public static void CalcESOs()
{
int gridSize = 3072;
var psfCuts = new int[] { 4, 8, 16, 32, 64 };
var psf = new float[gridSize, gridSize];
var factor = 4;
foreach (var cut in psfCuts)
{
var psfCut = PSF.Cut(psf, cut);
var ESO = CalcESO(8, psfCut.Length * factor, gridSize * gridSize);
}
Console.WriteLine("");
Console.WriteLine("");
Console.WriteLine("");
var processors = new int[] { 1, 4, 8, 16, 32, 64 };
var psfCut32 = PSF.Cut(psf, 32);
foreach (var p in processors)
{
var ESO = CalcESO(p, psfCut32.Length * factor, gridSize * gridSize);
Console.WriteLine(ESO);
}
}
private static void ReconstructRandom(MeasurementData input, GriddingConstants c, float[,] psf, int blockSize, int iterCount, string file)
{
var cutFactor = 8;
var totalSize = new Rectangle(0, 0, c.GridSize, c.GridSize);
var psfCut = PSF.Cut(psf, cutFactor);
var maxSidelobe = PSF.CalcMaxSidelobe(psf, cutFactor);
var maxLipschitzCut = PSF.CalcMaxLipschitz(psfCut);
var lambda = (float)(LAMBDA * PSF.CalcMaxLipschitz(psfCut));
var lambdaTrue = (float)(LAMBDA * PSF.CalcMaxLipschitz(psf));
var alpha = ALPHA;
ApproxFast.LAMBDA_TEST = lambdaTrue;
ApproxFast.ALPHA_TEST = alpha;
var metadata = Partitioner.CreatePartition(c, input.UVW, input.Frequencies);
var random = new Random(123);
var approx = new ApproxFast(totalSize, psfCut, 8, blockSize, 0.0f, 0.0f, false, true, false);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1)));
var data = new ApproxFast.TestingData(new StreamWriter(file + "_tmp.txt"));
var xImage = new float[c.GridSize, c.GridSize];
var xCorr = Copy(xImage);
var residualVis = input.Visibilities;
var dirtyGrid = IDG.GridW(c, metadata, residualVis, input.UVW, input.Frequencies);
var dirtyImage = FFT.WStackIFFTFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
var maxDirty = Residuals.GetMax(dirtyImage);
var bMap = bMapCalculator.Convolve(dirtyImage);
var maxB = Residuals.GetMax(bMap);
var correctionFactor = Math.Max(maxB / (maxDirty * maxLipschitzCut), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
var currentLambda = (float)Math.Max(currentSideLobe / alpha, lambda);
var gCorr = new float[c.GridSize, c.GridSize];
var shared = new ApproxFast.SharedData(currentLambda, alpha, 1, 1, 8, CountNonZero(psfCut), approx.psf2, approx.aMap, xImage, xCorr, bMap, gCorr, new Random());
shared.ActiveSet = ApproxFast.GetActiveSet(xImage, bMap, shared.YBlockSize, shared.XBlockSize, lambda, alpha, shared.AMap);
shared.BlockLock = new int[shared.ActiveSet.Count];
shared.maxLipschitz = (float)PSF.CalcMaxLipschitz(psfCut);
shared.MaxConcurrentIterations = 1000;
approx.DeconvolveConcurrentTest(data, 0, 0, 0.0, shared, 1, 1e-5f, Copy(xImage), dirtyImage, psfCut, psf);
var output = Tools.LMC.CutN132Remnant(xImage);
Tools.WriteToMeltCSV(output.Item1, file + "_1k.csv", output.Item2, output.Item3);
FitsIO.Write(output.Item1, file + "_1k.fits");
FitsIO.Write(xImage, file + "_1k2.fits");
approx.DeconvolveConcurrentTest(data, 0, 0, 0.0, shared, iterCount, 1e-5f, Copy(xImage), dirtyImage, psfCut, psf);
output = Tools.LMC.CutN132Remnant(xImage);
Tools.WriteToMeltCSV(output.Item1, file + "_10k.csv", output.Item2, output.Item3);
FitsIO.Write(output.Item1, file + "_10k.fits");
FitsIO.Write(xImage, file + "_10k2.fits");
}
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);
}
private static void ReconstructMinorCycle(MeasurementData input, GriddingConstants c, int cutFactor, float[,] fullPsf, string folder, string file, int minorCycles, float searchPercent, bool useAccelerated = true, int blockSize = 1, int maxCycle = 6)
