public static void Run()
{
var folder = @"C:\dev\GitHub\p9-data\small\fits\simulation_point\";
var data = DataLoading.SimulatedPoints.Load(folder);
var gridSizes = new int[] { 256, 512, 1024, 2048, 4096 };
Directory.CreateDirectory("GPUSpeedup");
var writer = new StreamWriter("GPUSpeedup/GPUSpeedup.txt", false);
writer.WriteLine("imgSize;iterCPU;timeCPU;iterGPU;timeGPU");
foreach (var gridSize in gridSizes)
{
var visibilitiesCount = data.visibilitiesCount;
int subgridsize = 8;
int kernelSize = 4;
int max_nr_timesteps = 1024;
double cellSize = (1.0 * 256 / gridSize) / 3600.0 * Math.PI / 180.0;
var c = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)cellSize, 1, 0.0f);
var metadata = Partitioner.CreatePartition(c, data.uvw, data.frequencies);
var frequencies = FitsIO.ReadFrequencies(Path.Combine(folder, "freq.fits"));
var uvw = FitsIO.ReadUVW(Path.Combine(folder, "uvw.fits"));
var flags = new bool[uvw.GetLength(0), uvw.GetLength(1), frequencies.Length];
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(Path.Combine(folder, "vis.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
var psfGrid = IDG.GridPSF(c, metadata, uvw, flags, frequencies);
var psf = FFT.BackwardFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var residualVis = data.visibilities;
var dirtyGrid = IDG.Grid(c, metadata, residualVis, data.uvw, data.frequencies);
var dirtyImage = FFT.BackwardFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
var totalSize = new Rectangle(0, 0, gridSize, gridSize);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psf, totalSize), new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1)));
var bMapCPU = bMapCalculator.Convolve(dirtyImage);
var bMapGPU = bMapCalculator.Convolve(dirtyImage);
var fastCD = new FastSerialCD(totalSize, psf);
var gpuCD = new GPUSerialCD(totalSize, psf, 1000);
var lambda = 0.5f * fastCD.MaxLipschitz;
var alpha = 0.5f;
var xCPU = new float[gridSize, gridSize];
var cpuResult = fastCD.Deconvolve(xCPU, bMapCPU, lambda, alpha, 10000, 1e-8f);
FitsIO.Write(xCPU, "GPUSpeedup/cpuResult" + gridSize + ".fits");
var xGPU = new float[gridSize, gridSize];
var gpuResult = gpuCD.Deconvolve(xGPU, bMapGPU, lambda, alpha, 10000, 1e-8f);
FitsIO.Write(xCPU, "GPUSpeedup/gpuResult" + gridSize + ".fits");
writer.WriteLine(gridSize + ";" + cpuResult.IterationCount + ";" + cpuResult.ElapsedTime.TotalSeconds + ";" + gpuResult.IterationCount + ";" + gpuResult.ElapsedTime.TotalSeconds);
writer.Flush();
}
writer.Close();
}
public static void GenerateSerialCDExample(string simulatedLocation, string outputFolder)
{
var data = MeasurementData.LoadSimulatedPoints(simulatedLocation);
var cellSize = 1.0 / 3600.0 * Math.PI / 180.0;
var c = new GriddingConstants(data.VisibilitiesCount, 256, 8, 4, 512, (float)cellSize, 1, 0.0);
var metadata = Partitioner.CreatePartition(c, data.UVW, data.Frequencies);
var psfGrid = IDG.GridPSF(c, metadata, data.UVW, data.Flags, data.Frequencies);
var psf = FFT.BackwardFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var corrKernel = PSF.CalcPaddedFourierCorrelation(psf, new Rectangle(0, 0, c.GridSize, c.GridSize));
Directory.CreateDirectory(outputFolder);
var reconstruction = new float[c.GridSize, c.GridSize];
var residualVis = data.Visibilities;
var totalSize = new Rectangle(0, 0, c.GridSize, c.GridSize);
var fastCD = new FastSerialCD(totalSize, psf);
