void Start()
{
if (!Optics.RefractiveObjectIsValid(this))
{
throw new UnityException("RefractiveObject \"" + gameObject.name + "\" is invalid!");
}
}
private void CreateSimilarityMatrix()
{
if (!_projectService.AreAllVarietiesCompared)
{
return;
}
var optics = new Optics <Variety>(variety => variety.VarietyPairs.Select(pair =>
{
double score = 0;
switch (_similarityMetric)
{
case SimilarityMetric.Lexical:
score = pair.LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
score = pair.PhoneticSimilarityScore;
break;
}
return(Tuple.Create(pair.GetOtherVariety(variety), 1.0 - score));
}).Concat(Tuple.Create(variety, 0.0)), 2);
_modelVarieties.Clear();
_modelVarieties.AddRange(optics.ClusterOrder(_projectService.Project.Varieties).Select(oe => oe.DataObject));
SimilarityMatrixVarietyViewModel[] vms = _modelVarieties.Select(v => new SimilarityMatrixVarietyViewModel(_similarityMetric, _modelVarieties, v)).ToArray();
Varieties = new ReadOnlyList <SimilarityMatrixVarietyViewModel>(vms);
IsEmpty = false;
}
private void WriteOutput(TextWriter outputWriter)
{
var optics = new Optics <Variety>(variety => variety.VarietyPairs
.Select(pair => Tuple.Create(pair.GetOtherVariety(variety), 1.0 - pair.LexicalSimilarityScore))
.Concat(Tuple.Create(variety, 0.0)), 2);
Variety[] varieties = optics.ClusterOrder(Project.Varieties).Select(e => e.DataObject).ToArray();
foreach (Variety variety in varieties)
{
outputWriter.Write("\t");
outputWriter.Write(variety.Name);
}
outputWriter.WriteLine();
for (int i = 0; i < varieties.Length; i++)
{
outputWriter.Write(varieties[i].Name);
int len = IsHalf ? i + 1 : varieties.Length;
for (int j = 0; j < len; j++)
{
outputWriter.Write("\t");
if (i == j)
{
outputWriter.Write(IsDistance ? "0.00" : "1.00");
}
else
{
VarietyPair varietyPair = varieties[i].VarietyPairs[varieties[j]];
double score = IsDistance ? 1.0 - varietyPair.LexicalSimilarityScore : varietyPair.LexicalSimilarityScore;
outputWriter.Write("{0:0.00}", score);
}
}
outputWriter.WriteLine();
}
}
/// <summary>
/// Define SimulationInput to describe homogeneous and two layer tissue
/// </summary>
/// <returns></returns>
private void GenerateReferenceDatabase()
{
var simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
DatabaseType.pMCDiffuseReflectance
},
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
0);
var sourceInput = new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
1);
var detectorInputs = new List <IDetectorInput>()
{
new ROfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101)
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Time = new DoubleRange(0.0, 1.0, 101)
}
};
_referenceInputTwoLayerTissue = new SimulationInput(
100,
"",
simulationOptions,
sourceInput,
new MultiLayerTissueInput(
new ITissueRegion[]
{
new LayerRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
new LayerRegion(
new DoubleRange(0.0, _layerThickness),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerRegion(
new DoubleRange(_layerThickness, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectorInputs);
_factor = 1.0 - Optics.Specular(
_referenceInputTwoLayerTissue.TissueInput.Regions[0].RegionOP.N,
_referenceInputTwoLayerTissue.TissueInput.Regions[1].RegionOP.N);
