public void CalculateProposedTauTest7()
{
Model model;
MethodInfo calculateProposedTauMethod;
MethodInfo resetModelStateMethod;
var solver = DefaultSetupForCalculateProposedTauTests(out model, out calculateProposedTauMethod, out resetModelStateMethod);
object[] inputArray1 = new object[1];
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
resetModelStateMethod.Invoke(solver, null);
ReflectionUtility.SetHiddenField("_regime", solver, 3);
model.Species[0].Count = 3;
model.Species[1].Count = 3;
inputArray1[0] = 10;
ReflectionUtility.SetHiddenField("nc", solver, 10);
var tauOut7 = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.023677467222359326, tauOut7);
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_regime", solver, 3);
inputArray1[0] = 0.001;
var tauOut7B = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.001, tauOut7B);
}
public void CalculateProposedTauTest5()
{
Model model;
MethodInfo calculateProposedTauMethod;
MethodInfo resetModelStateMethod;
var solver = DefaultSetupForCalculateProposedTauTests(out model, out calculateProposedTauMethod, out resetModelStateMethod);
object[] inputArray1 = new object[1];
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
//resetModelStateMethod.Invoke(solver, null);
ReflectionUtility.SetHiddenField("_regime", solver, 3);
model.Species[0].Count = 200;
model.Species[1].Count = 10;
ReflectionUtility.SetHiddenField("epsilon", solver, 0.1);
inputArray1[0] = 10;
var tauOut5 = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.052564102564102565, tauOut5);
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_regime", solver, 3);
inputArray1[0] = 0.01;
var tauOut5B = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.01, tauOut5B);
}
public TransportSSA(ModelInfo modelInfo, float duration, int repeats, int samples)
: base(modelInfo, duration, repeats, samples)
{
Configuration config = Configuration.CurrentConfiguration;
// Transport events
diffusionReactions = model.Reactions.Where(r => !r.IsLocal).ToList();
// Reaction events
reactionReactions = model.Reactions.Where(r => r.IsLocal).ToList();
currentReactionRates = new float[reactionReactions.Count];
kernels = new float[model.Species.Count][]; // jagged array (i.e. array of arrays)
speciesIndices = new int[model.Species.Count][]; // nearest neighbors of the current species
lambda = new int[model.Species.Count]; // X(t+tau) = X(t) + lambda, diffusion changes
diffusionRates = new float[diffusionReactions.Count];
_partiallyExecutedReactionsEvents = new List <int>();
// Jagged array initialization
for (int iSpecies = 0; iSpecies < model.Species.Count; ++iSpecies)
{
int numberOfNieghbors = NumberOfNeighbors(iSpecies) + 1; // +1 for the current node + number of neighbors
kernels[iSpecies] = new float[numberOfNieghbors];
speciesIndices[iSpecies] = new int[numberOfNieghbors];
}
epsilon = config.GetParameterWithDefault("tssa.epsilon", 0.01f);
greensFunctionIterations = config.GetParameterWithDefault("tssa.greensFunctionIterations", 100);
verbose = config.GetParameterWithDefault("tssa.verbose", false);
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
// Get the transport rates
FindDiffusionRates(ref diffusionRates);
}
// ReSharper restore InconsistentNaming
// The algorithm presented here (specifically in regards to the critical
// versus noncritical reactions to avoid negative species) can be found in
// 'Avoiding Negative Populations in Explicit Poisson Tau-Leaping'
// Cao, Gillespie, Petzold. Journal of Chemical Physics.
