Ytemp, 0, result[i - 1].length - 1); + else Ytemp = result[i - 1].clone(); + + for (int j = 0; j < inbetweensteps; j++) { + if (useLinearCalc) rkTerm2(DES, h, x, Ytemp, change); + else change = rkTerm(DES, h, x, Ytemp); +// System.out.println("aft change 0 " + change[0]); + + Mathematics.vvAdd(Ytemp, change, Ytemp); + + if (this.nonnegative) { + for (int k = 0; k < Ytemp.length; k++) { + if (Ytemp[k] < 0) Ytemp[k] = 0; + } + } + + x += h; + } + + h = timePoints[i] - x; + + if (useLinearCalc) rkTerm2(DES, h, x, Ytemp, change); + else change = rkTerm(DES, h, x, Ytemp); + + Mathematics.vvAdd(Ytemp, change, Ytemp); + + if (this.nonnegative) { + for (int k = 0; k < Ytemp.length; k++) { + if (Ytemp[k] < 0) Ytemp[k] = 0; + } + } + + if (includeTimes) { + result[i][0] = timePoints[i]; + System.arraycopy(Ytemp, 0, result[i], 1, Ytemp.length); + } else result[i] = Ytemp; + x += h; + + } + + return result; + } + + /** + * + */ + public double[][] solveAtTimePointsWithInitialConditions(DESystem DES, + double[][] initConditions, double[] timePoints) { + int order = DES.getDESystemDimension(); + double[][] result = new double[timePoints.length][order]; + result[0] = initConditions[0]; + double x = timePoints[0]; + for (int i = 1; i < timePoints.length; i++) { + double h = stepSize; + double[] Ytemp = new double[order]; + int inbetweensteps = (int) Math.floor((timePoints[i] - timePoints[i - 1]) + / h); + Ytemp = (double[]) initConditions[i - 1].clone(); + for (int j = 0; j < inbetweensteps; j++) { + double change[] = rkTerm(DES, h, x, Ytemp); + Ytemp = Mathematics.vvAdd(Ytemp, change); + x += h; + } + + h = timePoints[i] - x; + double change[] = rkTerm(DES, h, x, Ytemp); + + Ytemp = Mathematics.vvAdd(Ytemp, change); + + if (this.nonnegative) { + for (int k = 0; k < Ytemp.length; k++) { + if (Ytemp[k] < 0) { + Ytemp[k] = 0; + } + } + } + result[i] = Ytemp; + x += h; + } + + return result; + + } + + /** + * @param DES + * @param h + * @param x + * @param Ytemp + * @return + */ + public double[] rkTerm(DESystem DES, double h, double x, double[] Ytemp) { + double[][] K = new double[4][]; + K[0] = Mathematics.svMult(h, DES.getValue(x, Ytemp)); + K[1] = Mathematics.svMult(h, DES.getValue(x + h / 2, Mathematics.vvAdd(Ytemp, Mathematics.svMult(0.5, K[0])))); + K[2] = Mathematics.svMult(h, DES.getValue(x + h / 2, Mathematics.vvAdd(Ytemp, Mathematics.svMult(0.5, K[1])))); + K[3] = Mathematics.svMult(h, DES.getValue(x + h, Mathematics.vvAdd(Ytemp, K[2]))); + + double[] change = Mathematics.svDiv(6, Mathematics.vvAdd(K[0], Mathematics.vvAdd(Mathematics.svMult(2, K[1]), Mathematics.vvAdd(Mathematics.svMult( + 2, K[2]), K[3])))); + for (int k = 0; k < change.length; k++) { + if (Double.isNaN(change[k])) { + unstableFlag = true; + change[k] = 0; + // return result; + } + } + return change; + } + + /** + * Linearized code for speed-up (no allocations). + * + * @param DES + * @param h + * @param x + * @param Ytemp + * @return + */ + public void rkTerm2(DESystem DES, double h, double x, double[] Ytemp, + double[] res) { + if (kVals == null) { // "static" vectors which are allocated only once + k0tmp = new double[DES.getDESystemDimension()]; + k1tmp = new double[DES.getDESystemDimension()]; + k2tmp = new double[DES.getDESystemDimension()]; + kVals = new double[4][DES.getDESystemDimension()]; + } + +// double[][] K = new double[4][]; + DES.getValue(x, Ytemp, kVals[0]); + Mathematics.svMult(h, kVals[0], kVals[0]); + +// K[0] = svMult(h, DES.getValue(x, Ytemp)); + + Mathematics.svMult(0.5, kVals[0], k0tmp); + Mathematics.vvAdd(Ytemp, k0tmp, k0tmp); + DES.getValue(x + h / 2, k0tmp, kVals[1]); + Mathematics.svMult(h, kVals[1], kVals[1]); + +// K[1] = svMult(h, DES.getValue(x + h / 2, vvAdd(Ytemp, svMult(0.5, K[0])))); + + Mathematics.svMult(0.5, kVals[1], k1tmp); + Mathematics.vvAdd(Ytemp, k1tmp, k1tmp); + DES.getValue(x + h / 2, k1tmp, kVals[2]); + Mathematics.svMult(h, kVals[2], kVals[2]); + +// K[2] = svMult(h, DES.getValue(x + h / 2, vvAdd(Ytemp, svMult(0.5, K[1])))); + + Mathematics.vvAdd(Ytemp, kVals[2], k2tmp); + DES.getValue(x + h, k2tmp, k1tmp); + Mathematics.svMult(h, k1tmp, kVals[3]); + +// K[3] = svMult(h, DES.getValue(x + h, vvAdd(Ytemp, K[2]))); + + Mathematics.svMult(2, kVals[2], k0tmp); + Mathematics.vvAdd(k0tmp, kVals[3], k0tmp); + + Mathematics.svMult(2, kVals[1], k1tmp); + Mathematics.vvAdd(k1tmp, k0tmp, k2tmp); + + Mathematics.vvAdd(kVals[0], k2tmp, k1tmp); + Mathematics.svDiv(6, k1tmp, res); + +// double[] change = svDiv(6, vvAdd(K[0], vvAdd(svMult(2, K[1]), vvAdd(svMult(2, K[2]), K[3])))); +// for (int i=0; i 0.00000001) System.out.println("!!! "); +// } + + // double[] change = svdiv(6, vvadd(kVals[0], vvadd(svmult(2, kVals[1]), + // vvadd(svmult(2, kVals[2]), kVals[3])))); + for (int k = 0; k < res.length; k++) { + if (Double.isNaN(res[k])) { + unstableFlag = true; + res[k] = 0; + // return result; + } + } + } + + public static void main(String args[]) { + new RKSolver(0.01); + } + + /** + * @return + */ + public boolean isUnstable() { + return unstableFlag; + } + + /** + * @param unstableFlag + */ + public void setUnstableFlag(boolean unstableFlag) { + this.unstableFlag = unstableFlag; + } + + /** + * @return + */ + public double getStepSize() { + return stepSize; + } + + /** + * @param stepSize + */ + public void setStepSize(double stepSize) { + this.stepSize = stepSize; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/AbstractCurveFittingProblem.java b/src/eva2/server/go/problems/inference/metabolic/AbstractCurveFittingProblem.java new file mode 100644 index 00000000..1f904568 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/AbstractCurveFittingProblem.java @@ -0,0 +1,303 @@ +package eva2.server.go.problems.inference.metabolic; + +import java.io.Serializable; +import java.util.LinkedList; + +import eva2.server.go.individuals.AbstractEAIndividual; +import eva2.server.go.individuals.ESIndividualDoubleData; +import eva2.server.go.individuals.InterfaceDataTypeDouble; +import eva2.server.go.problems.AbstractOptimizationProblem; +import eva2.server.go.problems.InterfaceHasInitRange; +import eva2.server.go.problems.inference.des.RKSolver; +import eva2.tools.Mathematics; + +/** + * This class provides a collection of methods to compare two curves one against + * the other and to optimize the parameters of a model to be fitted as close as + * possible to given measurements. + * + * @author Andreas Dräger + * @date Sept. 18 2006 + */ +public abstract class AbstractCurveFittingProblem extends + AbstractOptimizationProblem implements InterfaceHasInitRange, + Serializable { + + protected AbstractEAIndividual best = null; + + protected LinkedList metabolites; + + protected TimeSeries timeSeries; + + protected double xOffSet, noise, yOffSet; + + protected int m_ProblemDimension; + + protected double t0, t1; + + protected RKSolver solver; + + protected boolean monteCarlo = false; + + protected double[][] initRange; + + public AbstractCurveFittingProblem() { + m_Template = new ESIndividualDoubleData(); + } + + public void setIndividualTemplate(AbstractEAIndividual indy) { + m_Template = indy; + } + + /** + * This method allows you to swich to an initialization that is completely + * by chance instead of a certain more sophisticated initialization + * procedure. + * + * @param mc + * If true, a Monte Carlo initialization will be performed. Else + * another possibly more sophisticated procedure will be started. + */ + public void setMonteCarloInitialization(boolean mc) { + this.monteCarlo = mc; + } + + /** + * @param individual + */ + public void evaluate(AbstractEAIndividual individual) { + double fitness[] = this + .doEvaluation(((InterfaceDataTypeDouble) individual) + .getDoubleData()); + individual.SetFitness(fitness); + if (this.best == null) { + this.best = (AbstractEAIndividual) individual.clone(); + } else if (this.best.getFitness(0) > individual.getFitness(0)) { + this.best = (AbstractEAIndividual) individual.clone(); + } + } + + /** + * @param x + * @return + */ + public double[] doEvaluation(double[] x) { + double freturn[] = new double[1], fitness = 0; + double[][] result = model(x); + + if (result == null) { + freturn[0] = Double.MAX_VALUE; + return freturn; + } + + freturn[0] = 0; + if (this.solver != null) + if (this.solver.isUnstable()) { + freturn[0] += 10000; + } + + // for all metabolites + for (int i = 0; (i < metabolites.size()) + && (freturn[0] < Double.MAX_VALUE); i++) + try { + /* + * Metabolites, which have not been considered in the + * measurements should also be able to be simulated. They + * shouldn't contribute to the overall fitness. + */ + if (!timeSeries.containsMetabolite(metabolites.get(i) + .toString()) + || (timeSeries.numberOfMeasurements(metabolites.get(i) + .toString()) == 0)) + continue; + + double[] times = this.timeSeries.getTimes(this.metabolites.get( + i).toString()), values = this.timeSeries + .getValues(this.metabolites.get(i).toString()), mvalue = new double[times.length]; + // model + // values + + // tests: + // boolean name = false; + // for all measured times for the current metabolite + for (int j = 0, min = 0, max = result.length - 1; j < times.length; j++) { + /* + * This happens, if the parameters are that bad that the + * model function doesn't even produce sensefull values. + */ + if (result[result.length - 1][0] < times[j]) { + mvalue[j] = Math.max(10000, 10000 * values[j]); + continue; + } + + if (times[j] != result[j][0]) { + /* + * Seek the right time value from the model with an + * approach in logarithmic time. The variables min and + * max have been defined as an additional start + * condition of the for loop. + */ + int t = Math.round((max + min) / 2), t_old = t; + do { + t_old = t; + if (result[t][0] < times[j]) { + if (t < max) + min = t + 1; + else + min = t; + } else if (result[t][0] > times[j]) { + if (t > min) + max = t - 1; + else + max = t; + } else + break; + t = Math.round((max + min) / 2); + } while (t != t_old); + /* + * Set min and max for the search of the next time + * (measured times are assumed to be ordered). + */ + min = t; + max = result.length - 1; + + /* + * The model value for the i-th metabolite at time j is + * given by result[t][i+1] because the first column of + * result is the time. + */ + mvalue[j] = result[t][i + 1]; + + /* + * These steps are necessary if the resulting model + * contains more than one value for one time step. In + * this case for the fitness the model value with the + * greatest distance to the measured value should be + * considered. + */ + int up = t, down = t; + while (up < result.length - 1) + if (result[up][0] == result[up + 1][0]) + up++; + else + break; + while (down > 0) + if (result[down][0] == result[down - 1][0]) + down--; + else + break; + if (up != down) + for (int k = down; k < up; k++) + if (Math.abs(result[k][i + 1] - values[j]) > Math + .abs(mvalue[j] - values[j])) + mvalue[j] = result[k][i + 1]; + + if ((result[t][0] > times[j]) && (t > 0)) { + if (result[t - 1][0] < times[j]) { + /* + * if (!name) { + * System.out.println(metabolites.get(i)+":"); + * name=true; } System.out.println( + * result[t-1][0]+" = result["+(t-1)+"][0] < + * times["+j+"] = "+times[j]+" & " + * +result[t][0]+" = result["+t+"][0] > + * times["+j+"] = "+times[j]);// + */ + mvalue[j] = Mathematics.linearInterpolation( + times[j], result[t - 1][0], + result[t][0], result[t - 1][i + 1], + mvalue[j]); + } // otherwise we don't want to interpolate with + // the value before + // down. + } else if ((result[t][0] < times[j]) + && (t < result.length - 1)) { + if (result[t + 1][0] > times[j]) { + mvalue[j] = Mathematics.linearInterpolation( + times[j], result[t][0], + result[t + 1][0], mvalue[j], + result[t + 1][i + 1]); + } /* + * we don't want to interpolate with the value + * after up. This would propably smooth the + * error function. + */ + } + /* + * We are lucky: the model already contains this time + * point. So we just need to use the given model value. + */ + } else { + mvalue[j] = result[j][i + 1]; + } + + /* + * This also happens with bad parameter values. Give a heavy + * weight to the current metabolite. + */ + if ((mvalue[j] == Double.NaN) || (mvalue[j] < 0)) + mvalue[j] = Math.max(10000, 10000 * values[j]); + } // end for all times + + // average distance over all times + /* + * fitness = Mathematics.dist(mvalue, values, 2)/times.length; + * freturn[0] += fitness;// + */ + + // fitness = Mathematics.dist(mvalue, values, 2); + fitness = Mathematics.relDist(mvalue, values, 10000); + System.out.println(metabolites.get(i) + "\t" + fitness); + + if (!Double.isNaN(fitness) && !Double.isInfinite(fitness)) { + freturn[0] += fitness; + } else + freturn[0] += 99999999999999999.; // TODO this should be + // managed + // differently? + + } catch (Exception exc) { + exc.printStackTrace(); + } + if (Double.isInfinite(freturn[0]) + || (freturn[0] > 99999999999999999999.)) + freturn[0] = 99999999999999999999.; + return freturn; + } + + /** + * This method returns the best individual of the complete optimization + * process, which is not neccessarily identical to the best individual of + * the final population. + * + * @return best The best Individual of the complete optimization process + */ + public AbstractEAIndividual getOverallBestIndividual() { + return this.best; + } + + /** + * Computes the differential equation describing the decrease or increase of + * the current metabolite in the metabolic network. + * + * @param x + * The current parameters of the differential equation describing + * the metabolic network. + * @return + */ + public abstract double[][] model(double[] x); + + + public int getProblemDimension() { + return m_ProblemDimension; + } + + public double[][] getInitRange() { + return initRange; + } + + public void SetInitRange(double[][] initRange) { + this.initRange = initRange; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/AbstractValineCurveFittingProblem.java b/src/eva2/server/go/problems/inference/metabolic/AbstractValineCurveFittingProblem.java new file mode 100644 index 00000000..35fe380f --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/AbstractValineCurveFittingProblem.java @@ -0,0 +1,195 @@ +package eva2.server.go.problems.inference.metabolic; + +import java.io.Serializable; +import java.util.LinkedList; + +import wsi.ra.math.RNG; +import eva2.server.go.individuals.AbstractEAIndividual; +import eva2.server.go.individuals.ESIndividualDoubleData; +import eva2.server.go.individuals.InterfaceDataTypeDouble; +import eva2.server.go.populations.Population; +import eva2.server.go.problems.inference.des.RKSolver; +import eva2.server.go.problems.inference.metabolic.odes.AbstractValineSystem; + +/** + * Super class for many simulation problems of the valine and leucine reaction + * network in C. glutamicum. + * + * @since 2.0 + * @version + * @author Andreas Dräger (draeger) + * Copyright (c) ZBiT, University of Tübingen, Germany Compiler: + * JDK 1.6.0 + * @date Sep 6, 2007 + */ +public abstract class AbstractValineCurveFittingProblem extends + AbstractCurveFittingProblem implements Serializable { + + /** + * Start values for: DHIV, IPM, AcLac, Val, Leu, KIV, KIC in this order. + */ + protected final double[] y = { 0.132, 0.0227, 0.236, 29.4, 0.209, 13.1, + 0.0741 }; + + protected AbstractValineSystem system; + + /** + * Generated serial id. + */ + private static final long serialVersionUID = -194867821991525140L; + + public AbstractValineCurveFittingProblem( + AbstractValineCurveFittingProblem avcfp) { + if (avcfp.m_Template != null) + this.m_Template = (AbstractEAIndividual) ((AbstractEAIndividual) avcfp.m_Template) + .clone(); + this.system = avcfp.system; + this.m_ProblemDimension = this.system.getNumberOfParameters(); + } + + /** + * Default constructor for simulation of the valine data. + */ + public AbstractValineCurveFittingProblem(AbstractValineSystem system) { + this.solver = new RKSolver(.01); + this.system = system; + this.m_ProblemDimension = system.getNumberOfParameters(); + solver.setWithLinearCalc(false); + m_Template = new ESIndividualDoubleData(); + } + + /* + * (non-Javadoc) + * + * @see eva2.server.go.OptimizationProblems.AbstractOptimizationProblem#initProblem() + */ + public void initProblem() { + this.best = null; + this.metabolites = new LinkedList(); + + this.timeSeries = new TimeSeries(); + + // this.metabolites.add("PYR"); // 46 + // this.metabolites.add("AKG"); // 47 + // this.metabolites.add("ALA"); // 47 + // this.metabolites.add("NAD"); // 47 + this.metabolites.add("DHIV"); // 44 + // this.metabolites.add("nadp"); // 45 + // this.metabolites.add("Glut"); // 47 + this.metabolites.add("2IPM"); // 45 + this.metabolites.add("AcLac"); // 40 + this.metabolites.add("Val"); // 46 + this.metabolites.add("Leu"); // 47 + this.metabolites.add("KIV"); // 47 + this.metabolites.add("KIC"); // 47 + + this.t0 = -3.894; + this.t1 = 20.643; + + this.xOffSet = RNG.gaussianDouble(0.1); + this.yOffSet = RNG.gaussianDouble(0.1); + this.noise = RNG.gaussianDouble(0.1); + initRange = new double[m_ProblemDimension][2]; + for (int i = 0; i < initRange.length; i++) { + initRange[i][0] = 0; + initRange[i][1] = 2; + } + this.SetInitRange(initRange); + } + + public void initPopulation(Population population) { + int i; + AbstractEAIndividual tmpIndy; + + best = null; + population.clear(); + + ((InterfaceDataTypeDouble) m_Template) + .setDoubleDataLength(initRange.length); + + // set the range + double[][] range = getParameterRanges(); + ((InterfaceDataTypeDouble) m_Template).SetDoubleRange(range); + + // for (i = 0; i < population.getPopulationSize(); i++) { + Population tmpPop = new Population(); + if (population.getPopulationSize() < 250) + tmpPop.setPopulationSize(250); + else + tmpPop.setPopulationSize(population.getPopulationSize()); + for (i = 0; i < tmpPop.getPopulationSize(); i++) { + tmpIndy = (AbstractEAIndividual) ((AbstractEAIndividual) m_Template) + .clone(); + // tmpIndy.init(this); + + // ////// B E G I N N /////////////// + int j; + double params[] = new double[m_ProblemDimension]; + + for (j = 0; j < params.length; j++) { + if (monteCarlo) + params[j] = RNG.randomDouble(range[j][0], range[j][1]); + else + params[j] = RNG.gaussianDouble(1) + 1; + if (params[j] < range[j][0]) { + params[j] = range[j][0]; + } + if (params[j] > range[j][1]) { + params[j] = range[j][1]; + } + } + + tmpIndy.initByValue(params, this); + // ////////E N D E ///////////////// + + tmpPop.add(tmpIndy); + // population.add(tmpIndy); + + // /////// B E G I N N ////////////// + ((InterfaceDataTypeDouble) tmpIndy).SetDoublePhenotype(params); + tmpIndy.resetConstraintViolation(); + params = ((InterfaceDataTypeDouble) tmpIndy).getDoubleData(); + for (j = 0; j < params.length; j++) + if ((params[j] < ((InterfaceDataTypeDouble) m_Template) + .getDoubleRange()[j][0]) + || (params[j] > ((InterfaceDataTypeDouble) m_Template) + .getDoubleRange()[j][1])) + System.out.println("Verletzung!\t" + params[j]);// */ + if (tmpIndy.violatesConstraint()) + System.out.println("Constraint violation: " + + tmpIndy.getConstraintViolation()); + // ////// E N D E ///////////////// + } + tmpPop.init(); + + for (i = 0; i < population.getPopulationSize(); i++) + population.add(tmpPop.remove(tmpPop.getIndexOfBestIndividual())); + population.init(); + } + + /** + * This method returns a matrix whose number of lines equals the number of + * parameters and which has two columns. In each line of this matrix the + * lower and the upper bound for the corresponding parameter are defined. + * + * @return + */ + protected abstract double[][] getParameterRanges(); + + /* + * (non-Javadoc) + * + * @see simulation.AbstractCurveFittingProblem#model(double[]) + */ + public double[][] model(double[] x) { + try { + system.setParameters(x); + return solver.solveAtTimePointsIncludingTime(system, y, + this.timeSeries.getTimes()); + } catch (Exception e) { + e.printStackTrace(); + return null; + } + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/AbstractValineSplineFittingProblem.java b/src/eva2/server/go/problems/inference/metabolic/AbstractValineSplineFittingProblem.java new file mode 100644 index 00000000..391f3fed --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/AbstractValineSplineFittingProblem.java @@ -0,0 +1,81 @@ +package eva2.server.go.problems.inference.metabolic; + +import java.io.Serializable; + +import eva2.server.go.individuals.AbstractEAIndividual; +import eva2.server.go.individuals.InterfaceDataTypeDouble; +import eva2.server.go.problems.inference.metabolic.odes.AbstractValineSystem; + +// Created at 2007-01-23 + +/** + * @author Andreas Dräger + */ +public abstract class AbstractValineSplineFittingProblem extends + AbstractValineCurveFittingProblem implements Serializable { + + /** + * Default Serial Verison ID + */ + private static final long serialVersionUID = 1L; + + /** + * Constructor + * + * @param s + */ + public AbstractValineSplineFittingProblem(AbstractValineSystem s) { + super(s); + } + + public AbstractValineSplineFittingProblem(GMAKiProblem problem) { + super(problem); + } + + @Override + public void evaluate(AbstractEAIndividual individual) { + double fitness[] = new double[] { 0 }, result[][] = model(((InterfaceDataTypeDouble) individual) + .getDoubleData()); + + if (result == null) { + fitness[0] = Double.MAX_VALUE; + } else { + + if (solver != null) + if (solver.isUnstable()) { + fitness[0] += 10000; + } + + for (int i = 0; i < result.length; i++) { // for all times + double spline[] = new double[] { // get the spline values at + // the + // current time point. + system.getDHIV(result[i][0]), + system.getIPM(result[i][0]), + system.getAcLac(result[i][0]), + system.getVal(result[i][0]), + system.getLeu(result[i][0]), + system.getKIV(result[i][0]), + system.getKIC(result[i][0]) }; + for (int j = 1; j < result[i].length + && (fitness[0] < Double.MAX_VALUE); j++) + // for all metabolites + if (spline[j - 1] != 0) + fitness[0] += Math + .pow( + ((result[i][j] - spline[j - 1]) / spline[j - 1]), + 2); + else + fitness[0] += 10000; + } + } + + individual.SetFitness(fitness); + if (this.best == null) { + this.best = (AbstractEAIndividual) individual.clone(); + } else if (this.best.getFitness(0) > individual.getFitness(0)) { + this.best = (AbstractEAIndividual) individual.clone(); + } + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/CKMMiProblem.java b/src/eva2/server/go/problems/inference/metabolic/CKMMiProblem.java new file mode 100644 index 00000000..a970d4b6 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/CKMMiProblem.java @@ -0,0 +1,81 @@ +package eva2.server.go.problems.inference.metabolic; + +import eva2.server.go.problems.inference.metabolic.odes.CKMMiSystem; +import eva2.server.go.strategies.InterfaceOptimizer; + +/** + * Created at 2007-02-04 + * + * @author Andreas Dräger + */ +public class CKMMiProblem extends AbstractValineCurveFittingProblem { + + /** + * Generated serial id. + */ + private static final long serialVersionUID = 3459101339325025847L; + + public CKMMiProblem() { + super(new CKMMiSystem()); + } + + public CKMMiProblem(CKMMiProblem ckmmiProblem) { + super(ckmmiProblem); + } + + protected double[][] getParameterRanges() { + int i; + double[][] range = new double[m_ProblemDimension][2]; + for (i = 0; i < 9; i++) { // double[] kcat = new double[9]; // -> + // p[0..9] + range[i][0] = 0; + range[i][1] = 2000; + } + for (i = 9; i < 12; i++) { // double[] ki = new double[3]; // -> + // p[9..11] + range[i][0] = 0; + range[i][1] = 2000; + } + for (i = 12; i < 28; i++) { // double[] km = new double[16]; // -> + // p[12..27] + range[i][0] = 1E-8; + range[i][1] = 2000; + } + for (i = 28; i < 31; i++) { // double[] vm = new double[3]; // -> + // p[28..30] + range[i][0] = 0; + range[i][1] = 2000; + } + for (i = 31; i < 34; i++) { // double[] kmm = new double[3]; // -> + // p[31..33] + range[i][0] = 1E-8; + range[i][1] = 2000; + } + for (i = 34; i < 37; i++) { // double[] kia = new double[3]; // -> + // p[34..36] + range[i][0] = 0; + range[i][1] = 1E+8; + } + for (i = 37; i < 40; i++) { // double[] kib = new double[3]; // -> + // p[37..39] + range[i][0] = 0; + range[i][1] = 1E+8; + } + range[40][0] = 1E-8; // double acCoA = 0; // -> p[40] + range[40][1] = 2000; + return range; + } + + @Override + public Object clone() { + return new CKMMiProblem(this); + } + + public String getStringRepresentationForProblem(InterfaceOptimizer opt) { + return "Parameter optimization problem for the valine and leucine biosynthesis" + + " in C. glutamicum, where 7 reactions are modeled using convenience kinetics" + + " and 3 reactions with Michaelis Menten kinetics. Only two reactions" + + " are considered reversible."; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/CKMMrProblem.java b/src/eva2/server/go/problems/inference/metabolic/CKMMrProblem.java new file mode 100644 index 00000000..2cefaff3 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/CKMMrProblem.java @@ -0,0 +1,76 @@ +package eva2.server.go.problems.inference.metabolic; + +import eva2.server.go.problems.InterfaceMultimodalProblem; +import eva2.server.go.problems.inference.metabolic.odes.CKMMrSystem; +import eva2.server.go.strategies.InterfaceOptimizer; + +/** + * This class is a problem of the valine/leucine biosynthesis in C. glutamicum + * and uses a combination of Michaelis-Menten and Convenience Kinetic, both + * reversible. + * + * @date 2007-02-05 + * @author Andreas Dräger + */ +public class CKMMrProblem extends AbstractValineCurveFittingProblem implements + InterfaceMultimodalProblem { + + /** + * + */ + private static final long serialVersionUID = -3120557413810402464L; + + public CKMMrProblem() { + super(new CKMMrSystem()); + } + + public CKMMrProblem(CKMMrProblem ckmmrprob) { + super(ckmmrprob); + } + + /* + * (non-Javadoc) + * @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges() + */ + public double[][] getParameterRanges() { + int i; + + // double[] kcat = null, // <- params[0..6] + // bcat = null, // <- params[7..13] + // km = null, // <- params[14..43] + // ki = null, // <- params[44..46] + // vm = null, // <- params[47..50] + // kia = null, // <- params[51..53] + // kib = null; // 50) && (i < 57)) + range[i][1] = 1E+8; + else + range[i][1] = 2000; + } + return range; + } + + @Override + public Object clone() { + return new CKMMrProblem(this); + } + + public String getStringRepresentationForProblem(InterfaceOptimizer opt) { + return "Parameter optimization problem for the valine and leucine biosynthesis" + + " in C. glutamicum, where 7 reactions are modeled using convenience kinetics" + + " and 3 reactions with Michaelis Menten kinetics. Only two reactions" + + " are considered irreversible."; + } +} diff --git a/src/eva2/server/go/problems/inference/metabolic/GMAKiProblem.java b/src/eva2/server/go/problems/inference/metabolic/GMAKiProblem.java new file mode 100644 index 00000000..fd72f293 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/GMAKiProblem.java @@ -0,0 +1,65 @@ +package eva2.server.go.problems.inference.metabolic; + +import eva2.server.go.problems.inference.metabolic.odes.GMAKiSystem; +import eva2.server.go.strategies.InterfaceOptimizer; + +// Created at 2007-02-02 + +/** + * @author Andreas Dräger + */ +public class GMAKiProblem extends AbstractValineSplineFittingProblem { + + /** + * Generated serial version id. + */ + private static final long serialVersionUID = -3635573692736142863L; + + /** + * Default constructor for simulation of the valine data. + */ + public GMAKiProblem() { + super(new GMAKiSystem()); + m_ProblemDimension = system.getNumberOfParameters(); + } + + public GMAKiProblem(GMAKiProblem gmakiProblem) { + super(gmakiProblem); + this.m_ProblemDimension = system.getNumberOfParameters(); + } + + /* + * (non-Javadoc) + * @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges() + */ + protected double[][] getParameterRanges() { + int i; + // set the range + double[][] range = new double[this.m_ProblemDimension][2]; + for (i = 0; i < 12; i++) { + range[i][0] = 0; + range[i][1] = 2000; + } + for (i = 12; i < this.m_ProblemDimension; i++) { + range[i][0] = 0; + range[i][1] = 8; + } + return range; + } + + /* + * (non-Javadoc) + * + * @see eva2.server.go.problems.AbstractOptimizationProblem#clone() + */ + public Object clone() { + return new GMAKiProblem(this); + } + + public String getStringRepresentationForProblem(InterfaceOptimizer opt) { + return "Parameter optimization problem for the valine and leucine biosynthesis" + + " in C. glutamicum, where all reactions are modeled using generalized" + + " mass-action kinetics. Only two reactions are considered irreversible."; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/GMAKrProblem.java b/src/eva2/server/go/problems/inference/metabolic/GMAKrProblem.java new file mode 100644 index 00000000..aa9217b0 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/GMAKrProblem.java @@ -0,0 +1,66 @@ +package eva2.server.go.problems.inference.metabolic; + +import eva2.server.go.problems.InterfaceMultimodalProblem; +import eva2.server.go.problems.inference.metabolic.odes.GMAKrSystem; +import eva2.server.go.strategies.InterfaceOptimizer; + +/** + * In this problem, the valine/leucine reaction network in C. glutamicum is + * simulated using a generalized mass-action approach. + * + * @since 2.0 + * @version + * @author Andreas Dräger (draeger) + * Copyright (c) ZBiT, University of Tübingen, Germany Compiler: + * JDK 1.6.0 + * @date Sep 6, 2007 + */ +public class GMAKrProblem extends AbstractValineCurveFittingProblem implements + InterfaceMultimodalProblem { + + /** + * Generated id. + */ + private static final long serialVersionUID = 3423204672190772267L; + + /** + * Default constructor for simulation of the valine data. + */ + public GMAKrProblem() { + super(new GMAKrSystem()); + } + + public GMAKrProblem(GMAKrProblem gmakrprob) { + super(gmakrprob); + } + + /* + * (non-Javadoc) + * @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges() + */ + protected double[][] getParameterRanges() { + int i; + double[][] range = new double[m_ProblemDimension][2]; + for (i = 0; i < 18; i++) { + range[i][0] = 0; + range[i][1] = 2000; + } + for (i = 18; i < m_ProblemDimension; i++) { + range[i][0] = 0; + range[i][1] = 2000; + } + return range; + } + + @Override + public Object clone() { + return new GMAKrProblem(this); + } + + public String getStringRepresentationForProblem(InterfaceOptimizer opt) { + return "Parameter optimization problem for the valine and leucine biosynthesis" + + " in C. glutamicum, where all reactions are modeled using generalized" + + " mass-action kinetics. Only two reactions are considered irreversible."; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/GMMiProblem.java b/src/eva2/server/go/problems/inference/metabolic/GMMiProblem.java new file mode 100644 index 00000000..c6a34ccd --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/GMMiProblem.java @@ -0,0 +1,107 @@ +package eva2.server.go.problems.inference.metabolic; + +import eva2.server.go.problems.inference.metabolic.odes.GMMiSystem; +import eva2.server.go.strategies.InterfaceOptimizer; + +// Created at 2007-01-18 + +/** + * @author Andreas Dräger + */ +public class GMMiProblem extends AbstractValineCurveFittingProblem { + + /** + * Generated serial id. + */ + private static final long serialVersionUID = -7344294007783094303L; + + public GMMiProblem() { + super(new GMMiSystem()); + } + + public GMMiProblem(GMMiProblem gmmiproblem) { + super(gmmiproblem); + this.m_ProblemDimension = system.getNumberOfParameters(); + } + + /* + * (non-Javadoc) + * + * @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges() + */ + protected double[][] getParameterRanges() { + int i; + double[][] range = new double[m_ProblemDimension][2]; + + // BRENDA: k+1 k+2 k+4 k+5 k+7 k+8 k+9 l1 l2 Ki1 Ki2 Ki3 vm1 vm2 vm3 + // kia1 + // kia2 kia3 kib1 kib2 kib3 km1 km2 km3 + double[] min = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 1E-8, 1E-8, 1E-8 }, max = { 2000, 2000, 2000, 2000, + 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, + 2000, 1E+8, 1E+8, 1E+8, 1E+8, 1E+8, 1E+8, 2000, 2000, 2000 }; + + /* + * for (i = 0; i < 7; i++) { // k, rate constants for forward reaction + * range[i][0] = 0.01; range[i][1] = 2000; } for (i = 7; i < 9; i++) { // + * l, rate constants for backward reaction range[i][0] = 0.01; + * range[i][1] = 2000; } for (i = 9; i < 12; i++) { // ki, Rate constant + * for inhibition of uncertain mechanism range[i][0] = 0.01; range[i][1] = + * 100; } for (i = 12; i < 15; i++) { // vm, Maximal reaction velocity + * range[i][0] = 0.1; range[i][1] = 2000; } for (i = 15; i < 18; i++) { // + * kia' == km/kia, Rate constant for inhibition of the enzyme + * range[i][0] = 0; range[i][1] = 100; } for (i = 18; i < 21; i++) { // + * kib' == 1/kib, Rate constant for inhibition of the enzyme substrate + * complex range[i][0] = 0; range[i][1] = 100; } for (i = 21; i < 24; + * i++) { // km, the Michaelis-Menten constant range[i][0] = 0.01; + * range[i][1] = 2000; } // AcCo, Constant specifying the concentration + * of Acetyl-CoA // This is not needed because p_4 already includes + * Acetyl-CoA implicitely. //range[24][0] = 0.01; //range[24][1] = 100; // + */ + for (i = 0; i < range.length; i++) { + range[i][0] = min[i]; + range[i][1] = max[i]; + } + + /* + * double initial[] = new double[] { 15.625991725476922, 2000.0, 0.0010, + * 0.0010, 0.0014167666438540366, 0.03914648793059087, + * 0.0012346931715111, 0.0010, 0.002922248833329009, 50.00050096623751, + * 25.00075000582071, 87.5001338911313, 0.0010, 0.0010, 0.0010, + * 99.99692409764904, 99.9470190144952, 100.0, 99.99999925494194, + * 99.99994374811648, 99.9999922933057, 2000.0, 1999.9999939464062, + * 2000.0, + * + * 30.76270291064184, 1000.0004988358473, 0.0010, 0.0010, + * 0.0017147897187887668, 0.04677587864891996, 0.0010852159729891494, + * 0.0010, 0.0025497199896605963, 96.87503280922212, 12.500875002910353, + * 93.75044398773962, 0.0010, 0.0010, 0.0010, 99.9999962747097, + * 99.98389044776184, 100.0, 99.90234374997726, 99.9999973224476, + * 99.99389646109051, 1999.9999701976926, 1999.9999664724041, 2000.0 + * + * + * //10.773224990548886, 505.8899553355387, 0.0010, 0.0010, 0.0010, + * 0.0010, 0.0010, 0.0010, 0.0010, 100.0, //100.0, 100.0, 0.0010, + * 0.0010, 0.0010, 100.0, 0.0, 100.0, 100.0, 100.0, 0.0, 2000.0, 2000.0, + * 2000.0, + * + * //0.016, 6.1, 0.001, 0.001, 0.001, 0.01, 0.005, 0.7, 0.011, 0.011, + * 0.012, 35.3, 0.4, 0.001, 0.001, 0.001, 5, 5, 0.001, 5, 5, 10.9, 5, 5 }; + * double choice = RandomNumberGenerator.randomDouble(0, 1);// + */ + return range; + } + + @Override + public Object clone() { + return new GMMiProblem(this); + } + + public String getStringRepresentationForProblem(InterfaceOptimizer opt) { + return "Parameter optimization problem for the valine and leucine biosynthesis" + + " in C. glutamicum, where 7 reactions are modeled using generalized" + + " mass-action kinetics and 3 reactions using Michaelis-Menten kinetics. " + + "Only two reactions are considered reversible."; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/GMMrProblem.java b/src/eva2/server/go/problems/inference/metabolic/GMMrProblem.java new file mode 100644 index 00000000..937f0e3d --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/GMMrProblem.java @@ -0,0 +1,81 @@ +package eva2.server.go.problems.inference.metabolic; + +import eva2.server.go.problems.inference.metabolic.odes.GMMrSystem; +import eva2.server.go.strategies.InterfaceOptimizer; + +/** + * Created at 2007-02-04. + * + * @author Andreas Dräger + */ +public class GMMrProblem extends AbstractValineCurveFittingProblem { + + /** + * Generated serial version id. + */ + private static final long serialVersionUID = -3889975340670999788L; + + public GMMrProblem() { + super(new GMMrSystem()); + } + + public GMMrProblem(GMMrProblem gmmrproblem) { + super(gmmrproblem); + } + + /* + * (non-Javadoc) + * @see eva2.server.go.problems.inference.metabolic.AbstractValineCurveFittingProblem#getParameterRanges() + */ + protected double[][] getParameterRanges() { + int i; + /* + * a // <- params[0..6] 7 b // <- params[7..13] 7 vm // <- + * params[14..17] 4 km // <- params[18..21] 4 kia // <- params[22..24] 3 + * kib // <- params[25..27] 3 ki // <- params[28..30] 3 + */ + double[][] range = new double[m_ProblemDimension][2]; + double min[] = { 0.000000000, 0.000000000, 0.000000000, 0.000000000, + 0.000000000, 0.000000000, 0.000000000, 0.000000000, + 0.000000000, 0.000000000, 0.000000000, 0.000000000, + 0.000000000, 0.000000000, 0.000000000, 0.000000000, + 0.000000000, 0.000000000, 0.000000000, 0.000000000, + 0.407343614, 0.059890210, 0.008423761, 0.000000000, + 0.000000000, 0.000000000, 0.432428055, 0.000000000, + 0.000000000, 0.000000000, 0.000000000 }; + double max[] = { 16.012590, 10.398386, 14.983890, 9.367657, 16.011989, + 7.229141, 409.395094, 3.348859, 65.532900, 424.407762, + 426.881040, 2.753995, 6.234599, 8.552800, 2.216418, 16.623249, + 1031.701261, 9.238867, 9.110233, 6.765434, 8.693675, 6.798338, + 5.628473, 5.364593, 8.438638, 9.185120, 6.459422, 12.470121, + 426.124248, 335.241456, 638.222487 }; + for (i = 0; i < range.length; i++) { + // Reaktionen, bei denen CO2 entsteht, sind wahrscheinlich + // irreversibel. + // if (i < 18) //((i == 7) || (i == 12)) + range[i][0] = min[i]; // 0; + /* + * else range[i][0] = 1E-8;/ + */ + if ((21 < i) && (i < 28)) + range[i][1] = max[i]; // 1E+8; + else + range[i][1] = max[i]; // 2000; + } + return range; + } + + + @Override + public Object clone() { + return new GMMrProblem(this); + } + + public String getStringRepresentationForProblem(InterfaceOptimizer opt) { + return "Parameter optimization problem for the valine and leucine biosynthesis" + + " in C. glutamicum, where 7 reactions are modeled using generalized" + + " mass-action kinetics and 3 reactions using Michaelis-Menten kinetics. " + + "Only two reactions are considered irreversible."; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/TimeSeries.java b/src/eva2/server/go/problems/inference/metabolic/TimeSeries.java new file mode 100644 index 00000000..5986b0e8 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/TimeSeries.java @@ -0,0 +1,243 @@ +package eva2.server.go.problems.inference.metabolic; + +import java.io.Serializable; +import java.util.HashMap; +import java.util.Iterator; +import java.util.Map; +import java.util.StringTokenizer; + +/** + * This class is able to parse and store the contents of a time series data + * file. These files are supposed to be a matrix, whose columns are separated by + * spaces or tabs. Every row is supposed to contain a time value in the first + * column. Every other column contains the concentration of the appropriate + * metabolite. Because the concentrations of different metabolites are not + * measured at the same time, it is possible that some values equal -1 or "NaN". + * For a given metabolite this class provides methods to extract the appropriate + * times as well as the concentration values from this file. + * + * @since 2.0 + * @version + * @author Andreas Dräger (draeger) + * Copyright (c) ZBiT, University of Tübingen, Germany Compiler: + * JDK 1.6.0 + * @date Sep 6, 2007 + */ +public class TimeSeries implements Serializable { + + /** + * + */ + private static final long serialVersionUID = 5744947450930268722L; + +String dataFile[] = new String[] { + "\"Time\" \"PYR\" \"AKG\" \"ALA\" \"NAD\" \"DHIV\" \"nadp\" \"Glut\" \"2IPM\" \"AcLac\" \"Val\" \"Leu\" \"KIV\" \"KIC\"", + "-3.894 0.69956151035 4.640691888659999 0.8149815245099999 0.43831117728999996 0.12919945973 NaN 39.91322431689 0.01634219825 0.22846321864000002 25.300234279730002 0.31457001595 13.06542276153 0.12828553615000002", + "-3.429 0.6887305946 4.4648219135 0.9733354471500001 0.46283738647 0.09269418599 0.01751164422 34.689755620279996 0.016622587119999997 0.26529057205 27.855794163480002 0.21601742448 12.27325468611 0.08806661914", + "-2.964 0.648489239 5.16446833661 1.08501337472 0.510398731 0.09668719327 0.015292064690000001 35.94424701067 0.0218616059 0.24372308822 27.512009946459997 0.19358187491999998 11.83868901432 0.08060740248 ", + "-2.499 0.66263247645 5.46655875364 1.25007291486 0.54709779337 0.27832465706 0.02017254508 42.09304165779 0.02271131235 0.31096389668 31.734637769419997 0.26547698208000003 9.74938023117 0.09527994438000001 ", + "-2.037 0.78148116407 5.01023940504 1.1274704877 0.54374513457 0.10951589105 0.023434610419999998 38.623872567220005 0.02310058527 0.23193180999000002 28.68948017027 0.27338539200999995 9.8790791622 0.06856701949 ", + "-1.572 0.7976018096 4.50327160849 0.9657423536899999 0.58804343197 0.07731788865 0.021456078269999998 39.65098562534 0.02122962586 NaN 23.23780871601 0.21372010324 7.35589658416 0.04408823098 ", + "-1.107 0.62138120097 5.527992933249999 1.11320796018 0.55468774709 0.19127059402000002 0.02151432978 37.43103867786 0.021760915119999998 0.1961057295 28.06503640484 0.25571800238 7.77390798856 0.08440327591999999 ", + "-0.642 0.61440001723 5.6582676904 1.0934342424799999 0.57892866745 0.29234911798 0.02163427084 41.73288066597 0.02358943064 0.23458068462 31.36134966529 0.28291670151 8.77177024347 0.0732470093 ", + "0.363 0.70849102753 5.6485532671600005 1.07913785266 0.5494837929699999 0.22848890345 0.03264048454 40.837490521439996 0.02034557524 0.25195151103 27.336329975730003 0.1981060288 6.68584901645 0.07329936145 ", + "0.828 0.7284264133299999 5.24081938936 1.20656221781 0.56038053457 0.50828902006 0.02799224444 43.839863948079994 0.02365648322 0.33595864068 30.40658463215 0.25328480297 7.46481961657 0.06727204061 ", + "1.293 2.35072982926 12.865197464340001 2.91425720335 0.6174561459100001 0.51680611101 0.0180555805 45.00192868082 0.02789099302 0.27282017381999996 35.47319021015 0.34012252203000004 12.4841096615 0.12075887125 ", + "1.758 1.9467590874099998 9.85083850897 2.38135380833 0.47990344534 0.36137563687 0.02083824064 39.874833705 0.02603135155 0.24703878422 35.24799025209 0.25499939033 10.58660287792 0.12011356083 ", + "2.220 1.76234822871 12.57168005228 2.535743239 0.58312865514 0.17860360497 0.018047886200000002 48.82282962176 0.02531203769 NaN 31.84083292177 0.21013809416 8.79825645884 0.10474495756 ", + "2.685 1.92439252352 11.2260038878 2.8335955021 0.49220203429 0.16592713605 0.0278326992 39.75506759172 0.02115183034 0.24700121622 29.23941353308 0.22169631684000002 8.94277598226 0.09683684583 ", + "3.150 1.92008237217 10.682979534100001 2.63737602928 0.36735949621999997 0.1546055768 0.01515236183 39.012881840679995 NaN 0.46143297886 35.812552779769995 0.28387244697 14.20243053826 0.10933158105 ", + "4.620 1.81992606678 11.067551454270001 2.70934921435 0.40725580208999995 0.24849740356 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"6.942 1.70200349375 11.29511994965 2.47410519106 0.61838724543 0.74141022319 0.02954918008 52.05561320903 0.03626130189 0.36020464648 47.08381733066 0.39496256108 15.29065040412 0.12714131781000002 ", + "7.407 1.4797133761099999 10.85092666902 2.73282139173 0.60893255673 0.91712444816 0.031866304570000004 54.69762263264 0.034354864900000004 0.50457338093 42.85485451463 0.35559022922000005 11.0783278302 0.11004224548000001 ", + "7.872 1.9687388421799998 12.632535436100001 3.38129932903 0.6733727859399999 1.19712426006 0.02287925851 69.03104100976 0.0346858365 0.6115046939 52.213982982809995 0.44352030561 14.17030298056 0.11817042216 ", + "8.877 1.66191235556 10.202950777509999 2.7018080554500004 0.65431385574 0.8630518573 0.03842550936 55.18740808596 0.03100906854 0.43636326995 44.94372991982 0.39590424030000004 11.87915174242 0.12055380252 ", + "9.342 1.79446423883 9.223736902639999 2.82086676673 0.46928184702 0.25036756575999997 0.0261580927 44.218411563610005 0.02307492312 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3.6051454388299997 0.54389398527 0.54313316601 0.01479252184 63.04737515095 0.02776861734 0.48410774037 46.409590689800005 0.44546516811 12.6862786223 0.10475246765999999 ", + "12.129 1.58626042008 9.99097297113 3.7513050785999997 0.42925918395999996 0.44080053731999996 0.013039703409999999 54.332315720649994 0.02735050961 0.31900140121 39.37877381533 0.32147277213000003 10.09758737431 0.08824555916 ", + "13.134 1.09179740401 6.491353226309999 2.26052958713 0.5481437170200001 NaN 0.01664214786 49.99620609066 0.02341309598 NaN 32.90242406736 0.29822072293 7.4630383928 0.0570285326 ", + "13.599 1.3879954566700001 9.38597812226 3.67292411291 0.41493699655 0.35712124297000003 0.01725904447 49.842043735 0.025759816390000002 0.40749356039 36.65749859265 0.31221753187 8.49907762956 0.08041935436999999 ", + "14.064 1.46398956252 9.514586394050001 3.8008622291900003 0.48941052945 0.43946239095 0.01718538349 54.47374486523 0.02895368422 0.48952197341 40.26813532416 0.40474806982 9.42390966864 0.07994405853 ", + "14.529 1.38755206855 8.484590611449999 3.2508142214399998 0.4940005127 0.48337445569 0.01893519951 47.84282639242 0.026125384369999997 0.4407703548 41.362606442570005 0.27053364357 11.06193869222 0.1028701703 ", + "14.991 0.88675119894 6.09938485345 2.0966046727900003 0.51403159937 0.25811120551 0.0228857864 44.11169057688 0.021193232819999998 NaN 31.027494313570003 0.22906790417 7.52510837768 0.053950203 ", + "15.456 1.8352414024800001 8.469270941569999 3.3301897663099997 0.4957035248 0.26361946777 0.025116875570000002 36.08517318062 0.025039514409999998 0.38916760052 38.21581762714 0.2746950311 11.731711424339998 0.12009641433 ", + "15.921 1.3283163392300001 8.17980680802 3.0409809543 0.38135485122 0.31612171511000003 0.01517163163 32.72595199009 0.02671292861 0.34622186149 42.07802536984 0.2542175654 12.23730313735 0.13746139785 ", + "16.386 1.0127505969 4.742491586150001 1.84597095725 0.34553761760999996 NaN NaN 23.37116254961 NaN NaN NaN 0.15254582637 6.9247716185999995 0.07113160166999999 ", + "17.391 1.3310395982 7.94156759798 2.53879431682 0.48983728463 0.28795700924 0.026527055879999997 26.57827416636 0.02478540202 0.32631108383999996 33.95632215865 0.23618178628 10.48083524196 0.12515650878 ", + "17.856 0.95469028533 6.2343780867100005 2.17715476263 0.38729195765999996 0.28723013923 0.02123345169 24.49108607053 0.02618426224 0.29861161455 32.8592195428 0.2472225415 10.106162722479999 0.10923677735 ", + "18.321 NaN 5.49182392648 1.46886777519 0.34474738841999997 0.40646871383 0.01547566737 20.63720807926 0.02820014831 0.24058040600000002 40.71516409524 0.24270642545999999 14.12503277772 0.12899961175000002 ", + "18.786 0.82619105906 5.95530539146 2.299362329 0.44373946309 0.41075036223 0.02555677606 38.20802369971 0.02900285112 0.32959703389 34.213028316030005 0.25023635697 9.12222153162 0.10197379988000001 ", + "19.248 1.25881942787 6.53214282703 2.7302141736600003 0.50467802709 0.41526312678 0.02242466065 36.837815626659996 0.02483324467 0.38040913803 33.23783851764 0.23737761148 9.48079376972 0.11263783924000001 ", + "19.713 1.18507811227 5.4310205666 2.1360162267800002 0.46222885974 NaN 0.03397622205 28.19338823663 0.02104977904 NaN 25.47234120703 0.14638363385 7.60817737184 0.08542327693999999 ", + "20.178 0.98680703323 6.8729957498 2.34703189167 0.36189835991 0.35197395012 0.0170510439 26.43911126279 0.03024185129 0.30588350463 36.72827117308 0.23660664184000002 13.6943897464 0.16017717532 ", + "20.643 0.97377667062 6.15710096877 2.10492373702 0.46580796436000005 0.38984610765 0.020551778160000003 28.19084298145 0.02817732147 0.31660453741 33.563565688000004 0.17354025424 11.137671980450001 0.11278907400999999"}; + + private int dataFileLineCnt = dataFile.length; + + private Map names; + + public static void main(String args[]) { + new TimeSeries(); + } + + /** + * This constructs a new instance of this class from the given file. All + * entries of the given file are stored in a String matrix. The + * names of the metabolites are stored in a hash for easy retrival. + * + * @param fileName + */ + public TimeSeries() { + try { + int i = 0, j = 0; + this.names = new HashMap(); + StringTokenizer st = new StringTokenizer(dataFile[0]); + + // Lesen zum ersten Mal und initialisieren Matrix. + for (j = 0; st.hasMoreElements(); st.nextElement(), j++); + + String data[][] = new String[dataFileLineCnt - 1][j]; + + // Jetzt in Matrix einlesen. + for (i = 0; i < dataFileLineCnt; i++) { + j = 0; + st = new StringTokenizer(dataFile[i]); + while (st.hasMoreElements()) + if (i > 0) + data[i - 1][j++] = st.nextElement().toString(); + else names.put(st.nextElement().toString().replaceAll("\"", ""), + new Integer(j++)); + } + + /* + * for (i=0; i iter = names.keySet().iterator(); + while (iter.hasNext()) + try { + double values[][]; + int tcol = 0, count = 0; + String name = iter.next().toString(); + + i = 0; + j = ((Integer) names.get(name)).intValue(); + for (i = 0; i < data.length; i++) + if (!data[i][j].equals("-1") && !data[i][j].equals("NaN")) count++; + values = new double[2][count]; +// /* +// * The times are supposed to be the first column. However, this +// * enables the times to be any column. +// */ +// try { +// tcol = ((Integer) this.names.get("Time")).intValue(); +// } catch (Exception exc) { +// }; + for (i = 0, count = 0; i < data.length; i++) + if (!data[i][j].equals("-1") && !data[i][j].equals("NaN")) { + values[0][count] = Double.parseDouble(data[i][tcol]); + values[1][count++] = Double.parseDouble(data[i][j]); + } + names.put(name, values); + } catch (Exception exc) { + exc.printStackTrace(); + } + data = null; + } catch (Exception exc) { + exc.printStackTrace(); + } + } + + /** + * With this method one retrives the time data of the given metabolite or + * throws an exception if the given metabolite does not exist. + * + * @param metabolite + * The name of the metabolite, whose time data are desired. + * @return The time data. + * @throws Exception + * @throws If + * there is no entry for the desired metabolite, an exeption + * is thrown. + */ + public double[] getTimes(String metabolite) throws Exception { + if (names == null) System.err.println("getTimes: names is null"); + if (!names.containsKey(metabolite)) + throw new Exception("No data measured for this metabolite"); + if (names.get(metabolite) == null) System.err.println("get(metabolite) is null"); + return ((double[][]) names.get(metabolite))[0]; + } + + /** + * This method returns all measured times. + * + * @return An array containing all measurement times. + * @throws Exception + * If there is no column called "Time" in the data matrix. + */ + public double[] getTimes() throws Exception { + return getTimes("Time"); + } + + /** + * This method returns the concentration values of the given metabolite or + * throws an exception if there is no measurement for the desired metabolite. + * + * @param metabolite + * The name of the desired metabolite. + * @return an array of concentration values. + * @throws Exception + * @throws If + * there is no entry for the desired metabolite, an exception + * is thrown. + */ + public double[] getValues(String metabolite) throws Exception { + if (!names.containsKey(metabolite)) + throw new Exception("No data measured for this metabolite"); + return ((double[][]) names.get(metabolite))[1]; + } + + + /** + * With this method it can be checked wheather measurements have been + * perforemd for the given metabolite. + * + * @param name + * The name of the metabolite + * @return true if an entry for this metabolite exists or + * false otherwise. + */ + public boolean containsMetabolite(String name) { + return names.keySet().contains(name); + } + + /** + * Returns the number of measurements taken for this specific metabolite or + * zero if there are no measurements. + * + * @param name + * The name of the metabolite + * @return The number of available measurement data for this metabolite + */ + public int numberOfMeasurements(String name) { + try { + return getTimes(name).length; + } catch (Exception exc) { + return 0; + } + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/odes/AbstractValineSystem.java b/src/eva2/server/go/problems/inference/metabolic/odes/AbstractValineSystem.java new file mode 100644 index 00000000..7944f465 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/odes/AbstractValineSystem.java @@ -0,0 +1,3755 @@ +package eva2.server.go.problems.inference.metabolic.odes; + +import eva2.server.go.problems.inference.des.DESystem; + +/** + * This class contains the necessary splines for selected metabolites of the + * valine/leucin reaction network in C. glutamicum. + * + * @since 2.0 + * @version + * @author Andreas Dräger (draeger) + * Copyright (c) ZBiT, University of Tübingen, Germany Compiler: + * JDK 1.6.0 + * @date + * @date 2006-12-06 + */ +public abstract class AbstractValineSystem implements DESystem { + + /** + * The metabolite concenterations in mM at the reference state transformed + * in the intervall [0,1] according to table 3 from the valine paper. + */ + protected final double pyr = 0.0430; // .689 + + protected final double akg = 0.0763; // 5.12 + + protected final double ala = 0.0787; // 1.05 + + protected final double nad = 0.5301; // .528 + + protected final double nadp = 0.1757; // .0175 + + protected final double glut = 0.3732; // 38.7 + + protected double[] p; + + /* + * (non-Javadoc) + * + * @see eva2.server.