_referenceOutputTwoLayerTissue = new MonteCarloSimulation(_referenceInputTwoLayerTissue).Run();
_databaseTwoLayerTissue = pMCDatabase.FromFile("DiffuseReflectanceDatabase", "CollisionInfoDatabase");
}
public void UpdateTest()
{
Dictionary <long, float?> reachabilityDistances = new Dictionary <long, float?>();
Dictionary <long, float?> coreDistancesCache = new Dictionary <long, float?>();
HashSet <long> processed = new HashSet <long>();
var points3D = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 2, 55
}),
new PointIdFloat(new List <float>()
{
0, 1, 0.1f
}),
new PointIdFloat(new List <float>()
{
3, 3, 0.01f
}),
new PointIdFloat(new List <float>()
{
1.5f, 2, -0.03f
}),
new PointIdFloat(new List <float>()
{
5, -1, 44
}),
new PointIdFloat(new List <float>()
{
15, -51, 73
}),
new PointIdFloat(new List <float>()
{
0.5f, -21, 144
})
};
PointIdFloat.SetIds(points3D);
var p = points3D[2];
var epsilon = 10;
var minPoints = 3;
Optics algo = new Optics(points3D);
var seeds = new PriorityQueue <float, IPointIdFloat>();
processed.Add(p.id);
var coreDistance = algo.CoreDistance(algo.kdt, coreDistancesCache, p, epsilon, minPoints);
algo.Update(ref seeds, processed, reachabilityDistances, coreDistancesCache, p, epsilon, minPoints);
Assert.AreEqual(2, seeds.Count);
KeyValuePair <float, IPointIdFloat> top;
seeds.TryPeek(out top);
Assert.AreEqual(top.Key, coreDistance);
Assert.AreEqual(points3D[3], top.Value);
}
public void ClusterTest()
{
var points3D = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 2, 55
}),
new PointIdFloat(new List <float>()
{
0, 1, 0.1f
}),
new PointIdFloat(new List <float>()
{
3, 3, 0.01f
}),
new PointIdFloat(new List <float>()
{
1.5f, 2, -0.03f
}),
new PointIdFloat(new List <float>()
{
5, -1, 44
}),
new PointIdFloat(new List <float>()
{
15, -51, 73
}),
new PointIdFloat(new List <float>()
{
0.5f, -21, 144
})
};
PointIdFloat.SetIds(points3D);
var ids = points3D.Select(p => p.id).ToList();
ids.Sort();
var epsilon = 100;
var epsilonPrime = 10;
var minPoints = 3;
Optics algo = new Optics(points3D);
var seeds = new PriorityQueue <float, IPointIdFloat>();
OpticsOrdering oo = algo.Ordering(epsilon, minPoints);
var results = oo.Cluster(epsilonPrime);
Assert.AreEqual(points3D.Count, results.Count);
var keys = results.Keys.ToList();
keys.Sort();
Assert.IsTrue(keys.SequenceEqual(ids));
}
public void execute_Monte_Carlo()
{
// delete previously generated files
clear_folders_and_files();
var input = new SimulationInput(
100,
"Output",
new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Analog,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
}, // databases to be written
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
0),
new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
0 // start in air
),
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, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
new List <IDetectorInput>
{
new RSpecularDetectorInput(),
}
);
_specularReflectance = Optics.Specular(input.TissueInput.Regions[0].RegionOP.N,
input.TissueInput.Regions[1].RegionOP.N);
_output = new MonteCarloSimulation(input).Run();
}
void Update()
{
if (Input.GetKeyDown(KeyCode.Space))
{
Debug.Log("bang"); //TODO shoot (ABSTRACTION!!!!)