public TauLeaping(ModelInfo modelInfo, float duration, int repeats, int samples, ModelBuilder modelBuilder = null)
: base(modelInfo, duration, repeats, samples, modelBuilder)
{
Configuration config = Configuration.CurrentConfiguration;
_currentRates = new float[model.Reactions.Count];
_jacobianMatrix = new float[model.Reactions.Count, model.Species.Count];
_nonCriticalReactions = new List <Reaction>();
_criticalReactions = new List <Reaction>();
// We'll need at most reactions.Count values in one or the other array
_criticalRates = new float[model.Reactions.Count];
_noncriticalRates = new float[model.Reactions.Count];
epsilon = config.GetParameterWithDefault("tau-leaping.epsilon", 0.001f);
nc = config.GetParameterWithDefault("tau-leaping.Nc", 2);
multiple = config.GetParameterWithDefault("tau-leaping.Multiple", 10);
SSAruns = config.GetParameterWithDefault("tau-leaping.SSARuns", 100);
CheckParameters();
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
Console.WriteLine("epsilon = {0}", epsilon);
Console.WriteLine("nc = {0}", nc);
Console.WriteLine("multiple = {0}", multiple);
Console.WriteLine("SSA runs = {0}", SSAruns);
}
public void CalculateProposedTauTest1()
{
Model model;
MethodInfo calculateProposedTauMethod;
MethodInfo resetModelStateMethod;
var solver = DefaultSetupForCalculateProposedTauTests(out model, out calculateProposedTauMethod, out resetModelStateMethod);
ReflectionUtility.SetHiddenField("_regime", solver, 0);
double tauIn = 5;
object[] inputArray1 = new object[1];
inputArray1[0] = tauIn;
var tauOut1 = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.017758100416769496, tauOut1);
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
inputArray1[0] = 0.0001;
var tauOut1B = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(inputArray1[0], tauOut1B);
}
public ExitTimes(ModelInfo modelInfo, double duration, int repeats, int samples)
: base(modelInfo, duration, repeats, samples, new ModelBuilder())
{
currentRates = new double[model.Reactions.Count];
lambda = new List <double>();
lambdaLists = new List <List <double> >();
exitTimes = new List <double>();
numberOfGammaRNs = 0.0;
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
Configuration config = Configuration.CurrentConfiguration;
_epsilon = Math.Min(config.GetParameterWithDefault("et.epsilon", 0.5), 1.0);
_eventName = config.GetParameterWithDefault("et.eventName", "exitTimeEvent");
_verbose = config.GetParameterWithDefault("et.verbose", false);
_testing = config.GetParameterWithDefault("et.testing", false);
_convergenceTest = config.GetParameterWithDefault("et.convergence", false);
_efficiencyCutoff = config.GetParameterWithDefault("et.efficiencyCutoff", 0.2);
targetCondition = model.Predicates.First(s => s.Name == _eventName);
sampleNumber = 1;
eventAchieved = false;
if (_verbose)
{
Console.WriteLine("\n predicate: {0} with initial evaluation: {1} \n", targetCondition.Name, targetCondition.Value);
Console.WriteLine("\n epsilon = {0} \n", _epsilon);
}
}
public BLeaping(ModelInfo modelInfo, double duration, int repeats, int samples)
: base(modelInfo, duration, repeats, samples)
{
_currentRates = new double[model.Reactions.Count];
_tau = Configuration.CurrentConfiguration.GetParameterWithDefault("b-leaping.Tau", 0.1);
_step = 1;
_deltaTau = 0.0;
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
}
public Normal(float mean, float variance)
{
if (variance <= 0.0f)
{
throw new ArgumentException("Variance must be greater than 0.");
}
_mean = mean;
_variance = variance;
_distributionSampler = RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG());
