public void validate_SolveInverse_can_be_called_using_IForwardSolver_IOptimizer_and_bounds()
{
object[] initialGuessOPsAndXAxis = new object[] {
new [] { new OpticalProperties(0.01, 1.0, 0.8, 1.4) },
new double[] { 1, 2, 3 }
};
double[] measuredData = new double[] { 4, 5, 6 };
double[] lowerBounds = new double[] { 0, 0, 0, 0 }; // one for each OP even if not optimized
double[] upperBounds = new double[]
{
double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity
};
var solution = ComputationFactory.SolveInverse(
new PointSourceSDAForwardSolver(),
new MPFitLevenbergMarquardtOptimizer(),
SolutionDomainType.ROfRho,
measuredData,
measuredData,
InverseFitType.MuaMusp,
initialGuessOPsAndXAxis,
lowerBounds,
upperBounds
);
// solution is a double array with converged solution OPs
Assert.IsTrue(Math.Abs(solution[1] - 3.75530) < 0.00001);
}
public void validate_GetAbsorbedEnergy_can_be_called_for_homogeneous_complex_fluence_solutions()
{
double mua = 0.1;
IEnumerable <Complex> absorbedEnergy = ComputationFactory.GetAbsorbedEnergy(complexFluence, mua);
Assert.IsTrue(Math.Abs(absorbedEnergy.First().Real - 0.018829) < 0.000001);
}
public void VerifyROfRhoMonteCarloMeasuredNoNoiseMonteCarloModel()
{
var independentValues = new double[] { 1, 2, 3, 4, 5, 6 }; // rho [mm]
var actualProperties = new OpticalProperties(mua: 0.01, musp: 1.0, g: 0.8, n: 1.4);
var initialGuess = new OpticalProperties(mua: 0.02, musp: 1.2, g: 0.8, n: 1.4);
var simulatedMeasured = ComputationFactory.ComputeReflectance(
ForwardSolverType.MonteCarlo,
SolutionDomainType.ROfRho,
ForwardAnalysisType.R,
new object[] { new[] { actualProperties }, independentValues });
var standardDeviation = simulatedMeasured;
double[] fit = ComputationFactory.SolveInverse(
ForwardSolverType.MonteCarlo,
OptimizerType.MPFitLevenbergMarquardt,
SolutionDomainType.ROfRho,
simulatedMeasured,
standardDeviation,
InverseFitType.MuaMusp,
new object[] { new[] { initialGuess }, independentValues });
var convergedMua = fit[0];
var convergedMusp = fit[1];
Assert.Less(Math.Abs(convergedMua - 0.01), 1e-6);
Assert.Less(Math.Abs(convergedMusp - 1.0), 1e-6);
}
public void VerifyROfRhoMonteCarloMeasuredNoNoiseSDAModel()
{
var independentValues = new double[] { 10, 11, 12, 13, 14, 15 }; // rho [mm]
var actualProperties = new OpticalProperties(mua: 0.01, musp: 1.0, g: 0.8, n: 1.4);
var initialGuess = new OpticalProperties(mua: 0.02, musp: 1.2, g: 0.8, n: 1.4);
var lowerBounds = new double[] { 0, 0, 0, 0 };
var upperBounds = new double[] { double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity };
var simulatedMeasured = ComputationFactory.ComputeReflectance(
ForwardSolverType.MonteCarlo,
SolutionDomainType.ROfRho,
ForwardAnalysisType.R,
new object[] { new[] { actualProperties }, independentValues });
var standardDeviation = simulatedMeasured;
double[] fit = ComputationFactory.SolveInverse(
ForwardSolverType.DistributedPointSourceSDA,
OptimizerType.MPFitLevenbergMarquardt,
SolutionDomainType.ROfRho,
simulatedMeasured,
standardDeviation,
InverseFitType.MuaMusp,
new object[] { new[] { initialGuess }, independentValues },
lowerBounds,
upperBounds);
var convergedMua = fit[0];
var convergedMusp = fit[1];
Assert.Less(Math.Abs(convergedMua - 0.01), 0.002);
Assert.Less(Math.Abs(convergedMusp - 1.0), 0.11);
}
private IDataPoint[][] GetDataPoints(double[] reflectance)
{
var plotIsVsWavelength = _allRangeVMs.Any(vm => vm.AxisType == IndependentVariableAxis.Wavelength);
var isComplexPlot = ComputationFactory.IsComplexSolver(SolutionDomainTypeOptionVM.SelectedValue);
var primaryIdependentValues = _allRangeVMs.First().Values.ToArray();
var numPointsPerCurve = primaryIdependentValues.Length;
var numForwardValues = isComplexPlot ? reflectance.Length / 2 : reflectance.Length;
// complex reported as all reals, then all imaginaries
var numCurves = numForwardValues / numPointsPerCurve;
var points = new IDataPoint[numCurves][];
Func<int, int, IDataPoint> getReflectanceAtIndex = (i, j) =>
{
// man, this is getting hacky...
var index = plotIsVsWavelength
? i * numCurves + j
: j * numPointsPerCurve + i;
return isComplexPlot
? (IDataPoint)
new ComplexDataPoint(primaryIdependentValues[i],
new Complex(reflectance[index], reflectance[index + numForwardValues]))
: (IDataPoint)new DoubleDataPoint(primaryIdependentValues[i], reflectance[index]);
};
for (int j = 0; j < numCurves; j++)
{
points[j] = new IDataPoint[numPointsPerCurve];
for (int i = 0; i < numPointsPerCurve; i++)
{
points[j][i] = getReflectanceAtIndex(i, j);
}
}
return points;
}
public void GetVectorizedIndependentVariableQueryNew_can_be_called_using_enum_inputs()
{
var test = ComputationFactory.ComputeReflectance(
ForwardSolverType.MonteCarlo,
SolutionDomainType.ROfRho,
ForwardAnalysisType.R,
new object[] { new[] { new OpticalProperties(0.01, 1, 0.8, 1.4) }, new double[] { 1, 2, 3 } });
}
public void validate_ROfRhoAndTime_With_Wavelength()
{
// used values for tissue=liver
var scatterer = new PowerLawScatterer(0.84, 0.55);
var hbAbsorber = new ChromophoreAbsorber(ChromophoreType.Hb, 66);
var hbo2Absorber = new ChromophoreAbsorber(ChromophoreType.HbO2, 124);
var fatAbsorber = new ChromophoreAbsorber(ChromophoreType.Fat, 0.02);
var waterAbsorber = new ChromophoreAbsorber(ChromophoreType.H2O, 0.87);
var n = 1.4;
var wvs = new double[] { 650, 700 };
var rhos = new double[] { 0.5, 1.625 };
var times = new double[] { 0.05, 0.10 };
var tissue = new Tissue(
new IChromophoreAbsorber[] { hbAbsorber, hbo2Absorber, fatAbsorber, waterAbsorber },
scatterer,
"test_tissue",
n);
var ops = wvs.Select(wv => tissue.GetOpticalProperties(wv)).ToArray();
var rVsWavelength = ComputationFactory.ComputeReflectance(
new PointSourceSDAForwardSolver(),
SolutionDomainType.ROfRhoAndTime,
ForwardAnalysisType.R,
new object[]
{
ops,
rhos,
times
});
// return from ROfRhoAndTime is new double[ops.Length * rhos.Length * ts.Length];
// order is: (ops0,rhos0,ts0), (ops0,rhos0,ts1)...(ops0,rhos0,tsnt-1)
// (ops0,rhos1,ts0), (ops0,rhos1,ts1)...(ops0,rhos1,tsnt-1)
// ...