}
float rayLength = 5f;
Optics.LightcastHit lightcastHit;
Vector3[] points;
Optics.Lightcast(muzzle.position, muzzle.forward, out lightcastHit, out points, rayLength);
for (int i = 1; i < points.Length; i++)
{
Debug.DrawLine(points[i - 1], points[i], Color.magenta, 0f, true);
}
}
public void Export(Stream stream, CogProject project, SimilarityMetric similarityMetric)
{
var optics = new Optics <Variety>(variety => variety.VarietyPairs.Select(pair =>
{
double score = 0;
switch (similarityMetric)
{
case SimilarityMetric.Lexical:
score = pair.LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
score = pair.PhoneticSimilarityScore;
break;
}
return(Tuple.Create(pair.GetOtherVariety(variety), 1.0 - score));
}).Concat(Tuple.Create(variety, 0.0)), 2);
Variety[] varietyArray = optics.ClusterOrder(project.Varieties).Select(oe => oe.DataObject).ToArray();
using (var writer = new StreamWriter(new NonClosingStreamWrapper(stream)))
{
foreach (Variety variety in varietyArray)
{
writer.Write("\t");
writer.Write(variety.Name);
}
writer.WriteLine();
for (int i = 0; i < varietyArray.Length; i++)
{
writer.Write(varietyArray[i].Name);
for (int j = 0; j < varietyArray.Length; j++)
{
writer.Write("\t");
if (i != j)
{
VarietyPair varietyPair = varietyArray[i].VarietyPairs[varietyArray[j]];
double score = similarityMetric == SimilarityMetric.Lexical ? varietyPair.LexicalSimilarityScore : varietyPair.PhoneticSimilarityScore;
writer.Write("{0:0.00}", score);
}
}
writer.WriteLine();
}
}
}
public void Export(Stream stream, CogProject project, SimilarityMetric similarityMetric)
{
var optics = new Optics<Variety>(variety => variety.VarietyPairs.Select(pair =>
{
double score = 0;
switch (similarityMetric)
{
case SimilarityMetric.Lexical:
score = pair.LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
score = pair.PhoneticSimilarityScore;
break;
}
return Tuple.Create(pair.GetOtherVariety(variety), 1.0 - score);
}).Concat(Tuple.Create(variety, 0.0)), 2);
Variety[] varietyArray = optics.ClusterOrder(project.Varieties).Select(oe => oe.DataObject).ToArray();
using (var writer = new StreamWriter(new NonClosingStreamWrapper(stream)))
{
foreach (Variety variety in varietyArray)
{
writer.Write("\t");
writer.Write(variety.Name);
}
writer.WriteLine();
for (int i = 0; i < varietyArray.Length; i++)
{
writer.Write(varietyArray[i].Name);
for (int j = 0; j < varietyArray.Length; j++)
{
writer.Write("\t");
if (i != j)
{
VarietyPair varietyPair = varietyArray[i].VarietyPairs[varietyArray[j]];
double score = similarityMetric == SimilarityMetric.Lexical ? varietyPair.LexicalSimilarityScore : varietyPair.PhoneticSimilarityScore;
writer.Write("{0:0.00}", score);
}
}
writer.WriteLine();
}
}
}
public void execute_Monte_Carlo()
{
// delete any previously generated files
clear_folders_and_files();
_input = new SimulationInput(
100,
"Output",
new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Analog,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
}, // databases to be written
true, // track statistics
0.0, // RR threshold -> 0 = no RR performed
0),
new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
1 // start off inside 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, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
new List <IDetectorInput>
{
new RDiffuseDetectorInput()
{
TallySecondMoment = true
},
new ROfAngleDetectorInput()
{
Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), TallySecondMoment = true
},
new ROfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Time = new DoubleRange(0.0, 1.0, 101)
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101), Y = new DoubleRange(-10.0, 10.0, 101)
},
new ROfRhoAndOmegaDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Omega = new DoubleRange(0.05, 1.0, 20)
}, // frequency sampling points (not bins)
new TDiffuseDetectorInput(),
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101)
},
new TOfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new AOfRhoAndZDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Z = new DoubleRange(0.0, 10.0, 101)
