public void FourierBluesteinIsReversible(FourierOptions options) { var samples = Generate.RandomComplex(0x7FFF, GetUniform(1)); var work = new Complex[samples.Length]; samples.CopyTo(work, 0); Fourier.BluesteinForward(work, options); Assert.IsFalse(work.ListAlmostEqual(samples, 6)); Fourier.BluesteinInverse(work, options); AssertHelpers.AlmostEqual(samples, work, 10); }
private (double[] real, double[] imaginary) CalculateFFT() { const int roundDecimals = 2; var real = GetRealPart(); var imaginary = GetImaginaryPart(); Fourier.Forward(real, imaginary); Round(real, roundDecimals); Round(imaginary, roundDecimals); return(real, imaginary); }
public void FourierRadix2IsReversible32(FourierOptions options) { var samples = Generate.RandomComplex32(0x8000, GetUniform(1)); var work = new Complex32[samples.Length]; samples.CopyTo(work, 0); Fourier.Forward(work, options); Assert.IsFalse(work.ListAlmostEqual(samples, 6)); Fourier.Inverse(work, options); AssertHelpers.AlmostEqual(samples, work, 12); }
public void FourierRealIsReversible(FourierOptions options) { var samples = Generate.Random(0x7FFF, GetUniform(1)); var work = new double[samples.Length + 2]; samples.CopyTo(work, 0); Fourier.ForwardReal(work, samples.Length, options); Assert.IsFalse(work.ListAlmostEqual(samples, 6)); Fourier.InverseReal(work, samples.Length, options); AssertHelpers.AlmostEqual(samples, work, 10); }
private void ComputeKernelTilde() { //Evaluate the kernel at the interpolation nodes and form the embedded generating kernel vector for a circulant matrix for (int i = 0; i < n_interpolation_points_1d; i++) { kernel_tilde[n_interpolation_points_1d + i] = kernel_tilde[n_interpolation_points_1d - i] = SquaredCauchy(tilde[0], tilde[i]); } // Precompute the FFT of the kernel generating matrix Fourier.ForwardReal(kernel_tilde, 2 * n_interpolation_points_1d, FourierOptions.NoScaling); }
public static void AnalyseGenerated(int frequency) { var samples = Generate.Sinusoidal(1024, 44100, frequency, 1.0) .Select(sample => new Complex32((float)sample, 0)) .ToArray(); Fourier.Forward(samples); var results = samples.Take(256).Select(result => result.Magnitude); Console.WriteLine($"FFT results: {FormatResults(results)}"); Console.WriteLine(new String('-', 80)); SaveResultsToCsvFile($"../js/signals/{frequency}_from_generated_data.csv", results); }
public void FourierNaiveIsReversible(FourierOptions options) { var samples = Generate.RandomComplex(0x80, GetUniform(1)); var work = new Complex[samples.Length]; samples.CopyTo(work, 0); work = Fourier.NaiveForward(work, options); Assert.IsFalse(work.ListAlmostEqual(samples, 6)); work = Fourier.NaiveInverse(work, options); AssertHelpers.AlmostEqual(samples, work, 12); }
public static IObservable <FftResult> AudioSampleFft(this IObservable <double[]> obs, double sampleRate) { return(obs.Select(x => { var copy = x.Select(y => new Complex(y, 0)).ToArray(); Fourier.Forward(copy, FourierOptions.AsymmetricScaling); return new FftResult( Fourier.FrequencyScale(x.Length, sampleRate), copy ); })); }
private static Complex[] fourier(double[] data) { Complex[] complexData = new Complex[data.Length]; int count = 0; foreach (double x in data) { complexData[count] = x; count++; } Fourier.Forward(complexData, FourierOptions.Default);//フーリエ変換 return(complexData); }