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getDESSystemOrder() + */ + public int getDESystemDimension() { + return 7; + } + + /** + * Allows to set the parameters in this system to the designated value. + * + * @param x + * the new parameters for this system. + */ + public void setParameters(double[] params) { + if (params.length == getNumberOfParameters()) + this.p = params; + else + throw new IllegalArgumentException("This system requires exactly " + + getNumberOfParameters() + " parameters but received " + + params.length + "."); + } + + /** + * This method tells the required number of parameters of this system. + * @return + */ + public abstract int getNumberOfParameters(); + + /** + * @param v + * @return + */ + public double[] linearCombinationOfVelocities(double[] v) { + return new double[] { v[1] - v[2], // DHIV + v[6] - v[7], // 2IPM + v[0] - v[1], // AcLac + v[3] + v[4] - v[5], // Val + v[8] - v[9], // Leu + v[2] - v[3] - v[4] - v[6], // KIV + v[7] - v[8] // KIC + }; + } + + public void linearCombinationOfVelocities(double[] v, double[] res) { + res[0] = v[1] - v[2]; // DHIV + res[1] = v[6] - v[7]; // 2IPM + res[2] = v[0] - v[1]; // AcLac + res[3] = v[3] + v[4] - v[5]; // Val + res[4] = v[8] - v[9]; // Leu + res[5] = v[2] - v[3] - v[4] - v[6]; // KIV + res[6] = v[7] - v[8]; // KIC + } + + /** + * Returns the approximation spline for akg or the default value 5.12 mM + * with S=26 and all weights equal to one. + * + * @param t + * @return + */ + public double getAKG(double t) { + double y = this.akg; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.00969646 + 0.0299456 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 0.0624106 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.0298962 + 0.0704298 * (t + 3.429) + 8.706283e-02 + * Math.pow(t + 3.429, 2) - 0.110594 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.0703517 + 0.0796589 * (t + 2.964) - 6.721530e-02 + * Math.pow(t + 2.964, 2) - 0.0462005 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.0882142 - 0.0128204 * (t + 2.499) - 1.316651e-01 + * Math.pow(t + 2.499, 2) + 0.11841 * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.0658646 - 0.0586573 * (t + 2.037) + 3.245109e-02 + * Math.pow(t + 2.037, 2) + 0.104831 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.0561459 + 0.0395234 * (t + 1.572) + 1.786901e-01 + * Math.pow(t + 1.572, 2) - 0.194164 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.0936394 + 0.0797561 * (t + 1.107) - 9.216813e-02 + * Math.pow(t + 1.107, 2) - 0.019546 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.108832 - 0.0186393 * (t + 0.642) - 1.194349e-01 + * Math.pow(t + 0.642, 2) + 0.154951 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.126754 + 0.21081 * (t - 0.363) + 3.477431e-01 + * Math.pow(t - 0.363, 2) + 0.219188 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.32201 + 0.676393 * (t - 0.828) + 6.535099e-01 + * Math.pow(t - 0.828, 2) - 1.12127 * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.665101 + 0.556819 * (t - 1.293) - 9.106580e-01 + * Math.pow(t - 1.293, 2) + 0.691096 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.7966 + 0.158204 * (t - 1.758) + 5.342020e-02 + * Math.pow(t - 1.758, 2) - 0.288946 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.852599 + 0.0225423 * (t - 2.22) - 3.470596e-01 + * Math.pow(t - 2.22, 2) + 0.240586 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.812228 - 0.144161 * (t - 2.685) - 1.144258e-02 + * Math.pow(t - 2.685, 2) + 0.0968603 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.752458 - 0.0919719 * (t - 3.15) + 1.236776e-01 + * Math.pow(t - 3.15, 2) - 0.0669395 * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.671879 - 0.162309 * (t - 4.62) - 1.715257e-01 + * Math.pow(t - 4.62, 2) + 0.494727 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.609059 - 0.00091066 * (t - 5.085) + 5.186183e-01 + * Math.pow(t - 5.085, 2) - 0.394601 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.681099 + 0.225436 * (t - 5.55) - 3.185070e-02 + * Math.pow(t - 5.55, 2) + 0.0649101 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.785567 + 0.237921 * (t - 6.015) + 5.869895e-02 + * Math.pow(t - 6.015, 2) - 0.358464 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.872666 + 0.0626224 * (t - 6.477) - 4.381325e-01 + * Math.pow(t - 6.477, 2) + 0.378394 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.845096 - 0.0993862 * (t - 6.942) + 8.972702e-02 + * Math.pow(t - 6.942, 2) + 0.0910474 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.827437 + 0.0431201 * (t - 7.407) + 2.167382e-01 + * Math.pow(t - 7.407, 2) - 0.39346 * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.854792 - 0.0105413 * (t - 7.872) - 3.321390e-01 + * Math.pow(t - 7.872, 2) + 0.164397 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.675605 - 0.180004 * (t - 8.877) + 1.635190e-01 + * Math.pow(t - 8.877, 2) + 0.121832 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.639509 + 0.0510979 * (t - 9.342) + 3.334752e-01 + * Math.pow(t - 9.342, 2) - 0.371766 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.697996 + 0.120074 * (t - 9.807) - 1.851385e-01 + * Math.pow(t - 9.807, 2) - 0.0565306 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.708115 - 0.0887743 * (t - 10.272) - 2.639986e-01 + * Math.pow(t - 10.272, 2) + 0.426366 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.652797 - 0.059693 * (t - 10.734) + 3.269452e-01 + * Math.pow(t - 10.734, 2) - 0.101866 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.685492 + 0.178288 * (t - 11.199) + 1.848428e-01 + * Math.pow(t - 11.199, 2) - 0.552669 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.752796 - 0.00831021 * (t - 11.664) - 5.861302e-01 + * Math.pow(t - 11.664, 2) + 0.303168 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.652677 - 0.356754 * (t - 12.129) - 1.632107e-01 + * Math.pow(t - 12.129, 2) + 0.250424 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.383492 + 0.0739952 * (t - 13.134) + 5.918165e-01 + * Math.pow(t - 13.134, 2) - 0.603056 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.485231 + 0.233197 * (t - 13.599) - 2.494464e-01 + * Math.pow(t - 13.599, 2) - 0.0945359 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.530226 - 0.060111 * (t - 14.064) - 3.813240e-01 + * Math.pow(t - 14.064, 2) + 0.240277 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.443981 - 0.258881 * (t - 14.529) - 4.613781e-02 + * Math.pow(t - 14.529, 2) + 0.380038 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.352007 - 0.0581613 * (t - 14.991) + 4.805954e-01 + * Math.pow(t - 14.991, 2) - 0.555397 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.373036 + 0.0285202 * (t - 15.456) - 2.941836e-01 + * Math.pow(t - 15.456, 2) - 0.0154123 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.321138 - 0.255068 * (t - 15.921) - 3.156838e-01 + * Math.pow(t - 15.921, 2) + 0.629819 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.197598 - 0.140106 * (t - 16.386) + 5.629136e-01 + * Math.pow(t - 16.386, 2) - 0.326519 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.293907 + 0.00197248 * (t - 17.391) - 4.215417e-01 + * Math.pow(t - 17.391, 2) + 0.315744 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.235422 - 0.185246 * (t - 17.856) + 1.892171e-02 + * Math.pow(t - 17.856, 2) + 0.145936 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.168047 - 0.0729833 * (t - 18.321) + 2.225031e-01 + * Math.pow(t - 18.321, 2) - 0.134407 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.168707 + 0.0467579 * (t - 18.786) + 3.500489e-02 + * Math.pow(t - 18.786, 2) - 0.106218 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.187306 + 0.0110874 * (t - 19.248) - 1.122138e-01 + * Math.pow(t - 19.248, 2) + 0.203081 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.188617 + 0.0384625 * (t - 19.713) + 1.710848e-01 + * Math.pow(t - 19.713, 2) - 0.237274 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.219639 + 0.0436577 * (t - 20.178) - 1.599124e-01 + * Math.pow(t - 20.178, 2) + 0.114633 + * Math.pow(t - 20.178, 3); + + return y * (13.0516 - 4.4648) + 4.4648; + } + + /** + * Computes the approximation spline for ala or returns the default value + * 1.05 if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=5.9718 (= 2*(max(y) - min(y)) + * + * @param t + * @return + */ + public double getAla(double t) { + double y = this.ala; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.0111417 + 0.0976193 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) - 0.0137179 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.0551554 + 0.0887208 * (t + 3.429) - 1.913651e-02 + * Math.pow(t + 3.429, 2) - 0.0163295 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.0906309 + 0.0603313 * (t + 2.964) - 4.191618e-02 + * Math.pow(t + 2.964, 2) - 0.0165692 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.107956 + 0.0106012 * (t + 2.499) - 6.503016e-02 + * Math.pow(t + 2.499, 2) + 0.0299227 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.101924 - 0.0303262 * (t + 2.037) - 2.355734e-02 + * Math.pow(t + 2.037, 2) + 0.033286 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.0860752 - 0.0306427 * (t + 1.572) + 2.287664e-02 + * Math.pow(t + 1.572, 2) - 0.0105259 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.0757145 - 0.0161953 * (t + 1.107) + 8.193019e-03 + * Math.pow(t + 1.107, 2) + 0.0192259 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.0718883 + 0.00389554 * (t + 0.642) + 3.501316e-02 + * Math.pow(t + 0.642, 2) + 0.045535 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.157389 + 0.212246 * (t - 0.363) + 1.723011e-01 + * Math.pow(t - 0.363, 2) - 0.0393216 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.289386 + 0.346979 * (t - 0.828) + 1.174474e-01 + * Math.pow(t - 0.828, 2) - 0.234153 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.452583 + 0.304317 * (t - 1.293) - 2.091957e-01 + * Math.pow(t - 1.293, 2) + 0.0742497 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.556323 + 0.157928 * (t - 1.758) - 1.056173e-01 + * Math.pow(t - 1.758, 2) + 0.0351834 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.610212 + 0.0828671 * (t - 2.22) - 5.685313e-02 + * Math.pow(t - 2.22, 2) - 0.0065701 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.635791 + 0.0257318 * (t - 2.685) - 6.601841e-02 + * Math.pow(t - 2.685, 2) + 0.0430014 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.637805 - 0.00777138 * (t - 3.15) - 6.031459e-03 + * Math.pow(t - 3.15, 2) + 0.0091822 * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.642515 + 0.0340216 * (t - 4.62) + 3.446205e-02 + * Math.pow(t - 4.62, 2) - 0.000758179 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.665711 + 0.0655795 * (t - 5.085) + 3.340439e-02 + * Math.pow(t - 5.085, 2) - 0.104524 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.692919 + 0.0288435 * (t - 5.55) - 1.124066e-01 + * Math.pow(t - 5.55, 2) + 0.0559302 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.687649 - 0.0394141 * (t - 6.015) - 3.438390e-02 + * Math.pow(t - 6.015, 2) + 0.0394099 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.665987 - 0.0459494 * (t - 6.477) + 2.023820e-02 + * Math.pow(t - 6.477, 2) + 0.0644611 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.655478 + 0.0146864 * (t - 6.942) + 1.101615e-01 + * Math.pow(t - 6.942, 2) - 0.0584419 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.680251 + 0.0792268 * (t - 7.407) + 2.863499e-02 + * Math.pow(t - 7.407, 2) - 0.105164 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.712709 + 0.0376398 * (t - 7.872) - 1.180694e-01 + * Math.pow(t - 7.872, 2) + 0.075549 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.707972 + 0.0292394 * (t - 8.877) + 1.097108e-01 + * Math.pow(t - 8.877, 2) - 0.0180929 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.743471 + 0.119534 * (t - 9.342) + 8.447119e-02 + * Math.pow(t - 9.342, 2) - 0.106349 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.806627 + 0.129106 * (t - 9.807) - 6.388543e-02 + * Math.pow(t - 9.807, 2) - 0.0296704 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.849864 + 0.0504465 * (t - 10.272) - 1.052756e-01 + * Math.pow(t - 10.272, 2) + 0.102264 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.860785 + 0.0186546 * (t - 10.734) + 3.646185e-02 + * Math.pow(t - 10.734, 2) - 0.0173023 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.875603 + 0.0413405 * (t - 11.199) + 1.232517e-02 + * Math.pow(t - 11.199, 2) - 0.0739829 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.890053 + 0.00481211 * (t - 11.664) - 9.088094e-02 + * Math.pow(t - 11.664, 2) - 0.0193171 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.870698 - 0.0922377 * (t - 12.129) - 1.178283e-01 + * Math.pow(t - 12.129, 2) + 0.119448 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.780238 + 0.0328649 * (t - 13.134) + 2.423084e-01 + * Math.pow(t - 13.134, 2) - 0.245131 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.823267 + 0.0992016 * (t - 13.599) - 9.964895e-02 + * Math.pow(t - 13.599, 2) - 0.0802879 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.839777 - 0.0455527 * (t - 14.064) - 2.116505e-01 + * Math.pow(t - 14.064, 2) + 0.116983 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.784593 - 0.166504 * (t - 14.529) - 4.845904e-02 + * Math.pow(t - 14.529, 2) + 0.155386 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.712648 - 0.111781 * (t - 14.991) + 1.669064e-01 + * Math.pow(t - 14.991, 2) - 0.193567 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.677297 - 0.0821204 * (t - 15.456) - 1.031202e-01 + * Math.pow(t - 15.456, 2) + 0.00967204 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.617786 - 0.171748 * (t - 15.921) - 8.962768e-02 + * Math.pow(t - 15.921, 2) + 0.166928 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.535327 - 0.14682 * (t - 16.386) + 1.432362e-01 + * Math.pow(t - 16.386, 2) - 0.0670531 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.464381 - 0.0620913 * (t - 17.391) - 5.892878e-02 + * Math.pow(t - 17.391, 2) + 0.0720023 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.430006 - 0.0701889 * (t - 17.856) + 4.151449e-02 + * Math.pow(t - 17.856, 2) + 0.104259 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.416827 + 0.0360499 * (t - 18.321) + 1.869560e-01 + * Math.pow(t - 18.321, 2) - 0.13932 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.460007 + 0.119546 * (t - 18.786) - 7.395260e-03 + * Math.pow(t - 18.786, 2) - 0.0936091 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.504428 + 0.0527714 * (t - 19.248) - 1.371375e-01 + * Math.pow(t - 19.248, 2) + 0.0750712 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.506862 - 0.0260696 * (t - 19.713) - 3.241318e-02 + * Math.pow(t - 19.713, 2) - 0.00426263 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.487302 - 0.058979 * (t - 20.178) - 3.835956e-02 + * Math.pow(t - 20.178, 2) + 0.0274979 + * Math.pow(t - 20.178, 3); + + return y * (3.8009 - .8150) + .8150; + } + + /** + * Computes the spline equation for pyr with the parameter S=2.5 and all + * weights equal to one. For values of t smaller or greater than the + * considered time the default steady-state value of 0.689 mM will be + * returned. + * + * @param t + * @return y the concentration of pyr at the given time t. + */ + public double getPyr(double t) { + double y = this.pyr; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.0468378 - 0.0255021 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 0.00825634 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.0358095 - 0.0201464 * (t + 3.429) + 1.151759e-02 + * Math.pow(t + 3.429, 2) + 0.0344174 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.0323923 + 0.0128907 * (t + 2.964) + 5.952992e-02 + * Math.pow(t + 2.964, 2) - 0.0132711 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.0499239 + 0.0596449 * (t + 2.499) + 4.101675e-02 + * Math.pow(t + 2.499, 2) - 0.0960409 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.076764 + 0.0360463 * (t + 2.037) - 9.209594e-02 + * Math.pow(t + 2.037, 2) - 0.0232984 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.0712695 - 0.0647161 * (t + 1.572) - 1.245973e-01 + * Math.pow(t + 1.572, 2) + 0.10468 * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.0247605 - 0.112688 * (t + 1.107) + 2.143171e-02 + * Math.pow(t + 1.107, 2) + 0.0271642 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = -0.0202741 - 0.0751358 * (t + 0.642) + 5.932581e-02 + * Math.pow(t + 0.642, 2) + 0.10294 * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.0686263 + 0.356024 * (t - 0.363) + 3.696886e-01 + * Math.pow(t - 0.363, 2) + 0.0489817 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.319038 + 0.731607 * (t - 0.828) + 4.380181e-01 + * Math.pow(t - 0.828, 2) - 0.897988 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.663658 + 0.556462 * (t - 1.293) - 8.146750e-01 + * Math.pow(t - 1.293, 2) + 0.359103 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.782366 + 0.031755 * (t - 1.758) - 3.137269e-01 + * Math.pow(t - 1.758, 2) + 0.302952 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.759948 - 0.064139 * (t - 2.22) + 1.061641e-01 + * Math.pow(t - 2.22, 2) - 0.0663651 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.746406 - 0.00845576 * (t - 2.685) + 1.358482e-02 + * Math.pow(t - 2.685, 2) - 0.0362597 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.741766 - 0.0193426 * (t - 3.15) - 3.699747e-02 + * Math.pow(t - 3.15, 2) + 0.00191105 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.639454 - 0.115726 * (t - 4.62) - 2.856973e-02 + * Math.pow(t - 4.62, 2) + 0.206882 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.600265 - 0.00809734 * (t - 5.085) + 2.600302e-01 + * Math.pow(t - 5.085, 2) - 0.229079 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.629692 + 0.0851332 * (t - 5.55) - 5.953446e-02 + * Math.pow(t - 5.55, 2) - 0.0383844 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.652547 + 0.00486712 * (t - 6.015) - 1.130807e-01 + * Math.pow(t - 6.015, 2) + 0.0637543 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.636946 - 0.0587955 * (t - 6.477) - 2.471716e-02 + * Math.pow(t - 6.477, 2) + 0.0556625 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.609858 - 0.0456756 * (t - 6.942) + 5.293199e-02 + * Math.pow(t - 6.942, 2) + 0.117416 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.61187 + 0.0797159 * (t - 7.407) + 2.167272e-01 + * Math.pow(t - 7.407, 2) - 0.30684 * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.664948 + 0.0822328 * (t - 7.872) - 2.113145e-01 + * Math.pow(t - 7.872, 2) + 0.123173 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.659189 + 0.0307141 * (t - 8.877) + 1.600521e-01 + * Math.pow(t - 8.877, 2) - 0.0857489 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.699457 + 0.123939 * (t - 9.342) + 4.043249e-02 + * Math.pow(t - 9.342, 2) - 0.159848 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.74976 + 0.0578521 * (t - 9.807) - 1.825560e-01 + * Math.pow(t - 9.807, 2) + 0.0836857 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.745602 - 0.0576401 * (t - 10.272) - 6.581435e-02 + * Math.pow(t - 10.272, 2) + 0.200744 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.72472 + 0.0100902 * (t - 10.734) + 2.124167e-01 + * Math.pow(t - 10.734, 2) - 0.18139 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.757104 + 0.0899747 * (t - 11.199) - 4.062200e-02 + * Math.pow(t - 11.199, 2) - 0.307885 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.759203 - 0.147521 * (t - 11.664) - 4.701216e-01 + * Math.pow(t - 11.664, 2) + 0.34237 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.623377 - 0.362647 * (t - 12.129) + 7.484435e-03 + * Math.pow(t - 12.129, 2) + 0.104455 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.372505 - 0.0310977 * (t - 13.134) + 3.224157e-01 + * Math.pow(t - 13.134, 2) - 0.260175 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.4016 + 0.0999801 * (t - 13.599) - 4.052806e-02 + * Math.pow(t - 13.599, 2) - 0.0959657 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.429678 + 3.84408e-05 * (t - 14.064) - 1.744002e-01 + * Math.pow(t - 14.064, 2) + 0.126879 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.404744 - 0.0798503 * (t - 14.529) + 2.596388e-03 + * Math.pow(t - 14.529, 2) + 0.278383 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.395859 + 0.100806 * (t - 14.991) + 3.884357e-01 + * Math.pow(t - 14.991, 2) - 0.614904 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.464898 + 0.0631788 * (t - 15.456) - 4.693555e-01 + * Math.pow(t - 15.456, 2) + 0.275445 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.420484 - 0.194647 * (t - 15.921) - 8.510932e-02 + * Math.pow(t - 15.921, 2) + 0.240611 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.335763 - 0.117721 * (t - 16.386) + 2.505425e-01 + * Math.pow(t - 16.386, 2) - 0.156845 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.311298 - 0.0893822 * (t - 17.391) - 2.223447e-01 + * Math.pow(t - 17.391, 2) + 0.222271 + * Math.pow(t - 17.391, 3); + else if (t <= 18.786) + y = 0.244007 - 0.151981 * (t - 17.856) + 8.772290e-02 + * Math.pow(t - 17.856, 2) + 0.0427783 + * Math.pow(t - 17.856, 3); + else if (t <= 19.248) + y = 0.212945 + 0.12218 * (t - 18.786) + 2.070743e-01 + * Math.pow(t - 18.786, 2) - 0.297219 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.284281 + 0.123198 * (t - 19.248) - 2.048717e-01 + * Math.pow(t - 19.248, 2) + 0.0274136 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.300026 - 0.0495505 * (t - 19.713) - 1.666297e-01 + * Math.pow(t - 19.713, 2) + 0.134468 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.254476 - 0.11729 * (t - 20.178) + 2.095292e-02 + * Math.pow(t - 20.178, 2) - 0.01502 + * Math.pow(t - 20.178, 3); + + return y * (2.3507 - 0.6144) + 0.6144; // Rücktransformation. + } + + /** + * Computes the approximation spline for glut or returns the default value + * 38.7 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=798.4982 (= 16.5*(max(y) - min(y))). + * + * @param t + * @return + */ + public double getGlut(double t) { + double y = this.glut; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.346152 + 0.0125877 * (t + 3.894) + 0 * Math.pow(t + 3.894, 2) + + 0.00386753 * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.352394 + 0.0150965 * (t + 3.429) + 5.395202e-03 + * Math.pow(t + 3.429, 2) - 0.000730397 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.360507 + 0.0196402 * (t + 2.964) + 4.376298e-03 + * Math.pow(t + 2.964, 2) - 0.00400789 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.370183 + 0.0211104 * (t + 2.499) - 1.214705e-03 + * Math.pow(t + 2.499, 2) + 0.00141752 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.379816 + 0.0208957 * (t + 2.037) + 7.499822e-04 + * Math.pow(t + 2.037, 2) + 0.000813723 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.389777 + 0.022121 * (t + 1.572) + 1.885125e-03 + * Math.pow(t + 1.572, 2) + 0.00104503 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.400576 + 0.0245521 * (t + 1.107) + 3.342938e-03 + * Math.pow(t + 1.107, 2) - 0.00292541 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.412421 + 0.0257634 * (t + 0.642) - 7.380070e-04 + * Math.pow(t + 0.642, 2) - 0.00118342 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.436367 + 0.0206941 * (t - 0.363) - 4.306028e-03 + * Math.pow(t - 0.363, 2) - 0.00258859 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.444798 + 0.0150103 * (t - 0.828) - 7.917118e-03 + * Math.pow(t - 0.828, 2) - 1.90838e-05 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.450064 + 0.00763503 * (t - 1.293) - 7.943740e-03 + * Math.pow(t - 1.293, 2) + 0.00394034 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.452293 + 0.00280336 * (t - 1.758) - 2.446961e-03 + * Math.pow(t - 1.758, 2) - 0.000120479 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.453054 + 0.00046522 * (t - 2.22) - 2.613945e-03 + * Math.pow(t - 2.22, 2) + 0.00947111 + * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.453657 + 0.00417793 * (t - 2.685) + 1.059826e-02 + * Math.pow(t - 2.685, 2) + 0.00512564 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.458407 + 0.0173592 * (t - 3.15) + 1.774853e-02 + * Math.pow(t - 3.15, 2) - 0.000709047 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.520026 + 0.0649433 * (t - 4.62) + 1.462163e-02 + * Math.pow(t - 4.62, 2) - 0.00895174 