public ActionResult MonteCarloSimulationModal(int id)
{
MonteCarloSimulationDto model = new MonteCarloSimulationDto();
if (id == 0)
{
TradingAccountDto tradingAccount = this._tradingAccountAppService.GetActive();
List <Market> markets = this._marketRepository.GetAll().Where(x => x.Active).ToList();
model.TimeStamp = DateTime.Now;
model.TradingAccountId = tradingAccount.Id;
model.NumberOfTradesInSample = this._tradeRepository.GetAll().Count(x => x.TradingAccountId == model.TradingAccountId && x.ExitReason != TradeExitReasons.None);
model.NumberOfTradesPerIteration = 30;
model.NumberOfIterations = 1000;
model.CumulativeProfitK = .95m;
model.ConsecutiveLossesK = 1m;
model.MaxDrawdownK = .99m;
model.AccountSize = tradingAccount.CurrentCapital;
model.RuinPoint = markets.Average(x => x.InitialMargin) + 10m;
model.MaxDrawdownMultiple = 2m;
}
else
{
MonteCarloSimulation monteCarloSimulation = this._repository.Single(x => x.Id == id);
monteCarloSimulation.MapTo(model);
}
return(PartialView("Modals/_MonteCarloSimulationModal", model));
}
public static void RunSimulation(SimulationInput input, string outputFolderPath)
{
var mc = new MonteCarloSimulation(input);
// locate root folder for output, creating it if necessary
var path = string.IsNullOrEmpty(outputFolderPath)
? Path.GetFullPath(Directory.GetCurrentDirectory())
: Path.GetFullPath(outputFolderPath);
if (!Directory.Exists(path))
{
Directory.CreateDirectory(path);
}
// locate destination folder for output, creating it if necessary
var resultsFolder = Path.Combine(path, input.OutputName);
if (!Directory.Exists(resultsFolder))
{
Directory.CreateDirectory(resultsFolder);
}
mc.SetOutputPathForDatabases(path);
SimulationOutput detectorResults = mc.Run();
input.ToFile(Path.Combine(resultsFolder, input.OutputName + ".txt"));
foreach (var result in detectorResults.ResultsDictionary.Values)
{
// save all detector data to the specified folder
DetectorIO.WriteDetectorToFile(result, resultsFolder);
}
}
public void validate_photon_database_postprocessor_ROfRhoAndTime_results()
{
// DAW postprocssing
var DAWinput = GenerateReferenceDAWInput();
var onTheFlyDAWOutput = new MonteCarloSimulation(DAWinput).Run();
var DAWdatabase = PhotonDatabase.FromFile("DiffuseReflectanceDatabase");
var DAWpostProcessor = new PhotonDatabasePostProcessor(
VirtualBoundaryType.DiffuseReflectance,
_detectorInputs,
DAWdatabase,
onTheFlyDAWOutput.Input);
var postProcessedDAWOutput = DAWpostProcessor.Run();
ValidateROfRhoAndTime(onTheFlyDAWOutput, postProcessedDAWOutput);
// CAW postprocessing
var CAWinput = GenerateReferenceCAWInput();
var onTheFlyCAWOutput = new MonteCarloSimulation(CAWinput).Run();
var CAWdatabase = PhotonDatabase.FromFile("DiffuseReflectanceDatabase");
var CAWpostProcessor = new PhotonDatabasePostProcessor(
VirtualBoundaryType.DiffuseReflectance,
_detectorInputs,
CAWdatabase,
onTheFlyCAWOutput.Input);
var postProcessedCAWOutput = CAWpostProcessor.Run();
ValidateROfRhoAndTime(onTheFlyCAWOutput, postProcessedCAWOutput);
}
public void GetMoveParallel_Simulation_()
{
var map = MapTest.Map18;
var state = State.Create(map);
var simParallel = new MonteCarloSimulation(17, true);
var act = simParallel.GetMove(map, PlayerType.Hero1, state, SimulationTime, 300);
Console.WriteLine("{0:#,##0.00}k/s (Parallel)", simParallel.Simulations / SimulationTime.TotalMilliseconds);
}
public void GetMoveParallel_Simulation_()
{
var state = State.Create(MapTest.Map16);
var simParallel = new MonteCarloSimulation(17, true);
var act = simParallel.GetMove(MapTest.Map16, state, SimulationTime);