{
var metadata = Partitioner.CreatePartition(c, input.UVW, input.Frequencies);
var totalSize = new Rectangle(0, 0, c.GridSize, c.GridSize);
var psfCut = PSF.Cut(fullPsf, cutFactor);
var maxSidelobe = PSF.CalcMaxSidelobe(fullPsf, cutFactor);
var sidelobeHalf = PSF.CalcMaxSidelobe(fullPsf, 2);
var random = new Random(123);
var approx = new ApproxFast(totalSize, psfCut, 8, blockSize, 0.1f, searchPercent, false, useAccelerated);
using (var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1))))
using (var bMapCalculator2 = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(fullPsf, totalSize), new Rectangle(0, 0, fullPsf.GetLength(0), fullPsf.GetLength(1))))
using (var residualsConvolver = new PaddedConvolver(totalSize, fullPsf))
{
var currentBMapCalculator = bMapCalculator;
var maxLipschitz = PSF.CalcMaxLipschitz(psfCut);
var lambda = (float)(LAMBDA * maxLipschitz);
var lambdaTrue = (float)(LAMBDA * PSF.CalcMaxLipschitz(fullPsf));
var alpha = ALPHA;
ApproxFast.LAMBDA_TEST = lambdaTrue;
ApproxFast.ALPHA_TEST = alpha;
var switchedToOtherPsf = false;
var writer = new StreamWriter(folder + "/" + file + "_lambda.txt");
var data = new ApproxFast.TestingData(new StreamWriter(folder + "/" + file + ".txt"));
var xImage = new float[c.GridSize, c.GridSize];
var residualVis = input.Visibilities;
for (int cycle = 0; cycle < maxCycle; 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");
var minLambda = 0.0f;
var dirtyCopy = Copy(dirtyImage);
var xCopy = Copy(xImage);
var currentLambda = 0f;
//var residualsConvolver = new PaddedConvolver(PSF.CalcPaddedFourierConvolution(fullPsf, totalSize), new Rectangle(0, 0, fullPsf.GetLength(0), fullPsf.GetLength(1)));
for (int minorCycle = 0; minorCycle < minorCycles; minorCycle++)
{
FitsIO.Write(dirtyImage, folder + "/dirtyMinor_" + minorCycle + ".fits");
var maxDirty = Residuals.GetMax(dirtyImage);
var bMap = currentBMapCalculator.Convolve(dirtyImage);
var maxB = Residuals.GetMax(bMap);
var correctionFactor = Math.Max(maxB / (maxDirty * maxLipschitz), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
currentLambda = (float)Math.Max(currentSideLobe / alpha, lambda);
if (minorCycle == 0)
{
minLambda = (float)(maxB * sidelobeHalf * correctionFactor / alpha);
}
if (currentLambda < minLambda)
{
currentLambda = minLambda;
}
writer.WriteLine(cycle + ";" + minorCycle + ";" + currentLambda + ";" + minLambda);
writer.Flush();
approx.DeconvolveTest(data, cycle, minorCycle, xImage, dirtyImage, psfCut, fullPsf, currentLambda, alpha, random, 15, 1e-5f);
FitsIO.Write(xImage, folder + "/xImageMinor_" + minorCycle + ".fits");
if (currentLambda == lambda | currentLambda == minLambda)
{
break;
}
Console.WriteLine("resetting residuals!!");
//reset dirtyImage with full PSF
var residualsUpdate = new float[xImage.GetLength(0), xImage.GetLength(1)];
Parallel.For(0, xCopy.GetLength(0), (i) =>
{
for (int j = 0; j < xCopy.GetLength(1); j++)
{
residualsUpdate[i, j] = xImage[i, j] - xCopy[i, j];
}
});
residualsConvolver.ConvolveInPlace(residualsUpdate);
Parallel.For(0, xCopy.GetLength(0), (i) =>
{
for (int j = 0; j < xCopy.GetLength(1); j++)
{
dirtyImage[i, j] = dirtyCopy[i, j] - residualsUpdate[i, j];
}
});
}
if (currentLambda == lambda & !switchedToOtherPsf)
{
approx.ResetAMap(fullPsf);
currentBMapCalculator = bMapCalculator2;
lambda = lambdaTrue;
switchedToOtherPsf = true;
writer.WriteLine("switched");