// (ops0,rhosnr-1,ts0),.................(ops0,rhosnr-1,tsnt-1)
// ... repeat above with ops1...
// [0] -> ops0=650, rho0=0.5, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[0] - 0.044606) < 0.000001); // API match
// [1] -> ops0=650, rho0=0.5, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[1] - 0.005555) < 0.000001);
// [2] -> ops0=650, rho1=1.635, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[2] - 0.036900) < 0.000001); // API match
// [3] -> ops0=650, rho1=1.635, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[3] - 0.005053) < 0.000001);
// [4] -> ops1=700, rho0=0.5, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[4] - 0.057894) < 0.000001); // API match
// [5] -> ops1=700, rho0=0.5, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[5] - 0.010309) < 0.000001);
// [6] -> ops1=700, rho1=1.635, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[6] - 0.048493) < 0.000001); // API match
// [7] -> ops1=700, rho1=1.635, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[7] - 0.009434) < 0.000001);
}
public void validate_ROfFxAndTime_With_Wavelength()
{
// used values for tissue=liver
var scatterer = new PowerLawScatterer(0.84, 0.55);
var hbAbsorber = new ChromophoreAbsorber(ChromophoreType.Hb, 66);
var hbo2Absorber = new ChromophoreAbsorber(ChromophoreType.HbO2, 124);
var fatAbsorber = new ChromophoreAbsorber(ChromophoreType.Fat, 0.02);
var waterAbsorber = new ChromophoreAbsorber(ChromophoreType.H2O, 0.87);
var n = 1.4;
var wvs = new double[] { 650, 700 };
var fxs = new double[] { 0.0, 0.5 };
var times = new double[] { 0.05, 0.10 };
var tissue = new Tissue(
new IChromophoreAbsorber[] { hbAbsorber, hbo2Absorber, fatAbsorber, waterAbsorber },
scatterer,
"test_tissue",
n);
var ops = wvs.Select(wv => tissue.GetOpticalProperties(wv)).ToArray();
var rVsWavelength = ComputationFactory.ComputeReflectance(
new DistributedPointSourceSDAForwardSolver(),
SolutionDomainType.ROfFxAndTime,
ForwardAnalysisType.R,
new object[]
{
ops,
fxs,
times
});
// return from ROfFxAndTime is new double[ops.Length * fxs.Length * ts.Length];
// order is: (ops0,fxs0,ts0), (ops0,fxs0,ts1)...(ops0,fxs0,tsnt-1)
// (ops0,fxs1,ts0), (ops0,fxs1,ts1)...(ops0,fxs1,tsnt-1)
// ...
// (ops0,fxsnf-1,ts0),................(ops0,fxsnf-1,tsnt-1)
// ... repeat above with ops1...
// [0] -> ops0=650, fx0=0.0, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[0] - 1.558702) < 0.000001);
// [1] -> ops0=650, fx0=0.0, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[1] - 0.391871) < 0.000001);
// [2] -> ops0=650, fx1=0.5, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[2] - 5.023055e-12) < 0.000001e-12);
// [3] -> ops0=650, fx1=0.5, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[3] - 1.032586e-13) < 0.000001e-13);
// [4] -> ops1=700, fx0=0.0, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[4] - 2.218329) < 0.000001);
// [5] -> ops1=700, fx1=0.5, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[5] - 0.797200) < 0.000001);
// [6] -> ops1=700, fx0=0.0, ts0=0.05
Assert.IsTrue(Math.Abs(rVsWavelength[6] - 1.347053e-12) < 0.000001e-12);
// [7] -> ops1=700, fx1=0.5, ts1=0.10
Assert.IsTrue(Math.Abs(rVsWavelength[7] - 2.052883e-13) < 0.000001e-13);
}
public InverseSolutionResult SolveInverse()
{
var lowerBounds = new double[] { 0, 0, 0, 0 };
var upperBounds = new double[] { double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity };
var measuredOpticalProperties = GetMeasuredOpticalProperties();
var measuredDataValues = GetSimulatedMeasuredData();
var dependentValues = measuredDataValues.ToArray();
var initGuessOpticalProperties = GetInitialGuessOpticalProperties();
var initGuessParameters = GetParametersInOrder(initGuessOpticalProperties);
// replace unconstrained L-M optimization with constrained version
// this solves problem of when distributed source solution produces neg OPs during inversion
//var fit = ComputationFactory.SolveInverse(
// InverseForwardSolverTypeOptionVM.SelectedValue,
// OptimizerTypeOptionVM.SelectedValue,
// SolutionDomainTypeOptionVM.SelectedValue,
// dependentValues,
// dependentValues, // set standard deviation, sd, to measured (works w/ or w/o noise)
// InverseFitTypeOptionVM.SelectedValue,
// initGuessParameters.Values.ToArray());
var fit = ComputationFactory.SolveInverse(
InverseForwardSolverTypeOptionVM.SelectedValue,
OptimizerTypeOptionVM.SelectedValue,
SolutionDomainTypeOptionVM.SelectedValue,
dependentValues,
dependentValues, // set standard deviation, sd, to measured (works w/ or w/o noise)
InverseFitTypeOptionVM.SelectedValue,
initGuessParameters.Values.ToArray(),
lowerBounds, upperBounds);
var fitOpticalProperties = ComputationFactory.UnFlattenOpticalProperties(fit);
var fitParameters = GetParametersInOrder(fitOpticalProperties);
var resultDataValues = ComputationFactory.ComputeReflectance(
InverseForwardSolverTypeOptionVM.SelectedValue,
SolutionDomainTypeOptionVM.SelectedValue,
ForwardAnalysisType.R,
fitParameters.Values.ToArray());
var resultDataPoints = GetDataPoints(resultDataValues);
return(new InverseSolutionResult
{
FitDataPoints = resultDataPoints,
MeasuredOpticalProperties = (OpticalProperties[])measuredOpticalProperties,
// todo: currently only supports homog OPs
GuessOpticalProperties = (OpticalProperties[])initGuessOpticalProperties,
// todo: currently only supports homog OPss
FitOpticalProperties = fitOpticalProperties
});
}
public void validate_GetAbsorbedEnergy_can_be_called_for_heterogeneous_real_fluence_solutions()
{
// use real Fluence as fluence for two layer tissue which is a 4x3 and
// define muas so that top 2 rows have one value, bottom have different value (matrix is column major)
double[] muas = new double[12] {