Console.WriteLine("{0:#,##0.00}k/s (Parallel)", simParallel.Simulations / SimulationTime.TotalMilliseconds);
Assert.AreEqual(MoveDirection.E, act);
}
public void RunSimulation(MonteCarloSimulationDto dto)
{
MonteCarloSimulation sim = _repository.Get(dto.Id);
sim.MapTo(dto);
List <Trade> sample = this._tradeRepository.GetAll().Where(x => x.TradingAccountId == dto.TradingAccountId && x.ExitReason != TradeExitReasons.None).ToList();
List <Market> markets = this._marketRepository.GetAll().Where(x => x.Active).ToList();
dto.Simulate(sample, markets, this._consoleHubProxy);
dto.MapTo(sim);
}
public void Run()
{
var data = new[] {
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
4,
4,
5,
6,
7,
8,
9,
10,
11,
15,
15,
15
};
var dist = Statistics.Distribution(data);
var deDE = new CultureInfo("de-DE");
foreach (var x in dist.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
var mcs = new MonteCarloSimulation <int, int>(values => values.Sum());
var results = mcs.Simulate(data, data, data, data, data);
_testOutputHelper.WriteLine("---");
dist = Statistics.Distribution(results);
foreach (var x in dist.OrderBy(o => o.value))
{
_testOutputHelper.WriteLine($"{x.value}\t{x.frequency}\t{x.probability.ToString("0.000", deDE)}\t{x.percentile.ToString("0.0", deDE)}");
}
}
public void GetMove_Performance_()
{
var state = State.Create(MapTest.Map16);
var simSingle = new MonteCarloSimulation(17, false);
var time = TimeSpan.FromSeconds(2);
var moveSingle = simSingle.GetMove(MapTest.Map16, state, time);
Console.WriteLine("{0:#,##0.00}k/s (Single)", simSingle.Simulations / time.TotalMilliseconds);
}
public void Build_random_number_generator()
{
var sut = MonteCarloSimulation.Build_random_number_generator();
const int MAX = 10;
for (var i = 0; i < 100; i++)
{
var r = sut(MAX);
Assert.IsTrue(r <= (MAX - 1));
Console.WriteLine(r);
}
}
public double HW_CAP_CM(double t0, double tAlpha, double[] taoArray, double x, double n, int num)
{
// 3.37
Func <double, double> alphaFunc = t => MarketF(t) +
Pow(this.Sigma(), 2) / (2 * Pow(this.A(), 2)) *
Pow(1 - Exp(-this.A() * t), 2);
var mu = MarketF(0) * Exp(-this.A() * t0) + alphaFunc(t0) -
alphaFunc(0) * Exp(-this.A() * t0);
var sigma = Pow(this.Sigma(), 2) / (2 * Pow(this.A(), 2)) *
Pow(1 - Exp(-2 * this.A() * t0), 2);
var sampleFunc = MonteCarloSimulation.GenerateSamples(
num, 0.Wrap(), (t, rt) => HW_Caps(t0, tAlpha, taoArray, x, n, rt), mu.Wrap(), sigma.Wrap());
var samples = sampleFunc();
return(samples.Average());
}
public void Save(MonteCarloSimulationDto dto)
{
if (dto.IsNew)
{
MonteCarloSimulation monteCarloSimulation = dto.MapTo <MonteCarloSimulation>();
int id = this._repository.InsertAndGetId(monteCarloSimulation);
this.RunSimulationEnqueue(new MonteCarloSimulationDto()
{
Id = id
});
}
else
{
MonteCarloSimulation monteCarloSimulation = this._repository.Get(dto.Id);
dto.MapTo(monteCarloSimulation);
}
}
public void validate_RunAll_given_two_simulations_runs_without_crashing()
{
var si1 = new SimulationInput {
N = 30
};
var si2 = new SimulationInput {
N = 20
};
var sim1 = new MonteCarloSimulation(si1);
var sim2 = new MonteCarloSimulation(si2);
var sims = new[] { sim1, sim2 };
var outputs = MonteCarloSimulation.RunAll(sims);
Assert.NotNull(outputs[0]);
Assert.NotNull(outputs[1]);
Assert.True(outputs[0].Input.N == 30);
Assert.True(outputs[1].Input.N == 20);
}
public void Run_simulations()
{
var featureValues = new[] {
new[] { 1f, 2f, 3f }, // F 1
new[] { 5f, 6f }, // F 2
new[] { 10f, 20f, 30f, 40f } // F3
};
// Values to be selected from each feat. per simulation: (F1,F2,F3), ...