public void FourierDefaultTransformSatisfiesParsevalsTheorem32(int count) { var samples = Generate.RandomComplex32(count, GetUniform(1)); var timeSpaceEnergy = (from s in samples select s.MagnitudeSquared()).Mean(); var spectrum = new Complex32[samples.Length]; samples.CopyTo(spectrum, 0); Fourier.Forward(spectrum); var frequencySpaceEnergy = (from s in spectrum select s.MagnitudeSquared()).Mean(); Assert.AreEqual(timeSpaceEnergy, frequencySpaceEnergy, 1e-7); }
private void button2_Click(object sender, EventArgs e) { if (GlobalVar.refresh == 0) { button1_Click(sender, e); // Refresh first GlobalVar.refresh = 1; button2_Click(sender, e); // Try again } else { GFFT1.Series[0].Points.Clear(); GFFT2.Series[0].Points.Clear(); double dintervals = 1000; int att = 30; int fs1 = 2700; int fs2 = 3500; Fourier.Forward(GlobalVar.compoutputA1); for (int q = 0; q < dintervals; q++) { GFFT1.ChartAreas[0].AxisX.Title = "Frequency (Hz)"; GFFT1.ChartAreas[0].AxisY.Title = "Amplitude"; GFFT1.Series[0].IsVisibleInLegend = false; // Saves space GFFT1.ChartAreas[0].AxisY.Minimum = 0; GFFT1.ChartAreas[0].AxisX.Minimum = 0; GFFT1.ChartAreas[0].AxisX.Maximum = 5500; GFFT2.ChartAreas[0].AxisX.Title = "Frequency (Hz)"; GFFT2.ChartAreas[0].AxisY.Title = "Amplitude"; GFFT2.Series[0].IsVisibleInLegend = false; // Saves space GFFT2.ChartAreas[0].AxisY.Minimum = 0; GFFT2.ChartAreas[0].AxisX.Minimum = 0; GFFT2.ChartAreas[0].AxisX.Maximum = 5500; if (GlobalVar.samplef / dintervals * q < fs2 && fs1 < GlobalVar.samplef / dintervals * q) { GFFT1.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 15 * Math.Log10(GlobalVar.compoutputA1[q].Magnitude) - att)); GFFT2.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 15 * Math.Log10(GlobalVar.compoutputA1[q].Magnitude) - att)); } else if (20 * Math.Log10(GlobalVar.compoutputA1[q].Magnitude) > 0) { GFFT1.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 15 * Math.Log10(GlobalVar.compoutputA1[q].Magnitude))); GFFT2.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 15 * Math.Log10(GlobalVar.compoutputA1[q].Magnitude))); } else { GFFT1.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 0)); } } } }
protected override void OnResize(EventArgs e) { base.OnResize(e); if (ClientRectangle.Width <= AxisMargin || ClientRectangle.Height <= AxisMargin) { return; } var temp = new byte[ClientRectangle.Width - 2 * AxisMargin]; Fourier.SmoothCopy(_powerSpectrum, temp, _powerSpectrum.Length, (_temp.Length + temp.Length) / (float)_temp.Length, 0); _powerSpectrum = temp; _temp = new byte[_powerSpectrum.Length]; var oldBuffer = _buffer; _buffer = new Bitmap(ClientRectangle.Width, ClientRectangle.Height, PixelFormat.Format32bppPArgb); var oldBuffer2 = _buffer2; _buffer2 = new Bitmap(ClientRectangle.Width, ClientRectangle.Height, PixelFormat.Format32bppPArgb); _graphics.Dispose(); _graphics = Graphics.FromImage(_buffer); SpectrumAnalyzer.ConfigureGraphics(_graphics); _graphics2.Dispose(); _graphics2 = Graphics.FromImage(_buffer2); SpectrumAnalyzer.ConfigureGraphics(_graphics2); _graphics.Clear(Color.Black); var rect = new Rectangle(AxisMargin, 0, _buffer.Width - 2 * AxisMargin, _buffer.Height); _graphics.DrawImage(oldBuffer, rect, AxisMargin, 0, oldBuffer.Width - 2 * AxisMargin, oldBuffer.Height, GraphicsUnit.Pixel); oldBuffer.Dispose(); oldBuffer2.Dispose(); if (_spectrumWidth > 0) { _xIncrement = _scale * (ClientRectangle.Width - 2 * AxisMargin) / _spectrumWidth; } _gradientBrush.Dispose(); _gradientBrush = new LinearGradientBrush(new Rectangle(AxisMargin / 2, AxisMargin / 2, Width - AxisMargin / 2, ClientRectangle.Height - AxisMargin / 2), Color.White, Color.Black, LinearGradientMode.Vertical); _gradientPixels = null; _gradientBrush.InterpolationColors = _gradientColorBlend; DrawGradient(); BuildGradientVector(); _performNeeded = true; var oldMouseIn = _mouseIn; _mouseIn = true; Perform(); _mouseIn = oldMouseIn; }