0.1, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.2
};
IEnumerable <double> absorbedEnergy = ComputationFactory.GetAbsorbedEnergy(realFluence.AsEnumerable(), muas.AsEnumerable());
Assert.IsTrue(Math.Abs(absorbedEnergy.First() - 0.018829) < 0.000001);
}
public double[] CalculateInitialGuess()
{
var opticalProperties = GetInitialGuessOpticalProperties();
var parameters = GetParametersInOrder(opticalProperties);
return ComputationFactory.ComputeReflectance(
InverseForwardSolverTypeOptionVM.SelectedValue,
SolutionDomainTypeOptionVM.SelectedValue,
ForwardAnalysisType.R,
parameters.Values.ToArray());
}
public void validate_ComputeReflectance_can_be_called_using_IForwardSolver()
{
var reflectance = ComputationFactory.ComputeReflectance(
new NurbsForwardSolver(),
SolutionDomainType.ROfFx,
ForwardAnalysisType.dRdMua,
new object[]
{
new [] { new OpticalProperties(0.01, 1, 0.8, 1.4) },
new double[] { 1, 2, 3 }
});
Assert.IsTrue(Math.Abs(reflectance[0] + 0.005571) < 0.000001);
}
public void validate_GetPHD_can_be_called_using_enum_forward_solver()
{
double sourceDetectorSeparation = 3;
double[] phd = ComputationFactory.GetPHD(
ForwardSolverType.PointSourceSDA,
realFluence,
sourceDetectorSeparation,
new[] { new OpticalProperties(0.01, 1.0, 0.8, 1.4) },
xAxis,
zAxis);
// solution is linearized PHD, column major
Assert.IsTrue(Math.Abs(phd[0] - 0.010336) < 0.000001);
}
public IDataPoint[][] ExecuteForwardSolver()
{
var opticalProperties = GetOpticalProperties();
var parameters = GetParametersInOrder(opticalProperties);
var reflectance = ComputationFactory.ComputeReflectance(
ForwardSolverTypeOptionVM.SelectedValue,
SolutionDomainTypeOptionVM.SelectedValue,
ForwardAnalysisTypeOptionVM.SelectedValue,
parameters.Values.ToArray());
return(GetDataPoints(reflectance));
}
private double[] GetSimulatedMeasuredData()
{
var opticalProperties = GetMeasuredOpticalProperties();
var parameters = GetParametersInOrder(opticalProperties);
var measuredData = ComputationFactory.ComputeReflectance(
MeasuredForwardSolverTypeOptionVM.SelectedValue,
SolutionDomainTypeOptionVM.SelectedValue,
ForwardAnalysisType.R,
parameters.Values.ToArray());
return measuredData.AddNoise(PercentNoise);
}
public void validate_ComputeReflectance_can_be_called_using_enum_forward_solver()
{
var reflectance = ComputationFactory.ComputeReflectance(
ForwardSolverType.MonteCarlo,
SolutionDomainType.ROfRho,
ForwardAnalysisType.R,
new object[]
{
// could have array of OPs, one set for each tissue region
new[] { new OpticalProperties(0.01, 1, 0.8, 1.4) },
new double[] { 1, 2, 3 }
});
Assert.IsTrue(Math.Abs(reflectance[0] - 0.021093) < 0.000001);
}
private async Task InitializeFields(IEnumerable <SkillNode> skillNodes)
{
_gameData.PassiveNodes = skillNodes;
var computationFactory = new ComputationFactory(GameData);
var calculator = computationFactory.CreateCalculator();
_builderFactories = await computationFactory.CreateBuilderFactoriesAsync();
_parser = await computationFactory.CreateParserAsync();
_schedulers = new ComputationSchedulerProvider();
_observables = new ComputationObservables(_parser);
_calculator = new ObservableCalculator(calculator, _schedulers.CalculationThread);
}
public void validate_ComputeFluenceComplex_can_be_called_using_IForwardSolver_and_single_optical_properties()
{
double[] xAxis = new double[] { 1, 2, 3 };
double[] zAxis = new double[] { 1, 2, 3, 4 };
double[][] independentValues = new double[][] { xAxis, zAxis };
var fluence = ComputationFactory.ComputeFluenceComplex(
new PointSourceSDAForwardSolver(),
FluenceSolutionDomainType.FluenceOfRhoAndZAndFt,
new IndependentVariableAxis[] { IndependentVariableAxis.Rho, IndependentVariableAxis.Z },
independentValues,
new OpticalProperties(0.01, 1, 0.8, 1.4), // single OPs
new double[] { 0 }
);
// fluence is linearized to be [0-3]=>(x=1,z=1,2,3,4), [4-7]=>(x=2,z=1,2,3,4), [8-11]=>(x=3,z=1,2,3,4)
Assert.IsTrue(Math.Abs(fluence[0].Real - 0.188294) < 0.000001);
}
public void validate_GetPHD_can_be_called_using_IForwardSolver_and_temporal_modulation_frequency()
{
double sourceDetectorSeparation = 3;
double modulationFrequency = 0;
var phd = ComputationFactory.GetPHD(
new PointSourceSDAForwardSolver(),
complexFluence,
sourceDetectorSeparation,
modulationFrequency,
new[] { new OpticalProperties(0.01, 1.0, 0.8, 1.4) },
xAxis,
zAxis
);
// solution is linearized PHD, column major
Assert.IsTrue(Math.Abs(phd[0] - 0.010336) < 0.000001);
}
public string GetPlotData(SolutionDomainPlotParameters plotParameters)
{
try
{
var inverseSolver = plotParameters.InverseSolverType;
var initialGuessParams = _parameterTools.GetParametersInOrder(
_parameterTools.GetOpticalPropertiesObject(plotParameters.OpticalProperties),
plotParameters.XAxis.AsEnumerable().ToArray(),
plotParameters.SolutionDomain,
plotParameters.IndependentAxes.Label,
plotParameters.IndependentAxes.Value);
var initialGuessParamsConvert = initialGuessParams.Values.ToArray();
// get measured data from inverse solver analysis component
var measuredPoints = plotParameters.MeasuredData;
var dependentValues = measuredPoints.Select(p => p.Last()).ToArray(); // get y value
var lowerBounds = new double[] { 0, 0, 0, 0 };
var upperBounds = new []
{
double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity
};
var fit = ComputationFactory.SolveInverse(
plotParameters.InverseSolverType,
plotParameters.OptimizerType,
plotParameters.SolutionDomain,
dependentValues,
dependentValues, // set standard deviation to measured to match WPF
plotParameters.OptimizationParameters,
initialGuessParamsConvert,
lowerBounds,
upperBounds);
var fitops = ComputationFactory.UnFlattenOpticalProperties(fit);
//var fitparms =