// Values to be selected: (1f,5f,10f), (2f,6f,20f), (3f,6f,40f)
// Simulation results expected: 16f, 28f, 49f
// Indexes of values: 0,0,0, 1,1,1, 2,1,3
var randomIndexes = new Queue <int>(new[] { 0, 0, 0, 1, 1, 1, 2, 1, 3 });
var sut = new MonteCarloSimulation(_ => randomIndexes.Dequeue());
var result = sut.Run(3, featureValues);
Assert.AreEqual(new[] { 16f, 28f, 49f }, result);
}
public void MonteCarlo(double[] data, string key, int span, int index)
{
double[] daSubData = data;
if (index != data.Length - 1)
{
daSubData = data.Take(index + 1).ToArray();
}
//if (!MonteCarloData.ContainsKey(key))
MonteCarloData.Add(key, new ThreadedList <double[]>());
try
{
for (int i = 0; i < 1000000; i++)
{
MonteCarloData[key].Add(MonteCarloSimulation.Next(daSubData, span).ToArray());
while (Performance.CPU > 50)
{
Thread.Sleep(1);
if (StopSimulations)
{
return;
}
}
//while (Performance.CPU > 50 && Performance.CPU < 100)
// if (!this.Sleep(Indexes.Length)) return;
if (StopSimulations)
{
return;
}
//
}
}
catch { }
}
public void validate_fluent_constructed_SimulationInput_runs_simulation_without_crashing()
{
var si = new SimulationInput("demoInput")
{
N = 30
}
.WithSourceInput(SourceInputProvider.DirectionalPointSourceInput())
.WithTissueInput(TissueInputProvider.MultiLayerTissueInput())
.WithDetectorInputs(DetectorInputProvider.RDiffuseDetectorInput());
Assert.NotNull(si.SourceInput);
Assert.NotNull(si.TissueInput);
Assert.NotNull(si.DetectorInputs);
Assert.IsTrue(si.DetectorInputs.Count == 1);
var mc = new MonteCarloSimulation(si);
var output = mc.Run();
Assert.NotNull(output);
Assert.True(output.Input.N == 30);
}
public void validate_database_input_with_no_detectors_specified_still_generates_database()
{
// make sure databases generated from previous tests are deleted
if (FileIO.FileExists("DiffuseReflectanceDatabase.txt"))
{
FileIO.FileDelete("DiffuseReflectanceDatabase.txt");
}
if (FileIO.FileExists("DiffuseReflectanceDatabase"))
{
FileIO.FileDelete("DiffuseReflectanceDatabase");
}
var input = new SimulationInput(
100,
"", // can't give folder name when writing to isolated storage
new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
DatabaseType.DiffuseReflectance
}, // SPECIFY DATABASE
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
1),
_sourceInput,
_tissueInput,
new List <IDetectorInput>()
{
} // specify NO DETECTORS
);
var output = new MonteCarloSimulation(input).Run();
Assert.IsTrue(FileIO.FileExists("DiffuseReflectanceDatabase"));
Assert.IsFalse(FileIO.FileExists("DiffuseTransmittanceDatabase"));
}
private double[] HW_R_CM(double s, double rs, double t0, int n)
{
// 3.37
if (t0 == s)
{
return new[] { rs }
}
;
var sigma = this.Sigma();
var a = this.A();
double AlphaFunc(double t) => MarketF(t) +
Pow(sigma, 2) / (2 * Pow(a, 2)) * Pow(1 - Exp(-a * t), 2);
var mu = rs * Exp(-a * (t0 - s)) + AlphaFunc(t0) -
AlphaFunc(s) * Exp(-a * (t0 - s));
var stdvar = Pow(sigma, 2) / (2 * Pow(a, 2)) *
Pow(1 - Exp(-2 * a * (t0 - s)), 2);
var sampleFunc = MonteCarloSimulation.GenerateSamples(
n, 0.Wrap(), (t, x) => x, mu.Wrap(), stdvar.Wrap());
var samples = sampleFunc();
return(samples);
}
private void MC_ExecuteMonteCarloSolver_Executed(object sender, ExecutedEventArgs e)
{
if (!EnoughRoomInIsolatedStorage(50))
{
logger.Info(() => "\rSimulation not run. Please allocate more than 50MB of storage space.\r");
Commands.IsoStorage_IncreaseSpaceQuery.Execute();
return;
}
_newResultsAvailable = false;
var input = _simulationInputVM.SimulationInput;
var validationResult = SimulationInputValidation.ValidateInput(input);
if (!validationResult.IsValid)
{
logger.Info(() => "\rSimulation input not valid.\rRule: " + validationResult.ValidationRule +
(!string.IsNullOrEmpty(validationResult.Remarks) ? "\rDetails: " + validationResult.Remarks : "") + ".\r");
return;
}
_simulation = new MonteCarloSimulation(input);
_currentCancellationTokenSource = new CancellationTokenSource();
CancellationToken cancelToken = _currentCancellationTokenSource.Token;
TaskScheduler scheduler = TaskScheduler.FromCurrentSynchronizationContext();
var t = Task.Factory.StartNew(() => _simulation.Run(), TaskCreationOptions.LongRunning);
var c = t.ContinueWith((antecedent) =>
{
SolverDemoView.Current.Dispatcher.BeginInvoke(delegate()
{
_output = antecedent.Result;
_newResultsAvailable = _simulation.ResultsAvailable;
var rOfRhoDetectorInputs = _simulationInputVM.SimulationInput.DetectorInputs.
