public static Data Load(string folder)
{
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 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;
var d = new Data();
d.frequencies = frequencies;
d.visibilities = visibilities;
d.uvw = uvw;
d.flags = flags;
d.visibilitiesCount = visibilitiesCount;
return(d);
}
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 LocalDataset LoadSubsetTinyMeerKAT(string folder)
{
var frequencies = FitsIO.ReadFrequencies(Path.Combine(folder, "freq.fits"));
var uvw = FitsIO.ReadUVW(Path.Combine(folder, "uvw0.fits"));
var flags = FitsIO.ReadFlags(Path.Combine(folder, "flags0.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length);
var visibilities = FitsIO.ReadVisibilities(Path.Combine(folder, "vis0.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, 2.0);
return(new LocalDataset(frequencies, uvw, flags, visibilities));
}
public static LocalDataset LoadSimulated(string folder)
{
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]; //completely unflagged dataset
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(Path.Combine(folder, "vis.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, 2.0);
return(new LocalDataset(frequencies, uvw, flags, visibilities));
}
public static MeasurementData LoadSimulatedPoints(string folder)
{
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);
return(new MeasurementData(visibilities, uvw, frequencies, flags));
}
public static Data Load(string folder)
{
var frequencies = FitsIO.ReadFrequencies(Path.Combine(folder, "freq.fits"));
var uvw = FitsIO.ReadUVW(Path.Combine(folder, "uvw0.fits"));
var flags = FitsIO.ReadFlags(Path.Combine(folder, "flags0.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length);
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(Path.Combine(folder, "vis0.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
for (int i = 1; i < 8; i++)
{
var uvw0 = FitsIO.ReadUVW(Path.Combine(folder, "uvw" + i + ".fits"));
var flags0 = FitsIO.ReadFlags(Path.Combine(folder, "flags" + i + ".fits"), uvw0.GetLength(0), uvw0.GetLength(1), frequencies.Length);
var visibilities0 = FitsIO.ReadVisibilities(Path.Combine(folder, "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 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;
var d = new Data();
d.frequencies = frequencies;
d.visibilities = visibilities;
d.uvw = uvw;
d.flags = flags;
d.visibilitiesCount = visibilitiesCount;
return(d);
}
public static MeasurementData LoadLMC(string folder)
{
var frequencies = FitsIO.ReadFrequencies(Path.Combine(folder, "freq.fits"));
var uvw = FitsIO.ReadUVW(Path.Combine(folder, "uvw0.fits"));
var flags = FitsIO.ReadFlags(Path.Combine(folder, "flags0.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length);
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(Path.Combine(folder, "vis0.fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
for (int i = 1; i < 8; i++)
{
var uvw0 = FitsIO.ReadUVW(Path.Combine(folder, "uvw" + i + ".fits"));
var flags0 = FitsIO.ReadFlags(Path.Combine(folder, "flags" + i + ".fits"), uvw0.GetLength(0), uvw0.GetLength(1), frequencies.Length);
var visibilities0 = FitsIO.ReadVisibilities(Path.Combine(folder, "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);
}
return(new MeasurementData(visibilities, uvw, frequencies, flags));
}
public static LocalDataset LoadTinyMeerKAT(Intracommunicator comm, string folder)
{
var beginIdx = comm.Rank * 8 / comm.Size;
var frequencies = FitsIO.ReadFrequencies(Path.Combine(folder, "freq.fits"));
var uvw = FitsIO.ReadUVW(Path.Combine(folder, "uvw" + comm.Rank + ".fits"));
var flags = FitsIO.ReadFlags(Path.Combine(folder, "flags" + comm.Rank + ".fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length);
var visibilities = FitsIO.ReadVisibilities(Path.Combine(folder, "vis" + comm.Rank + ".fits"), uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, 2.0);
for (int i = beginIdx + 1; i < beginIdx + 8 / comm.Size; i++)
{
var uvw0 = FitsIO.ReadUVW(Path.Combine(folder, "uvw" + i + ".fits"));
var flags0 = FitsIO.ReadFlags(Path.Combine(folder, "flags" + i + ".fits"), uvw0.GetLength(0), uvw0.GetLength(1), frequencies.Length);
var visibilities0 = FitsIO.ReadVisibilities(Path.Combine(folder, "vis" + i + ".fits"), uvw0.GetLength(0), uvw0.GetLength(1), frequencies.Length, 2.0);