public void ReverseFFT(Signal signalIn, out Signal signal) { if (signalIn.Length == 0) { signal = new Signal(0); return; } float[] values = new float[signalIn.Length + 2]; Buffer.BlockCopy(signalIn.Values, 0, values, 0, signalIn.Length); Fourier.InverseReal(values, signalIn.Length); signal = new Signal(values); }
public static double[] padded_FFT(Complex[] complexSignal) { int N = complexSignal.Length; Fourier.FFT(complexSignal, N, FourierDirection.Forward); // get the result double[] fft_real = new double[N]; for (int j = 0; j < N; j++) { fft_real[j] = complexSignal[j].Re; } return(fft_real); }
private static void SyncLookupTableLength(int length) { Debug.Assert(length < 1024 * 10); Debug.Assert(length >= 0); if (length > _lookupTabletLength) { int level = (int)Math.Ceiling(Math.Log(length, 2)); Fourier.InitializeReverseBits(level); Fourier.InitializeComplexRotations(level); //_cFFTData = new Complex[ Math2.CeilingBase( length, 2 ) ]; //_cFFTDataF = new ComplexF[ Math2.CeilingBase( length, 2 ) ]; _lookupTabletLength = length; } }
public static List <double> AutoCorr(List <double> input, int n) { double avg = input.Average(); Complex[] inputAsComplex = input.Select(x => new Complex(x - avg, 0)).ToArray(); Fourier.Forward(inputAsComplex, FourierOptions.Matlab); var conjugate = inputAsComplex.Select(Complex.Conjugate); var S = conjugate.Select((x, i) => x * inputAsComplex[i]).ToArray(); Fourier.Inverse(S, FourierOptions.Matlab); double first = S[0].Real; return(S.Take(n).Select(x => x.Real / first).ToList()); }
private double[] PrepareVerticalFouirier(double[][] source, int line = 0) { double[] oneLine; //take string x oneLine = new double[source.Length]; for (int i = 0; i < source.Length; i++) { oneLine[i] = source[i][line]; } //oneLine = Fourier.Derivative(oneLine); //oneLine = Fourier.AutoCrossCorrelation(oneLine, oneLine); oneLine = Fourier.FourierFunction(oneLine); return(oneLine); }
public void GenerateSignature() { int width = Sprite.DefaultSize; int height = Sprite.DefaultSize; if (this.Width > 1 || this.Height > 1) { width = Sprite.DefaultSize * 2; height = Sprite.DefaultSize * 2; } Bitmap canvas = new Bitmap(width, height, PixelFormat.Format24bppRgb); Graphics g = Graphics.FromImage(canvas); // draw sprite for (int l = 0; l < this.Layers; l++) { for (int h = 0; h < this.Height; ++h) { for (int w = 0; w < this.Width; ++w) { int index = w + h * this.Width + l * this.Width * this.Height; Bitmap bitmap = ImageUtils.GetBitmap(this.SpriteList[index].GetRGBData(), PixelFormat.Format24bppRgb, Sprite.DefaultSize, Sprite.DefaultSize); if (canvas.Width == Sprite.DefaultSize) { Rect.X = 0; Rect.Y = 0; Rect.Width = bitmap.Width; Rect.Height = bitmap.Height; } else { Rect.X = Math.Max(Sprite.DefaultSize - w * Sprite.DefaultSize, 0); Rect.Y = Math.Max(Sprite.DefaultSize - h * Sprite.DefaultSize, 0); Rect.Width = bitmap.Width; Rect.Height = bitmap.Height; } g.DrawImage(bitmap, Rect); } } } g.Save(); Bitmap ff2dBmp = Fourier.fft2dRGB(canvas, false); this.SpriteSignature = ImageUtils.CalculateEuclideanDistance(ff2dBmp, 1); }