writer.Flush();
}
FitsIO.Write(xImage, folder + "/xImage_" + cycle + ".fits");
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);
}
writer.Close();
}
}
private static void Reconstruct(Data input, int cutFactor, float[,] fullPsf, string folder, string file, int threads, int blockSize, bool accelerated, float randomPercent, float searchPercent)
{
var totalSize = new Rectangle(0, 0, input.c.GridSize, input.c.GridSize);
var psfCut = PSF.Cut(fullPsf, cutFactor);
var maxSidelobe = PSF.CalcMaxSidelobe(fullPsf, cutFactor);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1)));
var random = new Random(123);
var approx = new ApproxFast(totalSize, psfCut, threads, blockSize, randomPercent, searchPercent, false, true);
var maxLipschitzCut = PSF.CalcMaxLipschitz(psfCut);
var lambda = (float)(LAMBDA * PSF.CalcMaxLipschitz(psfCut));
var lambdaTrue = (float)(LAMBDA * PSF.CalcMaxLipschitz(fullPsf));
var alpha = ALPHA;
ApproxFast.LAMBDA_TEST = lambdaTrue;
ApproxFast.ALPHA_TEST = alpha;
var switchedToOtherPsf = false;
var writer = new StreamWriter(folder + "/" + file + "_lambda.txt");
var data = new ApproxFast.TestingData(new StreamWriter(folder + "/" + file + ".txt"));
var xImage = new float[input.c.GridSize, input.c.GridSize];
var residualVis = input.visibilities;
for (int cycle = 0; cycle < 7; 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 + "/dirty" + cycle + ".fits");
var maxDirty = Residuals.GetMax(dirtyImage);
var bMap = bMapCalculator.Convolve(dirtyImage);
var maxB = Residuals.GetMax(bMap);
var correctionFactor = Math.Max(maxB / (maxDirty * maxLipschitzCut), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
var currentLambda = (float)Math.Max(currentSideLobe / alpha, lambda);
writer.WriteLine("cycle" + ";" + currentLambda);
writer.Flush();
approx.DeconvolveTest(data, cycle, 0, xImage, dirtyImage, psfCut, fullPsf, currentLambda, alpha, random, 15, 1e-5f);
FitsIO.Write(xImage, folder + "/xImage_" + cycle + ".fits");
if (currentLambda == lambda & !switchedToOtherPsf)
{
approx.ResetAMap(fullPsf);
lambda = lambdaTrue;
switchedToOtherPsf = true;
writer.WriteLine("switched");
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);
}
writer.Close();
}
public static void GenerateCLEANExample(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);
Directory.CreateDirectory(outputFolder);
var reconstruction = new float[c.GridSize, c.GridSize];
var residualVis = data.Visibilities;
for (int cycle = 0; cycle < 10; 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);
//FitsIO.Write(dirtyImage, Path.Combine(outputFolder, "dirty_CLEAN_" + cycle + ".fits"));
Tools.WriteToMeltCSV(dirtyImage, Path.Combine(outputFolder, "dirty_CLEAN_" + cycle + ".csv"));
var maxY = -1;
var maxX = -1;
var max = 0.0f;
for (int y = 0; y < dirtyImage.GetLength(0); y++)
{
for (int x = 0; x < dirtyImage.GetLength(1); x++)
{
if (max < Math.Abs(dirtyImage[y, x]))
{
maxY = y;
maxX = x;
max = Math.Abs(dirtyImage[y, x]);
}
}
}
//FitsIO.Write(reconstruction, Path.Combine(outputFolder, "model_CLEAN_" + cycle + ".fits"));
Tools.WriteToMeltCSV(PSF.Cut(reconstruction), Path.Combine(outputFolder, "model_CLEAN_" + cycle + ".csv"));
reconstruction[maxY, maxX] += 0.5f * dirtyImage[maxY, maxX];
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);
}
var cleanbeam = new float[c.GridSize, c.GridSize];
var x0 = c.GridSize / 2;
var y0 = c.GridSize / 2;
for (int y = 0; y < cleanbeam.GetLength(0); y++)
{
for (int x = 0; x < cleanbeam.GetLength(1); x++)
