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 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);
}
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 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_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 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
};
}
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 string GetPlotData(SolutionDomainPlotParameters plotParameters)
{
try
{
var inverseSolver = plotParameters.InverseSolverType;
var igparms = GetParametersInOrder(
GetInitialGuessOpticalProperties(plotParameters.OpticalProperties),
plotParameters.XAxis.AsEnumerable().ToArray(),
plotParameters.SolutionDomain.ToString(),
plotParameters.IndependentAxes.Label,
plotParameters.IndependentAxes.Value);
object[] igparmsConvert = igparms.Values.ToArray();
// get measured data from inverse solver analysis component
var measuredPoints = plotParameters.MeasuredData;
var meas = measuredPoints.Select(p => p.Last()).ToArray(); // get y value
var lbs = new double[] { 0, 0, 0, 0 };
var ubs = new double[]
{
double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity
};
double[] fit = ComputationFactory.SolveInverse(
plotParameters.InverseSolverType,
plotParameters.OptimizerType,
plotParameters.SolutionDomain,
meas,
meas, // set standard deviation to measured to match WPF
plotParameters.OptimizationParameters,
igparmsConvert,
lbs,
ubs);
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;
}
// this needs further development when add in wavelength refer to WPF code
object GetInitialGuessOpticalProperties(OpticalProperties igops)
{
return(new[] { igops });
}
// the following needs to change when Wavelength is added into independent variable list
IDictionary <IndependentVariableAxis, object> GetParametersInOrder(
object opticalProperties, double[] xs, string sd, string independentAxis, double independentValue)
{
// make list of independent vars with independent first then constant
var listIndepVars = new List <IndependentVariableAxis>();
string isConstant = "";
if (sd == "ROfRho")
{
listIndepVars.Add(IndependentVariableAxis.Rho);
}
else if (sd == "ROfRhoAndTime")
{
listIndepVars.Add(IndependentVariableAxis.Rho);
listIndepVars.Add(IndependentVariableAxis.Time);
if (independentAxis == "t")
{
isConstant = "t";
}
else
{
isConstant = "rho";
}
}
else if (sd == "ROfRhoAndFt")
{
listIndepVars.Add(IndependentVariableAxis.Ft);
listIndepVars.Add(IndependentVariableAxis.Rho);
if (independentAxis == "ft")
{
isConstant = "ft";
}
else
{
isConstant = "rho";
}
}
else if (sd == "ROfFx")
{
listIndepVars.Add(IndependentVariableAxis.Fx);
}
else if (sd == "ROfFxAndTime")
{
listIndepVars.Add(IndependentVariableAxis.Time);
listIndepVars.Add(IndependentVariableAxis.Fx);
if (independentAxis == "t")
{
isConstant = "t";
}
else
{
isConstant = "fx";
}
}
else if (sd == "ROfFxAndFt")
{
listIndepVars.Add(IndependentVariableAxis.Ft);
listIndepVars.Add(IndependentVariableAxis.Fx);
if (independentAxis == "ft")
{
isConstant = "ft";
}
else
{
isConstant = "fx";
}
}
// get all parameters in order
var allParameters =
from iva in listIndepVars
where iva != IndependentVariableAxis.Wavelength
orderby GetParameterOrder(iva)
select new KeyValuePair <IndependentVariableAxis, object>(iva,
GetParameterValues(iva, isConstant, independentValue, xs));
// OPs are always first in the list
return
(new KeyValuePair <IndependentVariableAxis, object>(IndependentVariableAxis.Wavelength,
opticalProperties)
.AsEnumerable()
.Concat(allParameters).ToDictionary());
}
int GetParameterOrder(IndependentVariableAxis axis)
{
switch (axis)
{
case IndependentVariableAxis.Wavelength:
return(0);
case IndependentVariableAxis.Rho:
return(1);
case IndependentVariableAxis.Fx:
return(1);
case IndependentVariableAxis.Time:
return(2);
case IndependentVariableAxis.Ft:
return(2);
case IndependentVariableAxis.Z:
return(3);
default:
throw new InvalidEnumArgumentException("There is no Enum of this type");
}
}
// this has commented out code that might come into play when we add wavelength as axis
double[] GetParameterValues(IndependentVariableAxis axis, string isConstant, double independentValue,
double[] xs)
{
if (((axis == IndependentVariableAxis.Rho) && (isConstant == "rho")) ||
((axis == IndependentVariableAxis.Time) && (isConstant == "t")) ||
((axis == IndependentVariableAxis.Fx) && (isConstant == "fx")) ||
((axis == IndependentVariableAxis.Ft) && (isConstant == "ft")))
{
return(new[] { independentValue });
}
else
{
if (axis != IndependentVariableAxis.Time)
{
return(xs.ToArray());
}
else
{
return(xs.ToArray());
}
}
//{
// var positionIndex = 0; //hard-coded for now
// switch (positionIndex)
// {
// case 0:
// default:
// return new[] {independentValue};
//case 1:
//return new[] { SolutionDomainTypeOptionVM.ConstantAxesVMs[1].AxisValue };
//case 2:
// return new[] { SolutionDomainTypeOptionVM.ConstantAxisThreeValue };
//}
//}
//else
//{
// //var numAxes = axis.Count();
// var numAxes = 1;
// var positionIndex = 0; //hard-coded for now
// //var positionIndex = SolutionDomainTypeOptionVM.IndependentVariableAxisOptionVM.SelectedValues.IndexOf(axis);
// switch (numAxes)
// {
// case 1:
// default:
// //return AllRangeVMs[0].Values.ToArray();
// return xs.ToArray();
//case 2:
// switch (positionIndex)
// {
// case 0:
// default:
// return AllRangeVMs[1].Values.ToArray();
// case 1:
// return AllRangeVMs[0].Values.ToArray();
// }
//case 3:
// switch (positionIndex)
// {
// case 0:
// default:
// return AllRangeVMs[2].Values.ToArray();
// case 1:
// return AllRangeVMs[1].Values.ToArray();
// case 2:
// return AllRangeVMs[0].Values.ToArray();
// }
//}
//}
}
}