var lambda = 0.50f * fastCD.MaxLipschitz;
var alpha = 0.2f;
for (int cycle = 0; cycle < 100; cycle++)
{
var dirtyGrid = IDG.Grid(c, metadata, residualVis, data.UVW, data.Frequencies);
var dirtyImage = FFT.BackwardFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
var gradients = Residuals.CalcGradientMap(dirtyImage, corrKernel, totalSize);
Tools.WriteToMeltCSV(Common.PSF.Cut(reconstruction), Path.Combine(outputFolder, "model_CD_" + cycle + ".csv"));
Tools.WriteToMeltCSV(gradients, Path.Combine(outputFolder, "gradients_CD_" + cycle + ".csv"));
fastCD.Deconvolve(reconstruction, gradients, lambda, alpha, 4);
FFT.Shift(reconstruction);
var xGrid = FFT.Forward(reconstruction);
FFT.Shift(reconstruction);
var modelVis = IDG.DeGrid(c, metadata, xGrid, data.UVW, data.Frequencies);
residualVis = Visibilities.Substract(data.Visibilities, modelVis, data.Flags);
}
}
private static ReconstructionInfo ReconstructGradientApprox(Data input, float[,] fullPsf, string folder, int cutFactor, int maxMajor, string dirtyPrefix, string xImagePrefix, StreamWriter writer, double objectiveCutoff, float epsilon)
{
var info = new ReconstructionInfo();
var psfCut = PSF.Cut(fullPsf, cutFactor);
var maxSidelobe = PSF.CalcMaxSidelobe(fullPsf, cutFactor);
var totalSize = new Rectangle(0, 0, input.c.GridSize, input.c.GridSize);
var psfBMap = psfCut;
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfBMap, totalSize), new Rectangle(0, 0, psfBMap.GetLength(0), psfBMap.GetLength(1)));
var bMapCalculator2 = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(fullPsf, totalSize), new Rectangle(0, 0, fullPsf.GetLength(0), fullPsf.GetLength(1)));
var fastCD = new FastSerialCD(totalSize, psfCut);
var fastCD2 = new FastSerialCD(totalSize, psfCut);
fastCD2.ResetLipschitzMap(fullPsf);
FitsIO.Write(psfCut, folder + cutFactor + "psf.fits");
var lambda = LAMBDA_GLOBAL * fastCD.MaxLipschitz;
var lambdaTrue = (float)(LAMBDA_GLOBAL * PSF.CalcMaxLipschitz(fullPsf));
var xImage = new float[input.c.GridSize, input.c.GridSize];
var residualVis = input.visibilities;
DeconvolutionResult lastResult = null;
var firstTimeConverged = false;
var lastLambda = 0.0f;
for (int cycle = 0; cycle < maxMajor; cycle++)
{
Console.WriteLine("cycle " + cycle);
var dirtyGrid = IDG.GridW(input.c, input.metadata, residualVis, input.uvw, input.frequencies);
var dirtyImage = FFT.WStackIFFTFloat(dirtyGrid, input.c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, folder + dirtyPrefix + cycle + ".fits");
//calc data and reg penalty
var dataPenalty = Residuals.CalcPenalty(dirtyImage);
var regPenalty = ElasticNet.CalcPenalty(xImage, lambdaTrue, alpha);
var regPenaltyCurrent = ElasticNet.CalcPenalty(xImage, lambda, alpha);
info.lastDataPenalty = dataPenalty;
info.lastRegPenalty = regPenalty;
var maxDirty = Residuals.GetMax(dirtyImage);
var bMap = bMapCalculator.Convolve(dirtyImage);
FitsIO.Write(bMap, folder + dirtyPrefix + "bmap_" + cycle + ".fits");
var maxB = Residuals.GetMax(bMap);
var correctionFactor = Math.Max(maxB / (maxDirty * fastCD.MaxLipschitz), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
var currentLambda = Math.Max(currentSideLobe / alpha, lambda);
writer.Write(cycle + ";" + currentLambda + ";" + currentSideLobe + ";" + ";" + fastCD2.GetAbsMaxDiff(xImage, bMap, lambdaTrue, alpha) + ";" + dataPenalty + ";" + regPenalty + ";" + regPenaltyCurrent + ";");;
writer.Flush();
