public static Tuple <double, double> EstimateObjectives(float[,] xImage, float[,] residuals, float[,] psf, float[,] xExplore, float[,] xAccelerated, float lambda, float alpha, float[,] psfCut, float[,] bMap)
{
Tuple <double, double> output = null;
var CONVKernel = PSF.CalcPaddedFourierConvolution(psf, new Rectangle(0, 0, residuals.GetLength(0), residuals.GetLength(1)));
using (var residualsCalculator = new PaddedConvolver(CONVKernel, new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1))))
{
var residualsExplore = new float[xImage.GetLength(0), xImage.GetLength(1)];
var residualsAccelerated = new float[xImage.GetLength(0), xImage.GetLength(1)];
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
residualsExplore[i, j] = xExplore[i, j] - xImage[i, j];
residualsAccelerated[i, j] = xAccelerated[i, j] - xImage[i, j];
}
}
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] - residualsExplore[i, j];
residualsAccelerated[i, j] = residuals[i, j] - residualsAccelerated[i, j];
}
}
var objectiveExplore = Residuals.CalcPenalty(residualsExplore) + ElasticNet.CalcPenalty(xExplore, lambda, alpha);
var objectiveAccelerated = Residuals.CalcPenalty(residualsAccelerated) + ElasticNet.CalcPenalty(xAccelerated, lambda, alpha);
/*
* var CORRKernel = PSF.CalcPaddedFourierCorrelation(psfCut, new Rectangle(0, 0, residuals.GetLength(0), residuals.GetLength(1)));
* var bMapFull = Residuals.CalcBMap(residualsExplore, CORRKernel, new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1)));
* FitsIO.Write(bMap, "bMapOriginal.fits");
* FitsIO.Write(bMapFull, "bMapSanity.fits");
* var diff = new float[bMap.GetLength(0), bMap.GetLength(1)];
* for (int i = 0; i < xImage.GetLength(0); i++)
* for (int j = 0; j < xImage.GetLength(1); j++)
* diff[i, j] = bMap[i, j] - bMapFull[i, j];
* FitsIO.Write(diff, "bmapSanityDiff.fits");*/
output = new Tuple <double, double>(objectiveExplore, objectiveAccelerated);
}
return(output);
}
public void ISTAStep(float[,] xImage, float[,] residuals, float[,] psf, float lambda, float alpha)
{
var xOld = Copy(xImage);
var corrKernel = PSF.CalcPaddedFourierCorrelation(psf, new Rectangle(0, 0, residuals.GetLength(0), residuals.GetLength(1)));
var gradients = Residuals.CalcGradientMap(residuals, corrKernel, new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1)));
var lipschitz = (float)PSF.CalcMaxLipschitz(psf) * xImage.Length;
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
var tmp = gradients[i, j] + xImage[i, j] * lipschitz;
tmp = ElasticNet.ProximalOperator(tmp, lipschitz, lambda, alpha);
xImage[i, j] = tmp;
}
}
//update residuals
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
xOld[i, j] = xImage[i, j] - xOld[i, j];
}
}
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)));
residualsCalculator.ConvolveInPlace(xOld);
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
residuals[i, j] -= xOld[i, j];
}
}
}
private static Tuple <double, double> CalcObjectives(float[,] xImage, float[,] residuals, float[,] psf, float[,] xExplore, float[,] xAccelerated, float lambda, float alpha)
{
Tuple <double, double> output = null;
var CONVKernel = PSF.CalcPaddedFourierConvolution(psf, new Rectangle(0, 0, residuals.GetLength(0), residuals.GetLength(1)));
using (var residualsCalculator = new PaddedConvolver(CONVKernel, new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1))))
{
var residualsExplore = new float[xImage.GetLength(0), xImage.GetLength(1)];
var residualsAccelerated = new float[xImage.GetLength(0), xImage.GetLength(1)];
for (int i = 0; i < xImage.GetLength(0); i++)
{
for (int j = 0; j < xImage.GetLength(1); j++)
{
residualsExplore[i, j] = xExplore[i, j] - xImage[i, j];
residualsAccelerated[i, j] = xAccelerated[i, j] - xImage[i, j];
}
}
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 objectiveAccelerated = Residuals.CalcPenalty(residualsAccelerated) + ElasticNet.CalcPenalty(xAccelerated, lambda, alpha);
output = new Tuple <double, double>(objectiveExplore, objectiveAccelerated);
}
return(output);
}
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);
}