},
new ATotalDetectorInput(),
new FluenceOfRhoAndZDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Z = new DoubleRange(0.0, 10.0, 101)
},
new FluenceOfRhoAndZAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101),
Z = new DoubleRange(0.0, 10.0, 101),
Time = new DoubleRange(0.0, 1.0, 11)
},
new RadianceOfRhoAndZAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101),
Z = new DoubleRange(0.0, 10.0, 101),
Angle = new DoubleRange(-Math.PI / 2, Math.PI / 2, 5)
}
}
);
_output = new MonteCarloSimulation(_input).Run();
_simulationStatistics = SimulationStatistics.FromFile(_input.OutputName + "/statistics.txt");
_factor = 1.0 - Optics.Specular(
_input.TissueInput.Regions[0].RegionOP.N,
_input.TissueInput.Regions[1].RegionOP.N);
}
public void execute_Monte_Carlo()
{
// delete previously generated files
clear_folders_and_files();
var simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Continuous,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
DatabaseType.pMCDiffuseReflectance
},
false, // track statistics
0.0, // RR threshold -> 0 = no RR performed
0);
var sourceInput = new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
1);
var detectorInputs = new List <IDetectorInput>()
{
new ROfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101)
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Time = new DoubleRange(0.0, 1.0, 101)
},
new ROfFxDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 11)
},
new ROfFxAndTimeDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 11), Time = new DoubleRange(0.0, 1.0, 101)
}
};
_referenceInputOneLayerTissue = new SimulationInput(
100,
"", // can't create folder in isolated storage
simulationOptions,
sourceInput,
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, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectorInputs);
_factor = 1.0 - Optics.Specular(
_referenceInputOneLayerTissue.TissueInput.Regions[0].RegionOP.N,
_referenceInputOneLayerTissue.TissueInput.Regions[1].RegionOP.N);
_referenceOutputOneLayerTissue = new MonteCarloSimulation(_referenceInputOneLayerTissue).Run();
_databaseOneLayerTissue = pMCDatabase.FromFile("DiffuseReflectanceDatabase", "CollisionInfoDatabase");
}
public void execute_Monte_Carlo()
{
// delete previously generated files
clear_folders_and_files();
// instantiate common classes
var simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
}, // databases to be written
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),
1); // start inside tissue
var detectors = 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, 101), TallySecondMoment = true
},
new ROfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Time = new DoubleRange(0.0, 1.0, 101), TallySecondMoment = true
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101), Y = new DoubleRange(-10.0, 10.0, 101)
},
new ROfRhoAndOmegaDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Omega = new DoubleRange(0.05, 1.0, 20)
}, // DJC - edited to reflect frequency sampling points (not bins)
new ROfFxDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 51)
},
new TDiffuseDetectorInput(),
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101)
},
new TOfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101), Y = new DoubleRange(-10.0, 10.0, 101)
},
new TOfFxDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 51)
},
new AOfRhoAndZDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Z = new DoubleRange(0.0, 10.0, 101)
},
new AOfXAndYAndZDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101),
Y = new DoubleRange(-10.0, 10.0, 101),
Z = new DoubleRange(0.0, 10.0, 101),
TallySecondMoment = true
},
new ATotalDetectorInput()
{
TallySecondMoment = true
},
new FluenceOfRhoAndZDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Z = new DoubleRange(0.0, 10.0, 101)
},
new FluenceOfXAndYAndZDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
TallySecondMoment = true
},
new FluenceOfXAndYAndZAndOmegaDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