protected override void _Initialize() { double factor = fftLength; for (int i = 0; i < Channels; i++) { Fourier.Forward(filterFD[i]); for (int j = 0; j < fftLength; j++) { filterFD[i][j] *= factor; } } }
public override double[] Spectrum(double[] input, bool scale) { var data = ToComplex(input); Fourier.Forward(data, FourierOptions.Default); var spectrum = ComputeSpectrum(data); Fourier.Inverse(data, FourierOptions.Default); ToDouble(data, input); return(spectrum); }
public override double[] Spectrum(double[] input, bool scale) { var data = ToComplex(input); Fourier.FFT(data, data.Length, FourierDirection.Forward); var spectrum = ComputeSpectrum(data); Fourier.FFT(data, data.Length, FourierDirection.Backward); ToDouble(data, input); return(spectrum); }
public static double Estimate(double[] Samples, int Decimate, out double Phase) { Complex[] data = DecimateSignal(Samples, Decimate); int N = data.Length; Fourier.Forward(data); // Zero the DC bin. data[0] = 0.0; double f = 0.0; double max = 0.0; Phase = 0.0; // Find largest frequency in FFT. for (int i = 1; i < N / 2 - 1; ++i) { double x; Complex m = LogParabolaMax(data[i - 1], data[i], data[i + 1], out x); if (m.Magnitude > max) { max = m.Magnitude; f = i + x; Phase = m.Phase; } } // Check if this is a harmonic of another frequency (the fundamental frequency). double f0 = f; for (int h = 2; h < 5; ++h) { int i = (int)Math.Round(f / h); if (i >= 1) { double x; Complex m = LogParabolaMax(data[i - 1], data[i], data[i + 1], out x); if (m.Magnitude * 5.0 > max) { f0 = f / h; Phase = m.Phase; } } } return(f0); }
public override void _Process(float delta) { long amountOfFramesPassed = (long)(stopwatch.Elapsed.TotalSeconds * sampleRate); stopwatch.Restart(); //Delta time might not be entirely accurate. Using stopwatch instead. for (int i = 0; i < amountOfFramesPassed; i++) { frameBuffer.Enqueue(samplesFrames[framePointer]); framePointer = (framePointer + 1) % samplesFrames.LongLength; if (frameBuffer.Count > MaxFFTSize) { frameBuffer.Dequeue(); } } if (frameBuffer.Count == 0) { return; } //Apply Hann window on the frames var frameBufferArray = frameBuffer.ToArray(); for (int i = 0; i < frameBufferArray.Length; i++) { frameBufferArray[i] *= (float)hann[i]; } //Convert frames to complex numbers var framesComplex = FramesToComplex(frameBufferArray); Complex[] samplesComplexLeft = framesComplex.Item1; Complex[] samplesComplexRight = framesComplex.Item2; //Do FFT Fourier.Forward(samplesComplexLeft); Fourier.Forward(samplesComplexRight); //Convert to magnitudes float[] freqMagnitudesLeft = Array.ConvertAll(samplesComplexLeft, x => (float)x.Magnitude); float[] freqMagnitudesRight = Array.ConvertAll(samplesComplexRight, x => (float)x.Magnitude); //Convert to cubes GetNode <Meshes>("Meshes").PlaceCubes(freqMagnitudesLeft, freqMagnitudesRight, sampleRate); }