//check wether we can minimize the objective further with the current psf
var objectiveReached = (dataPenalty + regPenalty) < objectiveCutoff;
var minimumReached = (lastResult != null && lastResult.Converged && fastCD2.GetAbsMaxDiff(xImage, dirtyImage, lambdaTrue, alpha) < MAJOR_EPSILON && currentLambda == lambda);
if (lambda == lastLambda & !firstTimeConverged)
{
firstTimeConverged = true;
minimumReached = false;
}
if (!objectiveReached & !minimumReached)
{
//writer.Write(firstTimeConverged + ";");
//writer.Flush();
info.totalDeconv.Start();
if (!firstTimeConverged)
{
lastResult = fastCD.Deconvolve(xImage, bMap, currentLambda, alpha, 30000, epsilon);
}
else
{
bMap = bMapCalculator2.Convolve(dirtyImage);
//FitsIO.Write(bMap, folder + dirtyPrefix + "bmap_" + cycle + "_full.fits");
maxB = Residuals.GetMax(bMap);
correctionFactor = Math.Max(maxB / (maxDirty * fastCD2.MaxLipschitz), 1.0f);
currentSideLobe = maxB * maxSidelobe * correctionFactor;
currentLambda = Math.Max(currentSideLobe / alpha, lambdaTrue);
info.totalDeconv.Start();
lastResult = fastCD.Deconvolve(xImage, bMap, currentLambda, alpha, 30000, epsilon);
info.totalDeconv.Stop();
}
info.totalDeconv.Stop();
FitsIO.Write(xImage, folder + xImagePrefix + cycle + ".fits");
writer.Write(lastResult.Converged + ";" + lastResult.IterationCount + ";" + lastResult.ElapsedTime.TotalSeconds + "\n");
writer.Flush();
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGridW(input.c, input.metadata, xGrid, input.uvw, input.frequencies);
residualVis = Visibilities.Substract(input.visibilities, modelVis, input.flags);
}
else
{
writer.Write(false + ";0;0\n");
writer.Flush();
break;
}
lastLambda = currentLambda;
}
bMapCalculator.Dispose();
bMapCalculator2.Dispose();
return(info);
}
public bool DeconvolveApprox(float[,] xImage, float[,] residuals, float[,] psf, float lambda, float alpha, Random random, int blockSize, int threadCount, int maxIteration = 100, float epsilon = 1e-4f, bool coldStart = false)
{
var xExplore = Copy(xImage);
var xCorrection = new float[xImage.GetLength(0), xImage.GetLength(1)];
//calculate gradients for each pixel
var PSFCorr = PSF.CalcPaddedFourierCorrelation(psf, new Rectangle(0, 0, residuals.GetLength(0), residuals.GetLength(1)));
var gExplore = Residuals.CalcGradientMap(residuals, PSFCorr, new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1)));
var gCorrection = new float[residuals.GetLength(0), residuals.GetLength(1)];
var psf2 = PSF.CalcPSFSquared(psf);
if (coldStart)
{
var rec = new Rectangle(0, 0, xImage.GetLength(0), xImage.GetLength(1));
var fastCD = new FastSerialCD(rec, rec, psf, psf2);
fastCD.Deconvolve(xExplore, gExplore, lambda, alpha, xImage.GetLength(0));
}
yBlockSize = blockSize;
xBlockSize = blockSize;
degreeOfSeperability = CountNonZero(psf);
tau = threadCount; //number of processors
var maxLipschitz = (float)PSF.CalcMaxLipschitz(psf);
var activeSet = GetActiveSet(xExplore, gExplore, lambda, alpha, maxLipschitz);
var theta = DeconvolveAccelerated(xExplore, xCorrection, gExplore, gCorrection, psf2, ref activeSet, maxLipschitz, lambda, alpha, random, maxIteration, epsilon);
var theta0 = tau / (float)activeSet.Count;
var tmpTheta = theta < 1.0f ? ((theta * theta) / (1.0f - theta)) : theta0;
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
xCorrection[i, j] = tmpTheta * xCorrection[i, j] + xExplore[i, j];
}
}