}
public RLeaping(ModelInfo modelInfo, float duration, int repeats, int samples)
: base(modelInfo, duration, repeats, samples)
{
Configuration config = Configuration.CurrentConfiguration;
reactions = new List <Reaction>(model.Reactions);
currentRates = new float[reactions.Count];
jacobianMatrix = new float[reactions.Count, model.Species.Count];
_probabilityVector = new float[reactions.Count];
_executionsPerReaction = new int[reactions.Count];
_epsilon = config.GetParameterWithDefault("r-leaping.epsilon", 0.1f);
_theta = config.GetParameterWithDefault("r-leaping.theta", 0.0f);
_sortingInterval = config.GetParameterWithDefault("r-leaping.sorting interval", 365.0f);
_verbose = config.GetParameterWithDefault("r-leaping.verbose", false);
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
}
public MidPoint(ModelInfo modelInfo, float duration, int repeats, int samples)
: base(modelInfo, duration, repeats, samples, new MpModelBuilder())
{
Configuration config = Configuration.CurrentConfiguration;
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
epsilon = config.GetParameterWithDefault("midpoint.epsilon", 0.01f);
nc = config.GetParameterWithDefault("midpoint.Nc", 2);
multiple = config.GetParameterWithDefault("midpoint.Multiple", 10);
SSAruns = config.GetParameterWithDefault("midpoint.SSARuns", 100);
CheckParameters();
Console.WriteLine("epsilon = {0}", epsilon);
Console.WriteLine("nc = {0}", nc);
Console.WriteLine("multiple = {0}", multiple);
Console.WriteLine("SSA runs = {0}", SSAruns);
}
public RLeapingFast(ModelInfo modelInfo, double duration, int repeats, int samples)
: base(modelInfo, duration, repeats, samples)
{
Configuration config = Configuration.CurrentConfiguration;
reactions = new List <Reaction>(model.Reactions);
currentRates = new double[reactions.Count];
_highestOrderReaction = new int[model.Species.Count];
_muHat = new double[model.Species.Count];
_sigmaHat2 = new double[model.Species.Count];
_varHat = new double[model.Species.Count];
_probabilityVector = new double[reactions.Count];
_executionsPerReaction = new int[reactions.Count];
_epsilon = config.GetParameterWithDefault("r-leaping.epsilon", 0.1);
_sortingInterval = config.GetParameterWithDefault("r-leaping.sorting interval", 365.0);
_verbose = config.GetParameterWithDefault("r-leaping.verbose", false);
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
}
public void CalculateProposedTauTest4()
{
Model model;
MethodInfo calculateProposedTauMethod;
MethodInfo resetModelStateMethod;
var solver = DefaultSetupForCalculateProposedTauTests(out model, out calculateProposedTauMethod, out resetModelStateMethod);
ReflectionUtility.SetHiddenField("_regime", solver, 0);
object[] inputArray1 = new object[1];
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
//resetModelStateMethod.Invoke(solver, null);
ReflectionUtility.SetHiddenField("_regime", solver, 3);
model.Species[0].Count = 3;
model.Species[1].Count = 2;
inputArray1[0] = 10;
ReflectionUtility.SetHiddenField("nc", solver, 3);
var tauOut4 = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.026637150625154243, tauOut4);
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_regime", solver, 3);
inputArray1[0] = 0.001;
var tauOut4B = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.001, tauOut4B);
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_regime", solver, 3);
inputArray1[0] = 6.29010763;
var tauOut4C = calculateProposedTauMethod.Invoke(solver, inputArray1);
Assert.AreEqual(0.13335417718069428, tauOut4C);
}
public FractionalDiffusion(ModelInfo modelInfo, float duration, int repeats, int samples)