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.552486 + 0.0727347 * (t - 5.085) + 2.133952e-03 + * Math.pow(t - 5.085, 2) - 0.0005377 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.586715 + 0.0743704 * (t - 5.55) + 1.383860e-03 + * Math.pow(t - 5.55, 2) + 0.000484168 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.621645 + 0.0759715 * (t - 6.015) + 2.059275e-03 + * Math.pow(t - 6.015, 2) - 0.00426907 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.656762 + 0.0751406 * (t - 6.477) - 3.857662e-03 + * Math.pow(t - 6.477, 2) - 0.00352595 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.690514 + 0.0692658 * (t - 6.942) - 8.776359e-03 + * Math.pow(t - 6.942, 2) - 0.00658733 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.720163 + 0.0568308 * (t - 7.407) - 1.796568e-02 + * Math.pow(t - 7.407, 2) - 0.00779921 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.74192 + 0.0350635 * (t - 7.872) - 2.884559e-02 + * Math.pow(t - 7.872, 2) + 0.0113354 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.759531 + 0.0114309 * (t - 8.877) + 5.330512e-03 + * Math.pow(t - 8.877, 2) + 0.00795504 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.766798 + 0.0215485 * (t - 9.342) + 1.642779e-02 + * Math.pow(t - 9.342, 2) - 0.0127692 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.779087 + 0.0285432 * (t - 9.807) - 1.385314e-03 + * Math.pow(t - 9.807, 2) - 0.0138529 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.790667 + 0.0182689 * (t - 10.272) - 2.071006e-02 + * Math.pow(t - 10.272, 2) + 0.00135784 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.794821 + 2.26621e-06 * (t - 10.734) - 1.882810e-02 + * Math.pow(t - 10.734, 2) - 0.0030271 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.790446 - 0.0194715 * (t - 11.199) - 2.305090e-02 + * Math.pow(t - 11.199, 2) - 0.00202981 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.776204 - 0.0422255 * (t - 11.664) - 2.588248e-02 + * Math.pow(t - 11.664, 2) + 0.00539561 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.751515 - 0.0627962 * (t - 12.129) - 1.835561e-02 + * Math.pow(t - 12.129, 2) + 0.00129954 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.671184 - 0.0957533 * (t - 13.134) - 1.443750e-02 + * Math.pow(t - 13.134, 2) - 0.00348381 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.623187 - 0.11144 * (t - 13.599) - 1.929741e-02 + * Math.pow(t - 13.599, 2) - 0.00494472 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.566697 - 0.132594 * (t - 14.064) - 2.619529e-02 + * Math.pow(t - 14.064, 2) + 0.00487863 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.499868 - 0.153791 * (t - 14.529) - 1.938961e-02 + * Math.pow(t - 14.529, 2) + 0.00949794 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.425614 - 0.165625 * (t - 14.991) - 6.225460e-03 + * Math.pow(t - 14.991, 2) + 0.0139063 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.348651 - 0.162394 * (t - 15.456) + 1.317376e-02 + * Math.pow(t - 15.456, 2) + 0.0117237 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.277164 - 0.142538 * (t - 15.921) + 2.952836e-02 + * Math.pow(t - 15.921, 2) + 0.00969481 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.218244 - 0.108788 * (t - 16.386) + 4.305262e-02 + * Math.pow(t - 16.386, 2) - 0.00229767 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.150064 - 0.029214 * (t - 17.391) + 3.612515e-02 + * Math.pow(t - 17.391, 2) - 0.0043217 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.143856 + 0.00157903 * (t - 17.856) + 3.009639e-02 + * Math.pow(t - 17.856, 2) - 0.00908315 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.150185 + 0.0236767 * (t - 18.321) + 1.742540e-02 + * Math.pow(t - 18.321, 2) - 0.0202182 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.16293 + 0.0267673 * (t - 18.786) - 1.077895e-02 + * Math.pow(t - 18.786, 2) - 0.00537863 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.172465 + 0.0133634 * (t - 19.248) - 1.823373e-02 + * Math.pow(t - 19.248, 2) + 0.0066547 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.175405 + 0.000722759 * (t - 19.713) - 8.950425e-03 + * Math.pow(t - 19.713, 2) + 0.00522627 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.174332 - 0.00421098 * (t - 20.178) - 1.659774e-03 + * Math.pow(t - 20.178, 2) + 0.0011898 + * Math.pow(t - 20.178, 3); + + return y * (69.0310 - 20.6372) + 20.6372; + } + + /** + * Computes the approximation spline for nadp or returns the default value + * 0.0175 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=0.0003 + * + * @param t + * @return + */ + public double getNADP(double t) { + double y = this.nadp; + + if ((-3.429 <= t) && (t <= -2.964)) + y = 0.152191 - 0.100851 * (t + 3.429) + 0 * Math.pow(t + 3.429, 2) + + 0.359178 * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.141409 + 0.132138 * (t + 2.964) + 5.010527e-01 + * Math.pow(t + 2.964, 2) - 0.430333 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.267926 + 0.318971 * (t + 2.499) - 9.926155e-02 + * Math.pow(t + 2.499, 2) - 0.234737 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.370956 + 0.0769439 * (t + 2.037) - 4.246067e-01 + * Math.pow(t + 2.037, 2) + 0.342541 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.349365 - 0.0957427 * (t + 1.572) + 5.323777e-02 + * Math.pow(t + 1.572, 2) + 0.0754021 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.323937 + 0.00267988 * (t + 1.107) + 1.584237e-01 + * Math.pow(t + 1.107, 2) + 0.223708 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.381931 + 0.295128 * (t + 0.642) + 4.704962e-01 + * Math.pow(t + 0.642, 2) - 0.426272 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.721049 - 0.0508122 * (t - 0.363) - 8.147149e-01 + * Math.pow(t - 0.363, 2) + 0.339015 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.555345 - 0.588587 * (t - 0.828) - 3.417892e-01 + * Math.pow(t - 0.828, 2) + 0.843539 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.292562 - 0.359268 * (t - 1.293) + 8.349473e-01 + * Math.pow(t - 1.293, 2) - 0.579768 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.247747 + 0.0411519 * (t - 1.758) + 2.617095e-02 + * Math.pow(t - 1.758, 2) + 0.311203 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.303033 + 0.264607 * (t - 2.22) + 4.574978e-01 + * Math.pow(t - 2.22, 2) - 1.27356 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.396948 - 0.136044 * (t - 2.685) - 1.319114e+00 + * Math.pow(t - 2.685, 2) + 1.5105 * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.200335 - 0.382996 * (t - 3.15) + 7.880343e-01 + * Math.pow(t - 3.15, 2) - 0.244541 * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.563404 + 0.348539 * (t - 4.62) - 2.903913e-01 + * Math.pow(t - 4.62, 2) + 0.122972 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.675049 + 0.158244 * (t - 5.085) - 1.188447e-01 + * Math.pow(t - 5.085, 2) - 0.177319 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.705106 - 0.0673041 * (t - 5.55) - 3.662051e-01 + * Math.pow(t - 5.55, 2) + 0.265379 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.62131 - 0.23573 * (t - 6.015) + 3.998480e-03 + * Math.pow(t - 6.015, 2) + 0.405744 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.553267 + 0.0277755 * (t - 6.477) + 5.663602e-01 + * Math.pow(t - 6.477, 2) - 0.572376 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.631094 + 0.183205 * (t - 6.942) - 2.321041e-01 + * Math.pow(t - 6.942, 2) - 0.283917 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.637551 - 0.216822 * (t - 7.407) - 6.281689e-01 + * Math.pow(t - 7.407, 2) + 1.27563 * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.529161 + 0.026452 * (t - 7.872) + 1.151340e+00 + * Math.pow(t - 7.872, 2) - 0.845774 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.860103 - 0.222113 * (t - 8.877) - 1.398668e+00 + * Math.pow(t - 8.877, 2) + 1.2507 * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.580145 - 0.711574 * (t - 9.342) + 3.460638e-01 + * Math.pow(t - 9.342, 2) + 0.300835 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.354338 - 0.194591 * (t - 9.807) + 7.657292e-01 + * Math.pow(t - 9.807, 2) - 0.442497 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.384933 + 0.230501 * (t - 10.272) + 1.484461e-01 + * Math.pow(t - 10.272, 2) - 0.0370582 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.519455 + 0.343936 * (t - 10.734) + 9.708344e-02 + * Math.pow(t - 10.734, 2) - 1.27811 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.57187 - 0.394852 * (t - 11.199) - 1.685874e+00 + * Math.pow(t - 11.199, 2) + 1.81482 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.206206 - 0.785485 * (t - 11.664) + 8.458034e-01 + * Math.pow(t - 11.664, 2) - 0.240625 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = -0.000354184 - 0.154975 * (t - 12.129) + 5.101319e-01 + * Math.pow(t - 12.129, 2) - 0.235317 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.120277 + 0.157362 * (t - 13.134) - 1.993487e-01 + * Math.pow(t - 13.134, 2) + 0.0888371 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.159278 + 0.0295938 * (t - 13.599) - 7.542095e-02 + * Math.pow(t - 13.599, 2) + 0.192695 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.176106 + 0.0844486 * (t - 14.064) + 1.933881e-01 + * Math.pow(t - 14.064, 2) + 0.000891114 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.257279 + 0.264878 * (t - 14.529) + 1.946312e-01 + * Math.pow(t - 14.529, 2) - 0.374409 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.384275 + 0.204971 * (t - 14.991) - 3.242994e-01 + * Math.pow(t - 14.991, 2) - 0.320716 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.377218 - 0.304668 * (t - 15.456) - 7.716985e-01 + * Math.pow(t - 15.456, 2) + 1.15579 + * Math.pow(t - 15.456, 3); + else if (t <= 17.391) + y = 0.184896 - 0.272614 * (t - 15.921) + 8.406312e-01 + * Math.pow(t - 15.921, 2) - 0.356506 + * Math.pow(t - 15.921, 3); + else if (t <= 17.856) + y = 0.468224 - 0.112279 * (t - 17.391) - 7.315592e-01 + * Math.pow(t - 17.391, 2) + 0.588669 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.317021 - 0.410774 * (t - 17.856) + 8.963419e-02 + * Math.pow(t - 17.856, 2) + 0.674812 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.213241 + 0.11032 * (t - 18.321) + 1.030997e+00 + * Math.pow(t - 18.321, 2) - 1.08278 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.378599 + 0.366776 * (t - 18.786) - 4.794776e-01 + * Math.pow(t - 18.786, 2) + 0.632722 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.508102 + 0.328891 * (t - 19.248) + 3.974756e-01 + * Math.pow(t - 19.248, 2) - 1.44145 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.60205 - 0.236491 * (t - 19.713) - 1.613351e+00 + * Math.pow(t - 19.713, 2) + 1.89565 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.333833 - 0.507245 * (t - 20.178) + 1.031084e+00 + * Math.pow(t - 20.178, 2) - 0.739129 + * Math.pow(t - 20.178, 3); + + return y * (0.0384 - 0.0130) + 0.0130; + } + + /** + * Computes the approximation spline for nad or returns the default value + * 0.528 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=0.0838 (= std(y)) + * + * @param t + * @return + */ + public double getNAD(double t) { + double y = this.nad; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.26415 + 0.195236 * (t + 3.894) + 0 * Math.pow(t + 3.894, 2) + + 0.0529289 * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.360257 + 0.22957 * (t + 3.429) + 7.383579e-02 + * Math.pow(t + 3.429, 2) - 0.09885 * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.473033 + 0.234116 * (t + 2.964) - 6.406001e-02 + * Math.pow(t + 2.964, 2) - 0.0487923 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.56314 + 0.142889 * (t + 2.499) - 1.321253e-01 + * Math.pow(t + 2.499, 2) + 0.131891 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.613959 + 0.105259 * (t + 2.037) + 5.067510e-02 + * Math.pow(t + 2.037, 2) - 0.179837 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.65578 + 0.0357315 * (t + 1.572) - 2.001977e-01 + * Math.pow(t + 1.572, 2) + 0.210804 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.650303 - 0.0137091 * (t + 1.107) + 9.387388e-02 + * Math.pow(t + 1.107, 2) - 0.139083 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.650242 - 0.0166259 * (t + 0.642) - 1.001466e-01 + * Math.pow(t + 0.642, 2) + 0.0820861 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.615706 + 0.0308066 * (t - 0.363) + 1.473431e-01 + * Math.pow(t - 0.363, 2) - 0.108785 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.650953 + 0.0972696 * (t - 0.828) - 4.411873e-03 + * Math.pow(t - 0.828, 2) - 0.329967 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.662053 - 0.120875 * (t - 1.293) - 4.647152e-01 + * Math.pow(t - 1.293, 2) + 0.701928 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.575938 - 0.0977365 * (t - 1.758) + 5.144744e-01 + * Math.pow(t - 1.758, 2) - 0.803084 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.561402 - 0.136602 * (t - 2.22) - 5.985996e-01 + * Math.pow(t - 2.22, 2) + 0.239797 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.39256 - 0.53775 * (t - 2.685) - 2.640832e-01 + * Math.pow(t - 2.685, 2) + 0.516334 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.13732 - 0.448414 * (t - 3.15) + 4.562028e-01 + * Math.pow(t - 3.15, 2) - 0.0687614 * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.245537 + 0.447062 * (t - 4.62) + 1.529651e-01 + * Math.pow(t - 4.62, 2) - 0.595339 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.426638 + 0.203138 * (t - 5.085) - 6.775328e-01 + * Math.pow(t - 5.085, 2) + 0.535904 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.42848 - 0.0793393 * (t - 5.55) + 7.005394e-02 + * Math.pow(t - 5.55, 2) + 0.682327 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.475339 + 0.428419 * (t - 6.015) + 1.021900e+00 + * Math.pow(t - 6.015, 2) - 1.40739 * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.752603 + 0.471457 * (t - 6.477) - 9.287462e-01 + * Math.pow(t - 6.477, 2) + 0.605586 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.8319 + 0.000551332 * (t - 6.942) - 8.395334e-02 + * Math.pow(t - 6.942, 2) + 0.277409 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.841896 + 0.102423 * (t - 7.407) + 3.030328e-01 + * Math.pow(t - 7.407, 2) - 0.354633 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.919389 + 0.154202 * (t - 7.872) - 1.916809e-01 + * Math.pow(t - 7.872, 2) - 0.100478 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.778767 - 0.535532 * (t - 8.877) - 4.946216e-01 + * Math.pow(t - 8.877, 2) + 0.849276 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.508185 - 0.444626 * (t - 9.342) + 6.901186e-01 + * Math.pow(t - 9.342, 2) - 0.35446 * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.415016 - 0.0327449 * (t - 9.807) + 1.956475e-01 + * Math.pow(t - 9.807, 2) - 0.209782 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.421001 + 0.0131269 * (t - 10.272) - 9.699846e-02 + * Math.pow(t - 10.272, 2) + 0.262128 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.432211 + 0.0913491 * (t - 10.734) + 2.663105e-01 + * Math.pow(t - 10.734, 2) - 0.365611 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.495511 + 0.101855 * (t - 11.199) - 2.437172e-01 + * Math.pow(t - 11.199, 2) - 0.105576 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.47956 - 0.193287 * (t - 11.664) - 3.909959e-01 + * Math.pow(t - 11.664, 2) + 0.67975 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.373484 - 0.115976 * (t - 12.129) + 5.572554e-01 + * Math.pow(t - 12.129, 2) - 0.36999 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.444203 - 0.116991 * (t - 13.134) - 5.582651e-01 + * Math.pow(t - 13.134, 2) + 0.803056 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.349834 - 0.115255 * (t - 13.599) + 5.619983e-01 + * Math.pow(t - 13.599, 2) - 0.391353 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.37841 + 0.153543 * (t - 14.064) + 1.606073e-02 + * Math.pow(t - 14.064, 2) - 0.0653872 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.446706 + 0.126064 * (t - 14.529) - 7.515447e-02 + * Math.pow(t - 14.529, 2) - 0.187084 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.470458 - 0.0631743 * (t - 14.991) - 3.344527e-01 + * Math.pow(t - 14.991, 2) - 0.0305672 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.365691 - 0.394043 * (t - 15.456) - 3.770939e-01 + * Math.pow(t - 15.456, 2) + 0.547005 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.155923 - 0.389912 * (t - 15.921) + 3.859784e-01 + * Math.pow(t - 15.921, 2) + 0.139954 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.0721433 + 0.0598327 * (t - 16.386) + 5.812147e-01 + * Math.pow(t - 16.386, 2) - 0.428449 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.284409 - 0.0701584 * (t - 17.391) - 7.105590e-01 + * Math.pow(t - 17.391, 2) + 0.672417 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.165752 - 0.294798 * (t - 17.856) + 2.274630e-01 + * Math.pow(t - 17.856, 2) + 0.325128 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.110544 + 0.127645 * (t - 18.321) + 6.810168e-01 + * Math.pow(t - 18.321, 2) - 0.574329 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.259406 + 0.388438 * (t - 18.786) - 1.201718e-01 + * Math.pow(t - 18.786, 2) - 0.355037 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.378204 + 0.050058 * (t - 19.248) - 6.122524e-01 + * Math.pow(t - 19.248, 2) + 0.331958 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.302473 - 0.304004 * (t - 19.713) - 1.491710e-01 + * Math.pow(t - 19.713, 2) + 0.636016 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.192805 - 0.0301656 * (t - 20.178) + 7.380708e-01 + * Math.pow(t - 20.178, 2) - 0.529083 + * Math.pow(t - 20.178, 3); + + return y * (0.6904 - 0.3447) + 0.3447; + } + + /* + * Alternative Splines. + * ------------------------------------------------------------------------------ + */ + + /** + * Computes the approximation spline for nad or returns the default value + * 5.12 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1. + * + * @param t + * @return + */ + public double getAKG_2(double t) { + double y = this.akg; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.0103873 + 0.0245023 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 0.000889259 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.0218703 + 0.0250792 * (t + 3.429) + 1.240516e-003 + * Math.pow(t + 3.429, 2) - 0.00103745 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.0336961 + 0.0255599 * (t + 2.964) - 2.067336e-004 + * Math.pow(t + 2.964, 2) + 0.0031721 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.0458557 + 0.0274253 * (t + 2.499) + 4.218348e-003 + * Math.pow(t + 2.499, 2) + 0.00940976 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.0603544 + 0.0373484 * (t + 2.037) + 1.726027e-002 + * Math.pow(t + 2.037, 2) + 0.00968846 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.0824277 + 0.0596851 * (t + 1.572) + 3.077568e-002 + * Math.pow(t + 1.572, 2) + 0.0028213 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.117119 + 0.0901366 * (t + 1.107) + 3.471139e-002 + * Math.pow(t + 1.107, 2) + 0.00341112 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.166881 + 0.124631 * (t + 0.642) + 3.946990e-002 + * Math.pow(t + 0.642, 2) + 0.00095361 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.332969 + 0.206855 * (t - 0.363) + 4.234504e-002 + * Math.pow(t - 0.363, 2) - 0.0162354 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.43668 + 0.235704 * (t - 0.828) + 1.969664e-002 + * Math.pow(t - 0.828, 2) - 0.0467443 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.545842 + 0.2237 * (t - 1.293) - 4.551161e-002 + * Math.pow(t - 1.293, 2) - 0.00864697 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.639152 + 0.175765 * (t - 1.758) - 5.757413e-002 + * Math.pow(t - 1.758, 2) - 0.00969614 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.707111 + 0.116358 * (t - 2.22) - 7.101299e-002 + * Math.pow(t - 2.22, 2) + 0.0111824 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.746987 + 0.0575699 * (t - 2.685) - 5.541349e-002 + * Math.pow(t - 2.685, 2) + 0.014742 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.763257 + 0.0155981 * (t - 3.15) - 3.484845e-002 + * Math.pow(t - 3.15, 2) + 0.0112969 * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.746768 - 0.0136219 * (t - 4.62) + 1.497086e-002 + * Math.pow(t - 4.62, 2) + 0.0132501 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.745003 + 0.00889596 * (t - 5.085) + 3.345475e-002 + * Math.pow(t - 5.085, 2) - 0.012265 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.75514 + 0.0320529 * (t - 5.55) + 1.634507e-002 + * Math.pow(t - 5.55, 2) - 0.0117923 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.772393 + 0.0396044 * (t - 6.015) - 1.051669e-004 + * Math.pow(t - 6.015, 2) - 0.0170771 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.788984 + 0.0285723 * (t - 6.477) - 2.377399e-002 + * Math.pow(t - 6.477, 2) + 0.00151285 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.797281 + 0.0074438 * (t - 6.942) - 2.166356e-002 + * Math.pow(t - 6.942, 2) + 0.00135068 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.796194 - 0.0118272 * (t - 7.407) - 1.977937e-002 + * Math.pow(t - 7.407, 2) - 0.00327297 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.786089 - 0.0323451 * (t - 7.872) - 2.434516e-002 + * Math.pow(t - 7.872, 2) + 0.0112722 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.740435 - 0.0471232 * (t - 8.877) + 9.640591e-003 + * Math.pow(t - 8.877, 2) + 0.00491285 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.721101 - 0.0349706 * (t - 9.342) + 1.649402e-002 + * Math.pow(t - 9.342, 2) - 0.0097896 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.707422 - 0.0259814 * (t - 9.807) + 2.837523e-003 + * Math.pow(t - 9.807, 2) - 0.00582093 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.695369 - 0.0271184 * (t - 10.272) - 5.282677e-003 + * Math.pow(t - 10.272, 2) + 0.00265349 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.681974 - 0.0303005 * (t - 10.734) - 1.604944e-003 + * Math.pow(t - 10.734, 2) - 0.0079586 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.666737 - 0.0369556 * (t - 11.199) - 1.270719e-002 + * Math.pow(t - 11.199, 2) - 0.0131693 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.645481 - 0.0573159 * (t - 11.664) - 3.107839e-002 + * Math.pow(t - 11.664, 2) + 0.0093138 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.613046 - 0.0801772 * (t - 12.129) - 1.808564e-002 + * Math.pow(t - 12.129, 2) + 0.0120022 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.526384 - 0.0801617 * (t - 13.134) + 1.810107e-002 + * Math.pow(t - 13.134, 2) - 0.0135793 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.491658 - 0.0721363 * (t - 13.599) - 8.420971e-004 + * Math.pow(t - 13.599, 2) - 0.00640392 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.457288 - 0.0770735 * (t - 14.064) - 9.775570e-003 + * Math.pow(t - 14.064, 2) + 0.00511879 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.41985 - 0.0828443 * (t - 14.529) - 2.634851e-003 + * Math.pow(t - 14.529, 2) + 0.00937238 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.381938 - 0.0792775 * (t - 14.991) + 1.035527e-002 + * Math.pow(t - 14.991, 2) - 0.0075053 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.346558 - 0.0745156 * (t - 15.456) - 1.146279e-004 + * Math.pow(t - 15.456, 2) + 0.00304791 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.31219 - 0.0726451 * (t - 15.921) + 4.137208e-003 + * Math.pow(t - 15.921, 2) + 0.0136591 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.280678 - 0.0599372 * (t - 16.386) + 2.319161e-002 + * Math.pow(t - 16.386, 2) - 0.00821908 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.235522 - 0.0382265 * (t - 17.391) - 1.588930e-003 + * Math.pow(t - 17.391, 2) + 0.00670205 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.218077 - 0.0353567 * (t - 17.856) + 7.760434e-003 + * Math.pow(t - 17.856, 2) + 0.00564499 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.203882 - 0.0244778 * (t - 18.321) + 1.563519e-002 + * Math.pow(t - 18.321, 2) - 0.00177981 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.195702 - 0.0110916 * (t - 18.786) + 1.315236e-002 + * Math.pow(t - 18.786, 2) - 0.00372881 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.193017 - 0.00132647 * (t - 19.248) + 7.984227e-003 + * Math.pow(t - 19.248, 2) + 0.00047682 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.194174 + 0.00640816 * (t - 19.713) + 8.649391e-003 + * Math.pow(t - 19.713, 2) - 0.00671658 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.198349 + 0.0100952 * (t - 20.178) - 7.202434e-004 + * Math.pow(t - 20.178, 2) + 0.000516303 + * Math.pow(t - 20.178, 3); + + return (13.05163236 - 4.464821914) * y + 4.464821914; + } + + /** + * Computes the approximation spline for nad or returns the default value + * 1.05 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1. + * + * @param t + * @return + */ + public double getAla_2(double t) { + double y = this.ala; + + if ((-3.894 <= t) && (t <= -3.429)) + y = -0.0257788 + 0.0757032 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 8.46192e-005 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.00943169 + 0.0757581 * (t + 3.429) + 1.180437e-004 + * Math.pow(t + 3.429, 2) + 0.000227745 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.0447076 + 0.0760156 * (t + 2.964) + 4.357480e-004 + * Math.pow(t + 2.964, 2) + 0.00037785 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.0801871 + 0.0766659 * (t + 2.499) + 9.628484e-004 + * Math.pow(t + 2.499, 2) + 0.00059295 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.115871 + 0.0779353 * (t + 2.037) + 1.784677e-003 + * Math.pow(t + 2.037, 2) + 0.000556136 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.152552 + 0.0799558 * (t + 1.572) + 2.560487e-003 + * Math.pow(t + 1.572, 2) + 0.000221119 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.190308 + 0.0824805 * (t + 1.107) + 2.868948e-003 + * Math.pow(t + 1.107, 2) - 7.57144e-005 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.229274 + 0.0850995 * (t + 0.642) + 2.763327e-003 + * Math.pow(t + 0.642, 2) - 0.000522193 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.31706 + 0.0890715 * (t - 0.363) + 1.188915e-003 + * Math.pow(t - 0.363, 2) - 0.00127255 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.358607 + 0.0893517 * (t - 0.828) - 5.862872e-004 + * Math.pow(t - 0.828, 2) - 0.0020192 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.399826 + 0.0874966 * (t - 1.293) - 3.403066e-003 + * Math.pow(t - 1.293, 2) - 0.0010238 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.439673 + 0.0836677 * (t - 1.758) - 4.831271e-003 + * Math.pow(t - 1.758, 2) - 0.000745052 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.477223 + 0.0787265 * (t - 2.22) - 5.863913e-003 + * Math.pow(t - 2.22, 2) - 0.000419832 + * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.512521 + 0.0730007 * (t - 2.685) - 6.449578e-003 + * Math.pow(t - 2.685, 2) + 0.000116966 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.545083 + 0.0670785 * (t - 3.15) - 6.286411e-003 + * Math.pow(t - 3.15, 2) + 0.000331164 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.631156 + 0.0507433 * (t - 4.62) - 4.825979e-003 + * Math.pow(t - 4.62, 2) + 0.000341949 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.653743 + 0.0464769 * (t - 5.085) - 4.348961e-003 + * Math.pow(t - 5.085, 2) + 0.00010459 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.674425 + 0.0425003 * (t - 5.55) - 4.203058e-003 + * Math.pow(t - 5.55, 2) + 0.000593076 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.693338 + 0.0389761 * (t - 6.015) - 3.375717e-003 + * Math.pow(t - 6.015, 2) + 0.000530359 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.710677 + 0.0361966 * (t - 6.477) - 2.640640e-003 + * Math.pow(t - 6.477, 2) + 0.000450453 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.726982 + 0.034033 * (t - 6.942) - 2.012259e-003 + * Math.pow(t - 6.942, 2) - 0.000111927 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.742361 + 0.032089 * (t - 7.407) - 2.168397e-003 + * Math.pow(t - 7.407, 2) - 0.00044037 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.75677 + 0.0297867 * (t - 7.872) - 2.782713e-003 + * Math.pow(t - 7.872, 2) - 0.000103208 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.78379 + 0.0238807 * (t - 8.877) - 3.093887e-003 + * Math.pow(t - 8.877, 2) - 0.000601735 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.794165 + 0.0206131 * (t - 9.342) - 3.933308e-003 + * Math.pow(t - 9.342, 2) - 0.00100343 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.802799 + 0.0163042 * (t - 9.807) - 5.333096e-003 + * Math.pow(t - 9.807, 2) - 0.000786438 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.809148 + 0.0108343 * (t - 10.272) - 6.430177e-003 + * Math.pow(t - 10.272, 2) - 0.000301043 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.812751 + 0.00470002 * (t - 10.734) - 6.847422e-003 + * Math.pow(t - 10.734, 2) - 0.000462141 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.81341 - 0.00196787 * (t - 11.199) - 7.492109e-003 + * Math.pow(t - 11.199, 2) - 0.000409103 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.810833 - 0.0092009 * (t - 11.664) - 8.062808e-003 + * Math.pow(t - 11.664, 2) - 3.32225e-006 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.804811 - 0.0167015 * (t - 12.129) - 8.067443e-003 + * Math.pow(t - 12.129, 2) + 0.000582906 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.78047 - 0.0311508 * (t - 13.134) - 6.309980e-003 + * Math.pow(t - 13.134, 2) - 0.000389839 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.764581 - 0.0372719 * (t - 13.599) - 6.853806e-003 + * Math.pow(t - 13.599, 2) + 0.000242278 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.745792 - 0.0434888 * (t - 14.064) - 6.515828e-003 + * Math.pow(t - 14.064, 2) + 0.00107672 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.724269 - 0.0488501 * (t - 14.529) - 5.013806e-003 + * Math.pow(t - 14.529, 2) + 0.00137712 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.700766 - 0.052601 * (t - 14.991) - 3.105123e-003 + * Math.pow(t - 14.991, 2) + 0.000485789 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.675684 - 0.0551737 * (t - 15.456) - 2.427447e-003 + * Math.pow(t - 15.456, 2) + 0.00103293 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.649607 - 0.0567612 * (t - 15.921) - 9.865110e-004 + * Math.pow(t - 15.921, 2) + 0.00134773 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.623135 - 0.0568044 * (t - 16.386) + 8.935673e-004 + * Math.pow(t - 16.386, 2) + 0.00043569 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.567392 - 0.0536882 * (t - 17.391) + 2.207173e-003 + * Math.pow(t - 17.391, 2) + 0.000468284 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.542951 - 0.0513317 * (t - 17.856) + 2.860429e-003 + * Math.pow(t - 17.856, 2) + 0.000183539 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.519719 - 0.0485525 * (t - 18.321) + 3.116466e-003 + * Math.pow(t - 18.321, 2) - 0.000803598 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.497735 - 0.0461754 * (t - 18.786) + 1.995447e-003 + * Math.pow(t - 18.786, 2) - 0.000805573 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.476748 - 0.0448475 * (t - 19.248) + 8.789230e-004 + * Math.pow(t - 19.248, 2) - 0.000265004 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.456058 - 0.044202 * (t - 19.713) + 5.092420e-004 + * Math.pow(t - 19.713, 2) - 0.000309747 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.435583 - 0.0439293 * (t - 20.178) + 7.714514e-005 + * Math.pow(t - 20.178, 2) - 5.53012e-005 + * Math.pow(t - 20.178, 3); + + return (3.800862229 - 0.814981525) * y + 0.814981525; + } + + /** + * Computes the spline equation for pyr with the parameter S=1 and all + * weights equal to one. For values of t smaller or greater than the + * considered time the default steady-state value of 0.689 mM will be + * returned. + * + * @param t + * @return y the concentration of pyr at the given time t. + */ + public double getPyr_2(double t) { + double y = this.pyr; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.00709166 + 0.0109621 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 0.00196669 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.0123868 + 0.0122378 * (t + 3.429) + 2.743530e-003 + * Math.pow(t + 3.429, 2) + 0.00339276 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.0190117 + 0.0169901 * (t + 2.964) + 7.476430e-003 + * Math.pow(t + 2.964, 2) + 0.00342188 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.0288727 + 0.0261629 * (t + 2.499) + 1.224995e-002 + * Math.pow(t + 2.499, 2) + 0.00337058 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.043907 + 0.0396401 * (t + 2.037) + 1.692158e-002 + * Math.pow(t + 2.037, 2) + 0.00582311 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.066584 + 0.0591545 * (t + 1.572) + 2.504482e-002 + * Math.pow(t + 1.572, 2) + 0.00764784 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.100275 + 0.0874071 * (t + 1.107) + 3.571356e-002 + * Math.pow(t + 1.107, 2) + 0.00313583 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.148957 + 0.122655 * (t + 0.642) + 4.008804e-002 + * Math.pow(t + 0.642, 2) - 0.00384667 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.30881 + 0.191576 * (t - 0.363) + 2.849033e-002 + * Math.pow(t - 0.363, 2) - 0.0157823 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.402467 + 0.207835 * (t - 0.828) + 6.474088e-003 + * Math.pow(t - 0.828, 2) - 0.0315699 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.497336 + 0.193377 * (t - 1.293) - 3.756588e-002 + * Math.pow(t - 1.293, 2) - 0.00800701 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.578328 + 0.153247 * (t - 1.758) - 4.873566e-002 + * Math.pow(t - 1.758, 2) + 0.000853208 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.63881 + 0.108761 * (t - 2.22) - 4.755311e-002 + * Math.pow(t - 2.22, 2) + 0.00189972 + * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.679293 + 0.0657692 * (t - 2.685) - 4.490300e-002 + * Math.pow(t - 2.685, 2) + 0.00542329 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.700711 + 0.0275273 * (t - 3.15) - 3.733752e-002 + * Math.pow(t - 3.15, 2) + 0.00782647 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.685355 - 0.0315083 * (t - 4.62) - 2.822794e-003 + * Math.pow(t - 4.62, 2) + 0.00824557 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.670922 - 0.0287848 * (t - 5.085) + 8.679771e-003 + * Math.pow(t - 5.085, 2) - 0.00053434 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.65936 - 0.0210592 * (t - 5.55) + 7.934367e-003 + * Math.pow(t - 5.55, 2) + 0.000466607 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.65133 - 0.0133776 * (t - 6.015) + 8.585283e-003 + * Math.pow(t - 6.015, 2) + 0.00180465 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.64716 - 0.00428923 * (t - 6.477) + 1.108653e-002 + * Math.pow(t - 6.477, 2) + 0.00122435 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.647686 + 0.00681544 * (t - 6.942) + 1.279450e-002 + * Math.pow(t - 6.942, 2) + 0.000225641 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.653645 + 0.0188607 * (t - 7.407) + 1.310927e-002 + * Math.pow(t - 7.407, 2) - 0.00705357 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.66454 + 0.0264768 * (t - 7.872) + 3.269535e-003 + * Math.pow(t - 7.872, 2) - 0.00164124 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.692786 + 0.0280755 * (t - 8.877) - 1.678801e-003 + * Math.pow(t - 8.877, 2) - 0.00583638 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.704891 + 0.0227283 * (t - 9.342) - 9.820550e-003 + * Math.pow(t - 9.342, 2) - 0.00702045 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.71263 + 0.00904123 * (t - 9.807) - 1.961407e-002 + * Math.pow(t - 9.807, 2) - 0.00222559 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.71237 - 0.0106435 * (t - 10.272) - 2.271877e-002 + * Math.pow(t - 10.272, 2) + 0.000800336 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.702682 - 0.0311232 * (t - 10.734) - 2.160950e-002 + * Math.pow(t - 10.734, 2) - 0.00295931 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.68324 - 0.0531397 * (t - 11.199) - 2.573774e-002 + * Math.pow(t - 11.199, 2) - 0.00108335 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.652856 - 0.0777785 * (t - 11.664) - 2.724901e-002 + * Math.pow(t - 11.664, 2) + 0.0120572 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.612009 - 0.0952989 * (t - 12.129) - 1.042920e-002 + * Math.pow(t - 12.129, 2) + 0.00960616 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.515451 - 0.0871542 * (t - 13.134) + 1.853336e-002 + * Math.pow(t - 13.134, 2) - 0.00166772 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.478764 - 0.071 * (t - 13.599) + 1.620689e-002 + * Math.pow(t - 13.599, 2) - 0.00322537 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.448929 - 0.0580198 * (t - 14.064) + 1.170750e-002 + * Math.pow(t - 14.064, 2) - 0.00133285 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.424347 - 0.0479964 * (t - 14.529) + 9.848170e-003 + * Math.pow(t - 14.529, 2) - 0.000351634 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.40424 - 0.0391218 * (t - 14.991) + 9.360805e-003 + * Math.pow(t - 14.991, 2) - 0.0119481 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.386871 - 0.0381667 * (t - 15.456) - 7.306757e-003 + * Math.pow(t - 15.456, 2) + 0.00287619 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.367833 - 0.0430963 * (t - 15.921) - 3.294470e-003 + * Math.pow(t - 15.921, 2) + 0.00490737 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.347574 - 0.0429768 * (t - 16.386) + 3.551307e-003 + * Math.pow(t - 16.386, 2) - 0.000631183 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.307329 - 0.0377513 * (t - 17.391) + 1.648289e-003 + * Math.pow(t - 17.391, 2) + 0.0043097 + * Math.pow(t - 17.391, 3); + else if (t <= 18.786) + y = 0.290564 - 0.0334227 * (t - 17.856) + 7.660326e-003 + * Math.pow(t - 17.856, 2) - 0.000123905 + * Math.pow(t - 17.856, 3); + else if (t <= 19.248) + y = 0.266007 - 0.019496 * (t - 18.786) + 7.314632e-003 + * Math.pow(t - 18.786, 2) - 0.00687547 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.257883 - 0.0171399 * (t - 19.248) - 2.214775e-003 + * Math.pow(t - 19.248, 2) - 0.0015665 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.249277 - 0.0202158 * (t - 19.713) - 4.400043e-003 + * Math.pow(t - 19.713, 2) + 0.0021551 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.239141 - 0.0229099 * (t - 20.178) - 1.393672e-003 + * Math.pow(t - 20.178, 2) + 0.000999048 + * Math.pow(t - 20.178, 3); + + return (2.350729829 - 0.614400017) * y + 0.614400017; + } + + /** + * Computes the approximation spline for glut or returns the default value + * 38.7 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1. + * + * @param t + * @return + */ + public double getGlut_2(double t) { + double y = this.glut; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.345758 + 0.0308006 * (t + 3.894) + 0 * Math.pow(t + 3.894, 2) + + 1.46278e-005 * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.360082 + 0.03081 * (t + 3.429) + 2.040578e-005 + * Math.pow(t + 3.429, 2) - 4.77212e-006 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.374413 + 0.0308259 * (t + 2.964) + 1.374867e-005 + * Math.pow(t + 2.964, 2) - 2.09457e-005 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.388748 + 0.0308251 * (t + 2.499) - 1.547064e-005 + * Math.pow(t + 2.499, 2) - 5.74626e-006 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.402985 + 0.0308071 * (t + 2.037) - 2.343495e-005 + * Math.pow(t + 2.037, 2) - 1.44612e-005 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.417304 + 0.030776 * (t + 1.572) - 4.360827e-005 + * Math.pow(t + 1.572, 2) - 2.12542e-005 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.431603 + 0.0307216 * (t + 1.107) - 7.325784e-005 + * Math.pow(t + 1.107, 2) - 4.47943e-005 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.445868 + 0.0306244 * (t + 0.642) - 1.357459e-004 + * Math.pow(t + 0.642, 2) - 4.75641e-005 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.47646 + 0.0302075 * (t - 0.363) - 2.791515e-004 + * Math.pow(t - 0.363, 2) - 6.39979e-005 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.49044 + 0.0299063 * (t - 0.828) - 3.684286e-004 + * Math.pow(t - 0.828, 2) - 6.70553e-005 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.50426 + 0.0295202 * (t - 1.293) - 4.619707e-004 + * Math.pow(t - 1.293, 2) - 6.72759e-005 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.51788 + 0.0290469 * (t - 1.758) - 5.558206e-004 + * Math.pow(t - 1.758, 2) - 0.000100774 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.531172 + 0.0284688 * (t - 2.22) - 6.954937e-004 + * Math.pow(t - 2.22, 2) - 8.65101e-005 + * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.54425 + 0.0277659 * (t - 2.685) - 8.161753e-004 + * Math.pow(t - 2.685, 2) - 0.000128037 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.556972 + 0.0269238 * (t - 3.15) - 9.947864e-004 + * Math.pow(t - 3.15, 2) - 0.000177372 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.593837 + 0.0228493 * (t - 4.62) - 1.776998e-003 + * Math.pow(t - 4.62, 2) - 0.000228858 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.604055 + 0.0210482 * (t - 5.085) - 2.096254e-003 + * Math.pow(t - 5.085, 2) - 0.000211625 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.613368 + 0.0189614 * (t - 5.55) - 2.391471e-003 + * Math.pow(t - 5.55, 2) - 0.000215207 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.621646 + 0.0165978 * (t - 6.015) - 2.691685e-003 + * Math.pow(t - 6.015, 2) - 0.000233051 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.628717 + 0.0139614 * (t - 6.477) - 3.014694e-003 + * Math.pow(t - 6.477, 2) - 0.000222455 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.634535 + 0.0110134 * (t - 6.942) - 3.325019e-003 + * Math.pow(t - 6.942, 2) - 0.000218367 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.638915 + 0.00777953 * (t - 7.407) - 3.629641e-003 + * Math.pow(t - 7.407, 2) - 0.000200303 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.641727 + 0.00427403 * (t - 7.872) - 3.909065e-003 + * Math.pow(t - 7.872, 2) - 0.000100588 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.641972 - 0.00388798 * (t - 8.877) - 4.212339e-003 + * Math.pow(t - 8.877, 2) - 8.05589e-005 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.639246 - 0.00785771 * (t - 9.342) - 4.324718e-003 + * Math.pow(t - 9.342, 2) - 0.000122855 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.634644 - 0.0119594 * (t - 9.807) - 4.496101e-003 + * Math.pow(t - 9.807, 2) - 8.67214e-005 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.628102 - 0.016197 * (t - 10.272) - 4.617077e-003 + * Math.pow(t - 10.272, 2) + 1.56232e-005 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.619635 - 0.0204532 * (t - 10.734) - 4.595424e-003 + * Math.pow(t - 10.734, 2) + 4.79204e-005 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.609136 - 0.0246959 * (t - 11.199) - 4.528575e-003 + * Math.pow(t - 11.199, 2) + 0.000102127 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.596683 - 0.0288412 * (t - 11.664) - 4.386108e-003 + * Math.pow(t - 11.664, 2) + 0.000179965 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.582342 - 0.0328035 * (t - 12.129) - 4.135056e-003 + * Math.pow(t - 12.129, 2) + 0.000211674 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.545413 - 0.0404736 * (t - 13.134) - 3.496860e-003 + * Math.pow(t - 13.134, 2) + 0.000228722 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.525859 - 0.0435773 * (t - 13.599) - 3.177793e-003 + * Math.pow(t - 13.599, 2) + 0.000250327 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.504934 - 0.0463703 * (t - 14.064) - 2.828587e-003 + * Math.pow(t - 14.064, 2) + 0.000304393 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.482791 - 0.0488034 * (t - 14.529) - 2.403960e-003 + * Math.pow(t - 14.529, 2) + 0.000326486 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.459763 - 0.0508156 * (t - 14.991) - 1.951450e-003 + * Math.pow(t - 14.991, 2) + 0.00033353 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.435745 - 0.0524141 * (t - 15.456) - 1.486176e-003 + * Math.pow(t - 15.456, 2) + 0.000301097 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.411081 - 0.0536009 * (t - 15.921) - 1.066146e-003 + * Math.pow(t - 15.921, 2) + 0.000256208 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.385952 - 0.0544263 * (t - 16.386) - 7.087358e-004 + * Math.pow(t - 16.386, 2) + 0.000164513 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.330705 - 0.0553523 * (t - 17.391) - 2.127293e-004 + * Math.pow(t - 17.391, 2) + 0.000106639 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.304931 - 0.055481 * (t - 17.856) - 6.396824e-005 + * Math.pow(t - 17.856, 2) + 4.39342e-005 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.279123 - 0.055512 * (t - 18.321) - 2.679996e-006 + * Math.pow(t - 18.321, 2) - 3.37517e-005 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.253306 - 0.0555364 * (t - 18.786) - 4.976359e-005 + * Math.pow(t - 18.786, 2) - 3.19922e-006 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.227637 - 0.0555844 * (t - 19.248) - 5.419771e-005 + * Math.pow(t - 19.248, 2) + 2.66171e-005 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.201781 - 0.0556175 * (t - 19.713) - 1.706686e-005 + * Math.pow(t - 19.713, 2) + 1.3914e-005 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.175917 - 0.0556244 * (t - 20.178) + 2.343138e-006 + * Math.pow(t - 20.178, 2) - 1.67967e-006 + * Math.pow(t - 20.178, 3); + + return (69.03104101 - 20.63720808) * y + 20.63720808; + } + + /** + * Computes the approximation spline for nadp or returns the default value + * 0.0175 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getNADP_2(double t) { + double y = this.nadp; + + if ((-3.429 <= t) && (t <= -2.964)) + y = 0.13595 + 0.0971795 * (t + 3.429) + 0 * Math.pow(t + 3.429, 2) + + 0.05691 * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.186861 + 0.134096 * (t + 2.964) + 7.938952e-002 + * Math.pow(t + 2.964, 2) - 0.0819871 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.258138 + 0.154745 * (t + 2.499) - 3.498256e-002 + * Math.pow(t + 2.499, 2) - 0.0496607 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.317266 + 0.0906217 * (t + 2.037) - 1.038122e-001 + * Math.pow(t + 2.037, 2) + 0.0808513 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.345087 + 0.0465225 * (t + 1.572) + 8.975385e-003 + * Math.pow(t + 1.572, 2) + 0.0616748 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.374862 + 0.0948765 * (t + 1.107) + 9.501172e-002 + * Math.pow(t + 1.107, 2) + 0.00360399 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.439886 + 0.185575 * (t + 0.642) + 1.000393e-001 + * Math.pow(t + 0.642, 2) - 0.139812 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.585512 - 0.0369863 * (t - 0.363) - 3.214935e-001 + * Math.pow(t - 0.363, 2) + 0.1243 * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.511296 - 0.255345 * (t - 0.828) - 1.480948e-001 + * Math.pow(t - 0.828, 2) + 0.234296 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.384096 - 0.241091 * (t - 1.293) + 1.787485e-001 + * Math.pow(t - 1.293, 2) - 0.0296831 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.307654 - 0.0941094 * (t - 1.758) + 1.373406e-001 + * Math.pow(t - 1.758, 2) - 0.0303251 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.2905 + 0.0133752 * (t - 2.22) + 9.531002e-002 + * Math.pow(t - 2.22, 2) - 0.162261 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.301013 - 0.00324113 * (t - 2.685) - 1.310440e-001 + * Math.pow(t - 2.685, 2) + 0.236466 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.294947 + 0.0282775 * (t - 3.15) + 1.988259e-001 + * Math.pow(t - 3.15, 2) - 0.0632008 * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.565399 + 0.203114 * (t - 4.62) - 7.988958e-002 + * Math.pow(t - 4.62, 2) - 0.0313141 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.639424 + 0.108504 * (t - 5.085) - 1.235727e-001 + * Math.pow(t - 5.085, 2) - 0.00925389 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.662228 - 0.0124214 * (t - 5.55) - 1.364819e-001 + * Math.pow(t - 5.55, 2) + 0.0932451 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.636317 - 0.0788638 * (t - 6.015) - 6.404951e-003 + * Math.pow(t - 6.015, 2) + 0.0852613 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.606922 - 0.0301864 * (t - 6.477) + 1.117673e-001 + * Math.pow(t - 6.477, 2) - 0.083061 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.608701 + 0.0198776 * (t - 6.942) - 4.102769e-003 + * Math.pow(t - 6.942, 2) - 0.0241232 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.614632 + 0.000413894 * (t - 7.407) - 3.775461e-002 + * Math.pow(t - 7.407, 2) + 0.15561 * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.622307 + 0.0662424 * (t - 7.872) + 1.793212e-001 + * Math.pow(t - 7.872, 2) - 0.176584 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.690753 - 0.108384 * (t - 8.877) - 3.530788e-001 + * Math.pow(t - 8.877, 2) + 0.261112 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.590264 - 0.26737 * (t - 9.342) + 1.117277e-002 + * Math.pow(t - 9.342, 2) + 0.157079 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.484146 - 0.155086 * (t - 9.807) + 2.302982e-001 + * Math.pow(t - 9.807, 2) - 0.0968507 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.452089 - 0.00373366 * (t - 10.272) + 9.519140e-002 + * Math.pow(t - 10.272, 2) - 0.153609 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.455535 - 0.0141377 * (t - 10.734) - 1.177110e-001 + * Math.pow(t - 10.734, 2) - 0.180352 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.405375 - 0.240598 * (t - 11.199) - 3.693013e-001 + * Math.pow(t - 11.199, 2) + 0.347415 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.248575 - 0.358689 * (t - 11.664) + 1.153421e-001 + * Math.pow(t - 11.664, 2) + 0.0933175 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.116107 - 0.190889 * (t - 12.129) + 2.455201e-001 + * Math.pow(t - 12.129, 2) - 0.071016 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.100159 + 0.087423 * (t - 13.134) + 3.140679e-002 + * Math.pow(t - 13.134, 2) - 0.0119264 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.146403 + 0.108895 * (t - 13.599) + 1.476952e-002 + * Math.pow(t - 13.599, 2) + 0.0161065 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.201852 + 0.133079 * (t - 14.064) + 3.723807e-002 + * Math.pow(t - 14.064, 2) - 0.0384482 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.267919 + 0.14277 * (t - 14.529) - 1.639710e-002 + * Math.pow(t - 14.529, 2) - 0.0889529 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.321607 + 0.0706593 * (t - 14.991) - 1.396858e-001 + * Math.pow(t - 14.991, 2) + 0.00481567 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.324744 - 0.0561247 * (t - 15.456) - 1.329680e-001 + * Math.pow(t - 15.456, 2) + 0.218536 + * Math.pow(t - 15.456, 3); + else if (t <= 17.391) + y = 0.291868 - 0.0380259 * (t - 15.921) + 1.718902e-001 + * Math.pow(t - 15.921, 2) - 0.0756988 + * Math.pow(t - 15.921, 3); + else if (t <= 17.856) + y = 0.366948 - 0.0234014 * (t - 17.391) - 1.619415e-001 + * Math.pow(t - 17.391, 2) + 0.156911 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.336828 - 0.0722227 * (t - 17.856) + 5.694952e-002 + * Math.pow(t - 17.856, 2) + 0.137013 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.329334 + 0.0696174 * (t - 18.321) + 2.480828e-001 + * Math.pow(t - 18.321, 2) - 0.193298 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.395912 + 0.174947 * (t - 18.786) - 2.156847e-002 + * Math.pow(t - 18.786, 2) - 0.0557802 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.466634 + 0.119299 * (t - 19.248) - 9.887981e-002 + * Math.pow(t - 19.248, 2) - 0.192986 