Omega = new DoubleRange(0.05, 1.0, 20)
},
new RadianceOfRhoAtZDetectorInput()
{
ZDepth = _dosimetryDepth, Rho = new DoubleRange(0.0, 10.0, 101)
},
new RadianceOfRhoAndZAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Z = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(0, Math.PI, 5)
},
new RadianceOfFxAndZAndAngleDetectorInput()
{
Fx = new DoubleRange(0.0, 0.5, 51), Z = new DoubleRange(0.0, 10, 101), Angle = new DoubleRange(0, Math.PI, 5)
},
new RadianceOfXAndYAndZAndThetaAndPhiDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 11),
Y = new DoubleRange(-10.0, 10.0, 11),
Z = new DoubleRange(0.0, 10.0, 11),
Theta = new DoubleRange(0.0, Math.PI, 5), // theta (polar angle)
Phi = new DoubleRange(-Math.PI, Math.PI, 5), // phi (azimuthal angle)
},
new ReflectedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), // rho bins MAKE SURE AGREES with ROfRho rho specification for unit test below
MTBins = new DoubleRange(0.0, 500.0, 51), // MT bins
FractionalMTBins = new DoubleRange(0.0, 1.0, 11)
},
new TransmittedMTOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), // rho bins MAKE SURE AGREES with TOfRho rho specification for unit test below
MTBins = new DoubleRange(0.0, 500.0, 51), // MT bins
FractionalMTBins = new DoubleRange(0.0, 1.0, 11)
},
new ReflectedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101),
Y = new DoubleRange(-10.0, 10.0, 101),
MTBins = new DoubleRange(0.0, 500.0, 51), // MT bins
FractionalMTBins = new DoubleRange(0.0, 1.0, 11),
TallySecondMoment = true
},
new TransmittedMTOfXAndYAndSubregionHistDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101),
Y = new DoubleRange(-10.0, 10.0, 101),
MTBins = new DoubleRange(0.0, 500.0, 51), // MT bins
FractionalMTBins = new DoubleRange(0.0, 1.0, 11)
}
};
_inputOneLayerTissue = new SimulationInput(
100,
"",
simulationOptions,
source,
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, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputOneLayerTissue = new MonteCarloSimulation(_inputOneLayerTissue).Run();
_inputTwoLayerTissue = new SimulationInput(
100,
"",
simulationOptions,
source,
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, _layerThickness),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(_layerThickness, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputTwoLayerTissue = new MonteCarloSimulation(_inputTwoLayerTissue).Run();
_factor = 1.0 - Optics.Specular(
_inputOneLayerTissue.TissueInput.Regions[0].RegionOP.N,
_inputOneLayerTissue.TissueInput.Regions[1].RegionOP.N);
}
/// <summary>
/// Method that determines whether photon reflects or refracts across interface. When this
/// method is called photon is sitting on boundary of region and CurrentRegionIndex is Index
/// of region photon had been in.
/// </summary>
public void CrossRegionOrReflect()
{
double cosTheta = _tissue.GetAngleRelativeToBoundaryNormal(this);
double nCurrent = _tissue.Regions[CurrentRegionIndex].RegionOP.N;
int neighborIndex = _tissue.GetNeighborRegionIndex(this);
double nNext = _tissue.Regions[neighborIndex].RegionOP.N;
double coscrit;
if (nCurrent > nNext)
{
coscrit = Math.Sqrt(1.0 - (nNext / nCurrent) * (nNext / nCurrent));
}
else
{
coscrit = 0.0;
}
double probOfReflecting;
double cosThetaSnell;
// call Fresnel be default to have uZSnell set, used to be within else
probOfReflecting = Optics.Fresnel(nCurrent, nNext, cosTheta, out cosThetaSnell);
if (cosTheta <= coscrit)
{
probOfReflecting = 1.0;
}
//else
// probOfReflecting = Optics.Fresnel(nCurrent, nNext, cosTheta, out cosThetaSnell);
/* Decide whether or not photon goes to next region */
// perform first check so that rng not called on pseudo-collisions
if ((probOfReflecting == 0.0) || (_rng.NextDouble() > probOfReflecting)) // transmitted
{
// if at border of system
if (_tissue.OnDomainBoundary(this.DP.Position) && !_firstTimeEnteringDomain)
{
DP.StateFlag = DP.StateFlag.Add(_tissue.GetPhotonDataPointStateOnExit(DP.Position));
// adjust CAW weight for portion of track prior to exit
