public MPIGreedyCD(Intracommunicator comm, Rectangle totalSize, Rectangle imageSection, float[,] psf)
{
this.comm = comm;
this.imageSection = imageSection;
psf2 = PSF.CalcPSFSquared(psf);
aMap = PSF.CalcAMap(psf, totalSize, imageSection);
}
/// <summary>
///
/// </summary>
/// <param name="totalSize"></param>
/// <param name="psf"></param>
/// <param name="processorCount">Number of async. processors to use</param>
/// <param name="concurrentIterations">Number of async iterations each processor performs in one active set iteration</param>
/// <param name="searchFraction"></param>
public ParallelCoordinateDescent(Rectangle totalSize, float[,] psf, int processorCount = 8, int concurrentIterations = 1000, float searchFraction = 0.1f)
{
this.totalSize = totalSize;
this.psf = psf;
this.psf2 = PSF.CalcPSFSquared(psf);
this.aMap = PSF.CalcAMap(psf, totalSize);
this.processorCount = processorCount;
this.concurrentIterations = concurrentIterations;
this.searchFraction = searchFraction;
}
public ApproxFast(Rectangle totalSize, float[,] psf, int threadCount, int blockSize, float randomFraction, float searchFraction, bool useCDColdStart, bool useAcceleration = true, bool useRestarting = true)
{
this.totalSize = totalSize;
this.psf = psf;
this.psf2 = PSF.CalcPSFSquared(psf);
MaxLipschitz = (float)PSF.CalcMaxLipschitz(psf);
this.aMap = PSF.CalcAMap(psf, totalSize);
this.threadCount = threadCount;
this.blockSize = blockSize;
this.randomFraction = randomFraction;
this.searchFraction = searchFraction;
this.useAcceleration = useAcceleration;
this.useRestarting = useRestarting;
}
public void ResetLipschitzMap(float[,] psf)
{
var psf2Local = PSF.CalcPSFSquared(psf);
var maxFull = Residuals.GetMax(psf2Local);
MaxLipschitz = maxFull;
aMap = PSF.CalcAMap(psf, patch);
var maxCut = Residuals.GetMax(psf2);
for (int i = 0; i < psf2.GetLength(0); i++)
{
for (int j = 0; j < psf2.GetLength(1); j++)
{
psf2[i, j] *= (maxFull / maxCut);
}
}
}
public void ResetAMap(float[,] psf)
{
var psf2Local = PSF.CalcPSFSquared(psf);
var maxFull = Residuals.GetMax(psf2Local);
aMap = PSF.CalcAMap(psf, totalSize);
this.psf = psf;
var maxCut = Residuals.GetMax(psf2);
for (int i = 0; i < psf2.GetLength(0); i++)
{
for (int j = 0; j < psf2.GetLength(1); j++)
{
psf2[i, j] *= (maxFull / maxCut);
}
}
}
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 bool DeconvolveGreedy(float[,] xImage, float[,] residuals, float[,] psf, float lambda, float alpha, Random random, int blockSize, int threadCount, int maxIteration = 100, float epsilon = 1e-4f)
{
var xExplore = Copy(xImage);
var xCorrection = new float[xImage.GetLength(0), xImage.GetLength(1)];
var PSFCorr = PSF.CalcPaddedFourierCorrelation(psf, new Rectangle(0, 0, residuals.GetLength(0), residuals.GetLength(1)));
//calculate gradients for each pixel
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);
FitsIO.Write(gExplore, "gExplore.fits");
yBlockSize = blockSize;
xBlockSize = blockSize;
degreeOfSeperability = CountNonZero(psf);
tau = threadCount; //number of processors
var maxLipschitz = (float)PSF.CalcMaxLipschitz(psf);
var theta = Greedy(xExplore, xCorrection, gExplore, gCorrection, psf2, maxLipschitz, lambda, alpha, random, maxIteration, epsilon);
//decide which version should be taken#
var CONVKernel = PSF.CalcPaddedFourierConvolution(psf, new Rectangle(0, 0, residuals.GetLength(0), residuals.GetLength(1)));
var residualsCalculator = new PaddedConvolver(CONVKernel, new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1)));
var theta0 = tau / (float)(xExplore.Length / (yBlockSize * xBlockSize));
var tmpTheta = theta < 1.0f ? ((theta * theta) / (1.0f - theta)) : theta0;
//calculate residuals
var residualsExplore = Copy(xExplore);
var residualsAccelerated = Copy(xExplore);
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
residualsExplore[i, j] -= xImage[i, j];
residualsAccelerated[i, j] += tmpTheta * xCorrection[i, j] - xImage[i, j];
xCorrection[i, j] = tmpTheta * xCorrection[i, j] + xExplore[i, j];
}
}
FitsIO.Write(xExplore, "xExplore.fits");
FitsIO.Write(xCorrection, "xAcc.fits");
residualsCalculator.ConvolveInPlace(residualsExplore);
residualsCalculator.ConvolveInPlace(residualsAccelerated);
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
residualsExplore[i, j] -= residuals[i, j];
residualsAccelerated[i, j] -= residuals[i, j];
}
}
var objectiveExplore = Residuals.CalcPenalty(residualsExplore) + ElasticNet.CalcPenalty(xExplore, lambda, alpha);
var objectiveAcc = Residuals.CalcPenalty(residualsAccelerated) + ElasticNet.CalcPenalty(xCorrection, lambda, alpha);
if (objectiveAcc < objectiveExplore)
{
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(objectiveAcc < objectiveExplore);
}
public FastSerialCD(Rectangle totalSize, float[,] psf, int processorLimit = -1) :
this(totalSize, totalSize, psf, PSF.CalcPSFSquared(psf), processorLimit)
{
}
public GPUSerialCD(Rectangle totalSize, float[,] psf, int nrBatchIterations) :
this(totalSize, psf, PSF.CalcPSFSquared(psf), nrBatchIterations)
{
}
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);
}