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.481324 - 0.097844 * (t - 19.713) - 3.680952e-001 + * Math.pow(t - 19.713, 2) + 0.293063 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.385701 - 0.25007 * (t - 20.178) + 4.072711e-002 + * Math.pow(t - 20.178, 2) - 0.0291951 + * Math.pow(t - 20.178, 3); + + return (0.038425509 - 0.013039703) * y + 0.013039703; + } + + /** + * Computes the approximation spline for nad or returns the default value + * 0.528 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getNAD_2(double t) { + double y = this.nad; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.285913 + 0.197505 * (t + 3.894) + 0 * Math.pow(t + 3.894, 2) + - 0.00471653 * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.377278 + 0.194445 * (t + 3.429) - 6.579559e-003 + * Math.pow(t + 3.429, 2) - 0.0157447 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.464689 + 0.178113 * (t + 2.964) - 2.854341e-002 + * Math.pow(t + 2.964, 2) - 0.0112637 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.540208 + 0.144261 * (t + 2.499) - 4.425629e-002 + * Math.pow(t + 2.499, 2) + 0.00268962 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.597675 + 0.105091 * (t + 2.037) - 4.052848e-002 + * Math.pow(t + 2.037, 2) - 0.00411981 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.637365 + 0.0647267 * (t + 1.572) - 4.627561e-002 + * Math.pow(t + 1.572, 2) + 0.0164141 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.659107 + 0.0323377 * (t + 1.107) - 2.337800e-002 + * Math.pow(t + 1.107, 2) + 0.000399521 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.66913 + 0.0108554 * (t + 0.642) - 2.282067e-002 + * Math.pow(t + 0.642, 2) + 0.00296352 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.659998 - 0.0260345 * (t - 0.363) - 1.388565e-002 + * Math.pow(t - 0.363, 2) - 0.0179798 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.643082 - 0.0506112 * (t - 0.828) - 3.896752e-002 + * Math.pow(t - 0.828, 2) - 0.0239499 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.608714 - 0.102387 * (t - 1.293) - 7.237756e-002 + * Math.pow(t - 1.293, 2) + 0.0317423 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.548646 - 0.149107 * (t - 1.758) - 2.809708e-002 + * Math.pow(t - 1.758, 2) - 0.0169992 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.472084 - 0.185954 * (t - 2.22) - 5.165802e-002 + * Math.pow(t - 2.22, 2) + 0.0502322 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.379497 - 0.201412 * (t - 2.685) + 1.841597e-002 + * Math.pow(t - 2.685, 2) + 0.0647767 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.296335 - 0.142266 * (t - 3.15) + 1.087795e-001 + * Math.pow(t - 3.15, 2) - 0.00660796 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.301275 + 0.134708 * (t - 4.62) + 7.963841e-002 + * Math.pow(t - 4.62, 2) - 0.0438438 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.376726 + 0.180332 * (t - 5.085) + 1.847627e-002 + * Math.pow(t - 5.085, 2) + 0.0145632 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.46604 + 0.206962 * (t - 5.55) + 3.879198e-002 + * Math.pow(t - 5.55, 2) + 0.00851535 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.571521 + 0.248562 * (t - 6.015) + 5.067090e-002 + * Math.pow(t - 6.015, 2) - 0.10061 * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.687251 + 0.230958 * (t - 6.477) - 8.877391e-002 + * Math.pow(t - 6.477, 2) - 0.00393058 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.775056 + 0.145849 * (t - 6.942) - 9.425707e-002 + * Math.pow(t - 6.942, 2) + 0.00117341 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.822613 + 0.0589508 * (t - 7.407) - 9.262016e-002 + * Math.pow(t - 7.407, 2) - 0.0168782 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.828301 - 0.0381344 * (t - 7.872) - 1.161653e-001 + * Math.pow(t - 7.872, 2) + 0.0209354 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.693897 - 0.208191 * (t - 8.877) - 5.304513e-002 + * Math.pow(t - 8.877, 2) + 0.0832539 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.59399 - 0.203518 * (t - 9.342) + 6.309407e-002 + * Math.pow(t - 9.342, 2) + 0.0109974 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.514102 - 0.137707 * (t - 9.807) + 7.843539e-002 + * Math.pow(t - 9.807, 2) - 0.014136 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.465606 - 0.0739317 * (t - 10.272) + 5.871560e-002 + * Math.pow(t - 10.272, 2) - 0.00999596 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.442997 - 0.0260793 * (t - 10.734) + 4.486120e-002 + * Math.pow(t - 10.734, 2) - 0.0371792 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.436832 - 0.00847559 * (t - 11.199) - 7.003852e-003 + * Math.pow(t - 11.199, 2) - 0.00916083 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.430455 - 0.0209316 * (t - 11.664) - 1.978321e-002 + * Math.pow(t - 11.664, 2) + 0.0358549 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.420049 - 0.0160718 * (t - 12.129) + 3.023440e-002 + * Math.pow(t - 12.129, 2) - 0.0184213 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.415736 - 0.0111184 * (t - 13.134) - 2.530570e-002 + * Math.pow(t - 13.134, 2) + 0.0349448 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.408608 - 0.0119849 * (t - 13.599) + 2.344233e-002 + * Math.pow(t - 13.599, 2) - 0.0286015 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.405228 - 0.00873664 * (t - 14.064) - 1.645683e-002 + * Math.pow(t - 14.064, 2) - 0.0245074 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.395143 - 0.0399389 * (t - 14.529) - 5.064472e-002 + * Math.pow(t - 14.529, 2) - 0.0131914 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.36458 - 0.0951814 * (t - 14.991) - 6.892794e-002 + * Math.pow(t - 14.991, 2) + 0.0254845 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.307979 - 0.142753 * (t - 15.456) - 3.337702e-002 + * Math.pow(t - 15.456, 2) + 0.065268 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.240944 - 0.131456 * (t - 15.921) + 5.767177e-002 + * Math.pow(t - 15.921, 2) + 0.0235208 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.194652 - 0.0625641 * (t - 16.386) + 9.048331e-002 + * Math.pow(t - 16.386, 2) - 0.0359447 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.186679 + 0.0103924 * (t - 17.391) - 1.788984e-002 + * Math.pow(t - 17.391, 2) + 0.03609 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.191272 + 0.0171655 * (t - 17.856) + 3.245578e-002 + * Math.pow(t - 17.856, 2) + 0.015007 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.207781 + 0.0570841 * (t - 18.321) + 5.339052e-002 + * Math.pow(t - 18.321, 2) - 0.0492235 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.24092 + 0.0748072 * (t - 18.786) - 1.527625e-002 + * Math.pow(t - 18.786, 2) - 0.0351776 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.268752 + 0.0381666 * (t - 19.248) - 6.403238e-002 + * Math.pow(t - 19.248, 2) + 0.0247573 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.275143 - 0.00532401 * (t - 19.713) - 2.949588e-002 + * Math.pow(t - 19.713, 2) + 0.0447577 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.27079 - 0.00372197 * (t - 20.178) + 3.294111e-002 + * Math.pow(t - 20.178, 2) - 0.0236137 + * Math.pow(t - 20.178, 3); + + return (0.690441264 - 0.344747388) * y + 0.344747388; + } + + /* + * Splines of the data which should be fit normally. + */ + + /** + * Computes the approximation spline for Leu or returns the default value + * 0.209 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getLeu(double t) { + double y = 0.209 / (0.523231194 - 0.146383634) - 1 + / (0.523231194 - 0.146383634) * 0.146383634; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.254575 + 0.00234099 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 0.00081644 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.255745 + 0.0028706 * (t + 3.429) + 1.138934e-003 + * Math.pow(t + 3.429, 2) + 0.000514239 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.257378 + 0.00426338 * (t + 2.964) + 1.856298e-003 + * Math.pow(t + 2.964, 2) - 4.84395e-005 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.259757 + 0.00595832 * (t + 2.499) + 1.788725e-003 + * Math.pow(t + 2.499, 2) + 0.000191175 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.26291 + 0.00773351 * (t + 2.037) + 2.053693e-003 + * Math.pow(t + 2.037, 2) + 0.000506726 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.267001 + 0.00997215 * (t + 1.572) + 2.760576e-003 + * Math.pow(t + 1.572, 2) + 0.000130631 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.272249 + 0.0126242 * (t + 1.107) + 2.942806e-003 + * Math.pow(t + 1.107, 2) + 0.000206773 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.278776 + 0.0154952 * (t + 0.642) + 3.231255e-003 + * Math.pow(t + 0.642, 2) + 0.000562468 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.298183 + 0.0236943 * (t - 0.363) + 4.927094e-003 + * Math.pow(t - 0.363, 2) - 0.000122853 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.310254 + 0.0281968 * (t - 0.828) + 4.755714e-003 + * Math.pow(t - 0.828, 2) - 0.00023605 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.32437 + 0.0324665 * (t - 1.293) + 4.426424e-003 + * Math.pow(t - 1.293, 2) + 0.000571918 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.340482 + 0.0369541 * (t - 1.758) + 5.224250e-003 + * Math.pow(t - 1.758, 2) + 0.000349374 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.358704 + 0.042005 * (t - 2.22) + 5.708482e-003 + * Math.pow(t - 2.22, 2) - 0.000457706 + * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.379425 + 0.047017 * (t - 2.685) + 5.069983e-003 + * Math.pow(t - 2.685, 2) - 0.00122241 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.402261 + 0.0509391 * (t - 3.15) + 3.364716e-003 + * Math.pow(t - 3.15, 2) - 0.00138177 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.480023 + 0.0518738 * (t - 4.62) - 2.728891e-003 + * Math.pow(t - 4.62, 2) - 0.00128619 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.503425 + 0.0485016 * (t - 5.085) - 4.523131e-003 + * Math.pow(t - 5.085, 2) - 0.00136501 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.524863 + 0.0434096 * (t - 5.55) - 6.427323e-003 + * Math.pow(t - 5.55, 2) - 0.00137626 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.54352 + 0.0365395 * (t - 6.015) - 8.347206e-003 + * Math.pow(t - 6.015, 2) - 0.00181134 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.558441 + 0.0276668 * (t - 6.477) - 1.085772e-002 + * Math.pow(t - 6.477, 2) + 6.90083e-005 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.568965 + 0.0176139 * (t - 6.942) - 1.076145e-002 + * Math.pow(t - 6.942, 2) + 0.000455085 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.574875 + 0.00790092 * (t - 7.407) - 1.012661e-002 + * Math.pow(t - 7.407, 2) + 0.000371085 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.576396 - 0.00127611 * (t - 7.872) - 9.608945e-003 + * Math.pow(t - 7.872, 2) + 0.00127423 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.566702 - 0.0167291 * (t - 8.877) - 5.767152e-003 + * Math.pow(t - 8.877, 2) + 0.00168058 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.557845 - 0.0210024 * (t - 9.342) - 3.422739e-003 + * Math.pow(t - 9.342, 2) + 0.000255359 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.547364 - 0.0240199 * (t - 9.807) - 3.066513e-003 + * Math.pow(t - 9.807, 2) + 0.000486931 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.535581 - 0.0265559 * (t - 10.272) - 2.387245e-003 + * Math.pow(t - 10.272, 2) + 0.000578468 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.52286 - 0.0283913 * (t - 10.734) - 1.585488e-003 + * Math.pow(t - 10.734, 2) - 0.000443537 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.50927 - 0.0301535 * (t - 11.199) - 2.204222e-003 + * Math.pow(t - 11.199, 2) - 0.0010496 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.494667 - 0.0328843 * (t - 11.664) - 3.668415e-003 + * Math.pow(t - 11.664, 2) + 0.000223556 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.478605 - 0.0361509 * (t - 12.129) - 3.356554e-003 + * Math.pow(t - 12.129, 2) + 0.000163983 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.43905 - 0.0424007 * (t - 13.134) - 2.862147e-003 + * Math.pow(t - 13.134, 2) + 1.01014e-005 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.418715 - 0.0450559 * (t - 13.599) - 2.848055e-003 + * Math.pow(t - 13.599, 2) + 0.000100977 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.397159 - 0.0476391 * (t - 14.064) - 2.707192e-003 + * Math.pow(t - 14.064, 2) + 0.00132926 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.374555 - 0.0492946 * (t - 14.529) - 8.528782e-004 + * Math.pow(t - 14.529, 2) + 0.00113715 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.351711 - 0.0493545 * (t - 14.991) + 7.232178e-004 + * Math.pow(t - 14.991, 2) + 0.000573763 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.328975 - 0.0483097 * (t - 15.456) + 1.523618e-003 + * Math.pow(t - 15.456, 2) + 0.000622783 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.306903 - 0.0464887 * (t - 15.921) + 2.392399e-003 + * Math.pow(t - 15.921, 2) + 0.000534396 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.285857 - 0.0439171 * (t - 16.386) + 3.137882e-003 + * Math.pow(t - 16.386, 2) - 0.00061327 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.244267 - 0.0394683 * (t - 17.391) + 1.288872e-003 + * Math.pow(t - 17.391, 2) - 0.000638732 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.226129 - 0.0386839 * (t - 17.856) + 3.978406e-004 + * Math.pow(t - 17.856, 2) - 0.000462192 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.20818 - 0.0386138 * (t - 18.321) - 2.469173e-004 + * Math.pow(t - 18.321, 2) - 0.000260252 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.190145 - 0.0390122 * (t - 18.786) - 6.099692e-004 + * Math.pow(t - 18.786, 2) + 0.000103577 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.172002 - 0.0395095 * (t - 19.248) - 4.664113e-004 + * Math.pow(t - 19.248, 2) + 0.000399364 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.153569 - 0.0396842 * (t - 19.713) + 9.070190e-005 + * Math.pow(t - 19.713, 2) - 0.000254598 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.13511 - 0.039765 * (t - 20.178) - 2.644617e-004 + * Math.pow(t - 20.178, 2) + 0.000189578 + * Math.pow(t - 20.178, 3); + + return (0.523231194 - 0.146383634) * y + 0.146383634; + } + + /** + * Computes the approximation spline for AcLac or returns the default value + * 0.236 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getAcLac(double t) { + double y = 0.236 / (0.611504694 - 0.19610573) - 0.19610573 + / (0.611504694 - 0.19610573); + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.0509318 + 0.0425125 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 3.31769e-005 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.0707035 + 0.0425341 * (t + 3.429) + 4.628182e-005 + * Math.pow(t + 3.429, 2) + 0.000151112 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.090507 + 0.0426751 * (t + 2.964) + 2.570833e-004 + * Math.pow(t + 2.964, 2) + 0.000180795 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.110425 + 0.0430315 * (t + 2.499) + 5.092922e-004 + * Math.pow(t + 2.499, 2) + 0.000385144 + * Math.pow(t + 2.499, 3); + else if (t <= -1.107) + y = 0.130452 + 0.0437487 * (t + 2.037) + 1.043102e-003 + * Math.pow(t + 2.037, 2) + 0.000330749 + * Math.pow(t + 2.037, 3); + else if (t <= -0.642) + y = 0.172306 + 0.0465471 * (t + 1.107) + 1.965893e-003 + * Math.pow(t + 1.107, 2) + 0.000118734 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.194388 + 0.0484524 * (t + 0.642) + 2.131527e-003 + * Math.pow(t + 0.642, 2) - 6.48454e-006 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.245229 + 0.0527171 * (t - 0.363) + 2.111976e-003 + * Math.pow(t - 0.363, 2) - 0.000142806 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.270185 + 0.0545886 * (t - 0.828) + 1.912761e-003 + * Math.pow(t - 0.828, 2) - 6.09975e-005 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.295976 + 0.0563279 * (t - 1.293) + 1.827670e-003 + * Math.pow(t - 1.293, 2) - 0.000197946 + * Math.pow(t - 1.293, 3); + else if (t <= 2.685) + y = 0.322543 + 0.0578992 * (t - 1.758) + 1.551535e-003 + * Math.pow(t - 1.758, 2) - 0.000443952 + * Math.pow(t - 1.758, 3); + else if (t <= 3.15) + y = 0.377196 + 0.0596313 * (t - 2.685) + 3.169039e-004 + * Math.pow(t - 2.685, 2) - 0.000757317 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.404917 + 0.0594347 * (t - 3.15) - 7.395529e-004 + * Math.pow(t - 3.15, 2) - 0.000469621 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.489196 + 0.054216 * (t - 4.62) - 2.810583e-003 + * Math.pow(t - 4.62, 2) - 0.000383922 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.51376 + 0.0513531 * (t - 5.085) - 3.346154e-003 + * Math.pow(t - 5.085, 2) - 0.000500431 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.536865 + 0.0479166 * (t - 5.55) - 4.044255e-003 + * Math.pow(t - 5.55, 2) - 0.000254017 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.558246 + 0.0439907 * (t - 6.015) - 4.398609e-003 + * Math.pow(t - 6.015, 2) - 0.000388999 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.577593 + 0.0396773 * (t - 6.477) - 4.937762e-003 + * Math.pow(t - 6.477, 2) - 0.000299882 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.594945 + 0.0348906 * (t - 6.942) - 5.356098e-003 + * Math.pow(t - 6.942, 2) - 0.000545857 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.609956 + 0.0295554 * (t - 7.407) - 6.117569e-003 + * Math.pow(t - 7.407, 2) - 0.000382667 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.622338 + 0.0236178 * (t - 7.872) - 6.651389e-003 + * Math.pow(t - 7.872, 2) + 8.20287e-005 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.639439 + 0.0104971 * (t - 8.877) - 6.404073e-003 + * Math.pow(t - 8.877, 2) + 6.89544e-006 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.642936 + 0.00454574 * (t - 9.342) - 6.394454e-003 + * Math.pow(t - 9.342, 2) - 0.000265083 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.643641 - 0.00157305 * (t - 9.807) - 6.764245e-003 + * Math.pow(t - 9.807, 2) + 0.000121087 + * Math.pow(t - 9.807, 3); + else if (t <= 11.199) + y = 0.641459 - 0.00778526 * (t - 10.272) - 6.595329e-003 + * Math.pow(t - 10.272, 2) + 0.000427353 + * Math.pow(t - 10.272, 3); + else if (t <= 11.664) + y = 0.628915 - 0.0189113 * (t - 11.199) - 5.406861e-003 + * Math.pow(t - 11.199, 2) + 0.000486639 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.619001 - 0.023624 * (t - 11.664) - 4.727999e-003 + * Math.pow(t - 11.664, 2) + 0.000578078 + * Math.pow(t - 11.664, 3); + else if (t <= 13.599) + y = 0.607052 - 0.027646 * (t - 12.129) - 3.921580e-003 + * Math.pow(t - 12.129, 2) + 0.000195159 + * Math.pow(t - 12.129, 3); + else if (t <= 14.064) + y = 0.558558 - 0.0379103 * (t - 13.599) - 3.060930e-003 + * Math.pow(t - 13.599, 2) + 0.000134031 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.540281 - 0.0406701 * (t - 14.064) - 2.873957e-003 + * Math.pow(t - 14.064, 2) + 0.000338369 + * Math.pow(t - 14.064, 3); + else if (t <= 15.456) + y = 0.520782 - 0.0431233 * (t - 14.529) - 2.401931e-003 + * Math.pow(t - 14.529, 2) + 0.000422293 + * Math.pow(t - 14.529, 3); + else if (t <= 15.921) + y = 0.479079 - 0.0464879 * (t - 15.456) - 1.227535e-003 + * Math.pow(t - 15.456, 2) + 0.000404677 + * Math.pow(t - 15.456, 3); + else if (t <= 17.391) + y = 0.457237 - 0.047367 * (t - 15.921) - 6.630106e-004 + * Math.pow(t - 15.921, 2) + 0.000286727 + * Math.pow(t - 15.921, 3); + else if (t <= 17.856) + y = 0.387086 - 0.0474575 * (t - 17.391) + 6.014537e-004 + * Math.pow(t - 17.391, 2) + 0.000196116 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.365168 - 0.0467709 * (t - 17.856) + 8.750362e-004 + * Math.pow(t - 17.856, 2) + 5.04267e-005 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.343614 - 0.0459244 * (t - 18.321) + 9.453814e-004 + * Math.pow(t - 18.321, 2) - 0.000240636 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.322439 - 0.0452013 * (t - 18.786) + 6.096940e-004 + * Math.pow(t - 18.786, 2) - 0.000241968 + * Math.pow(t - 18.786, 3); + else if (t <= 20.178) + y = 0.301663 - 0.0447929 * (t - 19.248) + 2.743264e-004 + * Math.pow(t - 19.248, 2) - 6.72243e-005 + * Math.pow(t - 19.248, 3); + else if (t <= 20.643) + y = 0.260188 - 0.044457 * (t - 20.178) + 8.677062e-005 + * Math.pow(t - 20.178, 2) - 6.22012e-005 + * Math.pow(t - 20.178, 3); + + return (0.611504694 - 0.19610573) * y + 0.19610573; + } + + /** + * Computes the approximation spline for KIC or returns the default value + * 0.0741 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getKIC(double t) { + double y = 0.0741 / (0.160177175 - 0.044088231) - 0.044088231 + / (0.160177175 - 0.044088231); + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.59717 - 0.253511 * (t + 3.894) + 0 * Math.pow(t + 3.894, 2) + + 0.030027 * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.482307 - 0.234033 * (t + 3.429) + 4.188770e-002 + * Math.pow(t + 3.429, 2) + 0.00577486 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.38312 - 0.191331 * (t + 2.964) + 4.994363e-002 + * Math.pow(t + 2.964, 2) - 0.0102897 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.303915 - 0.151558 * (t + 2.499) + 3.558955e-002 + * Math.pow(t + 2.499, 2) + 0.0218333 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.243645 - 0.104693 * (t + 2.037) + 6.585046e-002 + * Math.pow(t + 2.037, 2) + 0.0141498 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.210624 - 0.0342735 * (t + 1.572) + 8.558938e-002 + * Math.pow(t + 1.572, 2) - 0.0352163 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.209652 + 0.0224808 * (t + 1.107) + 3.646268e-002 + * Math.pow(t + 1.107, 2) - 0.00295964 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.227692 + 0.0544712 * (t + 0.642) + 3.233398e-002 + * Math.pow(t + 0.642, 2) + 0.00254454 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.317677 + 0.127173 * (t - 0.363) + 4.000576e-002 + * Math.pow(t - 0.363, 2) - 0.0129362 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.384162 + 0.155987 * (t - 0.828) + 2.195974e-002 + * Math.pow(t - 0.828, 2) - 0.0561687 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.455796 + 0.139974 * (t - 1.293) - 5.639562e-002 + * Math.pow(t - 1.293, 2) - 0.00820249 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.507865 + 0.0822052 * (t - 1.758) - 6.783810e-002 + * Math.pow(t - 1.758, 2) + 0.0262569 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.533954 + 0.036336 * (t - 2.22) - 3.144602e-002 + * Math.pow(t - 2.22, 2) + 0.0235729 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.546421 + 0.0223823 * (t - 2.685) + 1.438178e-003 + * Math.pow(t - 2.685, 2) + 0.00200063 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.557341 + 0.0250176 * (t - 3.15) + 4.229058e-003 + * Math.pow(t - 3.15, 2) + 0.00309548 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.613088 + 0.0575181 * (t - 4.62) + 1.788011e-002 + * Math.pow(t - 4.62, 2) - 0.0103307 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.642661 + 0.0674453 * (t - 5.085) + 3.468802e-003 + * Math.pow(t - 5.085, 2) - 0.0224198 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.672519 + 0.0561282 * (t - 5.55) - 2.780675e-002 + * Math.pow(t - 5.55, 2) - 0.00483441 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.69212 + 0.0271319 * (t - 6.015) - 3.455076e-002 + * Math.pow(t - 6.015, 2) - 0.00777974 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.696513 - 0.0097746 * (t - 6.477) - 4.533348e-002 + * Math.pow(t - 6.477, 2) + 0.0117564 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.683348 - 0.0443086 * (t - 6.942) - 2.893330e-002 + * Math.pow(t - 6.942, 2) + 0.019275 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.658426 - 0.0587134 * (t - 7.407) - 2.044715e-003 + * Math.pow(t - 7.407, 2) - 0.00188787 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.630492 - 0.0618396 * (t - 7.872) - 4.678294e-003 + * Math.pow(t - 7.872, 2) - 9.30383e-005 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.563524 - 0.0715249 * (t - 8.877) - 4.958805e-003 + * Math.pow(t - 8.877, 2) + 0.0222099 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.531426 - 0.0617296 * (t - 9.342) + 2.602397e-002 + * Math.pow(t - 9.342, 2) - 0.022373 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.506099 - 0.0520401 * (t - 9.807) - 5.186390e-003 + * Math.pow(t - 9.807, 2) - 0.00875147 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.479899 - 0.0625403 * (t - 10.272) - 1.739469e-002 + * Math.pow(t - 10.272, 2) + 0.0272157 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.449976 - 0.061186 * (t - 10.734) + 2.032624e-002 + * Math.pow(t - 10.734, 2) - 0.0227538 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.423632 - 0.0570424 * (t - 11.199) - 1.141533e-002 + * Math.pow(t - 11.199, 2) - 0.0117263 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.39346 - 0.0752652 * (t - 11.664) - 2.777356e-002 + * Math.pow(t - 11.664, 2) + 0.0185337 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.35432 - 0.0890723 * (t - 12.129) - 1.918985e-003 + * Math.pow(t - 12.129, 2) + 0.0246405 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.287876 - 0.0182668 * (t - 13.134) + 7.237216e-002 + * Math.pow(t - 13.134, 2) - 0.0167059 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.293351 + 0.0382026 * (t - 13.599) + 4.906743e-002 + * Math.pow(t - 13.599, 2) - 0.0121101 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.320507 + 0.0759798 * (t - 14.064) + 3.217383e-002 + * Math.pow(t - 14.064, 2) - 0.0148388 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.361303 + 0.0962759 * (t - 14.529) + 1.147372e-002 + * Math.pow(t - 14.529, 2) + 0.0191581 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.41012 + 0.119145 * (t - 14.991) + 3.802679e-002 + * Math.pow(t - 14.991, 2) - 0.0570551 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.468009 + 0.1175 * (t - 15.456) - 4.156503e-002 + * Math.pow(t - 15.456, 2) - 0.0132887 