// GetParametersInOrder(fitops, independentValues, sd, independentAxis, independentAxisValue);
plotParameters.ForwardSolverType = inverseSolver;
plotParameters.OpticalProperties = fitops[0]; // not sure [0] is always going to work here
plotParameters.NoiseValue = 0;
var msg = _plotFactory.GetPlot(PlotType.SolutionDomain, plotParameters);
return(msg);
}
catch (Exception e)
{
_logger.LogError("An error occurred: {Message}", e.Message);
throw;
}
}
public void validate_ComputeFluence_can_be_called_using_IForwardSolver_and_optical_property_array()
{
double[] xAxis = new double[] { 1, 2, 3 };
double[] zAxis = new double[] { 1, 2, 3, 4 };
double[][] independentValues = new double[][] { xAxis, zAxis };
var fluence = ComputationFactory.ComputeFluence(
new PointSourceSDAForwardSolver(),
FluenceSolutionDomainType.FluenceOfRhoAndZ,
new IndependentVariableAxis[] { IndependentVariableAxis.Rho, IndependentVariableAxis.Z },
independentValues,
// could have array of OPs, one set for each tissue region
new OpticalProperties[] { new OpticalProperties(0.01, 1, 0.8, 1.4) },
new double[] { 0 }
);
// fluence is linearized to be [0-3]=>(x=1,z=1,2,3,4), [4-7]=>(x=2,z=1,2,3,4), [8-11]=>(x=3,z=1,2,3,4)
Assert.IsTrue(Math.Abs(fluence[0] - 0.188294) < 0.000001);
}
public void validate_SolveInverse_can_be_called_using_IForwardSolver_and_IOptimizer()
{
object[] initialGuessOPsAndXAxis = new object[] {
new [] { new OpticalProperties(0.01, 1.0, 0.8, 1.4) },
new double[] { 1, 2, 3 }
};
double[] measuredData = new double[] { 4, 5, 6 };
double[] solution = ComputationFactory.SolveInverse(
new PointSourceSDAForwardSolver(),
new MPFitLevenbergMarquardtOptimizer(),
SolutionDomainType.ROfRho,
measuredData,
measuredData,
InverseFitType.MuaMusp,
initialGuessOPsAndXAxis
);
// solution is a double array with converged solution OPs
Assert.IsTrue(Math.Abs(solution[1] - 3.75515) < 0.00001);
}
public void validate_ComputeFluenceComplex_can_be_called_using_enum_forward_solver_and_IOpticalPropertyRegion_array()
{
double[] xAxis = new double[] { 1, 2, 3 };
double[] zAxis = new double[] { 1, 2, 3, 4 };
double[][] independentValues = new double[][] { xAxis, zAxis };
Complex[] fluence = ComputationFactory.ComputeFluenceComplex(
ForwardSolverType.PointSourceSDA,
FluenceSolutionDomainType.FluenceOfRhoAndZAndFt,
new IndependentVariableAxis[] { IndependentVariableAxis.Rho, IndependentVariableAxis.Z },
independentValues,
new IOpticalPropertyRegion[] {
new LayerTissueRegion(
new DoubleRange(0, 10, 10),
new OpticalProperties(0.01, 1.0, 0.8, 1.4)
)
},
new double[] { 0 }
);
// fluence is linearized to be [0-3]=>(x=1,z=1,2,3,4), [4-7]=>(x=2,z=1,2,3,4), [8-11]=>(x=3,z=1,2,3,4)
Assert.IsTrue(Math.Abs(fluence[0].Real - 0.188294) < 0.000001);
}
public InverseSolutionResult SolveInverse()
{
var measuredOpticalProperties = GetMeasuredOpticalProperties();
var measuredDataValues = GetSimulatedMeasuredData();
var dependentValues = measuredDataValues.ToArray();
var initGuessOpticalProperties = GetInitialGuessOpticalProperties();
var initGuessParameters = GetParametersInOrder(initGuessOpticalProperties);
double[] fit = ComputationFactory.SolveInverse(
InverseForwardSolverTypeOptionVM.SelectedValue,
OptimizerTypeOptionVM.SelectedValue,
SolutionDomainTypeOptionVM.SelectedValue,
dependentValues,
dependentValues, // set standard deviation, sd, to measured (works w/ or w/o noise)
InverseFitTypeOptionVM.SelectedValue,
initGuessParameters.Values.ToArray());
var fitOpticalProperties = ComputationFactory.UnFlattenOpticalProperties(fit);
var fitParameters = GetParametersInOrder(fitOpticalProperties);
var resultDataValues = ComputationFactory.ComputeReflectance(
InverseForwardSolverTypeOptionVM.SelectedValue,
SolutionDomainTypeOptionVM.SelectedValue,
ForwardAnalysisType.R,
fitParameters.Values.ToArray());
var resultDataPoints = GetDataPoints(resultDataValues);
return new InverseSolutionResult
{
FitDataPoints = resultDataPoints,
MeasuredOpticalProperties = (OpticalProperties[])measuredOpticalProperties, // todo: currently only supports homog OPs
GuessOpticalProperties = (OpticalProperties[])initGuessOpticalProperties, // todo: currently only supports homog OPss
FitOpticalProperties = fitOpticalProperties
};
}
public void Setup()
{
// need to generate fluence to send into GetPHD
xAxis = new double[] { 1, 2, 3 };
zAxis = new double[] { 1, 2, 3, 4 };
double[][] independentValues = new double[][] { xAxis, zAxis };
realFluence = ComputationFactory.ComputeFluence(
ForwardSolverType.PointSourceSDA,
FluenceSolutionDomainType.FluenceOfRhoAndZ,
new IndependentVariableAxis[] { IndependentVariableAxis.Rho, IndependentVariableAxis.Z },
independentValues,
// could have array of OPs, one set for each tissue region
new OpticalProperties[] { new OpticalProperties(0.01, 1, 0.8, 1.4) },
new double[] { 0 }
);
complexFluence = ComputationFactory.ComputeFluenceComplex(
ForwardSolverType.PointSourceSDA,
FluenceSolutionDomainType.FluenceOfRhoAndZAndFt,
new IndependentVariableAxis[] { IndependentVariableAxis.Rho, IndependentVariableAxis.Z },
independentValues,
new OpticalProperties(0.01, 1, 0.8, 1.4), // single OPs
new double[] { 0 }
);
}
public MapData ExecuteForwardSolver() // todo: simplify method calls to ComputationFactory, as with Forward/InverseSolver(s)
{
var opticalProperties = GetOpticalProperties(); // could be OpticalProperties[] or IOpticalPropertyRegion[][]
//double[] rhos = RhoRangeVM.Values.Reverse().Concat(RhoRangeVM.Values).ToArray();
double[] rhos = RhoRangeVM.Values.Reverse().Select(rho => - rho).Concat(RhoRangeVM.Values).ToArray();
double[] zs = ZRangeVM.Values.ToArray();
double[][] independentValues = new[] { rhos, zs };
var sd = GetSelectedSolutionDomain();
// todo: too much thinking at the VM layer?