if (Absorb == AbsorbContinuous)
{
AbsorbContinuous();
}
CurrentRegionIndex = neighborIndex;
//don't need to update these unless photon not dead upon exiting tissue
//DP.Direction.Ux *= nCurrent / nNext;
//DP.Direction.Uy *= nCurrent / nNext;
//DP.Direction.Uz = uZSnell;
}
else // not on domain boundary, at internal interface or first time enter tissue, pass to next
{
CurrentRegionIndex = neighborIndex;
DP.Direction = _tissue.GetRefractedDirection(DP.Position, DP.Direction,
nCurrent, nNext, cosThetaSnell);
if (_firstTimeEnteringDomain)
{
_firstTimeEnteringDomain = false;
}
}
}
else // don't cross, reflect
{
DP.Direction = _tissue.GetReflectedDirection(DP.Position, DP.Direction);
// check if specular reflection
if (_firstTimeEnteringDomain)
{
DP.StateFlag = DP.StateFlag.Add(PhotonStateType.PseudoSpecularTissueBoundary);
}
}
}
public void execute_Monte_Carlo()
{
// delete previously generated files
clear_folders_and_files();
// instantiate common classes
var simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
}, // databases to be written
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),
1); // start inside tissue
var detectors =
new List <IDetectorInput>
{
new RDiffuseDetectorInput(),
new ROfAngleDetectorInput()
{
Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-200.0, 200.0, 401), Y = new DoubleRange(-200.0, 200.0, 401)
},
new TDiffuseDetectorInput(),
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new ATotalDetectorInput(),
};
_inputOneRegionTissue = new SimulationInput(
100,
"",
simulationOptions,
source,
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, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputOneRegionTissue = new MonteCarloSimulation(_inputOneRegionTissue).Run();
_inputTwoRegionTissue = new SimulationInput(
100,
"",
simulationOptions,
source,
new SingleVoxelTissueInput(
new VoxelTissueRegion(
new DoubleRange(-5, 5),
new DoubleRange(-5, 5),
new DoubleRange(1e-9, 5), // smallest Z.Start with tests passing is 1e-9
new OpticalProperties(0.01, 1.0, 0.8, 1.4) //debug with g=1
),
new LayerTissueRegion[]
{
new LayerTissueRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
new LayerTissueRegion(
new DoubleRange(0.0, 20.0), // debug with thin slab d=2
new OpticalProperties(0.01, 1.0, 0.8, 1.4)), // debug with g=1
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputTwoRegionTissue = new MonteCarloSimulation(_inputTwoRegionTissue).Run();
_factor = 1.0 - Optics.Specular(
_inputOneRegionTissue.TissueInput.Regions[0].RegionOP.N,
_inputOneRegionTissue.TissueInput.Regions[1].RegionOP.N);
}
public void execute_Monte_Carlo()
{
// delete any previously generated files
clear_folders_and_files();
// instantiate common classes
var simulationOptions = new SimulationOptions(
0, // rng seed = same as linux (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
0);
var source = new DirectionalPointSourceInput(
new Position(0.0, 0.0, 0.0),
new Direction(0.0, 0.0, 1.0),
1); // start inside tissue
var detectors =
new List <IDetectorInput>
{
new ATotalDetectorInput(),
new RDiffuseDetectorInput()
{
TallySecondMoment = true
},
new ROfAngleDetectorInput()
{
Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), TallySecondMoment = true
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Time = new DoubleRange(0.0, 1.0, 101)
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101), Y = new DoubleRange(-10.0, 10.0, 101)
},
new ROfRhoAndOmegaDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Omega = new DoubleRange(0.05, 1.0, 20)
}, // DJC - edited to reflect frequency sampling points (not bins)
new TDiffuseDetectorInput(),
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101)
},
new TOfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfXAndYDetectorInput()
{
X = new DoubleRange(-10.0, 10.0, 101), Y = new DoubleRange(-10.0, 10.0, 101)
},
new ReflectedTimeOfRhoAndSubregionHistDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Time = new DoubleRange(0.0, 1.0, 101)
},
};
// one tissue layer
var inputOneLayerTissue = new SimulationInput(