uvw = FitsIO.Stitch(uvw, uvw0);
flags = FitsIO.Stitch(flags, flags0);
visibilities = FitsIO.Stitch(visibilities, visibilities0);
}
return(new LocalDataset(frequencies, uvw, flags, visibilities));
}
public static void Run()
{
//var frequencies = FitsIO.ReadFrequencies(@"C:\Users\Jon\github\p9-data\small\fits\simulation_point\freq.fits");
//var uvw = FitsIO.ReadUVW(@"C:\Users\Jon\github\p9-data\small\fits\simulation_point\uvw.fits");
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
var visibilitiesCount = flags.Length;
int gridSize = 64;
int subgridsize = 16;
int kernelSize = 8;
int max_nr_timesteps = 64;
double cellSize = 2.0 / 3600.0 * PI / 180.0;
var c = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)cellSize, 1, 0.0f);
var metadata = Partitioner.CreatePartition(c, uvw, frequencies);
var psfGrid = IDG.GridPSF(c, metadata, uvw, flags, frequencies);
var psf = FFT.Backward(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var maxPsf = psf[gridSize / 2, gridSize / 2];
for (int i = 0; i < psf.GetLength(0); i++)
{
for (int j = 0; j < psf.GetLength(1); j++)
{
psf[i, j] = psf[i, j] / maxPsf;
}
}
FitsIO.Write(psf, "psf.fits");
var truth = new double[64, 64];
//truth[40, 50] = 1.5;
truth[0, 0] = 1.7;
var dirty = ConvolveFFTPadded(truth, psf);
FitsIO.Write(truth, "truth.fits");
FitsIO.Write(dirty, "dirty.fits");
var psf2 = ConvolveFFT(psf, psf);
var b = ConvolveFFTPadded(dirty, psf);
var a = psf2[gridSize / 2, gridSize / 2];
/*
* var integral = CalcPSf2Integral(psf);
* FitsIO.Write(integral, "psfIntegral.fits");
* var c0 = new double[64, 64];
* var qY = 0;
* var qX = 0;
* c0[qY, qX] = 1.0;
* c0 = Convolve(c0, psf);
* FitsIO.Write(c0, "cx0.fits");
* var cx = ConvolveFFT(c0, psf);
* FitsIO.Write(cx, "cx1.fits");
* var a2 = cx[qY, qX];
* var res = QueryIntegral(integral, qY, qX);*/
var x = new double[gridSize, gridSize];
//Deconv(x, dirty, psf, psf2, a, 0.0);
var dCopy = new double[gridSize, gridSize];
for (int i = 0; i < b.GetLength(0); i++)
{
for (int j = 0; j < b.GetLength(1); j++)
{
dCopy[i, j] = dirty[i, j];
}
}
var x2 = new double[gridSize, gridSize];
var converged = GreedyCD.Deconvolve2(x2, dirty, psf, 0.0, 1.0, 500, dCopy);
}
public static LocalDataset LoadTinyMeerKAT2(int rank, int nodeCount, string folder)
{
var blSum = 0;
var blFileScans = new int[8];
var blFileCounts = new int[8];
for (int i = 0; i < 8; i++)
{
blFileCounts[i] = FitsIO.CountBaselines(Path.Combine(folder, "uvw" + i + ".fits"));
blSum += blFileCounts[i];
blFileScans[i] = blSum;
}
var frequencies = FitsIO.ReadFrequencies(Path.Combine(folder, "freq.fits"));
var blBeginIdx = rank * (int)(blSum / (double)nodeCount);
var blEndIdx = rank + 1 < nodeCount ? (rank + 1) * (int)(blSum / (double)nodeCount) : blSum;
var baselineCount = blEndIdx - blBeginIdx;
LocalDataset output = null;
for (int i = 0; i < 8; i++)
{
if (blBeginIdx < blFileScans[i])
{
var blBefore = i > 0 ? blFileScans[i - 1] : 0;
var start = blBeginIdx - blBefore;
var end = Math.Min(start + baselineCount, blFileCounts[i]);
var uvw = FitsIO.ReadUVW(Path.Combine(folder, "uvw" + i + ".fits"), start, end);
var flags = FitsIO.ReadFlags(Path.Combine(folder, "flags" + i + ".fits"), start, end, uvw.GetLength(1), frequencies.Length);
var visibilities = FitsIO.ReadVisibilities(Path.Combine(folder, "vis" + i + ".fits"), start, end, uvw.GetLength(1), frequencies.Length, 2.0);
if (blBeginIdx + baselineCount > blFileCounts[i])
{
//continue reading files until all baselines, which belong to the current node, are loaded
var baselinesLoaded = end - start;
for (int j = i + 1; j < 8; j++)
{
var end2 = Math.Min(baselineCount - baselinesLoaded, blFileCounts[j]);
var uvw0 = FitsIO.ReadUVW(Path.Combine(folder, "uvw" + j + ".fits"), 0, end2);
var flags0 = FitsIO.ReadFlags(Path.Combine(folder, "flags" + j + ".fits"), 0, end2, uvw.GetLength(1), frequencies.Length);
var visibilities0 = FitsIO.ReadVisibilities(Path.Combine(folder, "vis" + j + ".fits"), 0, end2, uvw.GetLength(1), frequencies.Length, 2.0);
uvw = FitsIO.Stitch(uvw, uvw0);
flags = FitsIO.Stitch(flags, flags0);
visibilities = FitsIO.Stitch(visibilities, visibilities0);
baselinesLoaded += end2;
if (baselinesLoaded >= baselineCount)
{
//last file read;
break;
}
}
}
output = new LocalDataset(frequencies, uvw, flags, visibilities);
break;
}
}
return(output);
}