: base(modelInfo, duration, repeats, samples, new ModelBuilder())
{
Configuration config = Configuration.CurrentConfiguration;
#region initializeFields
_reactions = new List <Reaction>(model.Reactions);
_alpha = config.GetParameterWithDefault("fd.alpha", 0.5f);
_verbose = config.GetParameterWithDefault("fd.verbose", false);
_Dalpha = config.GetParameterWithDefault("fd.Dalpha", 1.0f);
_constant = config.GetParameterWithDefault("fd.constant", 0.25f);
_h = config.GetParameterWithDefault("fd.h", 1.0f);
_truncationDistance = config.GetParameterWithDefault("fd.truncation", (int)Math.Round(((double)(modelInfo.Locales.Count() - 1)) / 4.0));
_fourierNumber = 0.5;
_n = modelInfo.Locales.Count() - 1;
_currentRates = new float[_reactions.Count];
_field = new int[_n];
_numberOfNonDiscretizedSpecies = modelInfo.Species.Count() / _n;
_tau = (float)TimeStep(_fourierNumber, _constant, _h, _alpha, _Dalpha);
_dispersalChanges = new int[model.Species.Count];
_executionsPerReaction = new int[model.Reactions.Count];
_distributionSampler = RandLibSampler.CreateRandLibSampler(rng);
#endregion
if (_verbose == true)
{
Console.WriteLine("number of locales = {0}", modelInfo.Locales.Count() - 1);
Console.WriteLine("number of species = {0}", _numberOfNonDiscretizedSpecies);
Console.WriteLine("truncation = {0}", _truncationDistance);
Console.WriteLine("alpha = {0}", _alpha);
Console.WriteLine("D_alpha = {0}", _Dalpha);
Console.WriteLine("Fouier number (1d) = {0}", _fourierNumber);
Console.WriteLine("h = {0}", _h);
Console.WriteLine("C = {0}", _constant);
Console.WriteLine("fractional diffusion tau = {0}", _tau);
}
}
public void LargeFactorialTest()
{
long fact = RandLibSampler.Factorial(20);
Assert.AreEqual(2432902008176640000, fact);
}
public void SmallFactorialTest()
{
long fact = RandLibSampler.Factorial(10);
Assert.AreEqual(2 * 3 * 4 * 5 * 6 * 7 * 8 * 9 * 10, fact);
}
private DistributionSampler GetAesCounterSampler()
{
DistributionSampler sampler = RandLibSampler.CreateRandLibSampler(AesCounterVariateGenerator.CreateAesCounterVariateGenerator(new uint[] { 20130110, 19680201 }));
return(sampler);
}
public void FireNonCriticalReactionsTest()
{
const string configString = @"{""solver"":""Tau"",""prng_seed"":123, ""prng_index"":1,""RNG"":{""type"":""PSEUDODES""}}";
Configuration.CurrentConfiguration = Configuration.ConfigurationFromString(configString);
ModelInfo modelInfo = EmodlLoader.LoadEMODLFile("resources\\testmodelMidPoint.emodl");
const double duration = 6.28318531;
const int repeats = 42;
const int samples = 100;
var solver = new TauLeaping(modelInfo, duration, repeats, samples);
//Inputs for the method
// Tau Step
double tau1 = 0.0;
var tau2 = (1.0 / 200.0);
//Need a list of reactions
var currentRates = new double[1];
var nonCriticalReaction = new List <Reaction>();
//Find Hidden Method to in order to initialize the population of each of the species
MethodInfo resetModelStateMethod = ReflectionUtility.GetHiddenMethod("ResetModelState", solver);
resetModelStateMethod.Invoke(solver, null);
MethodInfo updateAndSumRatesMethod = ReflectionUtility.GetHiddenMethod("UpdateAndSumRates", solver);
//Find the model object
var model = ReflectionUtility.GetHiddenField <Model>("model", solver);
currentRates[0] = model.Reactions[0].Rate;
nonCriticalReaction.Add(model.Reactions[0]);
object[] inputArray1 = new object[3];
inputArray1[0] = tau1; inputArray1[1] = nonCriticalReaction; inputArray1[2] = currentRates;
object[] inputArray2 = new object[3];
inputArray2[0] = tau2; inputArray2[1] = nonCriticalReaction; inputArray2[2] = currentRates;
//Find Hidden Method.