Where(di => di.Name == "ROfRho");
if (rOfRhoDetectorInputs.Any())
{
logger.Info(() => "Creating R(rho) plot...");
var detectorInput = (ROfRhoDetectorInput)rOfRhoDetectorInputs.First();
double[] independentValues = detectorInput.Rho.AsEnumerable().ToArray();
DoubleDataPoint[] points = null;
//var showPlusMinusStdev = true;
//if(showPlusMinusStdev && _output.R_r2 != null)
//{
// var stdev = Enumerable.Zip(_output.R_r, _output.R_r2, (r, r2) => Math.Sqrt((r2 - r * r) / nPhotons)).ToArray();
// var rMinusStdev = Enumerable.Zip(_output.R_r, stdev, (r,std) => r-std).ToArray();
// var rPlusStdev = Enumerable.Zip(_output.R_r, stdev, (r,std) => r+std).ToArray();
// points = Enumerable.Zip(
// independentValues.Concat(independentValues).Concat(independentValues),
// rMinusStdev.Concat(_output.R_r).Concat(rPlusStdev),
// (x, y) => new Point(x, y));
//}
//else
//{
points = Enumerable.Zip(
independentValues,
_output.R_r,
(x, y) => new DoubleDataPoint(x, y)).ToArray();
//}
PlotAxesLabels axesLabels = GetPlotLabels();
Commands.Plot_SetAxesLabels.Execute(axesLabels);
string plotLabel = GetPlotLabel();
Commands.Plot_PlotValues.Execute(new[] { new PlotData(points, plotLabel) });
logger.Info(() => "done.\r");
}
var fluenceDetectorInputs = _simulationInputVM.SimulationInput.DetectorInputs.
Where(di => di.Name == "FluenceOfRhoAndZ");
if (fluenceDetectorInputs.Any())
{
logger.Info(() => "Creating Fluence(rho,z) map...");
var detectorInput = (FluenceOfRhoAndZDetectorInput)fluenceDetectorInputs.First();
var rhosMC = detectorInput.Rho.AsEnumerable().ToArray();
var zsMC = detectorInput.Z.AsEnumerable().ToArray();
var rhos = Enumerable.Zip(rhosMC.Skip(1), rhosMC.Take(rhosMC.Length - 1), (first, second) => (first + second) / 2).ToArray();
var zs = Enumerable.Zip(zsMC.Skip(1), rhosMC.Take(zsMC.Length - 1), (first, second) => (first + second) / 2).ToArray();
var dRhos = Enumerable.Select(rhos, rho => 2 * Math.PI * Math.Abs(rho) * detectorInput.Rho.Delta).ToArray();
var dZs = Enumerable.Select(zs, z => detectorInput.Z.Delta).ToArray();
if (_mapArrayBuffer == null || _mapArrayBuffer.Length != _output.Flu_rz.Length * 2)
{
_mapArrayBuffer = new double[_output.Flu_rz.Length * 2];
}
// flip the array (since it goes over zs and then rhos, while map wants rhos and then zs
for (int zi = 0; zi < zs.Length; zi++)
{
for (int rhoi = 0; rhoi < rhos.Length; rhoi++)
{
_mapArrayBuffer[rhoi + rhos.Length + rhos.Length * 2 * zi] = _output.Flu_rz[rhoi, zi];
}
var localRhoiForReverse = 0;
for (int rhoi = rhos.Length - 1; rhoi >= 0; rhoi--, localRhoiForReverse++)
{
_mapArrayBuffer[localRhoiForReverse + rhos.Length * 2 * zi] = _output.Flu_rz[rhoi, zi];
}
}
var twoRhos = Enumerable.Concat(rhos.Reverse().Select(rho => - rho), rhos).ToArray();
var twoDRhos = Enumerable.Concat(dRhos.Reverse(), dRhos).ToArray();
var mapData = new MapData(_mapArrayBuffer, twoRhos, zs, twoDRhos, dZs);
Commands.Maps_PlotMap.Execute(mapData);
logger.Info(() => "done.\r");
}
// save results to isolated storage
logger.Info(() => "Saving simulation results to temporary directory...");
//var detectorFolder = Path.Combine(TEMP_RESULTS_FOLDER, input.OutputName);
//// create the root directory
//FileIO.CreateDirectory(TEMP_RESULTS_FOLDER);
// create the detector directory, removing stale files first if they exist
FileIO.CreateEmptyDirectory(TEMP_RESULTS_FOLDER);
// write detector to file
input.ToFile(Path.Combine(TEMP_RESULTS_FOLDER, "infile_" + input.OutputName + ".txt"));
foreach (var result in _output.ResultsDictionary.Values)
{
// save all detector data to the specified folder
DetectorIO.WriteDetectorToFile(result, TEMP_RESULTS_FOLDER);
}
var store = IsolatedStorageFile.GetUserStoreForApplication();
if (store.DirectoryExists(TEMP_RESULTS_FOLDER))
{
var currentAssembly = Assembly.GetExecutingAssembly();
// get all the files we want to zip up
var fileNames = store.GetFileNames(TEMP_RESULTS_FOLDER + @"\*");
// copy the MATLAB files to isolated storage and get their names so they can be included in the zip file
var matlabFiles = FileIO.CopyFolderFromEmbeddedResources("Matlab", TEMP_RESULTS_FOLDER, currentAssembly.FullName, false);
// then, zip all the files together and store *that* .zip to isolated storage as well (can't automatically copy to user folder due to security restrictions)
var allFiles = matlabFiles.Concat(fileNames).Distinct();
try
{
FileIO.ZipFiles(allFiles, TEMP_RESULTS_FOLDER, input.OutputName + ".zip");