{
cleanbeam[y, x] = (float)(1.0 * Math.Exp(-(Math.Pow(x0 - x, 2) / 16 + Math.Pow(y0 - y, 2) / 16)));
}
}
FitsIO.Write(cleanbeam, Path.Combine(outputFolder, "clbeam.fits"));
FFT.Shift(cleanbeam);
var CL = FFT.Forward(cleanbeam);
var REC = FFT.Forward(reconstruction);
var CONF = Common.Fourier2D.Multiply(REC, CL);
var cleaned = FFT.BackwardFloat(CONF, reconstruction.Length);
//FFT.Shift(cleaned);
//FitsIO.Write(cleaned, Path.Combine(outputFolder, "rec_CLEAN.fits"));
Tools.WriteToMeltCSV(PSF.Cut(cleaned), Path.Combine(outputFolder, "rec_CLEAN.csv"));
}
/// <summary>
/// Major cycle implementation for the Serial CD
/// </summary>
/// <param name="obsName"></param>
/// <param name="data"></param>
/// <param name="c"></param>
/// <param name="useGPU"></param>
/// <param name="psfCutFactor"></param>
/// <param name="maxMajorCycle"></param>
/// <param name="lambda"></param>
/// <param name="alpha"></param>
/// <param name="deconvIterations"></param>
/// <param name="deconvEpsilon"></param>
public static void ReconstructSerialCD(string obsName, MeasurementData data, GriddingConstants c, bool useGPU, int psfCutFactor, int maxMajorCycle, float lambda, float alpha, int deconvIterations, float deconvEpsilon)
{
var metadata = Partitioner.CreatePartition(c, data.UVW, data.Frequencies);
var psfVis = new Complex[data.UVW.GetLength(0), data.UVW.GetLength(1), data.Frequencies.Length];
for (int i = 0; i < data.Visibilities.GetLength(0); i++)
{
for (int j = 0; j < data.Visibilities.GetLength(1); j++)
{
for (int k = 0; k < data.Visibilities.GetLength(2); k++)
{
if (!data.Flags[i, j, k])
{
psfVis[i, j, k] = new Complex(1.0, 0);
}
else
{
psfVis[i, j, k] = new Complex(0, 0);
}
}
}
}
Console.WriteLine("gridding psf");
var psfGrid = IDG.GridW(c, metadata, psfVis, data.UVW, data.Frequencies);
var psf = FFT.WStackIFFTFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var totalWatch = new Stopwatch();
var currentWatch = new Stopwatch();
var totalSize = new Rectangle(0, 0, c.GridSize, c.GridSize);
var psfCut = PSF.Cut(psf, psfCutFactor);
var maxSidelobe = PSF.CalcMaxSidelobe(psf, psfCutFactor);
IDeconvolver deconvolver = null;
if (useGPU & GPUSerialCD.IsGPUSupported())
{
deconvolver = new GPUSerialCD(totalSize, psfCut, 1000);
}
else if (useGPU & !GPUSerialCD.IsGPUSupported())
{
Console.WriteLine("GPU not supported by library. Switching to CPU implementation");
deconvolver = new FastSerialCD(totalSize, psfCut);
}
else
{
deconvolver = new FastSerialCD(totalSize, psfCut);
}
var psfBMap = psfCut;
using (var gCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfBMap, totalSize), new Rectangle(0, 0, psfBMap.GetLength(0), psfBMap.GetLength(1))))
using (var gCalculator2 = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psf, totalSize), new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1))))
{
var currentGCalculator = gCalculator;
var maxLipschitz = PSF.CalcMaxLipschitz(psfCut);
var lambdaLipschitz = (float)(lambda * maxLipschitz);
var lambdaTrue = (float)(lambda * PSF.CalcMaxLipschitz(psf));
var switchedToOtherPsf = false;
var xImage = new float[c.GridSize, c.GridSize];
var residualVis = data.Visibilities;
DeconvolutionResult lastResult = null;
for (int cycle = 0; cycle < maxMajorCycle; cycle++)
{
Console.WriteLine("Beginning Major cycle " + cycle);
var dirtyGrid = IDG.GridW(c, metadata, residualVis, data.UVW, data.Frequencies);