100,
"",
simulationOptions,
source,
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, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputOneLayerTissue = new MonteCarloSimulation(inputOneLayerTissue).Run();
// two tissue layers with same optical properties
var inputTwoLayerTissue = new SimulationInput(
100,
"Output",
simulationOptions,
source,
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, _layerThickness),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(_layerThickness, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerTissueRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputTwoLayerTissue = new MonteCarloSimulation(inputTwoLayerTissue).Run();
_factor = 1.0 - Optics.Specular(
inputOneLayerTissue.TissueInput.Regions[0].RegionOP.N,
inputOneLayerTissue.TissueInput.Regions[1].RegionOP.N);
}
public void CoreDistanceTest()
{
var points3D = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 2, 55
}),
new PointIdFloat(new List <float>()
{
0, 1, 0.1f
}),
new PointIdFloat(new List <float>()
{
3, 3, 0.01f
}),
new PointIdFloat(new List <float>()
{
1.5f, 2, -0.03f
}),
new PointIdFloat(new List <float>()
{
5, -1, 44
}),
new PointIdFloat(new List <float>()
{
15, -51, 73
}),
new PointIdFloat(new List <float>()
{
0.5f, -21, 144
})
};
PointIdFloat.SetIds(points3D);
var p = new PointIdFloat(new List <float>()
{
1, 2, 3
});
Optics algo = new Optics(points3D);
Dictionary <long, float?> coreDist = new Dictionary <long, float?>();
// - Tests with no cache -
//Not enough points in the tree
Assert.IsNull(algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), p, 1000, 40));
Assert.IsNull(algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), p, 1000, points3D.Count + 1));
//Not enough points in the epsilon-neighbourhood
Assert.IsNull(algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), p, 1, 2));
Assert.IsNull(algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), p, 10, 6));
var result = algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), p, 1000, points3D.Count);
Assert.AreEqual(p.DistanceTo(points3D[6]), result);
points3D.ForEach(point =>
{
result = algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), point, 0.00001f, 1);
Assert.AreEqual(0, result);
});
Assert.IsNull(algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), p, 1, 1));
Assert.AreEqual(p.DistanceTo(points3D[3]), algo.CoreDistance(algo.kdt, new Dictionary <long, float?>(), p, 5, 1));
//Tests - using cache -
Assert.AreEqual(0, coreDist.Count);
float?tmp;
Assert.IsNull(algo.CoreDistance(algo.kdt, coreDist, p, 1000, 40));
Assert.IsTrue(coreDist.ContainsKey(p.id));
coreDist.TryGetValue(p.id, out tmp);
Assert.IsNull(tmp);
coreDist.Clear();
result = algo.CoreDistance(algo.kdt, coreDist, p, 1000, points3D.Count);
Assert.AreEqual(p.DistanceTo(points3D[6]), result);
Assert.IsTrue(coreDist.ContainsKey(p.id));
coreDist.TryGetValue(p.id, out tmp);
Assert.AreEqual(result, tmp);
}
public void execute_Monte_Carlo()
{
// instantiate common classes
var simulationOptions = new SimulationOptions(
0,
RandomNumberGeneratorType.MersenneTwister,
AbsorptionWeightingType.Discrete,
PhaseFunctionType.HenyeyGreenstein,
new List <DatabaseType>()
{
}, // databases to be written
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),
1); // start inside tissue
var detectors =
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, 101), TallySecondMoment = true
},
new ROfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(Math.PI / 2, Math.PI, 2)
},
new ROfRhoAndTimeDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Time = new DoubleRange(0.0, 1.0, 101)
},
new ROfXAndYDetectorInput()
{
X = new DoubleRange(-200.0, 200.0, 401), Y = new DoubleRange(-200.0, 200.0, 401)
},
new ROfRhoAndOmegaDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Omega = new DoubleRange(0.0, 1.0, 21)
},
new TDiffuseDetectorInput(),
new TOfAngleDetectorInput()
{
Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new TOfRhoDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101)