double[] constantValues = new double[0];
if (ComputationFactory.IsSolverWithConstantValues(sd.SelectedValue))
{
switch (sd.SelectedValue)
{
case FluenceSolutionDomainType.FluenceOfRhoAndZAndFt:
constantValues = new[] { TimeModulationFrequency };
break;
default:
constantValues = new[] { sd.ConstantAxesVMs[0].AxisValue };
break;
}
}
IndependentVariableAxis[] independentAxes =
GetIndependentVariableAxesInOrder(
sd.IndependentVariableAxisOptionVM.SelectedValue,
IndependentVariableAxis.Z);
double[] results = null;
if (ComputationFactory.IsComplexSolver(sd.SelectedValue))
{
Complex[] fluence;
if (IsMultiRegion)
{
fluence =
ComputationFactory.ComputeFluenceComplex(
ForwardSolverTypeOptionVM.SelectedValue,
sd.SelectedValue,
independentAxes,
independentValues,
((IOpticalPropertyRegion[][])opticalProperties)[0],
constantValues);
}
else
{
fluence =
ComputationFactory.ComputeFluenceComplex(
ForwardSolverTypeOptionVM.SelectedValue,
sd.SelectedValue,
independentAxes,
independentValues,
((OpticalProperties[])opticalProperties)[0],
constantValues);
}
switch (MapTypeOptionVM.SelectedValue)
{
case MapType.Fluence:
results = fluence.Select(f => f.Magnitude).ToArray();
break;
case MapType.AbsorbedEnergy:
results = ComputationFactory.GetAbsorbedEnergy(fluence, ((OpticalProperties[])opticalProperties)[0].Mua).Select(a => a.Magnitude).ToArray(); // todo: is this correct?? DC 12/08/12
break;
case MapType.PhotonHittingDensity:
switch (PhotonHittingDensitySolutionDomainTypeOptionVM.SelectedValue)
{
case FluenceSolutionDomainType.FluenceOfRhoAndZAndFt:
results = ComputationFactory.GetPHD(
ForwardSolverTypeOptionVM.SelectedValue,
fluence.ToArray(),
SourceDetectorSeparation,
TimeModulationFrequency,
(OpticalProperties[])opticalProperties,
independentValues[0],
independentValues[1]).ToArray();
break;
case FluenceSolutionDomainType.FluenceOfFxAndZAndFt:
break;
default:
throw new ArgumentOutOfRangeException("FluenceSolutionDomainType");
}
break;
default:
throw new ArgumentOutOfRangeException("MapType");
}
}
else
{
double[] fluence;
if (IsMultiRegion)
{
fluence = ComputationFactory.ComputeFluence(
ForwardSolverTypeOptionVM.SelectedValue,
sd.SelectedValue,
independentAxes,
independentValues,
((IOpticalPropertyRegion[][])opticalProperties)[0],
constantValues).ToArray();
}
else
{
fluence = ComputationFactory.ComputeFluence(
ForwardSolverTypeOptionVM.SelectedValue,
sd.SelectedValue,
independentAxes,
independentValues,
(OpticalProperties[])opticalProperties,
constantValues).ToArray();
}
switch (MapTypeOptionVM.SelectedValue)
{
case MapType.Fluence:
results = fluence;
break;
case MapType.AbsorbedEnergy:
if (IsMultiRegion)
{
if (ForwardSolver is TwoLayerSDAForwardSolver)
{
var regions = ((MultiRegionTissueViewModel)TissueInputVM).GetTissueInput().Regions
.Select(region => (ILayerOpticalPropertyRegion)region).ToArray();
var muas = getRhoZMuaArrayFromLayerRegions(regions, rhos, zs);
results = ComputationFactory.GetAbsorbedEnergy(fluence, muas).ToArray();
}
else
{
return(null);
}
}
else
{
// Note: the line below was originally overwriting the multi-region results. I think this was a bug (DJC 7/11/14)
results = ComputationFactory.GetAbsorbedEnergy(fluence, ((OpticalProperties[])opticalProperties)[0].Mua).ToArray();
}
break;
case MapType.PhotonHittingDensity:
switch (PhotonHittingDensitySolutionDomainTypeOptionVM.SelectedValue)
{
case FluenceSolutionDomainType.FluenceOfRhoAndZ:
if (IsMultiRegion)
{
var nop = (IOpticalPropertyRegion[][])opticalProperties;
results = ComputationFactory.GetPHD(
ForwardSolverTypeOptionVM.SelectedValue,
fluence,
SourceDetectorSeparation,
(from LayerTissueRegion tissue in nop[0] select tissue.RegionOP).ToArray(),
independentValues[0],
independentValues[1]).ToArray();
}
else
{
results = ComputationFactory.GetPHD(
ForwardSolverTypeOptionVM.SelectedValue,
fluence,
SourceDetectorSeparation,
(OpticalProperties[])opticalProperties,
independentValues[0],
independentValues[1]).ToArray();
}
break;
case FluenceSolutionDomainType.FluenceOfFxAndZ:
break;
case FluenceSolutionDomainType.FluenceOfRhoAndZAndTime:
break;
case FluenceSolutionDomainType.FluenceOfFxAndZAndTime:
break;
default:
throw new ArgumentOutOfRangeException("PhotonHittingDensitySolutionDomainTypeOptionVM.SelectedValue");
}
break;
default:
throw new ArgumentOutOfRangeException("MapTypeOptionVM.SelectedValue");
}
}
// flip the array (since it goes over zs and then rhos, while map wants rhos and then zs
double[] destinationArray = new double[results.Length];
long index = 0;
for (int rhoi = 0; rhoi < rhos.Length; rhoi++)
{
for (int zi = 0; zi < zs.Length; zi++)
{
destinationArray[rhoi + rhos.Length * zi] = results[index++];
}
}
var dRho = 1D;
var dZ = 1D;
var dRhos = Enumerable.Select(rhos, rho => 2 * Math.PI * Math.Abs(rho) * dRho).ToArray();
var dZs = Enumerable.Select(zs, z => dZ).ToArray();
//var twoRhos = Enumerable.Concat(rhos.Reverse(), rhos).ToArray();
//var twoDRhos = Enumerable.Concat(dRhos.Reverse(), dRhos).ToArray();
return(new MapData(destinationArray, rhos, zs, dRhos, dZs));
}
private static void ReportInverseSolverROfRhoAndTime(double dt,
double riseMarker,
double tailMarker,
string stDevMode,
InverseFitType IFT,
string projectName,
string inputPath,
ForwardSolverType[] forwardSolverTypes,
OptimizerType[] optimizerTypes,
IEnumerable <OpticalProperties> guessOps,
IEnumerable <OpticalProperties> realOps,
double[] rhos,
double noisePercentage,
bool stepByStep)
{
Console.WriteLine("#############################################");
Console.WriteLine("##### REPORT INVERSE SOLVER: ROfRhoAndT #####");
Console.WriteLine("#############################################");
//path definition