}
catch (SecurityException)
{
logger.Error(() => "\rProblem saving results to file.\r");
}
}
logger.Info(() => "done.\r");
});
},
cancelToken,
TaskContinuationOptions.OnlyOnRanToCompletion,
scheduler);
}
public void execute_Monte_Carlo()
{
// instantiate common classes
var simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
DatabaseType.pMCDiffuseReflectance
}, // write database for pMC tests
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
0);
var source = new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
0); // start in air
var tissue = new MultiLayerTissueInput(
new ITissueRegion[]
{
new LayerTissueRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
new LayerTissueRegion(
new DoubleRange(0.0, 10.0), // make tissue layer thin so transmittance results improved
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(10.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
});
var detectorsNA0 = new List <IDetectorInput>
{
new RDiffuseDetectorInput()
{
FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfAngleDetectorInput()
{
Angle = new DoubleRange(Math.PI / 2, Math.PI, 2), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Angle = new DoubleRange(Math.PI / 2, Math.PI, 2), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Time = new DoubleRange(0.0, 1.0, 11), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfRhoAndOmegaDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Omega = new DoubleRange(0.05, 1.0, 20), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfXAndYAndTimeDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), Time = new DoubleRange(0, 1, 11), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfXAndYAndMaxDepthDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), MaxDepth = new DoubleRange(0, 10.0, 11), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfFxDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 51), FinalTissueRegionIndex = 0, NA = 0.0
},
new ROfFxAndTimeDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 51), Time = new DoubleRange(0.0, 1.0, 11), FinalTissueRegionIndex = 0, NA = 0.0
},
new RSpecularDetectorInput()
{
FinalTissueRegionIndex = 0, NA = 0.0
},
new TDiffuseDetectorInput()
{
FinalTissueRegionIndex = 2, NA = 0.0
},
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2), FinalTissueRegionIndex = 2, NA = 0.0
},
new TOfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Angle = new DoubleRange(0.0, Math.PI / 2, 2), FinalTissueRegionIndex = 2, NA = 0.0
},
new TOfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), FinalTissueRegionIndex = 2, NA = 0.0
},
new TOfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), FinalTissueRegionIndex = 2, NA = 0.0
},
new RadianceOfRhoAtZDetectorInput()
{
ZDepth = _dosimetryDepth, Rho = new DoubleRange(0.0, 10.0, 11), FinalTissueRegionIndex = 1, NA = 0.0
},
new ReflectedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 0,
NA = 0.0
},
new ReflectedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 0,
NA = 0.0
},
new TransmittedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 2,
NA = 0.0
},
new TransmittedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 2,
NA = 0.0
},
};
var input = new SimulationInput(
100,
"",
simulationOptions,
source,
tissue,
detectorsNA0);
_outputNA0 = new MonteCarloSimulation(input).Run();
_inputForPMC = input; // set pMC input to one that specified database generation
_pMCDatabase = pMCDatabase.FromFile("DiffuseReflectanceDatabase", "CollisionInfoDatabase"); // grab database
var detectorsNA0p3 = new List <IDetectorInput>
{
new RDiffuseDetectorInput()
{
FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfAngleDetectorInput()
{
Angle = new DoubleRange(Math.PI / 2, Math.PI, 2), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Angle = new DoubleRange(Math.PI / 2, Math.PI, 2), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Time = new DoubleRange(0.0, 1.0, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfRhoAndOmegaDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Omega = new DoubleRange(0.05, 1.0, 20), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfXAndYAndTimeDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), Time = new DoubleRange(0, 1, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfXAndYAndMaxDepthDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), MaxDepth = new DoubleRange(0, 10.0, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfFxDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 51), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfFxAndTimeDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 5), Time = new DoubleRange(0.0, 1.0, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new RSpecularDetectorInput()