MethodInfo fireNonCriticalReactionsMethod = ReflectionUtility.GetHiddenMethod("FireNonCriticalReactions", solver);
// First Test if Tau = 0;
fireNonCriticalReactionsMethod.Invoke(solver, inputArray1);
Assert.AreEqual(400, model.Species[0].Value);
Assert.AreEqual(200, model.Species[1].Value);
Assert.AreEqual(900, model.Species[2].Value);
//Second Test if Tau = = 0.05. Set the RNG to a known seed number, in order to exactly know what is generated
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
fireNonCriticalReactionsMethod.Invoke(solver, inputArray2);
Assert.AreEqual(384.0, model.Species[0].Value);
Assert.AreEqual(184.0, model.Species[1].Value);
Assert.AreEqual(916.0, model.Species[2].Value);
resetModelStateMethod.Invoke(solver, null);
object[] inputArray3 = new object[2];
inputArray3[0] = model.Reactions; inputArray3[1] = currentRates;
updateAndSumRatesMethod.Invoke(solver, inputArray3);
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
fireNonCriticalReactionsMethod.Invoke(solver, inputArray2);
Assert.AreEqual(384.0, model.Species[0].Value);
Assert.AreEqual(184.0, model.Species[1].Value);
Assert.AreEqual(916.0, model.Species[2].Value);
}
public void ExecuteReactionsTest()
{
//0. Setup
const string configString = @"{""solver"":""Tau"",""prng_seed"":123, ""prng_index"":1,""RNG"":{""type"":""PSEUDODES""}}";
Configuration.CurrentConfiguration = Configuration.ConfigurationFromString(configString);
ModelInfo modelInfo = EmodlLoader.LoadEMODLFile("resources//testmodelTau.emodl");
const double duration = 6.28318531;
const int repeats = 42;
const int samples = 100;
var solver = new TauLeaping(modelInfo, duration, repeats, samples);
//Find Hidden Method to in order to initialize the population of each of the species
MethodInfo resetModelStateMethod = ReflectionUtility.GetHiddenMethod("ResetModelState", solver);
resetModelStateMethod.Invoke(solver, null);
//Find the model object
var model = ReflectionUtility.GetHiddenField <Model>("model", solver);
//Find the method
MethodInfo executeReactionsMethod = ReflectionUtility.GetHiddenMethod("ExecuteReactions", solver);
//1. ==Regime.SSA
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_regime", solver, 0);
ReflectionUtility.GetHiddenField <Reaction>("_ssaReaction", solver);
//a. _ssaReaction == null;
executeReactionsMethod.Invoke(solver, null);
var regime = ReflectionUtility.GetHiddenField <int>("_regime", solver);
Assert.AreEqual(0, regime);
//b. _ssaReaction != null;
ReflectionUtility.SetHiddenField("_remainingSSAsteps", solver, 1);
ReflectionUtility.SetHiddenField("_ssaReaction", solver, model.Reactions[0]);
ReflectionUtility.RunResetRngFactory();
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
executeReactionsMethod.Invoke(solver, null);
regime = ReflectionUtility.GetHiddenField <int>("_regime", solver);
Assert.AreEqual(3, regime);
Assert.AreEqual(6.0, model.Species[0].Count);
Assert.AreEqual(1.0, model.Species[1].Count);
//2. == Regime.NonCritical
ReflectionUtility.SetHiddenField("_regime", solver, 1);
ReflectionUtility.RunResetRngFactory();
resetModelStateMethod.Invoke(solver, null);
model.Species[0].Count = 100;
model.Species[1].Count = 50;
var nonCriticalReactionsList = new List <Reaction>();
nonCriticalReactionsList.Add(model.Reactions[0]);
nonCriticalReactionsList.Add(model.Reactions[1]);
var nonCriticalRates = new double[2];
nonCriticalRates[0] = model.Reactions[0].Rate;
nonCriticalRates[1] = model.Reactions[1].Rate;
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_leapTau", solver, 0.01);
ReflectionUtility.SetHiddenField("_nonCriticalReactions", solver, nonCriticalReactionsList);