string spaceDomainFolder = "Real";
string timeDomainFolder = "TimeDomain";
string noiseFolder = "noise" + noisePercentage.ToString();
string problemFolder = "dt" + (dt * 1000).ToString() + "markers" + riseMarker.ToString() +
tailMarker.ToString();
problemFolder = problemFolder.Replace(".", "p");
foreach (var fST in forwardSolverTypes)
{
//initialize forward solver
Console.WriteLine("Forward Solver Type: {0}", fST.ToString());
foreach (var oT in optimizerTypes)
{
Console.WriteLine("Optimizer Type: {0}", oT.ToString());
foreach (var rho in rhos)
{
string rhoFolder = rho.ToString();
Console.WriteLine("=================================================");
Console.WriteLine("SOURCE DETECTOR SEPARETION: R = {0} mm", rhoFolder);
if (stepByStep)
{
Console.WriteLine("Press enter to continue");
}
Console.WriteLine("=================================================");
if (stepByStep)
{
Console.ReadLine();
}
rhoFolder = rhoFolder.Replace(".", "p");
rhoFolder = "rho" + rhoFolder;
double[] constantVals = { rho };
foreach (var rOp in realOps)
{
//output
double bestMua = 0.0;
double meanMua = 0.0;
double guessBestMua = 0.0;
double bestMusp = 0.0;
double meanMusp = 0.0;
double guessBestMusp = 0.0;
double bestChiSquared = 10000000000000.0; //initialize very large to avoid if first
double meanChiSquared = 0.0;
DateTime start = new DateTime(); //processing start time
DateTime end = new DateTime(); //processing finish time
double elapsedSeconds; //processing time
//set filename based on real optical properties
var filename = "musp" + rOp.Musp.ToString() + "mua" + rOp.Mua.ToString();
filename = filename.Replace(".", "p");
Console.WriteLine("Looking for file {0}", filename);
if (File.Exists(inputPath + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "Range"))
{
Console.WriteLine("The file has been found for rho = {0} mm.", rho);
//read binary files
var timeRange = (double[])FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "Range", projectName, 2);
int numberOfPoints = Convert.ToInt32((timeRange[1] - timeRange[0]) / dt) + 1;
var T = new DoubleRange(timeRange[0], timeRange[1], numberOfPoints).AsEnumerable().ToArray();
var R = (double[])FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "R", projectName, numberOfPoints);
var S = GetStandardDeviationValues("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "S",
projectName, stDevMode, numberOfPoints, R.ToArray());
// add noise
if (noisePercentage != 0.0)
{
R = R.AddNoise(noisePercentage);
}
start = DateTime.Now;
int convergedCounter = 0;
foreach (var gOp in guessOps)
{
bool converged;
if (IFT == InverseFitType.Mua)
{
gOp.Musp = rOp.Musp;
}
if (IFT == InverseFitType.Musp)
{
gOp.Mua = rOp.Mua;
}
//solve inverse problem
double[] fit = ComputationFactory.SolveInverse(fST, oT, SolutionDomainType.ROfRhoAndTime, R, S, IFT, new object[] { new[] { gOp }, constantVals, T });
if (fit[0] != 0 && fit[1] != 0)
{
converged = true;
}
else
{
converged = false;
}
if (converged)
{
OpticalProperties fOp = new OpticalProperties(fit[0], fit[1], gOp.G, gOp.N);
//calculate chi squared and change best values if it improved
double chiSquared = EvaluateChiSquared(R.ToArray(), SolverFactory.GetForwardSolver(fST).ROfRhoAndTime(fOp.AsEnumerable(), rho.AsEnumerable(), T).ToArray(), S.ToArray());
if (chiSquared < bestChiSquared)
{
guessBestMua = gOp.Mua;
bestMua = fit[0];
guessBestMusp = gOp.Musp;
bestMusp = fit[1];
bestChiSquared = chiSquared;
}
meanMua += fit[0];
meanMusp += fit[1];
meanChiSquared += chiSquared;
convergedCounter += 1;
}
}
end = DateTime.Now;
meanMua /= convergedCounter;
meanMusp /= convergedCounter;
meanChiSquared /= convergedCounter;
elapsedSeconds = (end - start).TotalSeconds;
MakeDirectoryIfNonExistent(new string[] { spaceDomainFolder, timeDomainFolder, noiseFolder, problemFolder, fST.ToString(), oT.ToString(), IFT.ToString(), rhoFolder });
//write results to array
double[] inverseProblemValues = FillInverseSolverValuesArray(bestMua, meanMua, guessBestMua,
bestMusp, meanMusp, guessBestMusp,
bestChiSquared, meanChiSquared,
elapsedSeconds, numberOfPoints);
// write array to binary
LocalWriteArrayToBinary(inverseProblemValues, @"Output/" + spaceDomainFolder + "/" +
timeDomainFolder + "/" + noiseFolder + "/" + problemFolder + "/" + fST.ToString() + "/" +
oT.ToString() + "/" + IFT.ToString() + "/" + rhoFolder + "/" + filename, FileMode.Create);
Console.WriteLine("Real MUA = {0} - best MUA = {1} - mean MUA = {2}", rOp.Mua, bestMua, meanMua);
Console.WriteLine("Real MUSp = {0} - best MUSp = {1} - mean MUSp = {2}", rOp.Musp, bestMusp, meanMusp);
if (stepByStep)
{
Console.ReadLine();
}
}
else
{
Console.WriteLine("The file has not been found.");
}
Console.Clear();
}
}
}
}
}
private static void ReportInverseSolverROfRho(double drho,
double[] rhoRange,
InverseFitType IFT,
string projectName,
string inputPath,
ForwardSolverType[] forwardSolverTypes,
OptimizerType[] optimizerTypes,
IEnumerable <OpticalProperties> guessOps,
IEnumerable <OpticalProperties> realOps,
int ratioDetectors,
double noisePercentage,
bool stepByStep)
{
Console.WriteLine("#############################################");
Console.WriteLine("####### REPORT INVERSE SOLVER: ROfRho #######");
Console.WriteLine("#############################################");
//path definition
string spaceDomainFolder = "Real";
string timeDomainFolder = "SteadyState";
string problemFolder = "drho" + drho.ToString() + "/" + "ratioD" + ratioDetectors.ToString() + "/" +
"noise" + noisePercentage.ToString() + "/" + rhoRange[0].ToString() + "_" + rhoRange[1].ToString();
problemFolder = problemFolder.Replace(".", "p");
//rhos based on range