var dirtyImage = FFT.WStackIFFTFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, obsName + "_dirty_serial_majorCycle" + cycle + ".fits");
currentWatch.Restart();
totalWatch.Start();
var maxDirty = Residuals.GetMax(dirtyImage);
var gradients = gCalculator.Convolve(dirtyImage);
var maxB = Residuals.GetMax(gradients);
var correctionFactor = Math.Max(maxB / (maxDirty * maxLipschitz), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
var currentLambda = (float)Math.Max(currentSideLobe / alpha, lambdaLipschitz);
var objective = Residuals.CalcPenalty(dirtyImage) + ElasticNet.CalcPenalty(xImage, lambdaTrue, alpha);
var absMax = deconvolver.GetAbsMaxDiff(xImage, gradients, lambdaTrue, alpha);
if (absMax >= MAJOR_EPSILON)
{
lastResult = deconvolver.Deconvolve(xImage, gradients, currentLambda, alpha, deconvIterations, deconvEpsilon);
}
if (lambda == currentLambda & !switchedToOtherPsf)
{
currentGCalculator = gCalculator2;
lambda = lambdaTrue;
maxLipschitz = PSF.CalcMaxLipschitz(psf);
switchedToOtherPsf = true;
}
FitsIO.Write(xImage, obsName + "_model_serial_majorCycle" + cycle + ".fits");
currentWatch.Stop();
totalWatch.Stop();
if (absMax < MAJOR_EPSILON)
{
break;
}
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGridW(c, metadata, xGrid, data.UVW, data.Frequencies);
residualVis = Visibilities.Substract(data.Visibilities, modelVis, data.Flags);
}
Console.WriteLine("Reconstruction finished in (seconds): " + totalWatch.Elapsed.TotalSeconds);
}
}
/// <summary>
/// Major cycle implemnentation for the parallel coordinate descent algorithm
/// </summary>
/// <param name="data"></param>
/// <param name="c"></param>
/// <param name="psfCutFactor"></param>
/// <param name="maxMajorCycle"></param>
/// <param name="maxMinorCycle"></param>
/// <param name="lambda"></param>
/// <param name="alpha"></param>
/// <param name="deconvIterations"></param>
/// <param name="deconvEpsilon"></param>
public static void ReconstructPCDM(string obsName, MeasurementData data, GriddingConstants c, int psfCutFactor, int maxMajorCycle, int maxMinorCycle, float lambda, float alpha, int deconvIterations, float deconvEpsilon)
{
var metadata = Partitioner.CreatePartition(c, data.UVW, data.Frequencies);
var psfVis = new Complex[data.UVW.GetLength(0), data.UVW.GetLength(1), data.Frequencies.Length];
for (int i = 0; i < data.Visibilities.GetLength(0); i++)
{
for (int j = 0; j < data.Visibilities.GetLength(1); j++)
{
for (int k = 0; k < data.Visibilities.GetLength(2); k++)
{
if (!data.Flags[i, j, k])
{
psfVis[i, j, k] = new Complex(1.0, 0);
}
else
{
psfVis[i, j, k] = new Complex(0, 0);
}
}
}
}
Console.WriteLine("gridding psf");
var psfGrid = IDG.Grid(c, metadata, psfVis, data.UVW, data.Frequencies);
var psf = FFT.BackwardFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var totalWatch = new Stopwatch();
var currentWatch = new Stopwatch();
var totalSize = new Rectangle(0, 0, c.GridSize, c.GridSize);
var psfCut = PSF.Cut(psf, psfCutFactor);
var maxSidelobe = PSF.CalcMaxSidelobe(psf, psfCutFactor);
var sidelobeHalf = PSF.CalcMaxSidelobe(psf, 2);
var pcdm = new ParallelCoordinateDescent(totalSize, psfCut, Environment.ProcessorCount, 1000);
using (var gCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1))))
using (var gCalculator2 = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psf, totalSize), new Rectangle(0, 0, psf.GetLength(0), psf.GetLength(1))))
using (var residualsConvolver = new PaddedConvolver(totalSize, psf))
{
var currentGCalculator = gCalculator;
var maxLipschitz = PSF.CalcMaxLipschitz(psfCut);