{
FinalTissueRegionIndex = 0, NA = 0.3
},
new TDiffuseDetectorInput()
{
FinalTissueRegionIndex = 2, NA = 0.3
},
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2), FinalTissueRegionIndex = 2, NA = 0.3
},
new TOfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Angle = new DoubleRange(0.0, Math.PI / 2, 2), FinalTissueRegionIndex = 2, NA = 0.3
},
new TOfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), FinalTissueRegionIndex = 2, NA = 0.3
},
new TOfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), FinalTissueRegionIndex = 2, NA = 0.3
},
new RadianceOfRhoAtZDetectorInput()
{
ZDepth = _dosimetryDepth, Rho = new DoubleRange(0.0, 10.0, 11), FinalTissueRegionIndex = 1, NA = 0.3
},
new ReflectedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 0,
NA = 0.3
},
new ReflectedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 0,
NA = 0.3
},
new TransmittedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 2,
NA = 0.3
},
new TransmittedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
FinalTissueRegionIndex = 2,
NA = 0.3
},
};
input = new SimulationInput(
100,
"",
simulationOptions,
source,
tissue,
detectorsNA0p3);
_outputNA0p3 = new MonteCarloSimulation(input).Run();
var detectorsNoNASpecified = new List <IDetectorInput>
{
new RDiffuseDetectorInput()
{
},
new ROfAngleDetectorInput()
{
Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11)
},
new ROfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Time = new DoubleRange(0.0, 1.0, 11)
},
new ROfRhoAndOmegaDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Omega = new DoubleRange(0.05, 1.0, 20)
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11)
},
new ROfXAndYAndTimeDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), Time = new DoubleRange(0, 1, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfXAndYAndMaxDepthDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11), MaxDepth = new DoubleRange(0, 10.0, 11), FinalTissueRegionIndex = 0, NA = 0.3
},
new ROfFxDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 51)
},
new ROfFxAndTimeDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 5), Time = new DoubleRange(0.0, 1.0, 11)
},
new RSpecularDetectorInput()
{
},
new TDiffuseDetectorInput()
{
},
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11), Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11)
},
new TOfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11), Y = new DoubleRange(-10.0, 10.0, 11)
},
new RadianceOfRhoAtZDetectorInput()
{
ZDepth = _dosimetryDepth, Rho = new DoubleRange(0.0, 10.0, 11)
},
new ReflectedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
},
new ReflectedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
},
new TransmittedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
},
new TransmittedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
},
};
input = new SimulationInput(
100,
"",
simulationOptions,
source,
tissue,
detectorsNoNASpecified);
_outputNoNASpecified = new MonteCarloSimulation(input).Run();
}
public void execute_Monte_Carlo()
{
// instantiate common classes
_simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>(),
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
0);
_source = new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
1);
_tissue =
new MultiLayerWithSurfaceFiberTissueInput(
new SurfaceFiberTissueRegion(
new Position(0, 0, 0),
_detectorRadius, // needs to match SurfaceFiberDetectorInput
new OpticalProperties(0.01, 1.0, 0.8, 1.4)
),
new ITissueRegion[]
{
new LayerTissueRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
new LayerTissueRegion(
new DoubleRange(0.0, 100.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(100.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
);
// tissue specification to verify MultiLayerWithSurfaceFiberTissue
// increases reflectance. If use this tissue need to change FinalTissueRegion
// for ALL detectors to 0
//new MultiLayerTissueInput(
// new ITissueRegion[]
// {
// new LayerTissueRegion(
// new DoubleRange(double.NegativeInfinity, 0.0),
// new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
// new LayerTissueRegion(