int numberOfPoints = Convert.ToInt32((rhoRange[1] - rhoRange[0]) / drho) + 1;
var rhos = new DoubleRange(rhoRange[0], rhoRange[1], numberOfPoints).AsEnumerable().ToArray();
double[] R = new double[numberOfPoints];
double[] S = new double[numberOfPoints];
//based on range evaluate the index of first and last points to use
int firstInd = Convert.ToInt32((rhoRange[0] + drho / 2.0) / drho) - 1;
int lastInd = Convert.ToInt32((rhoRange[1] + drho / 2) / drho) - 1;
//execute
foreach (var fST in forwardSolverTypes)
{
Console.WriteLine("Forward Solver Type: {0}", fST.ToString());
foreach (var oT in optimizerTypes)
{
Console.WriteLine("Optimizer Type: {0}", oT.ToString());
if (stepByStep)
{
Console.WriteLine("Press enter to continue");
}
Console.WriteLine("=================================================");
if (stepByStep)
{
Console.ReadLine();
}
foreach (var rOp in realOps)
{
//output
double bestMua = 0.0;
double meanMua = 0.0;
double guessBestMua = 0.0;
double bestMusp = 0.0;
double meanMusp = 0.0;
double guessBestMusp = 0.0;
double bestChiSquared = 10000000000000.0; //initialize very large to avoid if first
double meanChiSquared = 0.0;
DateTime start = new DateTime(); //processing start time
DateTime end = new DateTime(); //processing finish time
double elapsedSeconds; //processing time
//set filename based on real optical properties
var filename = "musp" + rOp.Musp.ToString() + "mua" + rOp.Mua.ToString();
filename = filename.Replace(".", "p");
Console.WriteLine("Looking for file {0}", filename);
if (File.Exists(inputPath + spaceDomainFolder + "/" + timeDomainFolder + "/" + filename + "R"))
{
Console.WriteLine("The file has been found");
//read binary files
var Rtot = (IEnumerable <double>)FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + filename + "R", projectName, 88);
var Stot = (IEnumerable <double>)FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + filename + "S", projectName, 88);
// extract points within range
for (int i = firstInd; i <= lastInd; i++)
{
R[i - firstInd] = Rtot.ToArray()[i];
S[i - firstInd] = Stot.ToArray()[i];
}
// reduce number of measurements
var mrhos = FilterArray(rhos, ratioDetectors);
var mR = FilterArray(R, ratioDetectors);
var mS = FilterArray(S, ratioDetectors);
// add noise
if (noisePercentage != 0.0)
{
mR.AddNoise(noisePercentage);
}
start = DateTime.Now;
int covergedCounter = 0;
foreach (var gOp in guessOps)
{
bool converged;
//if fitting only one parameter change the guess to the true value
if (IFT == InverseFitType.Mua)
{
gOp.Musp = rOp.Musp;
}
if (IFT == InverseFitType.Musp)
{
gOp.Mua = rOp.Mua;
}
//solve inverse problem
double[] fit = ComputationFactory.SolveInverse(fST, oT, SolutionDomainType.ROfRho, mR, mS, IFT, new object[] { new[] { gOp }, mrhos });
if (fit[0] != 0 && fit[1] != 0)
{
converged = true;
}
else
{
converged = false;
}
// fitted op
if (converged)
{
OpticalProperties fOp = new OpticalProperties(fit[0], fit[1], gOp.G, gOp.N);
//calculate chi squared and change values if it improved
double chiSquared = EvaluateChiSquared(mR, SolverFactory.GetForwardSolver(fST).ROfRho(fOp.AsEnumerable(), mrhos).ToArray(), mS);
if (chiSquared < bestChiSquared)
{
guessBestMua = gOp.Mua;
bestMua = fit[0];
guessBestMusp = gOp.Musp;
bestMusp = fit[1];
bestChiSquared = chiSquared;
}
meanMua += fit[0];
meanMusp += fit[1];
meanChiSquared += chiSquared;
covergedCounter += 1;
}
}
end = DateTime.Now;
meanMua /= covergedCounter;
meanMusp /= covergedCounter;
meanChiSquared /= covergedCounter;
elapsedSeconds = (end - start).TotalSeconds;
MakeDirectoryIfNonExistent(new string[] { spaceDomainFolder, timeDomainFolder, problemFolder, fST.ToString(), oT.ToString(), IFT.ToString() });
//write results to array
double[] inverseProblemValues = FillInverseSolverValuesArray(bestMua, meanMua, guessBestMua,
bestMusp, meanMusp, guessBestMusp,
bestChiSquared, meanChiSquared,
elapsedSeconds, mR.Count());
// write array to binary
LocalWriteArrayToBinary(inverseProblemValues, @"Output/" + spaceDomainFolder + "/" +
timeDomainFolder + "/" + problemFolder + "/" + fST.ToString() + "/" +
oT.ToString() + "/" + IFT.ToString() + "/" + filename, FileMode.Create);
Console.WriteLine("Real MUA = {0} - best MUA = {1} - mean MUA = {2}", rOp.Mua, bestMua, meanMua);
Console.WriteLine("Real MUSp = {0} - best MUSp = {1} - mean MUSp = {2}", rOp.Musp, bestMusp, meanMusp);
if (stepByStep)
{
Console.ReadLine();
}
}
else
{
Console.WriteLine("The file has not been found.");
}
Console.Clear();
}
}
}
}
public void validate_ROfRhoAndFt_With_Wavelength()
{
// used values for tissue=liver
var scatterer = new PowerLawScatterer(0.84, 0.55);
var hbAbsorber = new ChromophoreAbsorber(ChromophoreType.Hb, 66);
var hbo2Absorber = new ChromophoreAbsorber(ChromophoreType.HbO2, 124);
var fatAbsorber = new ChromophoreAbsorber(ChromophoreType.Fat, 0.02);
var waterAbsorber = new ChromophoreAbsorber(ChromophoreType.H2O, 0.87);
var n = 1.4;
var wvs = new double[] { 650, 700 };
var rhos = new double[] { 0.5, 1.625 };
var fts = new double[] { 0.0, 0.50 };
var tissue = new Tissue(
new IChromophoreAbsorber[] { hbAbsorber, hbo2Absorber, fatAbsorber, waterAbsorber },
scatterer,
"test_tissue",
n);
var ops = wvs.Select(wv => tissue.GetOpticalProperties(wv)).ToArray();
var rVsWavelength = ComputationFactory.ComputeReflectance(
new PointSourceSDAForwardSolver(),
SolutionDomainType.ROfRhoAndFt,
ForwardAnalysisType.R,
new object[]
{
ops,
rhos,
fts
});
// return from ROfRhoAndFt is new double[ops.Length * rhos.Length * fts.Length];
// order is: (ops0,rhos0,fts0)real, (ops0,rhos0,fts1)real...(ops0,rhos0,ftsnt-1)real
// (ops0,rhos1,fts0)real, (ops0,rhos1,fts1)real...(ops0,rhos1,ftsnt-1)real
// ...