var lambdaLipschitz = (float)(lambda * maxLipschitz);
var lambdaTrue = (float)(lambda * PSF.CalcMaxLipschitz(psf));
var switchedToOtherPsf = false;
var xImage = new float[c.GridSize, c.GridSize];
var residualVis = data.Visibilities;
ParallelCoordinateDescent.PCDMStatistics lastResult = null;
for (int cycle = 0; cycle < maxMajorCycle; cycle++)
{
Console.WriteLine("Beginning Major cycle " + cycle);
var dirtyGrid = IDG.GridW(c, metadata, residualVis, data.UVW, data.Frequencies);
var dirtyImage = FFT.WStackIFFTFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, obsName + "_dirty_pcdm_majorCycle" + cycle + ".fits");
currentWatch.Restart();
totalWatch.Start();
var breakMajor = false;
var minLambda = 0.0f;
var dirtyCopy = Copy(dirtyImage);
var xCopy = Copy(xImage);
var currentLambda = 0f;
var currentObjective = 0.0;
var absMax = 0.0f;
for (int minorCycle = 0; minorCycle < maxMinorCycle; minorCycle++)
{
Console.WriteLine("Beginning Minor Cycle " + minorCycle);
var maxDirty = Residuals.GetMax(dirtyImage);
var bMap = currentGCalculator.Convolve(dirtyImage);
var maxB = Residuals.GetMax(bMap);
var correctionFactor = Math.Max(maxB / (maxDirty * maxLipschitz), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
currentLambda = (float)Math.Max(currentSideLobe / alpha, lambdaLipschitz);
if (minorCycle == 0)
{
minLambda = (float)(maxB * sidelobeHalf * correctionFactor / alpha);
}
if (currentLambda < minLambda)
{
currentLambda = minLambda;
}
currentObjective = Residuals.CalcPenalty(dirtyImage) + ElasticNet.CalcPenalty(xImage, lambdaTrue, alpha);
absMax = pcdm.GetAbsMax(xImage, bMap, lambdaTrue, alpha);
if (absMax < MAJOR_EPSILON)
{
breakMajor = true;
break;
}
lastResult = pcdm.Deconvolve(xImage, bMap, currentLambda, alpha, 40, deconvEpsilon);
if (currentLambda == lambda | currentLambda == minLambda)
{
break;
}
var residualsUpdate = new float[xImage.GetLength(0), xImage.GetLength(1)];
Parallel.For(0, xCopy.GetLength(0), (i) =>
{
for (int j = 0; j < xCopy.GetLength(1); j++)
{
residualsUpdate[i, j] = xImage[i, j] - xCopy[i, j];
}
});
residualsConvolver.ConvolveInPlace(residualsUpdate);
Parallel.For(0, xCopy.GetLength(0), (i) =>
{
for (int j = 0; j < xCopy.GetLength(1); j++)
{
dirtyImage[i, j] = dirtyCopy[i, j] - residualsUpdate[i, j];
}
});
}
currentWatch.Stop();
totalWatch.Stop();
if (breakMajor)
{
break;
}
if (currentLambda == lambda & !switchedToOtherPsf)
{
pcdm.ResetAMap(psf);
currentGCalculator = gCalculator2;
lambda = lambdaTrue;
switchedToOtherPsf = true;
}
FitsIO.Write(xImage, obsName + "_model_pcdm_majorCycle" + cycle + ".fits");
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGridW(c, metadata, xGrid, data.UVW, data.Frequencies);
residualVis = Visibilities.Substract(data.Visibilities, modelVis, data.Flags);
}
Console.WriteLine("Reconstruction finished in (seconds): " + totalWatch.Elapsed.TotalSeconds);
}
}
public static void DebugSimulatedApprox()
{
var frequencies = FitsIO.ReadFrequencies(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\freq.fits");
var uvw = FitsIO.ReadUVW(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\uvw.fits");
var flags = new bool[uvw.GetLength(0), uvw.GetLength(1), frequencies.Length]; //completely unflagged dataset
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\vis.fits", uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
var visibilitiesCount = visibilities.Length;
int gridSize = 256;
int subgridsize = 8;
int kernelSize = 4;