// new DoubleRange(0.0, 100.0),
// new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
// new LayerTissueRegion(
// new DoubleRange(100.0, double.PositiveInfinity),
// new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
// }
//);
_detectorOpen = new List <IDetectorInput>
{
new SurfaceFiberDetectorInput()
{
Center = new Position(0, 0, 0),
Radius = _detectorRadius,
TallySecondMoment = true,
N = 1.4,
NA = 1.4,
FinalTissueRegionIndex = 3
},
new ROfRhoDetectorInput() // 1mm wide ring to match fiber and 2 because beyond goes into 2nd
{
Rho = new DoubleRange(0.0, 2 * _detectorRadius, 3),
// since tissue w fiber specified -> photon will be in 3 upon exit
FinalTissueRegionIndex = 3,
NA = 1.4,
TallySecondMoment = true
},
};
_detectorNA = new List <IDetectorInput>
{
new SurfaceFiberDetectorInput()
{
Center = new Position(0, 0, 0),
Radius = _detectorRadius,
TallySecondMoment = true,
N = 1.4,
FinalTissueRegionIndex = 3,
NA = 0.39
},
new ROfRhoDetectorInput() // ring to match fiber detector
{
Rho = new DoubleRange(0.0, 2 * _detectorRadius, 3),
// since tissue w fiber specified -> photon will be in 3 upon exit
FinalTissueRegionIndex = 3,
NA = 0.39,
TallySecondMoment = true
},
};
_detectorNAOffCenter = new List <IDetectorInput>
{
new SurfaceFiberDetectorInput()
{
Center = new Position(_detectorRadius, 0, 0), // diam = [0, 2*radius]
Radius = _detectorRadius,
TallySecondMoment = true,
N = 1.4,
FinalTissueRegionIndex = 3,
NA = 1.4
},
new ROfRhoDetectorInput() // ring to match fiber detector
{
// place 1st rho bin center at _detectorRadius with width = 2*radius
Rho = new DoubleRange(_detectorRadius / 2, 2 * _detectorRadius + _detectorRadius / 2, 3),
// since tissue w fiber specified -> photon will be in 3 upon exit
FinalTissueRegionIndex = 3,
NA = 1.4,
TallySecondMoment = true
},
};
var _inputOpen = new SimulationInput(
100,
"",
_simulationOptions,
_source,
_tissue,
_detectorOpen);
_outputOpen = new MonteCarloSimulation(_inputOpen).Run();
var _inputNA = new SimulationInput(
100,
"",
_simulationOptions,
_source,
_tissue,
_detectorNA);
_outputNA = new MonteCarloSimulation(_inputNA).Run();
var _inputNAOffCenter = new SimulationInput(
100,
"",
_simulationOptions,
_source,
_tissue,
_detectorNAOffCenter);
_outputNAOffCenter = new MonteCarloSimulation(_inputNAOffCenter).Run();
}
public void execute_Monte_Carlo()
{
// instantiate common classes
var simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
},
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
0);
var source = new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
0); // start in air
var tissue = new MultiLayerTissueInput(
new ITissueRegion[]
{
new LayerTissueRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
new LayerTissueRegion(
new DoubleRange(0.0, 10.0), // make tissue layer thin so transmittance results improved
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(10.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
});
var detectorsNA0 = new List <IDetectorInput>
{
new ReflectedDynamicMTOfFxAndSubregionHistDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 0,
NA = 0.0
},
new TransmittedDynamicMTOfFxAndSubregionHistDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 2,
NA = 0.0
},
new ReflectedDynamicMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 0,
NA = 0.0
},
new TransmittedDynamicMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 2,
NA = 0.0
},
new ReflectedDynamicMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 21),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 0,
NA = 0.0
},
new TransmittedDynamicMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 2,
NA = 0.0
}
};
var input = new SimulationInput(
100,
"",
simulationOptions,
source,
tissue,
detectorsNA0);
_outputNA0 = new MonteCarloSimulation(input).Run();
var detectorsNA0p3 = new List <IDetectorInput>
{
new ReflectedDynamicMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 0,
NA = 0.3
},
new TransmittedDynamicMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 2,
NA = 0.3
},
new ReflectedDynamicMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 21),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 0,
NA = 0.3
},
new TransmittedDynamicMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
FinalTissueRegionIndex = 2,
NA = 0.3
}
};
input = new SimulationInput(
100,
"",
simulationOptions,
source,
tissue,
detectorsNA0p3);
_outputNA0p3 = new MonteCarloSimulation(input).Run();