// (ops0,rhosnr-1,fts0)real,.................(ops0,rhosnr-1,ftsnt-1)real
// ... repeat above with imag, then next ops1...
// [0] -> ops0=650, rho0=0.5, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[0] - 0.037575) < 0.000001);
// [1] -> ops0=650, rho0=0.5, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[1] - 0.037511) < 0.000001);
// [2] -> ops0=650, rho1=1.635, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[2] - 0.009306) < 0.000001);
// [3] -> ops0=650, rho1=1.635, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[3] - 0.009255) < 0.000001);
// [4] -> ops1=700, rho0=0.5, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[4] - 0.036425) < 0.000001);
// [5] -> ops1=700, rho0=0.5, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[5] - 0.036310) < 0.000001);
// [6] -> ops1=700, rho1=1.635, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[6] - 0.010657) < 0.000001);
// [7] -> ops1=700, rho1=1.635, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[7] - 0.010558) < 0.000001);
// [8] -> ops0=650, rho0=0.5, fts0=0.0 imag
Assert.IsTrue(Math.Abs(rVsWavelength[8] - 0.0) < 0.000001);
// [9] -> ops0=650, rho0=0.5, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[9] + 0.001200) < 0.000001);
// [10] -> ops0=650, rho1=1.635, fts0=0.0 imag
Assert.IsTrue(Math.Abs(rVsWavelength[10] - 0.0) < 0.000001);
// [11] -> ops1=650, rho1=1.635, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[11] + 0.000674) < 0.000001);
// [12] -> ops1=700, rho0=0.5, fts0=0.0
Assert.IsTrue(Math.Abs(rVsWavelength[12] - 0.0) < 0.000001);
// [13] -> ops1=700, rho0=0.5, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[13] + 0.001446) < 0.000001);
// [14] -> ops1=700, rho1=1.635, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[14] - 0.0) < 0.000001);
// [15] -> ops1=700, rho1=1.635, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[15] + 0.000929) < 0.000001);
}
public void validate_ROfFxAndFt_With_Wavelength()
{
// used values for tissue=liver
var scatterer = new PowerLawScatterer(0.84, 0.55);
var hbAbsorber = new ChromophoreAbsorber(ChromophoreType.Hb, 66);
var hbo2Absorber = new ChromophoreAbsorber(ChromophoreType.HbO2, 124);
var fatAbsorber = new ChromophoreAbsorber(ChromophoreType.Fat, 0.02);
var waterAbsorber = new ChromophoreAbsorber(ChromophoreType.H2O, 0.87);
var n = 1.4;
var wvs = new double[] { 650, 700 };
var fxs = new double[] { 0.0, 0.5 };
var fts = new double[] { 0.0, 0.50 };
var tissue = new Tissue(
new IChromophoreAbsorber[] { hbAbsorber, hbo2Absorber, fatAbsorber, waterAbsorber },
scatterer,
"test_tissue",
n);
var ops = wvs.Select(wv => tissue.GetOpticalProperties(wv)).ToArray();
var rVsWavelength = ComputationFactory.ComputeReflectance(
new DistributedPointSourceSDAForwardSolver(),
SolutionDomainType.ROfFxAndFt,
ForwardAnalysisType.R,
new object[]
{
ops,
fxs,
fts
});
// return from ROfFxAndFt is new double[ops.Length * fxs.Length * fts.Length];
// order is: (ops0,fxs0,fts0)real, (ops0,fxs0,ts1)real...(ops0,fxs0,ftsnt-1)real
// (ops0,fxs1,fts0)real, (ops0,fxs1,ts1)real...(ops0,fxs1,ftsnt-1)real
// ...
// (ops0,fxsnf-1,fts0)real,.................(ops0,fxsnf-1,ftsnt-1)real
// ... repeat above with imag, then with ops1...
// [0] -> ops0=650, fx0=0.0, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[0] - 1.890007) < 0.000001); // API match
// [1] -> ops0=650, fx0=0.0, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[1] - 1.888160) < 0.000001);
// [2] -> ops0=650, fx1=0.5, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[2] - 0.562537) < 0.000001); // API match
// [3] -> ops0=650, fx1=0.5, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[3] - 0.562543) < 0.000001);
// [4] -> ops1=700, fx0=0.0, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[4] - 2.118427) < 0.000001); // API match
// [5] -> ops1=700, fx0=0.0, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[5] - 2.113377) < 0.000001);
// [6] -> ops1=700, fx1=0.5, fts0=0.0 real
Assert.IsTrue(Math.Abs(rVsWavelength[6] - 0.543539) < 0.000001); // API match
// [7] -> ops1=700, fx1=0.5, fts1=0.5 real
Assert.IsTrue(Math.Abs(rVsWavelength[7] - 0.543546) < 0.000001);
// [8] -> ops0=650, fx0=0.0, fts0=0.0 imag
Assert.IsTrue(Math.Abs(rVsWavelength[8] - 0.0) < 0.000001); // API match
// [9] -> ops0=650, fx0=0.0, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[9] + 0.045122) < 0.000001);
// [10] -> ops0=650, fx1=0.5, fts0=0.0 imag
Assert.IsTrue(Math.Abs(rVsWavelength[10] - 0.0) < 0.000001); // API match
// [11] -> ops0=650, fx1=0.5, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[11] + 0.000799) < 0.000001);
// [12] -> ops1=700, fx0=0.0, fts0=0.0 imag
Assert.IsTrue(Math.Abs(rVsWavelength[12] - 0.0) < 0.000001); // API match
// [13] -> ops1=700, fx0=0.0, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[13] + 0.071758) < 0.000001);
// [14] -> ops1=700, fx1=0.5, fts0=0.0 imag
Assert.IsTrue(Math.Abs(rVsWavelength[14] - 0.0) < 0.000001); // API match
// [15] -> ops1=700, fx1=0.5, fts1=0.5 imag
Assert.IsTrue(Math.Abs(rVsWavelength[15] + 0.000651) < 0.000001);
}