int max_nr_timesteps = 1024;
double cellSize = 1.0 / 3600.0 * PI / 180.0;
var c = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)cellSize, 1, 0.0f);
var watchTotal = new Stopwatch();
var watchForward = new Stopwatch();
var watchBackwards = new Stopwatch();
var watchDeconv = new Stopwatch();
watchTotal.Start();
var metadata = Partitioner.CreatePartition(c, uvw, frequencies);
var psfGrid = IDG.GridPSF(c, metadata, uvw, flags, frequencies);
var psf = FFT.BackwardFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var psfCut = PSF.Cut(psf);
FitsIO.Write(psfCut, "psfCut.fits");
var random = new Random(123);
var totalSize = new Rectangle(0, 0, gridSize, gridSize);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1)));
var fastCD = new FastSerialCD(totalSize, psfCut);
//fastCD.ResetAMap(psf);
var lambda = 0.5f * fastCD.MaxLipschitz;
var alpha = 0.8f;
var approx = new ApproxParallel();
var approx2 = new ApproxFast(totalSize, psfCut, 4, 8, 0f, 0.25f, false, true);
var xImage = new float[gridSize, gridSize];
var residualVis = visibilities;
/*var truth = new double[gridSize, gridSize];
* truth[30, 30] = 1.0;
* truth[35, 36] = 1.5;
* var truthVis = IDG.ToVisibilities(c, metadata, truth, uvw, frequencies);
* visibilities = truthVis;
* var residualVis = truthVis;*/
var data = new ApproxFast.TestingData(new StreamWriter("approxConvergence.txt"));
for (int cycle = 0; cycle < 4; cycle++)
{
//FORWARD
watchForward.Start();
var dirtyGrid = IDG.Grid(c, metadata, residualVis, uvw, frequencies);
var dirtyImage = FFT.BackwardFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, "dirty_" + cycle + ".fits");
watchForward.Stop();
//DECONVOLVE
watchDeconv.Start();
//approx.ISTAStep(xImage, dirtyImage, psf, lambda, alpha);
//FitsIO.Write(xImage, "xIsta.fits");
//FitsIO.Write(dirtyImage, "dirtyFista.fits");
//bMapCalculator.ConvolveInPlace(dirtyImage);
//FitsIO.Write(dirtyImage, "bMap_" + cycle + ".fits");
//var result = fastCD.Deconvolve(xImage, dirtyImage, 0.5f * fastCD.MaxLipschitz, 0.8f, 1000, 1e-4f);
//var converged = approx.DeconvolveActiveSet(xImage, dirtyImage, psfCut, lambda, alpha, random, 8, 1, 1);
//var converged = approx.DeconvolveGreedy(xImage, dirtyImage, psfCut, lambda, alpha, random, 4, 4, 500);
//var converged = approx.DeconvolveApprox(xImage, dirtyImage, psfCut, lambda, alpha, random, 1, threads, 500, 1e-4f, cycle == 0);
approx2.DeconvolveTest(data, cycle, 0, xImage, dirtyImage, psfCut, psf, lambda, alpha, random, 10, 1e-4f);
if (data.converged)
{
Console.WriteLine("-----------------------------CONVERGED!!!!------------------------");
}
else
{
Console.WriteLine("-------------------------------not converged----------------------");
}
FitsIO.Write(xImage, "xImageApprox_" + cycle + ".fits");
watchDeconv.Stop();
//BACKWARDS
watchBackwards.Start();
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGrid(c, metadata, xGrid, uvw, frequencies);
residualVis = Visibilities.Substract(visibilities, modelVis, flags);
watchBackwards.Stop();
}
var dirtyGridCheck = IDG.Grid(c, metadata, residualVis, uvw, frequencies);
var dirtyCheck = FFT.Backward(dirtyGridCheck, c.VisibilitiesCount);
FFT.Shift(dirtyCheck);
var l2Penalty = Residuals.CalcPenalty(ToFloatImage(dirtyCheck));
var elasticPenalty = ElasticNet.CalcPenalty(xImage, (float)lambda, (float)alpha);
var sum = l2Penalty + elasticPenalty;
data.writer.Close();
}