var objectives = CalcObjectives(xImage, residuals, psf, xExplore, xCorrection, lambda, alpha);
//decide whether we take the correction or explore version
if (objectives.Item2 < objectives.Item1)
{
//correction has the lower objective than explore
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
xImage[i, j] = xCorrection[i, j];
}
}
}
else
{
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
xImage[i, j] = xExplore[i, j];
}
}
}
return(objectives.Item2 < objectives.Item1);
}
public static void GeneratePSFs(string simulatedLocation, string outputFolder)
{
var data = MeasurementData.LoadSimulatedPoints(simulatedLocation);
var c = MeasurementData.CreateSimulatedStandardParams(data.VisibilitiesCount);
var metadata = Partitioner.CreatePartition(c, data.UVW, data.Frequencies);
var psfGrid = IDG.GridPSF(c, metadata, data.UVW, data.Flags, data.Frequencies);
var psf = FFT.BackwardFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
Directory.CreateDirectory(outputFolder);
var maskedPsf = Copy(psf);
Tools.Mask(maskedPsf, 2);
var reverseMasked = Copy(psf);
Tools.ReverseMask(reverseMasked, 2);
var psf2 = PSF.CalcPSFSquared(psf);
var psf2Cut = PSF.CalcPSFSquared(maskedPsf);
Tools.WriteToMeltCSV(psf, Path.Combine(outputFolder, "psf.csv"));
Tools.WriteToMeltCSV(maskedPsf, Path.Combine(outputFolder, "psfCut.csv"));
Tools.WriteToMeltCSV(reverseMasked, Path.Combine(outputFolder, "psfReverseCut.csv"));
Tools.WriteToMeltCSV(psf2, Path.Combine(outputFolder, "psfSquared.csv"));
Tools.WriteToMeltCSV(psf2Cut, Path.Combine(outputFolder, "psfSquaredCut.csv"));
var x = new float[c.GridSize, c.GridSize];
x[10, 10] = 1.0f;
var convKernel = PSF.CalcPaddedFourierConvolution(psf, new Rectangle(0, 0, c.GridSize, c.GridSize));
var corrKernel = PSF.CalcPaddedFourierCorrelation(psf, new Rectangle(0, 0, c.GridSize, c.GridSize));
using (var convolver = new PaddedConvolver(convKernel, new Rectangle(0, 0, c.GridSize, c.GridSize)))
using (var correlator = new PaddedConvolver(corrKernel, new Rectangle(0, 0, c.GridSize, c.GridSize)))
{
var zeroPadded = convolver.Convolve(x);
var psf2Edge = correlator.Convolve(zeroPadded);
Tools.WriteToMeltCSV(zeroPadded, Path.Combine(outputFolder, "psfZeroPadding.csv"));
Tools.WriteToMeltCSV(psf2Edge, Path.Combine(outputFolder, "psfSquaredEdge.csv"));
}
convKernel = PSF.CalcPaddedFourierConvolution(psf, new Rectangle(0, 0, 0, 0));
using (var convolver = new PaddedConvolver(convKernel, new Rectangle(0, 0, 0, 0)))
Tools.WriteToMeltCSV(convolver.Convolve(x), Path.Combine(outputFolder, "psfCircular.csv"));
//================================================= Reconstruct =============================================================
var totalSize = new Rectangle(0, 0, c.GridSize, c.GridSize);
var reconstruction = new float[c.GridSize, c.GridSize];
var fastCD = new FastSerialCD(totalSize, psf);
var lambda = 0.50f * fastCD.MaxLipschitz;
var alpha = 0.2f;
var residualVis = data.Visibilities;
for (int cycle = 0; cycle < 5; cycle++)
{
Console.WriteLine("in cycle " + cycle);
var dirtyGrid = IDG.Grid(c, metadata, residualVis, data.UVW, data.Frequencies);
var dirtyImage = FFT.BackwardFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
var gradients = Residuals.CalcGradientMap(dirtyImage, corrKernel, totalSize);
if (cycle == 0)
{
Tools.WriteToMeltCSV(dirtyImage, Path.Combine(outputFolder, "dirty.csv"));
Tools.WriteToMeltCSV(gradients, Path.Combine(outputFolder, "gradients.csv"));