ReflectionUtility.SetHiddenField("_noncriticalRates", solver, nonCriticalRates);
executeReactionsMethod.Invoke(solver, null);
regime = ReflectionUtility.GetHiddenField <int>("_regime", solver);
Assert.AreEqual(3, regime);
Assert.AreEqual(103, model.Species[0].Count);
Assert.AreEqual(47, model.Species[1].Count);
//3. == Regime.Critical ) no noncriticalreactions
ReflectionUtility.SetHiddenField("_regime", solver, 2);
ReflectionUtility.RunResetRngFactory();
resetModelStateMethod.Invoke(solver, null);
model.Species[0].Count = 100;
model.Species[1].Count = 50;
var criticalReactionsList = new List <Reaction>();
criticalReactionsList.Add(model.Reactions[0]);
criticalReactionsList.Add(model.Reactions[1]);
var criticalRates = new double[2];
criticalRates[0] = model.Reactions[0].Rate;
criticalRates[1] = model.Reactions[1].Rate;
MethodInfo updateAndSumRatesMethod = ReflectionUtility.GetHiddenMethod("UpdateAndSumRates", solver);
object[] inputArray1 = new object[2];
inputArray1[0] = criticalReactionsList; inputArray1[1] = criticalRates;
var a0Critical = updateAndSumRatesMethod.Invoke(solver, inputArray1);
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_a0Critical", solver, a0Critical);
ReflectionUtility.SetHiddenField("_criticalReactions", solver, criticalReactionsList);
ReflectionUtility.SetHiddenField("_criticalRates", solver, criticalRates);
ReflectionUtility.SetHiddenField("_nonCriticalReactions", solver, null);
ReflectionUtility.SetHiddenField("_noncriticalRates", solver, null);
executeReactionsMethod.Invoke(solver, null);
regime = ReflectionUtility.GetHiddenField <int>("_regime", solver);
Assert.AreEqual(3, regime);
Assert.AreEqual(101, model.Species[0].Count);
Assert.AreEqual(49, model.Species[1].Count);
//3. == Regime.Critical b) noncriticalreactions and critical reactions
ReflectionUtility.SetHiddenField("_regime", solver, 2);
ReflectionUtility.RunResetRngFactory();
resetModelStateMethod.Invoke(solver, null);
model.Species[0].Count = 100;
model.Species[1].Count = 50;
criticalReactionsList = new List <Reaction>();
nonCriticalReactionsList = new List <Reaction>();
criticalReactionsList.Add(model.Reactions[0]);
nonCriticalReactionsList.Add(model.Reactions[1]);
criticalRates = new double[1];
nonCriticalRates = new double[1];
criticalRates[0] = model.Reactions[0].Rate;
nonCriticalRates[0] = model.Reactions[1].Rate;
inputArray1[0] = criticalReactionsList; inputArray1[1] = criticalRates;
a0Critical = updateAndSumRatesMethod.Invoke(solver, inputArray1);
ReflectionUtility.SetHiddenField("_distributionSampler", solver, RandLibSampler.CreateRandLibSampler(RNGFactory.GetRNG()));
ReflectionUtility.SetHiddenField("_a0Critical", solver, a0Critical);
ReflectionUtility.SetHiddenField("_leapTau", solver, 0.01);
ReflectionUtility.SetHiddenField("_criticalReactions", solver, criticalReactionsList);
ReflectionUtility.SetHiddenField("_criticalRates", solver, criticalRates);
ReflectionUtility.SetHiddenField("_nonCriticalReactions", solver, nonCriticalReactionsList);
ReflectionUtility.SetHiddenField("_noncriticalRates", solver, nonCriticalRates);
executeReactionsMethod.Invoke(solver, null);
regime = ReflectionUtility.GetHiddenField <int>("_regime", solver);
Assert.AreEqual(3, regime);
Assert.AreEqual(100, model.Species[0].Count);
Assert.AreEqual(50, model.Species[1].Count);
//4. == Regime.AnythingElse
ReflectionUtility.SetHiddenField("_regime", solver, 4);
try
{
executeReactionsMethod.Invoke(solver, null);
Assert.Fail();
}
catch (ApplicationException ex)
{
Assert.That(ex.GetBaseException().ToString(), Is.StringContaining("Bad solver mode."));
}
}