private static void ReconstructPCDM(MeasurementData input, GriddingConstants c, float[,] fullPsf, string folder, string file, int minorCycles, float searchPercent, 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_PCDM);
var maxSidelobe = PSF.CalcMaxSidelobe(fullPsf, CUT_FACTOR_PCDM);
var sidelobeHalf = PSF.CalcMaxSidelobe(fullPsf, 2);
var random = new Random(123);
var pcdm = new ParallelCoordinateDescent(totalSize, psfCut, 1, 1000, searchPercent);
var metadata = Partitioner.CreatePartition(c, input.UVW, input.Frequencies);
using (var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1))))
using (var bMapCalculator2 = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(fullPsf, totalSize), new Rectangle(0, 0, fullPsf.GetLength(0), fullPsf.GetLength(1))))
using (var residualsConvolver = new PaddedConvolver(totalSize, fullPsf))
{
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 writer = new StreamWriter(folder + "/" + file + ".txt");
var xImage = new float[c.GridSize, c.GridSize];
var residualVis = input.Visibilities;
ParallelCoordinateDescent.PCDMStatistics lastResult = null;
for (int cycle = 0; cycle < 6; cycle++)
{
Console.WriteLine("Beginning Major 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 breakMajor = false;
var minLambda = 0.0f;
var dirtyCopy = Copy(dirtyImage);
var xCopy = Copy(xImage);
var currentLambda = 0f;
var currentObjective = 0.0;
var absMax = 0.0f;
for (int minorCycle = 0; minorCycle < minorCycles; minorCycle++)
{
Console.WriteLine("Beginning Minor Cycle " + minorCycle);
var maxDirty = Residuals.GetMax(dirtyImage);
var bMap = currentBMapCalculator.Convolve(dirtyImage);
var maxB = Residuals.GetMax(bMap);
var correctionFactor = Math.Max(maxB / (maxDirty * maxLipschitz), 1.0f);
var currentSideLobe = maxB * maxSidelobe * correctionFactor;
currentLambda = (float)Math.Max(currentSideLobe / alpha, lambda);
if (minorCycle == 0)
{
minLambda = (float)(maxB * sidelobeHalf * correctionFactor / alpha);
}
if (currentLambda < minLambda)
{
currentLambda = minLambda;
}
currentObjective = Residuals.CalcPenalty(dirtyImage) + ElasticNet.CalcPenalty(xImage, lambdaTrue, alpha);
absMax = pcdm.GetAbsMax(xImage, bMap, lambdaTrue, alpha);
if (absMax < MAJOR_STOP)
{
breakMajor = true;
break;
}
lastResult = pcdm.Deconvolve(xImage, bMap, currentLambda, alpha, 100, 1e-5f);
if (currentLambda == lambda | currentLambda == minLambda)
{
break;
}
var residualsUpdate = new float[xImage.GetLength(0), xImage.GetLength(1)];
Parallel.For(0, xCopy.GetLength(0), (i) =>
{
for (int j = 0; j < xCopy.GetLength(1); j++)
{
residualsUpdate[i, j] = xImage[i, j] - xCopy[i, j];
}
});
residualsConvolver.ConvolveInPlace(residualsUpdate);
Parallel.For(0, xCopy.GetLength(0), (i) =>
{
for (int j = 0; j < xCopy.GetLength(1); j++)
{
dirtyImage[i, j] = dirtyCopy[i, j] - residualsUpdate[i, j];
}
});
}
currentWatch.Stop();
totalWatch.Stop();
writer.WriteLine(cycle + ";" + currentLambda + ";" + currentObjective + ";" + absMax + ";" + lastResult.IterationCount + ";" + totalWatch.Elapsed.TotalSeconds + ";" + currentWatch.Elapsed.TotalSeconds);
writer.Flush();
FitsIO.Write(xImage, folder + "/xImage_pcdm_" + cycle + ".fits");
if (breakMajor)
{
break;
}
if (currentLambda == lambda & !switchedToOtherPsf)
{
pcdm.ResetAMap(fullPsf);
currentBMapCalculator = bMapCalculator2;
lambda = lambdaTrue;
switchedToOtherPsf = true;
writer.WriteLine("switched");
writer.Flush();
}
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);
}
writer.Close();
}
}
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);
}
}
}