var detectorsNoNASpecified = new List <IDetectorInput>
{
new ReflectedDynamicMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
},
new TransmittedDynamicMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
},
new ReflectedDynamicMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 21),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
},
new TransmittedDynamicMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
MTBins = new DoubleRange(0.0, 500.0, 5),
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
BloodVolumeFraction = new List <double>()
{
0, 0.5, 0
},
}
};
input = new SimulationInput(
100,
"",
simulationOptions,
source,
tissue,
detectorsNoNASpecified);
_outputNoNASpecified = new MonteCarloSimulation(input).Run();
}
public void Forecast()
{
var rndNumbers = new Queue <int>(new[] { 0, 2, 1, 1, 5, 3, 3, 4, 2 });
var montecarlo = new MonteCarloSimulation(_ => rndNumbers.Dequeue());
var sut = new Forecasting(montecarlo, 3, 2);
//TODO: integrationstest forecasting
var historicalData = new[] {
new History.Datapoint {
Value = 1f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 2f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 2f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 3f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 3f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 4f, Tags = new[] { "a" }
},
new History.Datapoint {
Value = 10f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 10f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 20f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 20f, Tags = new[] { "b" }
},
new History.Datapoint {
Value = 30f, Tags = new[] { "b" }
}
};
var features = new[] {
new Feature {
Quantity = 2, Tags = new[] { "a" }
},
new Feature {
Quantity = 1, Tags = new[] { "b" }
},
};
/*
* 3 Simulationen, 2 Intervalle
* 2a(0..5), 1b(0..4)
*
* a: 0=1 1=2 3=3
* a: 2=2 5=4 4=3
* b: 1=10 3=20 2=20
* 13 26 26
*
* 26-13=13
* 13/2=6,5
* 13..19,5, 19,5..26
* (13,1,0.33), (26,2,1.0)
*/
var result = sut.Calculate(historicalData, features);
foreach (var f in result.Features)
{
Debug.WriteLine($"{string.Join(",", f)}");
}
foreach (var po in result.Distribution)
{
Debug.WriteLine($"{po.Prognosis}, {po.CummulatedProbability}");
}
Assert.AreEqual(new[] { "a", "a", "b" }, result.Features);
Assert.AreEqual(2, result.Distribution.Length);
Assert.AreEqual(3, result.Distribution.Select(g => g.Count).Sum());
Assert.AreEqual(13f, result.Distribution[0].Prognosis);
Assert.AreEqual(1, result.Distribution[0].Count);
Assert.AreEqual(0.33f, result.Distribution[0].CummulatedProbability, 0.01f);
Assert.AreEqual(26f, result.Distribution[1].Prognosis);
Assert.AreEqual(2, result.Distribution[1].Count);
Assert.AreEqual(1f, result.Distribution[1].CummulatedProbability, 0.01f);
}
static void Main(string[] args)
{
try {
var input = new SimulationInput(
1000000, // FIX 1e6 takes about 110 minutes my laptop
"", // if non-empty string here, need to create sub-folder
new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Continuous,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
}, // databases to be written
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
1),
new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
0),
new MultiLayerTissueInput(
new LayerTissueRegion[]
{
new LayerTissueRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 0.0, 1.0)
),
//new LayerTissueRegion(
// new DoubleRange(0.0, 0.1),
// new OpticalProperties(0.033, 1.0, 0.8, 1.38)
// ),
new LayerTissueRegion(
new DoubleRange(0.0, 100.0),
new OpticalProperties(0.0, 1.0, 0.8, 1.38)
),
new LayerTissueRegion(
new DoubleRange(100.0, double.PositiveInfinity),
new OpticalProperties(0, 1e-10, 0.0, 1.0)
)
}
),
new List <IDetectorInput>()
{
new ROfRhoAndTimeDetectorInput {
Rho = new DoubleRange(0.0, 40, 201), // numbers for scaled MC
Time = new DoubleRange(0.0, 4, 801)
} // numbers for scaled MC
}
);
SimulationOutput output = new MonteCarloSimulation(input).Run();
input.ToFile("infile.txt");
// the following gets are R(rho,time) for scaled.
//var rOfRhoAndTime = output.ResultsDictionary[TallyType.ROfRhoAndTime.ToString()];
//string folderPath = "results";
//if (!Directory.Exists(folderPath))
// Directory.CreateDirectory(folderPath);
//DetectorIO.WriteDetectorToFile(rOfRhoAndTime, folderPath);
}
catch (Exception e)
{
Console.WriteLine("Failed to run: Reason: " + e.Message);
throw;
//return false;
}
}