},
new TOfRhoAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Angle = new DoubleRange(0.0, Math.PI / 2, 2)
},
new AOfRhoAndZDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Z = new DoubleRange(0.0, 10.0, 101)
},
new ATotalDetectorInput(),
new FluenceOfRhoAndZDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101), Z = new DoubleRange(0.0, 10.0, 101)
},
new RadianceOfRhoAndZAndAngleDetectorInput()
{
Rho = new DoubleRange(0.0, 10.0, 101),
Z = new DoubleRange(0.0, 10.0, 101),
Angle = new DoubleRange(-Math.PI / 2, Math.PI / 2, 5)
}
};
_inputOneRegionTissue = new SimulationInput(
100,
"",
simulationOptions,
source,
new MultiLayerTissueInput(
new ITissueRegion[]
{
new LayerRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
new LayerRegion(
new DoubleRange(0.0, 20.0),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)),
new LayerRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputOneRegionTissue = new MonteCarloSimulation(_inputOneRegionTissue).Run();
_inputTwoRegionTissue = new SimulationInput(
100,
"",
simulationOptions,
source,
new SingleEllipsoidTissueInput(
new EllipsoidRegion(
new Position(0, 0, 1),
0.5,
0.5,
0.5,
new OpticalProperties(0.01, 1.0, 0.8, 1.4) //debug with g=1
),
new LayerRegion[]
{
new LayerRegion(
new DoubleRange(double.NegativeInfinity, 0.0),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0)),
new LayerRegion(
new DoubleRange(0.0, 20.0), // debug with thin slab d=2
new OpticalProperties(0.01, 1.0, 0.8, 1.4)), // debug with g=1
new LayerRegion(
new DoubleRange(20.0, double.PositiveInfinity),
new OpticalProperties(0.0, 1e-10, 1.0, 1.0))
}
),
detectors);
_outputTwoRegionTissue = new MonteCarloSimulation(_inputTwoRegionTissue).Run();
_factor = 1.0 - Optics.Specular(
_inputOneRegionTissue.TissueInput.Regions[0].RegionOP.N,
_inputOneRegionTissue.TissueInput.Regions[1].RegionOP.N);
}
public void OrderingTest()
{
IReadOnlyDictionary <long, long> orderingMapping;
IReadOnlyCollection <IPointIdFloat> ordering;
var points3D = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 2, 55
}),
new PointIdFloat(new List <float>()
{
0, 1, 0.1f
}),
new PointIdFloat(new List <float>()
{
3, 3, 0.01f
}),
new PointIdFloat(new List <float>()
{
1.5f, 2, -0.03f
}),
new PointIdFloat(new List <float>()
{
5, -1, 44
}),
new PointIdFloat(new List <float>()
{
15, -51, 73
}),
new PointIdFloat(new List <float>()
{
0.5f, -21, 144
})
};
PointIdFloat.SetIds(points3D);
var epsilon = 10;
var minPoints = 3;
Optics algo = new Optics(points3D);
var seeds = new PriorityQueue <float, IPointIdFloat>();
OpticsOrdering oo = algo.Ordering(epsilon, minPoints);
ordering = oo.ordering;
orderingMapping = oo.orderingMapping;
Assert.AreEqual(points3D.Count, ordering.Count);
Assert.IsTrue(SequenceEquivalent(points3D, ordering.ToList(), PointIdFloat.PointsComparison));
Assert.AreEqual(points3D.Count, orderingMapping.Count);
for (long i = 0; i < orderingMapping.Count; i++)
{
Assert.IsTrue(orderingMapping.Values.Contains(i));
}
foreach (var p in points3D)
{
Assert.IsTrue(orderingMapping.ContainsKey(p.id));
}
Assert.IsTrue(oo.reachabilityDistances.Count > 0);
foreach (var p in points3D)
{
Assert.IsTrue(oo.coreDistancesCache.ContainsKey(p.id));
}
}
private void CreateSimilarityMatrix()
{
var optics = new Optics<Variety>(variety => variety.VarietyPairs.Select(pair =>
{
double score = 0;
switch (_similarityMetric)
{
case SimilarityMetric.Lexical:
score = pair.LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
score = pair.PhoneticSimilarityScore;
break;
}
return Tuple.Create(pair.GetOtherVariety(variety), 1.0 - score);
}).Concat(Tuple.Create(variety, 0.0)), 2);
_modelVarieties.Clear();
_modelVarieties.AddRange(optics.ClusterOrder(_projectService.Project.Varieties).Select(oe => oe.DataObject));
SimilarityMatrixVarietyViewModel[] vms = _modelVarieties.Select(v => new SimilarityMatrixVarietyViewModel(_similarityMetric, _modelVarieties, v)).ToArray();
Varieties = new ReadOnlyList<SimilarityMatrixVarietyViewModel>(vms);
IsEmpty = false;
}