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.512323 + 0.0702243 * (t - 15.921) - 6.010273e-002 + * Math.pow(t - 15.921, 2) + 0.0551508 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.537526 + 0.0501038 * (t - 16.386) + 1.683269e-002 + * Math.pow(t - 16.386, 2) - 0.016235 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.588402 + 0.0347443 * (t - 17.391) - 3.211571e-002 + * Math.pow(t - 17.391, 2) + 0.00952971 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.598572 + 0.0110584 * (t - 17.856) - 1.882176e-002 + * Math.pow(t - 17.856, 2) + 0.000769216 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.599722 - 0.00594685 * (t - 18.321) - 1.774871e-002 + * Math.pow(t - 18.321, 2) + 0.0316399 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.5963 - 0.00192916 * (t - 18.786) + 2.638891e-002 + * Math.pow(t - 18.786, 2) + 0.00874813 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.601904 + 0.0280559 * (t - 19.248) + 3.851381e-002 + * Math.pow(t - 19.248, 2) + 0.00607335 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.623889 + 0.0678134 * (t - 19.713) + 4.698613e-002 + * Math.pow(t - 19.713, 2) - 0.0566996 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.659881 + 0.0747309 * (t - 20.178) - 3.210978e-002 + * Math.pow(t - 20.178, 2) + 0.0230178 + * Math.pow(t - 20.178, 3); + + return (0.160177175 - 0.044088231) * y + 0.044088231; + } + + /** + * Computes the approximation spline for 2IPM or returns the default value + * 0.0227 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getIPM(double t) { + double y = 0.0227 / (0.036305123 - 0.015512247) - 0.015512247 + / (0.036305123 - 0.015512247); + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.0802829 + 0.147166 * (t + 3.894) + 0 * Math.pow(t + 3.894, 2) + - 0.00476735 * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.148236 + 0.144073 * (t + 3.429) - 6.650452e-003 + * Math.pow(t + 3.429, 2) - 0.0159672 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.212186 + 0.127531 * (t + 2.964) - 2.892476e-002 + * Math.pow(t + 2.964, 2) - 0.00496333 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.264735 + 0.0974111 * (t + 2.499) - 3.584861e-002 + * Math.pow(t + 2.499, 2) + 0.00466081 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.302547 + 0.0672715 * (t + 2.037) - 2.938873e-002 + * Math.pow(t + 2.037, 2) + 0.0120304 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.328683 + 0.0477438 * (t + 1.572) - 1.260631e-002 + * Math.pow(t + 1.572, 2) + 0.00568681 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.34873 + 0.0397088 * (t + 1.107) - 4.673207e-003 + * Math.pow(t + 1.107, 2) - 6.69227e-006 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.366183 + 0.0353584 * (t + 0.642) - 4.682543e-003 + * Math.pow(t + 0.642, 2) + 0.00262408 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.399652 + 0.0338976 * (t - 0.363) + 3.229045e-003 + * Math.pow(t - 0.363, 2) - 0.0171221 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.414391 + 0.0257939 * (t - 0.828) - 2.065624e-002 + * Math.pow(t - 0.828, 2) - 0.0198038 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.419928 - 0.00626258 * (t - 1.293) - 4.828252e-002 + * Math.pow(t - 1.293, 2) + 0.000911555 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.406668 - 0.050574 * (t - 1.758) - 4.701090e-002 + * Math.pow(t - 1.758, 2) + 0.0126306 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.374514 - 0.0859243 * (t - 2.22) - 2.950484e-002 + * Math.pow(t - 2.22, 2) + 0.0240615 * Math.pow(t - 2.22, 3); + else if (t <= 4.62) + y = 0.330599 - 0.0977557 * (t - 2.685) + 4.060973e-003 + * Math.pow(t - 2.685, 2) + 0.0170498 + * Math.pow(t - 2.685, 3); + else if (t <= 5.085) + y = 0.280174 + 0.109475 * (t - 4.62) + 1.030351e-001 + * Math.pow(t - 4.62, 2) - 0.00730802 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.352624 + 0.200557 * (t - 5.085) + 9.284036e-002 + * Math.pow(t - 5.085, 2) - 0.0489522 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.461035 + 0.255145 * (t - 5.55) + 2.455205e-002 + * Math.pow(t - 5.55, 2) - 0.020076 * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.582968 + 0.264955 * (t - 6.015) - 3.453901e-003 + * Math.pow(t - 6.015, 2) - 0.0597257 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.69875 + 0.22352 * (t - 6.477) - 8.623371e-002 + * Math.pow(t - 6.477, 2) - 0.0241487 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.781613 + 0.127658 * (t - 6.942) - 1.199211e-001 + * Math.pow(t - 6.942, 2) + 0.00139353 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.815184 + 0.017035 * (t - 7.407) - 1.179771e-001 + * Math.pow(t - 7.407, 2) + 0.012143 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.798817 - 0.0848069 * (t - 7.872) - 1.010377e-001 + * Math.pow(t - 7.872, 2) + 0.0267053 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.638643 - 0.206974 * (t - 8.877) - 2.052130e-002 + * Math.pow(t - 8.877, 2) + 0.0393006 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.541914 - 0.200565 * (t - 9.342) + 3.430310e-002 + * Math.pow(t - 9.342, 2) + 0.0182556 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.457904 - 0.156821 * (t - 9.807) + 5.976968e-002 + * Math.pow(t - 9.807, 2) + 0.0154081 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.399455 - 0.0912406 * (t - 10.272) + 8.126393e-002 + * Math.pow(t - 10.272, 2) + 0.00115016 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.374761 - 0.0154163 * (t - 10.734) + 8.285805e-002 + * Math.pow(t - 10.734, 2) - 0.0285911 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.382634 + 0.0430954 * (t - 11.199) + 4.297352e-002 + * Math.pow(t - 11.199, 2) - 0.0330048 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.408646 + 0.0616514 * (t - 11.664) - 3.068147e-003 + * Math.pow(t - 11.664, 2) - 0.0116522 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.435479 + 0.0512395 * (t - 12.129) - 1.932292e-002 + * Math.pow(t - 12.129, 2) + 0.00415678 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.471678 + 0.0249958 * (t - 13.134) - 6.790222e-003 + * Math.pow(t - 13.134, 2) - 0.00667273 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.481162 + 0.0143525 * (t - 13.599) - 1.609868e-002 + * Math.pow(t - 13.599, 2) - 0.00529352 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.483823 - 0.00405304 * (t - 14.064) - 2.348313e-002 + * Math.pow(t - 14.064, 2) + 0.0139118 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.478259 - 0.0168681 * (t - 14.529) - 4.076173e-003 + * Math.pow(t - 14.529, 2) + 0.0177102 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.471342 - 0.00929412 * (t - 14.991) + 2.047010e-002 + * Math.pow(t - 14.991, 2) - 0.00568797 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.470875 + 0.00605343 * (t - 15.456) + 1.253539e-002 + * Math.pow(t - 15.456, 2) - 0.00718501 + * Math.pow(t - 15.456, 3); + else if (t <= 17.391) + y = 0.475678 + 0.0130506 * (t - 15.921) + 2.512302e-003 + * Math.pow(t - 15.921, 2) + 0.000255298 + * Math.pow(t - 15.921, 3); + else if (t <= 17.856) + y = 0.501102 + 0.0220918 * (t - 17.391) + 3.638165e-003 + * Math.pow(t - 17.391, 2) - 0.00625479 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.511532 + 0.021418 * (t - 17.856) - 5.087271e-003 + * Math.pow(t - 17.856, 2) - 0.00605154 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.519783 + 0.0127613 * (t - 18.321) - 1.352917e-002 + * Math.pow(t - 18.321, 2) + 0.004627 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.523257 + 0.0031806 * (t - 18.786) - 7.074514e-003 + * Math.pow(t - 18.786, 2) + 0.0194544 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.525135 + 0.00910103 * (t - 19.248) + 1.988931e-002 + * Math.pow(t - 19.248, 2) + 0.0103778 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.534711 + 0.0343299 * (t - 19.713) + 3.436634e-002 + * Math.pow(t - 19.713, 2) - 0.0213188 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.555962 + 0.0524616 * (t - 20.178) + 4.626590e-003 + * Math.pow(t - 20.178, 2) - 0.00331655 + * Math.pow(t - 20.178, 3); + + return (0.036305123 - 0.015512247) * y + 0.015512247; + } + + /** + * Computes the approximation spline for Val or returns the default value + * 29.4 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getVal(double t) { + double y = 29.4 / (52.21398298 - 23.23780872) - 23.23780872 + / (52.21398298 - 23.23780872); + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.0870064 + 0.0543556 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) - 8.90776e-006 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.112281 + 0.0543499 * (t + 3.429) - 1.242632e-005 + * Math.pow(t + 3.429, 2) + 1.75913e-005 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.137553 + 0.0543497 * (t + 2.964) + 1.211360e-005 + * Math.pow(t + 2.964, 2) + 2.31931e-005 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.16283 + 0.054376 * (t + 2.499) + 4.446802e-005 + * Math.pow(t + 2.499, 2) + 9.65747e-005 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.187971 + 0.054479 * (t + 2.037) + 1.783206e-004 + * Math.pow(t + 2.037, 2) + 9.66717e-005 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.213352 + 0.0547075 * (t + 1.572) + 3.131776e-004 + * Math.pow(t + 1.572, 2) - 2.3386e-005 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.238856 + 0.0549836 * (t + 1.107) + 2.805541e-004 + * Math.pow(t + 1.107, 2) - 6.40501e-005 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.264478 + 0.055203 * (t + 0.642) + 1.912042e-004 + * Math.pow(t + 0.642, 2) - 5.51172e-005 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.320094 + 0.0554203 * (t - 0.363) + 2.502589e-005 + * Math.pow(t - 0.363, 2) - 0.000155647 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.345854 + 0.0553426 * (t - 0.828) - 1.921018e-004 + * Math.pow(t - 0.828, 2) - 0.000211048 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.371526 + 0.055027 * (t - 1.293) - 4.865138e-004 + * Math.pow(t - 1.293, 2) - 0.000182501 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.39699 + 0.0544562 * (t - 1.758) - 7.411022e-004 + * Math.pow(t - 1.758, 2) - 0.000172656 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.421973 + 0.0536608 * (t - 2.22) - 9.804033e-004 + * Math.pow(t - 2.22, 2) - 0.000243037 + * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.446689 + 0.0525914 * (t - 2.685) - 1.319440e-003 + * Math.pow(t - 2.685, 2) - 0.000377847 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.470821 + 0.0511192 * (t - 3.15) - 1.846537e-003 + * Math.pow(t - 3.15, 2) - 0.000398585 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.54071 + 0.0431065 * (t - 4.62) - 3.604295e-003 + * Math.pow(t - 4.62, 2) - 0.000432186 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.559932 + 0.0394742 * (t - 5.085) - 4.207195e-003 + * Math.pow(t - 5.085, 2) - 0.000381397 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.577339 + 0.0353141 * (t - 5.55) - 4.739244e-003 + * Math.pow(t - 5.55, 2) - 0.00029943 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.592705 + 0.0307123 * (t - 6.015) - 5.156948e-003 + * Math.pow(t - 6.015, 2) - 0.0002747 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.605767 + 0.0257714 * (t - 6.477) - 5.537682e-003 + * Math.pow(t - 6.477, 2) - 6.94366e-005 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.616546 + 0.0205763 * (t - 6.942) - 5.634546e-003 + * Math.pow(t - 6.942, 2) + 4.67127e-005 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.6249 + 0.0153665 * (t - 7.407) - 5.569382e-003 + * Math.pow(t - 7.407, 2) + 7.60338e-005 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.630849 + 0.0102363 * (t - 7.872) - 5.463315e-003 + * Math.pow(t - 7.872, 2) + 0.000283763 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.635907 + 0.000114872 * (t - 8.877) - 4.607770e-003 + * Math.pow(t - 8.877, 2) + 0.000347457 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.634999 - 0.00394497 * (t - 9.342) - 4.123067e-003 + * Math.pow(t - 9.342, 2) + 0.000216058 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.632294 - 0.00763927 * (t - 9.807) - 3.821667e-003 + * Math.pow(t - 9.807, 2) + 0.000216223 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.627938 - 0.0110532 * (t - 10.272) - 3.520036e-003 + * Math.pow(t - 10.272, 2) + 0.000263321 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.622106 - 0.0141371 * (t - 10.734) - 3.155073e-003 + * Math.pow(t - 10.734, 2) + 0.000162254 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.614866 - 0.016966 * (t - 11.199) - 2.928729e-003 + * Math.pow(t - 11.199, 2) + 0.000177556 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.606361 - 0.0195746 * (t - 11.664) - 2.681039e-003 + * Math.pow(t - 11.664, 2) + 0.000286342 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.596708 - 0.0218822 * (t - 12.129) - 2.281591e-003 + * Math.pow(t - 12.129, 2) + 0.000264022 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.57268 - 0.0256682 * (t - 13.134) - 1.485566e-003 + * Math.pow(t - 13.134, 2) + 0.00012945 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.560436 - 0.0269658 * (t - 13.599) - 1.304983e-003 + * Math.pow(t - 13.599, 2) + 7.46932e-005 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.547623 - 0.028131 * (t - 14.064) - 1.200785e-003 + * Math.pow(t - 14.064, 2) + 9.72656e-005 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.534292 - 0.0291846 * (t - 14.529) - 1.065100e-003 + * Math.pow(t - 14.529, 2) + 0.000148594 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.520596 - 0.0300736 * (t - 14.991) - 8.591482e-004 + * Math.pow(t - 14.991, 2) + 6.92071e-006 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.506427 - 0.0308681 * (t - 15.456) - 8.494938e-004 + * Math.pow(t - 15.456, 2) + 1.28187e-005 + * Math.pow(t - 15.456, 3); + else if (t <= 17.391) + y = 0.49189 - 0.0316498 * (t - 15.921) - 8.316116e-004 + * Math.pow(t - 15.921, 2) + 0.000101901 + * Math.pow(t - 15.921, 3); + else if (t <= 17.856) + y = 0.443892 - 0.0334342 * (t - 17.391) - 3.822275e-004 + * Math.pow(t - 17.391, 2) + 6.02688e-005 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.428268 - 0.0337506 * (t - 17.856) - 2.981526e-004 + * Math.pow(t - 17.856, 2) + 6.12216e-006 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.41251 - 0.0340239 * (t - 18.321) - 2.896122e-004 + * Math.pow(t - 18.321, 2) + 0.000113406 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.396638 - 0.0342197 * (t - 18.786) - 1.314102e-004 + * Math.pow(t - 18.786, 2) + 0.00010335 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.380811 - 0.0342749 * (t - 19.248) + 1.183285e-005 + * Math.pow(t - 19.248, 2) + 8.32616e-005 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.364884 - 0.0342099 * (t - 19.713) + 1.279827e-004 + * Math.pow(t - 19.713, 2) - 7.86715e-005 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.348996 - 0.0341419 * (t - 20.178) + 1.823597e-005 + * Math.pow(t - 20.178, 2) - 1.30724e-005 + * Math.pow(t - 20.178, 3); + + return (52.21398298 - 23.23780872) * y + 23.23780872; + } + + /** + * Computes the approximation spline for DHIV or returns the default value + * 0.132 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getDHIV(double t) { + double y = 0.132 / (1.19712426 - 0.077317889) - 0.077317889 + / (1.19712426 - 0.077317889); + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.0260752 + 0.0333816 * (t + 3.894) + 0 + * Math.pow(t + 3.894, 2) + 0.000123214 + * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.0416101 + 0.0334615 * (t + 3.429) + 1.718840e-004 + * Math.pow(t + 3.429, 2) - 4.6372e-005 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.0572022 + 0.0335913 * (t + 2.964) + 1.071950e-004 + * Math.pow(t + 2.964, 2) - 0.000289114 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.0728163 + 0.0335035 * (t + 2.499) - 2.961193e-004 + * Math.pow(t + 2.499, 2) + 0.000359848 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.0882672 + 0.0334603 * (t + 2.037) + 2.026304e-004 + * Math.pow(t + 2.037, 2) - 2.17354e-006 + * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.10387 + 0.0336473 * (t + 1.572) + 1.995983e-004 + * Math.pow(t + 1.572, 2) - 0.00063401 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.119495 + 0.0334217 * (t + 1.107) - 6.848463e-004 + * Math.pow(t + 1.107, 2) - 0.000741887 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.134814 + 0.0323035 * (t + 0.642) - 1.719778e-003 + * Math.pow(t + 0.642, 2) - 0.000393869 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.165142 + 0.0276533 * (t - 0.363) - 2.907293e-003 + * Math.pow(t - 0.363, 2) - 0.000577236 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.177314 + 0.0245751 * (t - 0.828) - 3.712538e-003 + * Math.pow(t - 0.828, 2) + 0.000685274 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.188008 + 0.021567 * (t - 1.293) - 2.756581e-003 + * Math.pow(t - 1.293, 2) + 0.001929 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.197634 + 0.0202546 * (t - 1.758) - 6.562414e-005 + * Math.pow(t - 1.758, 2) + 0.00226985 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.207202 + 0.0216475 * (t - 2.22) + 3.080384e-003 + * Math.pow(t - 2.22, 2) + 0.00155965 + * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.21809 + 0.0255239 * (t - 2.685) + 5.256091e-003 + * Math.pow(t - 2.685, 2) + 0.000714349 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.231167 + 0.0308755 * (t - 3.15) + 6.252608e-003 + * Math.pow(t - 3.15, 2) - 0.000271997 + * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.289202 + 0.0474948 * (t - 4.62) + 5.053102e-003 + * Math.pow(t - 4.62, 2) - 0.00110133 + * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.312269 + 0.0514798 * (t - 5.085) + 3.516751e-003 + * Math.pow(t - 5.085, 2) - 0.00184457 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.336782 + 0.0535539 * (t - 5.55) + 9.435794e-004 + * Math.pow(t - 5.55, 2) - 0.00232587 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.361654 + 0.0529227 * (t - 6.015) - 2.301005e-003 + * Math.pow(t - 6.015, 2) - 0.00310515 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.385307 + 0.0488082 * (t - 6.477) - 6.604749e-003 + * Math.pow(t - 6.477, 2) - 0.00428991 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.406144 + 0.039883 * (t - 6.942) - 1.258918e-002 + * Math.pow(t - 6.942, 2) - 0.00315302 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.42165 + 0.0261298 * (t - 7.407) - 1.698764e-002 + * Math.pow(t - 7.407, 2) - 0.00115594 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.430011 + 0.0095815 * (t - 7.872) - 1.860017e-002 + * Math.pow(t - 7.872, 2) + 0.00231129 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.4232 - 0.0208015 * (t - 8.877) - 1.163163e-002 + * Math.pow(t - 8.877, 2) + 0.00400521 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.411415 - 0.0290208 * (t - 9.342) - 6.044358e-003 + * Math.pow(t - 9.342, 2) + 0.00244262 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.396859 - 0.0330576 * (t - 9.807) - 2.636904e-003 + * Math.pow(t - 9.807, 2) + 0.00186923 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.381105 - 0.0342974 * (t - 10.272) - 2.933415e-005 + * Math.pow(t - 10.272, 2) + 0.00120579 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.365372 - 0.0335524 * (t - 10.734) + 1.641895e-003 + * Math.pow(t - 10.734, 2) - 5.22663e-005 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.35012 - 0.0320593 * (t - 11.199) + 1.568984e-003 + * Math.pow(t - 11.199, 2) - 0.000303024 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.335521 - 0.0307967 * (t - 11.664) + 1.146266e-003 + * Math.pow(t - 11.664, 2) + 0.000186394 + * Math.pow(t - 11.664, 3); + else if (t <= 13.599) + y = 0.321468 - 0.0296098 * (t - 12.129) + 1.406286e-003 + * Math.pow(t - 12.129, 2) + 0.000205413 + * Math.pow(t - 12.129, 3); + else if (t <= 14.064) + y = 0.281632 - 0.0241437 * (t - 13.599) + 2.312159e-003 + * Math.pow(t - 13.599, 2) + 1.21887e-005 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.270907 - 0.0219855 * (t - 14.064) + 2.329162e-003 + * Math.pow(t - 14.064, 2) + 0.000331498 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.261221 - 0.0196043 * (t - 14.529) + 2.791602e-003 + * Math.pow(t - 14.529, 2) + 0.000948267 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.252853 - 0.0164177 * (t - 14.991) + 4.105900e-003 + * Math.pow(t - 14.991, 2) + 0.00039227 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.246146 - 0.0123447 * (t - 15.456) + 4.653117e-003 + * Math.pow(t - 15.456, 2) - 9.30071e-005 + * Math.pow(t - 15.456, 3); + else if (t <= 17.391) + y = 0.241402 - 0.00807765 * (t - 15.921) + 4.523372e-003 + * Math.pow(t - 15.921, 2) - 0.000264229 + * Math.pow(t - 15.921, 3); + else if (t <= 17.856) + y = 0.238463 + 0.00350815 * (t - 17.391) + 3.358124e-003 + * Math.pow(t - 17.391, 2) - 0.000570568 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.240763 + 0.00626109 * (t - 17.856) + 2.562182e-003 + * Math.pow(t - 17.856, 2) - 0.000894847 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.244139 + 0.00806346 * (t - 18.321) + 1.313871e-003 + * Math.pow(t - 18.321, 2) - 0.000591935 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.248113 + 0.00890138 * (t - 18.786) + 4.881216e-004 + * Math.pow(t - 18.786, 2) - 0.000289939 + * Math.pow(t - 18.786, 3); + else if (t <= 20.178) + y = 0.252301 + 0.00916675 * (t - 19.248) + 8.626680e-005 + * Math.pow(t - 19.248, 2) + 1.10962e-005 + * Math.pow(t - 19.248, 3); + else if (t <= 20.643) + y = 0.26091 + 0.009356 * (t - 20.178) + 1.172251e-004 + * Math.pow(t - 20.178, 2) - 8.40323e-005 + * Math.pow(t - 20.178, 3); + + return (1.19712426 - 0.077317889) * y + 0.077317889; + } + + /** + * Computes the approximation spline for KIV or returns the default value + * 13.1 mM if t is not in the measured time intervall. Parameter for the + * spline: weights all equal to one, S=1 + * + * @param t + * @return + */ + public double getKIV(double t) { + double y = 13.1 / (16.97282365372 - 6.68584901645) - 6.68584901645 + / (16.97282365372 - 6.68584901645); + ; + + if ((-3.894 <= t) && (t <= -3.429)) + y = 0.632703 - 0.200248 * (t + 3.894) + 0 * Math.pow(t + 3.894, 2) + - 0.00683388 * Math.pow(t + 3.894, 3); + else if (t <= -2.964) + y = 0.5389 - 0.204681 * (t + 3.429) - 9.533265e-03 + * Math.pow(t + 3.429, 2) - 0.00451643 + * Math.pow(t + 3.429, 3); + else if (t <= -2.499) + y = 0.441208 - 0.216476 * (t + 2.964) - 1.583368e-02 + * Math.pow(t + 2.964, 2) + 0.0280129 + * Math.pow(t + 2.964, 3); + else if (t <= -2.037) + y = 0.33994 - 0.21303 * (t + 2.499) + 2.324431e-02 + * Math.pow(t + 2.499, 2) + 0.00505588 + * Math.pow(t + 2.499, 3); + else if (t <= -1.572) + y = 0.24698 - 0.188315 * (t + 2.037) + 3.025176e-02 + * Math.pow(t + 2.037, 2) + 0.03962 * Math.pow(t + 2.037, 3); + else if (t <= -1.107) + y = 0.169938 - 0.134481 * (t + 1.572) + 8.552163e-02 + * Math.pow(t + 1.572, 2) - 0.0174838 + * Math.pow(t + 1.572, 3); + else if (t <= -0.642) + y = 0.124138 - 0.0662868 * (t + 1.107) + 6.113172e-02 + * Math.pow(t + 1.107, 2) - 0.0274919 + * Math.pow(t + 1.107, 3); + else if (t <= 0.363) + y = 0.103769 - 0.0272676 * (t + 0.642) + 2.278052e-02 + * Math.pow(t + 0.642, 2) + 0.0264526 + * Math.pow(t + 0.642, 3); + else if (t <= 0.828) + y = 0.126225 + 0.0986746 * (t - 0.363) + 1.025350e-01 + * Math.pow(t - 0.363, 2) - 0.0423241 + * Math.pow(t - 0.363, 3); + else if (t <= 1.293) + y = 0.190024 + 0.166578 * (t - 0.828) + 4.349295e-02 + * Math.pow(t - 0.828, 2) - 0.104603 + * Math.pow(t - 0.828, 3); + else if (t <= 1.758) + y = 0.26637 + 0.139173 * (t - 1.293) - 1.024283e-01 + * Math.pow(t - 1.293, 2) + 0.0573769 + * Math.pow(t - 1.293, 3); + else if (t <= 2.22) + y = 0.314706 + 0.0811332 * (t - 1.758) - 2.238759e-02 + * Math.pow(t - 1.758, 2) + 0.092514 + * Math.pow(t - 1.758, 3); + else if (t <= 2.685) + y = 0.356534 + 0.119687 * (t - 2.22) + 1.058368e-01 + * Math.pow(t - 2.22, 2) + 0.0101367 * Math.pow(t - 2.22, 3); + else if (t <= 3.15) + y = 0.436093 + 0.22469 * (t - 2.685) + 1.199775e-01 + * Math.pow(t - 2.685, 2) - 0.107935 + * Math.pow(t - 2.685, 3); + else if (t <= 4.62) + y = 0.555663 + 0.266255 * (t - 3.15) - 3.059157e-02 + * Math.pow(t - 3.15, 2) - 0.0125683 * Math.pow(t - 3.15, 3); + else if (t <= 5.085) + y = 0.84103 + 0.0948394 * (t - 4.62) - 8.601761e-02 + * Math.pow(t - 4.62, 2) - 0.012452 * Math.pow(t - 4.62, 3); + else if (t <= 5.55) + y = 0.865279 + 0.00676576 * (t - 5.085) - 1.033881e-01 + * Math.pow(t - 5.085, 2) + 0.0609538 + * Math.pow(t - 5.085, 3); + else if (t <= 6.015) + y = 0.852198 - 0.049846 * (t - 5.55) - 1.835756e-02 + * Math.pow(t - 5.55, 2) + 0.00816093 + * Math.pow(t - 5.55, 3); + else if (t <= 6.477) + y = 0.825871 - 0.0616247 * (t - 6.015) - 6.973056e-03 + * Math.pow(t - 6.015, 2) - 0.0446731 + * Math.pow(t - 6.015, 3); + else if (t <= 6.942) + y = 0.791507 - 0.0966734 * (t - 6.477) - 6.888994e-02 + * Math.pow(t - 6.477, 2) + 0.0172469 + * Math.pow(t - 6.477, 3); + else if (t <= 7.407) + y = 0.733392 - 0.149553 * (t - 6.942) - 4.483048e-02 + * Math.pow(t - 6.942, 2) + 0.0734141 + * Math.pow(t - 6.942, 3); + else if (t <= 7.872) + y = 0.661538 - 0.143624 * (t - 7.407) + 5.758222e-02 + * Math.pow(t - 7.407, 2) - 0.054382 + * Math.pow(t - 7.407, 3); + else if (t <= 8.877) + y = 0.601735 - 0.125349 * (t - 7.872) - 1.828061e-02 + * Math.pow(t - 7.872, 2) + 0.0141796 + * Math.pow(t - 7.872, 3); + else if (t <= 9.342) + y = 0.47169 - 0.119127 * (t - 8.877) + 2.447087e-02 + * Math.pow(t - 8.877, 2) + 0.0322437 + * Math.pow(t - 8.877, 3); + else if (t <= 9.807) + y = 0.424829 - 0.0754538 * (t - 9.342) + 6.945085e-02 + * Math.pow(t - 9.342, 2) - 0.0325761 + * Math.pow(t - 9.342, 3); + else if (t <= 10.272) + y = 0.401484 - 0.0319958 * (t - 9.807) + 2.400726e-02 + * Math.pow(t - 9.807, 2) + 0.00495286 + * Math.pow(t - 9.807, 3); + else if (t <= 10.734) + y = 0.392295 - 0.00645621 * (t - 10.272) + 3.091651e-02 + * Math.pow(t - 10.272, 2) + 0.013472 + * Math.pow(t - 10.272, 3); + else if (t <= 11.199) + y = 0.39724 + 0.0307372 * (t - 10.734) + 