}
fastCD.Deconvolve(reconstruction, gradients, lambda, alpha, 10000, 1e-5f);
FFT.Shift(reconstruction);
var xGrid = FFT.Forward(reconstruction);
FFT.Shift(reconstruction);
var modelVis = IDG.DeGrid(c, metadata, xGrid, data.UVW, data.Frequencies);
residualVis = Visibilities.Substract(data.Visibilities, modelVis, data.Flags);
}
//FitsIO.Write(reconstruction, Path.Combine(outputFolder,"xImage.fits"));
Tools.WriteToMeltCSV(reconstruction, Path.Combine(outputFolder, "elasticNet.csv"));
}
public static void DebugdWStack()
{
var frequencies = FitsIO.ReadFrequencies(@"C:\dev\GitHub\p9-data\large\fits\meerkat_tiny\freq.fits");
var uvw = FitsIO.ReadUVW(@"C:\dev\GitHub\p9-data\large\fits\meerkat_tiny\uvw0.fits");
var flags = FitsIO.ReadFlags(@"C:\dev\GitHub\p9-data\large\fits\meerkat_tiny\flags0.fits", uvw.GetLength(0), uvw.GetLength(1), frequencies.Length);
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(@"C:\dev\GitHub\p9-data\large\fits\meerkat_tiny\vis0.fits", uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
for (int i = 1; i < 8; i++)
{
var uvw0 = FitsIO.ReadUVW(@"C:\dev\GitHub\p9-data\large\fits\meerkat_tiny\uvw" + i + ".fits");
var flags0 = FitsIO.ReadFlags(@"C:\dev\GitHub\p9-data\large\fits\meerkat_tiny\flags" + i + ".fits", uvw0.GetLength(0), uvw0.GetLength(1), frequencies.Length);
var visibilities0 = FitsIO.ReadVisibilities(@"C:\dev\GitHub\p9-data\large\fits\meerkat_tiny\vis" + i + ".fits", uvw0.GetLength(0), uvw0.GetLength(1), frequencies.Length, norm);
uvw = FitsIO.Stitch(uvw, uvw0);
flags = FitsIO.Stitch(flags, flags0);
visibilities = FitsIO.Stitch(visibilities, visibilities0);
}
var maxW = 0.0;
for (int i = 0; i < uvw.GetLength(0); i++)
{
for (int j = 0; j < uvw.GetLength(1); j++)
{
maxW = Math.Max(maxW, Math.Abs(uvw[i, j, 2]));
}
}
maxW = Partitioner.MetersToLambda(maxW, frequencies[frequencies.Length - 1]);
var visCount2 = 0;
for (int i = 0; i < flags.GetLength(0); i++)
{
for (int j = 0; j < flags.GetLength(1); j++)
{
for (int k = 0; k < flags.GetLength(2); k++)
{
if (!flags[i, j, k])
{
visCount2++;
}
}
}
}
var visibilitiesCount = visCount2;
int gridSize = 4096;
int subgridsize = 16;
int kernelSize = 8;
int max_nr_timesteps = 1024;
double cellSize = 1.6 / 3600.0 * PI / 180.0;
int wLayerCount = 32;
double wStep = maxW / (wLayerCount);
var c = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)cellSize, wLayerCount, wStep);
var c2 = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)cellSize, 1, 0.0);
var metadata = Partitioner.CreatePartition(c, uvw, frequencies);
var psfVis = new Complex[uvw.GetLength(0), uvw.GetLength(1), frequencies.Length];
for (int i = 0; i < visibilities.GetLength(0); i++)
{
for (int j = 0; j < visibilities.GetLength(1); j++)
{
for (int k = 0; k < visibilities.GetLength(2); k++)
{
if (!flags[i, j, k])
{
psfVis[i, j, k] = new Complex(1.0, 0);
}
else
{
psfVis[i, j, k] = new Complex(0, 0);
}
}
}
}
var psfGrid = IDG.GridW(c, metadata, psfVis, uvw, frequencies);
var psf = FFT.WStackIFFTFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
FitsIO.Write(psf, "psfWStack.fits");
var totalSize = new Rectangle(0, 0, gridSize, gridSize);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psf, totalSize), new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1)));
var fastCD = new FastSerialCD(totalSize, psf);
var lambda = 0.4f * fastCD.MaxLipschitz;
