Esempio n. 1
0
        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);
            }
        }
Esempio n. 2
0
        public static void MeerKATFull()
        {
            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 frequencies = FitsIO.ReadFrequencies(@"freq.fits");
             * var uvw = FitsIO.ReadUVW("uvw0.fits");
             * var flags = FitsIO.ReadFlags("flags0.fits", uvw.GetLength(0), uvw.GetLength(1), frequencies.Length);
             * double norm = 2.0;
             * var visibilities = FitsIO.ReadVisibilities("vis0.fits", uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
             */
            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    = 1024;
            int subgridsize = 16;
            int kernelSize  = 4;
            //cell = image / grid
            int    max_nr_timesteps = 512;
            double scaleArcSec      = 2.5 / 3600.0 * PI / 180.0;

            var watchTotal     = new Stopwatch();
            var watchForward   = new Stopwatch();
            var watchBackwards = new Stopwatch();
            var watchDeconv    = new Stopwatch();

            watchTotal.Start();

            var c        = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)scaleArcSec, 1, 0.0f);
            var metadata = Partitioner.CreatePartition(c, uvw, frequencies);
            var psf      = IDG.CalculatePSF(c, metadata, uvw, flags, frequencies);

            FitsIO.Write(psf, "psf.fits");
            var psfCut = CutImg(psf, 2);

            FitsIO.Write(psfCut, "psfCut.fits");
            var maxSidelobe   = CommonDeprecated.PSF.CalcMaxSidelobe(psf);
            var psfCorrelated = CommonDeprecated.PSF.CalculateFourierCorrelation(psfCut, c.GridSize, c.GridSize);

            var xImage      = new double[gridSize, gridSize];
            var residualVis = visibilities;
            var maxCycle    = 2;

            for (int cycle = 0; cycle < maxCycle; cycle++)
            {
                watchForward.Start();
                var dirtyImage = IDG.ToImage(c, metadata, residualVis, uvw, frequencies);
                watchForward.Stop();
                FitsIO.Write(dirtyImage, "dirty" + cycle + ".fits");

                watchDeconv.Start();
                var sideLobe = maxSidelobe * GetMax(dirtyImage);
                Console.WriteLine("sideLobeLevel: " + sideLobe);
                var b             = CommonDeprecated.Residuals.CalculateBMap(dirtyImage, psfCorrelated, psfCut.GetLength(0), psfCut.GetLength(1));
                var lambda        = 0.8;
                var alpha         = 0.05;
                var currentLambda = Math.Max(1.0 / alpha * sideLobe, lambda);
                var converged     = SerialCDReference.DeconvolvePath(xImage, b, psfCut, currentLambda, 4.0, alpha, 5, 1000, 2e-5);
                //var converged = GreedyCD2.Deconvolve(xImage, b, psfCut, currentLambda, alpha, 5000);
                if (converged)
                {
                    Console.WriteLine("-----------------------------CONVERGED!!!! with lambda " + currentLambda + "------------------------");
                }
                else
                {
                    Console.WriteLine("-------------------------------not converged with lambda " + currentLambda + "----------------------");
                }

                watchDeconv.Stop();
                FitsIO.Write(xImage, "xImage_" + cycle + ".fits");

                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();
            }
            watchBackwards.Stop();
            watchTotal.Stop();

            var timetable = "total elapsed: " + watchTotal.Elapsed;

            timetable += "\n" + "idg forward elapsed: " + watchForward.Elapsed;
            timetable += "\n" + "idg backwards elapsed: " + watchBackwards.Elapsed;
            timetable += "\n" + "devonvolution: " + watchDeconv.Elapsed;
            File.WriteAllText("watches_single.txt", timetable);
        }