4.958866e-02 + * Math.pow(t - 10.734, 2) - 0.0875034 + * Math.pow(t - 10.734, 3); + else if (t <= 11.664) + y = 0.413457 + 0.0200934 * (t - 11.199) - 7.247858e-02 + * Math.pow(t - 11.199, 2) - 0.0360727 + * Math.pow(t - 11.199, 3); + else if (t <= 12.129) + y = 0.403502 - 0.0707112 * (t - 11.664) - 1.228001e-01 + * Math.pow(t - 11.664, 2) + 0.0618963 + * Math.pow(t - 11.664, 3); + else if (t <= 13.134) + y = 0.350292 - 0.144765 * (t - 12.129) - 3.645476e-02 + * Math.pow(t - 12.129, 2) + 0.0517422 + * Math.pow(t - 12.129, 3); + else if (t <= 13.599) + y = 0.220505 - 0.061256 * (t - 13.134) + 1.195480e-01 + * Math.pow(t - 13.134, 2) - 0.0272394 + * Math.pow(t - 13.134, 3); + else if (t <= 14.064) + y = 0.215132 + 0.0322542 * (t - 13.599) + 8.154909e-02 + * Math.pow(t - 13.599, 2) - 0.0484171 + * Math.pow(t - 13.599, 3); + else if (t <= 14.529) + y = 0.242895 + 0.0766879 * (t - 14.064) + 1.400728e-02 + * Math.pow(t - 14.064, 2) - 0.0357363 + * Math.pow(t - 14.064, 3); + else if (t <= 14.991) + y = 0.27799 + 0.0665334 * (t - 14.529) - 3.584491e-02 + * Math.pow(t - 14.529, 2) + 0.0445836 + * Math.pow(t - 14.529, 3); + else if (t <= 15.456) + y = 0.305474 + 0.061961 * (t - 14.991) + 2.594790e-02 + * Math.pow(t - 14.991, 2) - 0.0774076 + * Math.pow(t - 14.991, 3); + else if (t <= 15.921) + y = 0.332114 + 0.0358802 * (t - 15.456) - 8.203569e-02 + * Math.pow(t - 15.456, 2) + 0.00889788 + * Math.pow(t - 15.456, 3); + else if (t <= 16.386) + y = 0.331955 - 0.0346412 * (t - 15.921) - 6.962314e-02 + * Math.pow(t - 15.921, 2) + 0.12207 + * Math.pow(t - 15.921, 3); + else if (t <= 17.391) + y = 0.313066 - 0.020207 * (t - 16.386) + 1.006644e-01 + * Math.pow(t - 16.386, 2) - 0.0358557 + * Math.pow(t - 16.386, 3); + else if (t <= 17.856) + y = 0.358035 + 0.0734829 * (t - 17.391) - 7.440562e-03 + * Math.pow(t - 17.391, 2) - 0.0299292 + * Math.pow(t - 17.391, 3); + else if (t <= 18.321) + y = 0.387586 + 0.0471488 * (t - 17.856) - 4.919179e-02 + * Math.pow(t - 17.856, 2) - 0.0599498 + * Math.pow(t - 17.856, 3); + else if (t <= 18.786) + y = 0.392846 - 0.0374874 * (t - 18.321) - 1.328217e-01 + * Math.pow(t - 18.321, 2) + 0.120032 + * Math.pow(t - 18.321, 3); + else if (t <= 19.248) + y = 0.358764 - 0.0831502 * (t - 18.786) + 3.462234e-02 + * Math.pow(t - 18.786, 2) + 0.053599 + * Math.pow(t - 18.786, 3); + else if (t <= 19.713) + y = 0.333024 - 0.016838 * (t - 19.248) + 1.089105e-01 + * Math.pow(t - 19.248, 2) + 0.0201839 + * Math.pow(t - 19.248, 3); + else if (t <= 20.178) + y = 0.350773 + 0.0975416 * (t - 19.713) + 1.370670e-01 + * Math.pow(t - 19.713, 2) - 0.122089 + * Math.pow(t - 19.713, 3); + else if (t <= 20.643) + y = 0.413492 + 0.145818 * (t - 20.178) - 3.324745e-02 + * Math.pow(t - 20.178, 2) + 0.0238333 + * Math.pow(t - 20.178, 3); + + return (16.97282365372 - 6.68584901645) * y + 6.68584901645; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/odes/CKMMiSystem.java b/src/eva2/server/go/problems/inference/metabolic/odes/CKMMiSystem.java new file mode 100644 index 00000000..cb5dd66e --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/odes/CKMMiSystem.java @@ -0,0 +1,156 @@ +/** + * + */ +package eva2.server.go.problems.inference.metabolic.odes; + +/** + * Created at 2007-02-03 + * + * This class describes the valine/leucine biosynthesis in Corynebacterium + * glutamicum acording to the convenience kinetics as proposed by + * Liebermeister and Klipp. Three reaction velocities follow the traditional + * Michaelis Menten scheme the others are given in the convenience kinetics. All + * reactions are considered to be ehter reversible or irreversible according to + * what is written in the KEGG database. + * + * @author Andreas Dräger + * + */ +public class CKMMiSystem extends AbstractValineSystem { + + /** + * Generated serial id. + */ + private static final long serialVersionUID = 8595111459805099502L; + + + /** + * + */ + public CKMMiSystem() { + this.p = null; + } + + /** + * @param params + * The parameters of this system. + */ + public CKMMiSystem(double[] params) { + this.p = params; + } + + /* + * (non-Javadoc) + * + * @see javaeva.server.oa.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double, + * double[]) + */ + public double[] getValue(double t, double[] Y) { + // double[] kcat = new double[9]; // -> p[0..9] + // double[] ki = new double[3]; // -> p[9..11] + // double[] km = new double[16]; // -> p[12..27] + // double[] vm = new double[3]; // -> p[28..30] + // double[] kmm = new double[3]; // -> p[31..33] + // double[] kia = new double[3]; // -> p[34..36] + // double[] kib = new double[3]; // -> p[37..39] + // double acCoA = 0; // -> p[40] + + double pyr = getPyr_2(t), nadp = getNADP_2(t), nadph = 0.04 - nadp, glut = getGlut_2(t), ala = getAla_2(t); + /* + * Y[0] DHIV Y[1] IPM Y[2] AcLac Y[3] Val Y[4] Leu Y[5] KIV Y[6] KIC + */ + + return linearCombinationOfVelocities(new double[] { + /* Korrigiert: */ + // v_1: AHAS: convenience (ge�ndert) + (p[0] * p[9] * Math.pow(pyr / p[12], 2)) + / ((1 + pyr / p[12] + Math.pow(pyr / p[12], 2)) * (p[9] + Y[3])), + + // v_2: AHAIR: convenience (ge�ndert) + (p[1] * Y[2] / p[13] * nadph / p[14] * p[10] - p[2] * Y[0] + / p[15] * nadp / p[16] * p[10]) + / ((1 + Y[2] / p[13] + nadph / p[14] + (Y[2] * nadph) + / (p[13] * p[14]) + Y[0] / p[15] + nadp / p[16] + (Y[0] * nadp) + / (p[15] * p[16])) * (p[10] + Y[3])), + + // v_3: DHAD: reversible Michaelis Menten with two inhibitions + // (ge�ndert) + (p[28] * Y[0]) + / (p[31] + Y[0] + Y[3] * (p[31] * p[34] + p[37] * Y[0])), + + // v_4: BCAAT_ValB: convenience (ge�ndert) + (p[3] * Y[5] / p[17] * glut / p[18]) + / (1 + Y[5] / p[17] + glut / p[18] + (Y[5] * glut) + / (p[17] * p[18])), + + // v_5: BCAAT_ValC: convenience (ge�ndert) + (p[4] * Y[5] / p[19] * ala / p[20]) + / (1 + Y[5] / p[19] + ala / p[20] + (Y[5] * ala) + / (p[19] * p[20])), + + // v_6: Trans_Val: irreversible Michaelis Menten with two + // inhibitions + (p[29] * Y[3]) + / (p[32] + Y[3] + p[32] * p[35] * Y[4] + p[38] * Y[3] + * Y[4]), + + // v_7: IPMS: convenience (AcCoA/K_m = p40) (ge�ndert) + (p[5] * Y[5] / p[21] * p[40] * p[11]) + / ((1 + Y[5] / p[21] + p[40] + p[40] * Y[5] / p[21]) * (p[11] + Y[4])), + + // v_8: IPMDH: convenience (ge�ndert) + (p[6] * Y[1] / p[22] * nad / p[23]) + / (1 + Y[1] / p[22] + nad / p[23] + (Y[1] * nad) + / (p[22] * p[23])), + + // v_9: BCAAT_LeuB: convenience (ge�ndert) + (p[7] * Y[6] / p[24] * glut / p[25] - p[8] * Y[4] / p[26] * akg + / p[27]) + / (1 + Y[6] / p[24] + glut / p[25] + (Y[6] * glut) + / (p[24] * p[25]) + Y[4] / p[26] + akg / p[27] + (Y[4] * akg) + / (p[26] * p[27])), + + // v_10: Trans_Leu: irreversible Michaelis Menten with two + // inhibitions + (p[30] * Y[4]) + / (p[33] + Y[4] + p[33] * p[36] * Y[3] + p[39] * Y[3] + * Y[4]) + + /* + * zuvor: // AHAS: convenience (p[0] * pyr)/(p[9] * (1 + pyr/p[12] + + * Math.pow(pyr/p[12], 2)) + Y[3] * (1 + pyr/p[12] + Math.pow(pyr/p[12], + * 2))), // AHAIR: convenience (p[1] * Y[2] * nadph - p[2] * Y[0] * + * nadp)/((1 + Y[2]/p[13] + nadph/p[14] + (Y[2]*nadph)/(p[13]*p[14]) + + * Y[0]/p[15] + nadp/p[16] + (Y[0] * nadp)/(p[15] * p[16])) * p[10] * + * Y[3]), // DHAD: reversible Michaelis Menten with two inhibitions + * (p[28]* Y[0])/(p[31] + Y[0] + Y[3] * (p[28] * p[34] + p[37] * Y[0])), // + * BCAAT_ValB: convenience (p[3] * Y[5] * glut)/(1 + Y[5]/p[17] + + * glut/p[18] + (Y[5] * glut)/(p[17] * p[18])), // BCAAT_ValC: + * convenience (p[4] * Y[5] * ala)/(1 + Y[5]/p[19] + ala/p[20] + (Y[5] * + * ala)/(p[19] * p[20])), // Trans_Val: irreversible Michaelis Menten + * with two inhibitions (p[29] * Y[3])/(p[32] + Y[3] + p[32] * p[35] * + * Y[4] + p[38] * Y[3] * Y[4]), // IPMS: convenience (p[5] * Y[5])/((1 + + * Y[5]/p[21] + p[40] + (p[40] * Y[5])/p[21]) * (p[11] + Y[4])), // + * IPMDH: convenience (p[6] * Y[1] * nad)/(1 + Y[1]/p[22] + nad/p[23] + + * (Y[1] * nad)/(p[22] * p[23])), // BCAAT_LeuB: convenience (p[7] * + * Y[6] * glut - p[8] * Y[4] * akg)/(1 + Y[6]/p[24] + glut/p[25] + (Y[6] * + * glut)/(p[24] * p[25]) + Y[4]/p[26] + akg/p[27] + (Y[4] * akg)/(p[26] * + * p[27])), // Trans_Leu: irreversible Michaelis Menten with two + * inhibitions (p[30] * Y[4])/(p[33] + Y[4] + p[33] * p[36] * Y[3] + + * p[39] * Y[3] * Y[4]) + */ + }); + } + + + public void getValue(double t, double[] Y, double[] resultVector) { + double tmp[] = getValue(t, Y); + System.arraycopy(tmp, 0, resultVector, 0, tmp.length); + } + + @Override + public int getNumberOfParameters() { + return 41; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/odes/CKMMrSystem.java b/src/eva2/server/go/problems/inference/metabolic/odes/CKMMrSystem.java new file mode 100644 index 00000000..ed52c8a2 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/odes/CKMMrSystem.java @@ -0,0 +1,117 @@ +package eva2.server.go.problems.inference.metabolic.odes; + +/** + * This class describes the valine/leucine biosynthesis in Corynebacterium + * glutamicum acording to the convenience kinetics as proposed by + * Liebermeister and Klipp. Three reaction velocities follow the traditional + * Michaelis Menten scheme the others are given in the convenience kinetics. + * However, in this class all reactions are assumed to be reversible. + * + * @since 2.0 + * @version + * @author Andreas Dräger (draeger) + * Copyright (c) ZBiT, University of Tübingen, Germany Compiler: + * JDK 1.6.0 + * @date 2007-02-05 + */ +public class CKMMrSystem extends AbstractValineSystem { + + /** + * Generated serial id. + */ + private static final long serialVersionUID = 726678613269764327L; + transient protected double velocities[] = null; + + /** + * @param x + */ + public CKMMrSystem(double[] x) { + this.p = x; + } + + public CKMMrSystem() { + super(); + } + + /* + * (non-Javadoc) + * + * @see eva2.server.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double, + * double[]) + */ + public double[] getValue(double t, double[] Y) { + + double[] res = new double[7]; + getValue(t, Y, res); + return res; + + } + + public void getValue(double t, double[] Y, double[] res) { + double pyr = getPyr_2(t), nadp = getNADP_2(t), nadph = 0.04 - nadp, glut = getGlut_2(t), akg = getAKG_2(t), ala = getAla_2(t), nad = getNAD_2(t), nadh = 0.8 - nad; + + if (velocities == null) + velocities = new double[10]; + + // v_1 + velocities[0] = ((p[0] * p[44] * Math.pow(pyr, 2)) / Math.pow(p[14], 2) - (p[7] + * p[44] * Y[2]) + / p[15]) + / ((1 + pyr / p[14] + Math.pow(pyr / p[14], 2) + Y[2] / p[15]) * (p[44] + Y[3])); + // v_2 + velocities[1] = ((p[1] * p[45] * Y[2] * nadph) / (p[16] * p[17]) - (p[8] + * p[45] * Y[0] * nadp) + / (p[18] * p[19])) + / ((1 + Y[2] / p[16] + nadph / p[17] + (Y[2] * nadph) + / (p[16] * p[17]) + Y[0] / p[18] + nadp / p[19] + (Y[0] * nadp) + / (p[18] * p[19])) * (p[45] + Y[3])); + // v_3 + velocities[2] = ((p[47] * Y[0]) / p[20] - (p[48] * Y[5]) / p[21]) + / (1 + p[51] * Y[3] + (Y[0] / p[20] + Y[5] / p[21]) + * (1 + p[54] * Y[3])); + // v_4 + velocities[3] = ((p[2] * Y[5] * glut) / (p[22] * p[23]) - (p[9] * Y[3] * akg) + / (p[24] * p[25])) + / (1 + Y[5] / p[22] + glut / p[23] + (Y[5] * glut) + / (p[22] * p[23]) + Y[3] / p[24] + akg / p[25] + (Y[3] * akg) + / (p[24] * p[25])); + // v_5 + velocities[4] = ((p[3] * Y[5] * ala) / (p[26] * p[27]) - (p[10] * Y[3] * pyr) + / (p[28] * p[29])) + / (1 + Y[5] / p[26] + ala / p[27] + (Y[5] * ala) + / (p[26] * p[27]) + Y[3] / p[28] + pyr / p[29] + (Y[3] * pyr) + / (p[28] * p[29])); + // v_6 + velocities[5] = (p[49] * Y[3]) + / (p[30] + p[30] * p[52] * Y[4] + Y[3] + p[55] * Y[3] * Y[4]); + // v_7 + velocities[6] = ((p[4] * p[46] * Y[5] * p[57]) / (p[31] * p[32]) - (p[11] + * p[46] * Y[1] * p[58]) + / (p[33] * p[34])) + / ((1 + Y[5] / p[31] + p[57] / p[32] + (Y[5] * p[57]) + / (p[31] * p[32]) + Y[1] / p[33] + p[58] / p[34] + (Y[1] * p[58]) + / (p[33] * p[34])) * (p[46] + Y[4])); + // v_8 + velocities[7] = ((p[5] * Y[1] * nad) / (p[35] * p[36]) - (p[12] * Y[6] * nadh) + / (p[37] * p[38])) + / (1 + Y[1] / p[35] + nad / p[36] + (Y[1] * nad) + / (p[35] * p[36]) + Y[6] / p[37] + nadh / p[38] + (Y[6] * nadh) + / (p[37] * p[38])); + // v_9 + velocities[8] = ((p[6] * Y[6] * glut) / (p[39] * p[40]) - (p[13] * Y[4] * akg) + / (p[41] * p[42])) + / (1 + Y[6] / p[39] + glut / p[40] + (Y[6] * glut) + / (p[39] * p[40]) + Y[4] / p[41] + akg / p[42] + (Y[4] * akg) + / (p[41] * p[42])); + // v_10 + velocities[9] = (p[50] * Y[4]) + / (p[43] + p[43] * p[53] * Y[3] + Y[4] + p[56] * Y[3] * Y[4]); + + linearCombinationOfVelocities(velocities, res); + } + + @Override + public int getNumberOfParameters() { + return 59; + } +} diff --git a/src/eva2/server/go/problems/inference/metabolic/odes/GMAKiSystem.java b/src/eva2/server/go/problems/inference/metabolic/odes/GMAKiSystem.java new file mode 100644 index 00000000..308a8e41 --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/odes/GMAKiSystem.java @@ -0,0 +1,69 @@ +/** + * + */ +package eva2.server.go.problems.inference.metabolic.odes; + +// Created ad 2007-01-13 + +/** + * In this class the valine/leucine biosynthesis in Corynebacterium glutamicum + * is modeled to be in many reactions irreversible according to what is written + * in the KEGG database. Inhibition is modeled as negative exponential function. + * + * @author Andreas Dräger + * + */ +public class GMAKiSystem extends AbstractValineSystem { + + /** + * Generated serial id. + */ + private static final long serialVersionUID = -5893476466596099997L; + + /** + * + * @param params + */ + public GMAKiSystem(double params[]) { + this.p = params; + } + + public GMAKiSystem() { + this.p = null; + } + + /* + * (non-Javadoc) + * + * @see javaeva.server.oa.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double, + * double[]) + */ + public double[] getValue(double t, double[] Y) { + return linearCombinationOfVelocities(new double[] { + p[0] * Math.pow(getPyr_2(t), 2) + * Math.exp((-p[12]) * Y[3]), + (p[1] * Y[2] * (0.04 - getNADP_2(t)) - p[10] * Y[0] + * getNADP_2(t)) + * Math.exp((-p[13]) * Y[3]), + p[2] * Y[0] * Math.exp((-p[14]) * Y[3]), + p[3] * getGlut_2(t) * Y[5], + p[4] * getAla_2(t) * Y[5], + p[5] * Y[3] * Math.exp((-p[15]) * Y[4]), + p[6] * Y[5] * Math.exp((-p[16]) * Y[4]), + p[7] * getNAD_2(t) * Y[1], + p[8] * getGlut_2(t) * Y[6] - p[11] * getAKG_2(t) + * Y[4], + p[9] * Y[4] * Math.exp((-p[17]) * Y[3]) }); + } + + public void getValue(double t, double[] Y, double[] resultVector) { + double tmp[] = getValue(t, Y); + System.arraycopy(tmp, 0, resultVector, 0, tmp.length); + } + + @Override + public int getNumberOfParameters() { + return 18; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/odes/GMAKrSystem.java b/src/eva2/server/go/problems/inference/metabolic/odes/GMAKrSystem.java new file mode 100644 index 00000000..0344b70e --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/odes/GMAKrSystem.java @@ -0,0 +1,116 @@ +package eva2.server.go.problems.inference.metabolic.odes; + +/** + * This class describes the valine/leucine biosynthesis in Corynebacterium + * glutamicum. All 10 reactions are assumed to be reversible and modeled + * following the generalized mass action kinetic, where inhibition is modeled + * using the function 1/(1 + ki*[Inhibitor]). + * + * @date 2007-02-04 + * @author Andreas Dräger + */ +public class GMAKrSystem extends AbstractValineSystem { + + /** + * Generated serial id. + */ + private static final long serialVersionUID = -5668558229555497614L; + transient double[] veloc = null; + + public GMAKrSystem() { + super(); + } + + /** + * @param params + */ + public GMAKrSystem(double params[]) { + super(); + this.p = params; + } + + + /* + * (non-Javadoc) + * + * @see eva2.server.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double, + * double[]) + */ + public double[] getValue(double t, double[] Y) { + // a <- params[0..9] + // b <- params[10..17] + // g <- params[18..23] + double[] res = new double[7]; + getValue(t, Y, res); + return res; + // return linearCombinationOfVelocities(new double[] { + // (params[0] * Math.pow(getPyr_2(t), 2) - params[10] * Y[2])/ (1 + + // params[18] * Y[3]), // Math.exp((-params[18]) * Y[3]), + // (params[1] * Y[2] * (.04 - getNADP_2(t)) - params[11] * Y[0] * + // getNADP_2(t)) / (1 + params[19] * Y[3]), // * Math.exp((-params[19]) + // * + // Y[3]), + // (params[2] * Y[0] - params[12] * Y[5]) / (1 + params[20] * Y[3]), // + // * + // // Math.exp((-params[20]) + // // * + // // Y[3]), + // params[3] * getGlut_2(t) * Y[5] - params[13] * getAKG_2(t) * Y[3], + // params[4] * getAla_2(t) * Y[5] - params[14] * getPyr_2(t) * Y[3], + // (params[5] * Y[3]) / (1 + params[21] * Y[4]), // * + // // Math.exp((-params[21]) + // // * Y[4]), + // (params[6] * Y[5] - params[15] * Y[1]) / (1 + params[22] * Y[4]), // + // * + // // Math.exp((-params[22]) + // // * + // // Y[4]), + // params[7] * getNAD_2(t) * Y[1] - params[16] * Y[6] * (.8 - + // getNAD_2(t)), + // params[8] * getGlut_2(t) * Y[6] - params[17] * getAKG_2(t) * Y[4], + // (params[9] * Y[4]) / (1 + params[23] * Y[3]) // * + // // Math.exp((-params[23]) + // // * Y[3]) + // }); + } + + public void getValue(double t, double[] Y, double[] res) { + if (veloc == null) + veloc = new double[10]; + + veloc[0] = (p[0] * Math.pow(getPyr_2(t), 2) - p[10] * Y[2]) + / (1 + p[18] * Y[3]); // Math.exp((-params[18]) * Y[3]), + veloc[1] = (p[1] * Y[2] * (.04 - getNADP_2(t)) - p[11] * Y[0] + * getNADP_2(t)) + / (1 + p[19] * Y[3]); // * Math.exp((-params[19]) * Y[3]), + veloc[2] = (p[2] * Y[0] - p[12] * Y[5]) + / (1 + p[20] * Y[3]); // * + // Math.exp((-params[20]) + // * + // Y[3]), + veloc[3] = p[3] * getGlut_2(t) * Y[5] - p[13] * getAKG_2(t) + * Y[3]; + veloc[4] = p[4] * getAla_2(t) * Y[5] - p[14] * getPyr_2(t) + * Y[3]; + veloc[5] = (p[5] * Y[3]) / (1 + p[21] * Y[4]); // * + // Math.exp((-params[21]) + // * Y[4]), + veloc[6] = (p[6] * Y[5] - p[15] * Y[1]) + / (1 + p[22] * Y[4]); // * + // Math.exp((-params[22]) + // * + // Y[4]), + veloc[7] = p[7] * getNAD_2(t) * Y[1] - p[16] * Y[6] + * (.8 - getNAD_2(t)); + veloc[8] = p[8] * getGlut_2(t) * Y[6] - p[17] * getAKG_2(t) + * Y[4]; + veloc[9] = (p[9] * Y[4]) / (1 + p[23] * Y[3]); // * + linearCombinationOfVelocities(veloc, res); + } + + @Override + public int getNumberOfParameters() { + return 24; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/odes/GMMiSystem.java b/src/eva2/server/go/problems/inference/metabolic/odes/GMMiSystem.java new file mode 100644 index 00000000..77c8c87d --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/odes/GMMiSystem.java @@ -0,0 +1,120 @@ +package eva2.server.go.problems.inference.metabolic.odes; + +// Created at 2007-01-18 + +/** + * This class implements a system of ordinary differential equations describing + * the valine and leucin biosynthesis in Corynebacterium gluthamicum. + * + * @author Andreas Dräger + */ +public class GMMiSystem extends AbstractValineSystem { + + /** + * Generated version id. + */ + private static final long serialVersionUID = 6119930633376953563L; + + + /** + *

+ * A vector of parameters it needed to initialize this class. The parameters + * are given as: + *

+ *
    + *
  • params[0-6] = k, Rate constant for the forward reaction
    • + *
  • params[7-8] = l, Rate constant for the backward reaction
    • + *
  • params[9-11] = ki, Rate constant for inhibition of uncertain + * mechanism
    • + *
  • params[12-14] = vm, Maximal reaction velocity
    • + *
  • params[15-17] = kia, Rate constant for inhibition of the enzyme
    • + *
  • params[18-20] = kib, Rate constant for inhibition of the enzyme + * substrate complex
    • + *
  • params[21-23] = km, the Michaelis-Menten constant
    • + *
+ * + * @param params + */ + public GMMiSystem(double[] params) { + if (params != null) + if (params.length != 24) + throw new Error( + "This ODE system needs exactly 24 parameters. Given are " + + params.length); + this.p = params; + } + + /** + * + * + */ + public GMMiSystem() { + this.p = null; + } + + + /* + * (non-Javadoc) + * + * @see javaeva.server.oa.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double, + * double[]) + */ + public double[] getValue(double t, double[] Y) { + double[] v = new double[] { + // v_1 + (p[0] * Math.pow(getPyr_2(t), 2)) / (1 + p[9] * Y[3]), + // v_2 + (p[1] * Y[2] * (0.04 - getNADP_2(t)) - p[7] * Y[0] + * getNADP_2(t)) + / (1 + p[10] * Y[3]), + // v_3 + // ((p[12] * Y[0])/p[21])/(1 + p[15] * Y[3] + (Y[0] + p[18] * + // Y[3] * + // Y[0])/p[21]), + (p[12] * Y[0]) + / (p[21] + Y[0] + p[21] * p[15] * Y[3] + p[18] * Y[0] + * Y[3]), + // v_4 + p[2] * Y[5] * getGlut_2(t), + // v_5 + p[3] * Y[5] * getAla_2(t), + // v_6 + // (p[13]/p[22] * Y[3])/(1 + p[16] * Y[4] + (Y[3] + p[19] * Y[4] + // * + // Y[3])/p[22]), + (p[13] * Y[3]) + / (p[22] + Y[3] + p[22] * p[16] * Y[4] + p[19] * Y[3] + * Y[4]), + // v_7 + (p[4] * Y[5]) / (1 + p[11] * Y[4]), + // v_8 + p[5] * Y[1] * getNAD_2(t), + // v_9 + p[6] * Y[6] * getGlut_2(t) - p[8] * Y[4] * getAKG_2(t), + // v_10 + // ((p[14] * Y[4])/p[23])/(1 + p[17] * Y[3] + (Y[4] + p[20] * + // Y[3] * + // Y[4])/p[23]) + (p[14] * Y[4]) + / (p[23] + Y[4] + p[17] * p[23] * Y[3] + p[20] * Y[3] + * Y[4]) }; + + /* + * if ((Double.isNaN(v[0]) || Double.isInfinite(v[0])) && (t < -1.784)) { + * System.out.println(t+". Nenner: "+(1 + p[9] * Y[3])+"\tY[3] = + * "+Y[3]+"\tparam9 = "+p[9]); }// + */ + return linearCombinationOfVelocities(v); + } + + public void getValue(double t, double[] Y, double[] resultVector) { + double tmp[] = getValue(t, Y); + System.arraycopy(tmp, 0, resultVector, 0, tmp.length); + } + + @Override + public int getNumberOfParameters() { + return 24; + } + +} diff --git a/src/eva2/server/go/problems/inference/metabolic/odes/GMMrSystem.java b/src/eva2/server/go/problems/inference/metabolic/odes/GMMrSystem.java new file mode 100644 index 00000000..bf01712e --- /dev/null +++ b/src/eva2/server/go/problems/inference/metabolic/odes/GMMrSystem.java @@ -0,0 +1,87 @@ +package eva2.server.go.problems.inference.metabolic.odes; + +/** + * This class describes the valine/leucine biosynthesis in Corynebacterium + * gluthamicum where three reactions are modeled using traditional reversible + * Michaelis Menten kinetics and the remaining reactions are modeled using + * reversible generalized mass action kinetics, where inhibition is modeled + * using 1/(1 + ki*[Inhibitor]). Created at 2007-02-04. + * + * @author Andreas Dräger + */ +public class GMMrSystem extends AbstractValineSystem { + + /** + * Generated serial id. + */ + private static final long serialVersionUID = 7461506193192163451L; + + /** + * + */ + public GMMrSystem() { + } + + /** + * @param params + */ + public GMMrSystem(double[] params) { + this.p = params; + } + + /* + * (non-Javadoc) + * + * @see eva2.server.go.OptimizationProblems.InferenceRegulatoryNetworks.Des.DESystem#getValue(double, + * double[]) + */ + public double[] getValue(double t, double[] Y) { + /* + * double[] a = null, // <- params[0..6] 7 b = null, // <- params[7..13] + * 7 vm = null, // <- params[14..17] 4 km = null, // <- params[18..21] 4 + * kia = null, // <- params[22..24] 3 kib = null, // <- params[25..27] 3 + * ki = null; // <- params[28..30] 3 // + */ + + return linearCombinationOfVelocities(new double[] { + // v_1 + (p[0] * Math.pow(getPyr_2(t), 2) - p[7] * Y[2]) + / (1 + p[28] * Y[3]), + // v_2 + (p[1] * Y[2] * (.04 - getNADP_2(t)) - p[8] * Y[0] + * getNADP_2(t)) + / (1 + p[29] * Y[3]), + // v_3 + ((p[14] * Y[0]) / p[18] - (p[15] * Y[5]) / p[19]) + / (1 + p[22] * Y[3] + (Y[0] / p[18] + Y[5] / p[19]) + * (1 + p[25] * Y[3])), + // v_4 + p[2] * Y[5] * getGlut_2(t) - p[9] * Y[3] * getAKG_2(t), + // v_5 + p[3] * Y[5] * getAla_2(t) - p[10] * Y[3] * getPyr_2(t), + // v_6 + (p[16] * Y[3]) + / (p[20] + p[20] * p[23] * Y[4] + Y[3] + p[26] * Y[4] + * Y[3]), + // v_7 + (p[4] * Y[5] - p[11] * Y[1]) / (1 + p[30] * Y[4]), + // v_8 + p[5] * Y[1] * getNAD_2(t) - p[12] * Y[6] * (.8 - getNAD_2(t)), + // v_9 + p[6] * Y[6] * getGlut_2(t) - p[13] * Y[4] * getAKG_2(t), + // v_10 + (p[17] * Y[4]) + / (p[21] + p[21] * p[24] * Y[3] + Y[4] + p[27] * Y[3] + * Y[4]) }); + } + + public void getValue(double t, double[] Y, double[] resultVector) { + resultVector = getValue(t, Y); + } + + @Override + public int getNumberOfParameters() { + return 31; + } + +} \ No newline at end of file diff --git a/src/eva2/server/go/strategies/MonteCarloSearch.java b/src/eva2/server/go/strategies/MonteCarloSearch.java index 504ae1b2..43ece97b 100644 --- a/src/eva2/server/go/strategies/MonteCarloSearch.java +++ b/src/eva2/server/go/strategies/MonteCarloSearch.java @@ -20,7 +20,11 @@ import eva2.server.go.problems.InterfaceOptimizationProblem; * $Author: mkron $ */ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializable { - // These variables are necessary for the simple testcase + /** + * Generated serial version id. + */ + private static final long serialVersionUID = -751760624411490405L; + // These variables are necessary for the simple testcase private InterfaceOptimizationProblem m_Problem = new B1Problem(); private int m_MultiRuns = 100; private int m_FitnessCalls = 100; @@ -60,7 +64,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl */ public void initByPopulation(Population pop, boolean reset) { this.m_Population = (Population)pop.clone(); - if (reset) this.m_Population.init(); + if (reset) this.m_Population.init(); this.m_Problem.evaluate(this.m_Population); this.firePropertyChangedEvent("NextGenerationPerformed"); } @@ -171,7 +175,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl */ public void freeWilly() { - } + } /********************************************************************************************************************** * These are for GUI */ @@ -201,7 +205,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl public String populationTipText() { return "Change the number of best individuals stored."; } - + public InterfaceSolutionSet getAllSolutions() { return new SolutionSet(getPopulation()); }