var alpha = 0.1f;
var xImage = new float[gridSize, gridSize];
var residualVis = visibilities;
for (int cycle = 0; cycle < 8; cycle++)
{
var dirtyGrid = IDG.GridW(c, metadata, residualVis, uvw, frequencies);
var dirty = FFT.WStackIFFTFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirty);
FitsIO.Write(dirty, "dirty_" + cycle + ".fits");
bMapCalculator.ConvolveInPlace(dirty);
FitsIO.Write(dirty, "bMap_" + cycle + ".fits");
var result = fastCD.Deconvolve(xImage, dirty, lambda, alpha, 10000, 1e-4f);
FitsIO.Write(xImage, "xImageGreedy" + cycle + ".fits");
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGridW(c, metadata, xGrid, uvw, frequencies);
var modelGrid = IDG.GridW(c, metadata, modelVis, uvw, frequencies);
var model = FFT.WStackIFFTFloat(modelGrid, c.VisibilitiesCount);
FFT.Shift(model);
FitsIO.Write(model, "model_" + cycle + ".fits");
residualVis = Visibilities.Substract(visibilities, modelVis, flags);
}
}
private static ReconstructionInfo Reconstruct(Data input, float fullLipschitz, float[,] maskedPsf, string folder, float maskFactor, int maxMajor, string dirtyPrefix, string xImagePrefix, StreamWriter writer, double objectiveCutoff, float epsilon, bool maskPsf2)
{
var info = new ReconstructionInfo();
var totalSize = new Rectangle(0, 0, input.c.GridSize, input.c.GridSize);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(maskedPsf, totalSize), new Rectangle(0, 0, maskedPsf.GetLength(0), maskedPsf.GetLength(1)));
var maskedPsf2 = PSF.CalcPSFSquared(maskedPsf);
if (maskPsf2)
{
Mask(maskedPsf2, 1e-5f);
}
writer.WriteLine((CountNonZero(maskedPsf2) - maskedPsf2.Length) / (double)maskedPsf2.Length);
var fastCD = new FastSerialCD(totalSize, totalSize, maskedPsf, maskedPsf2);
FitsIO.Write(maskedPsf, folder + maskFactor + "psf.fits");
var lambda = 0.4f * fastCD.MaxLipschitz;
var lambdaTrue = 0.4f * fullLipschitz;
var alpha = 0.1f;
var xImage = new float[input.c.GridSize, input.c.GridSize];
var residualVis = input.visibilities;
DeconvolutionResult lastResult = null;
for (int cycle = 0; cycle < maxMajor; cycle++)
{
Console.WriteLine("cycle " + cycle);
var dirtyGrid = IDG.GridW(input.c, input.metadata, residualVis, input.uvw, input.frequencies);
var dirtyImage = FFT.WStackIFFTFloat(dirtyGrid, input.c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, folder + dirtyPrefix + cycle + ".fits");
//calc data and reg penalty
var dataPenalty = Residuals.CalcPenalty(dirtyImage);
var regPenalty = ElasticNet.CalcPenalty(xImage, lambdaTrue, alpha);
var regPenaltyCurrent = ElasticNet.CalcPenalty(xImage, lambda, alpha);
info.lastDataPenalty = dataPenalty;
info.lastRegPenalty = regPenalty;
bMapCalculator.ConvolveInPlace(dirtyImage);
//FitsIO.Write(dirtyImage, folder + dirtyPrefix + "bmap_" + cycle + ".fits");
var currentLambda = lambda;
writer.Write(cycle + ";" + currentLambda + ";" + Residuals.GetMax(dirtyImage) + ";" + dataPenalty + ";" + regPenalty + ";" + regPenaltyCurrent + ";");
writer.Flush();
//check wether we can minimize the objective further with the current psf
var objectiveReached = (dataPenalty + regPenalty) < objectiveCutoff;
var minimumReached = (lastResult != null && lastResult.IterationCount < 100 && lastResult.Converged);
if (!objectiveReached & !minimumReached)
{
info.totalDeconv.Start();
lastResult = fastCD.Deconvolve(xImage, dirtyImage, currentLambda, alpha, 50000, epsilon);
info.totalDeconv.Stop();
FitsIO.Write(xImage, folder + xImagePrefix + cycle + ".fits");
writer.Write(lastResult.Converged + ";" + lastResult.IterationCount + ";" + lastResult.ElapsedTime.TotalSeconds + "\n");
writer.Flush();
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGridW(input.c, input.metadata, xGrid, input.uvw, input.frequencies);
residualVis = Visibilities.Substract(input.visibilities, modelVis, input.flags);
}
else
{
writer.Write(false + ";0;0");
writer.Flush();
break;
}
}
return(info);
}
private static void ReconstructSerial(MeasurementData input, GriddingConstants c, float[,] fullPsf, string folder, string file, int processorCount)
{
var totalWatch = new Stopwatch();
var currentWatch = new Stopwatch();
var totalSize = new Rectangle(0, 0, c.GridSize, c.GridSize);
var psfCut = PSF.Cut(fullPsf, CUT_FACTOR_SERIAL);
var maxSidelobe = PSF.CalcMaxSidelobe(fullPsf, CUT_FACTOR_SERIAL);
var fastCD = new FastSerialCD(totalSize, psfCut, processorCount);
var metadata = Partitioner.CreatePartition(c, input.UVW, input.Frequencies);
var writer = new StreamWriter(folder + "/" + file + ".txt");
var psfBMap = psfCut;
using (var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfBMap, totalSize), new Rectangle(0, 0, psfBMap.GetLength(0), psfBMap.GetLength(1))))
using (var bMapCalculator2 = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(fullPsf, totalSize), new Rectangle(0, 0, fullPsf.GetLength(0), fullPsf.GetLength(1))))
{
var currentBMapCalculator = bMapCalculator;
var maxLipschitz = PSF.CalcMaxLipschitz(psfCut);
var lambda = (float)(LAMBDA * maxLipschitz);
var lambdaTrue = (float)(LAMBDA * PSF.CalcMaxLipschitz(fullPsf));
var alpha = ALPHA;
var switchedToOtherPsf = false;
var xImage = new float[c.GridSize, c.GridSize];
var residualVis = input.Visibilities;
DeconvolutionResult lastResult = null;
for (int cycle = 0; cycle < 6; cycle++)
{
Console.WriteLine("cycle " + cycle);
var dirtyGrid = IDG.GridW(c, metadata, residualVis, input.UVW, input.Frequencies);
var dirtyImage = FFT.WStackIFFTFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, folder + "/dirty" + cycle + ".fits");
currentWatch.Restart();
totalWatch.Start();
var maxDirty = Residuals.GetMax(dirtyImage);
var bMap = bMapCalculator.Convolve(dirtyImage);
var maxB = Residuals.GetMax(bMap);
var correctionFactor = Math.Max(maxB / (maxDirty * fastCD.MaxLipschitz), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
var currentLambda = Math.Max(currentSideLobe / alpha, lambda);
var objective = Residuals.CalcPenalty(dirtyImage) + ElasticNet.CalcPenalty(xImage, lambdaTrue, alpha);
var absMax = fastCD.GetAbsMaxDiff(xImage, bMap, lambdaTrue, alpha);
if (absMax >= MAJOR_STOP)
{
lastResult = fastCD.Deconvolve(xImage, bMap, currentLambda, alpha, 30000, 1e-5f);
}
if (lambda == currentLambda & !switchedToOtherPsf)
{
currentBMapCalculator = bMapCalculator2;
lambda = lambdaTrue;
switchedToOtherPsf = true;
}
currentWatch.Stop();
totalWatch.Stop();
writer.WriteLine(cycle + ";" + currentLambda + ";" + objective + ";" + absMax + ";" + lastResult.IterationCount + ";" + totalWatch.Elapsed.TotalSeconds + ";" + currentWatch.Elapsed.TotalSeconds);
writer.Flush();
if (absMax < MAJOR_STOP)
{
break;
}
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGridW(c, metadata, xGrid, input.UVW, input.Frequencies);
residualVis = Visibilities.Substract(input.Visibilities, modelVis, input.Flags);
}
}
}