public void CalculatePuls(List <double> dataList) { foreach (var value in dataList) { analysisList.Add(value); } if (analysisList.Count == 6 * _daqDTO.SampleRate) { Complex[] complexAnalysisListWithoutWindow = new Complex[6 * _daqDTO.SampleRate]; for (int i = 0; i < analysisList.Count; i++) { complexAnalysisListWithoutWindow[i] = new Complex(analysisList[i], 0); } Complex[] complexAnalysisListWithWindow = new Complex[6 * _daqDTO.SampleRate]; double[] hammingWindow = MathNet.Numerics.Window.Hamming(complexAnalysisListWithWindow.Length); for (int i = 0; i < complexAnalysisListWithoutWindow.Length; i++) { complexAnalysisListWithWindow[i] = hammingWindow[i] * complexAnalysisListWithoutWindow[i]; } Fourier.Forward(complexAnalysisListWithWindow, FourierOptions.NoScaling); double[] magnitudes = new double[complexAnalysisListWithWindow.Length / 2]; //vi kigger kun på den første halvdel af frekvensspekteret fordi efter nyquist frekvensen gentager spekteret sig. for (int i = 2; i < complexAnalysisListWithWindow.Length / 2; i++) { magnitudes[i] = complexAnalysisListWithWindow[i].Magnitude; } int maxIndex = 0; for (int i = 0; i < magnitudes.Length; i++) { if (magnitudes[i] == magnitudes.Max()) { maxIndex = i; } } double frequenceForMaxMagnitude = maxIndex * 1000.0 / complexAnalysisListWithWindow.Length; double bpm = 60 * frequenceForMaxMagnitude; _puls = bpm; Notify(); analysisList.Clear(); } }
public static double[] padded_IFFT(double[] signal) { int N = signal.Length; Complex[] complexSignal = FFTUtils.DoubleToComplex(signal); Fourier.FFT(complexSignal, N, FourierDirection.Backward); // get the result double[] fft_real = new double[N]; for (int j = 0; j < N; j++) { fft_real[j] = complexSignal[j].Re; } return(fft_real); }
public override void Load() { if (Fetched) { return; } var images = DoFetchImages(FileNames); _fft = Fourier.FFT2(Images[0], false); ViewColumns = 9; ViewRows = 9; Images = images; Rearrange(0, 0, 300, 150, 9); }
public FrequencyChart AnalyzeSound(Complex[] sound, int offset) { var sample = new Complex[window.Length]; Array.Copy(sound, offset, sample, 0, window.Length); Fourier.Radix2Forward(sample, FourierOptions.Default); var chart = new FrequencyChart( sample.Select(k => k.Magnitude * 2 / (sample.Length)) .Zip(frqs, (a, f) => new FrequencyPair(f, a)) .Where(k => k.Frequency > 0) .ToArray()); return(chart); }
private static double[] Analyse(double[] sliver) { var extra = sliver.Length - FFT_SIZE; var samples = sliver.Skip(extra).Select(sample => new Complex(sample, 0)).ToArray(); Fourier.Forward(samples); var results = samples.Select(result => result.Magnitude).Take(FFT_SIZE / 2).ToArray(); // The first result always looks massive and skews everything else so force it to 0. // TODO: figure out why // - something to do with the ffmpeg conversion from .mp3 to .pcm ? // - as comparison, FFT of generated sine wave looks fine // results[0] = 0; return(results); }
private void DoApply(UnmanagedImage image, UnmanagedImage output) { var fimg = Fourier.FFT2(image); if (Width != fimg.Width || Height != fimg.Height) { throw new DimensionMismatchException(); } fimg.C0.MultiplyWith(C0); fimg.C1.MultiplyWith(C1); fimg.C2.MultiplyWith(C2); output.Draw(fimg.InverseFFT2(false)); }
private void CreateSpectrum(ref float[] data) { for (int i = 0; i < data.Length; i++) { _complexBuffer[i] = new Complex32(data[i], 0); } Fourier.Forward(_complexBuffer, FourierOptions.NoScaling); for (int i = 0; i < _spectrum.Length; i++) { Complex32 fourierData = _complexBuffer[i]; _spectrum[i] = (float)Math.Sqrt(fourierData.Real * fourierData.Real) + (fourierData.Imaginary * fourierData.Imaginary); } }
public static void BenchFourier() { Setup(); FourierStruct t = new Fourier(); t.adjust = 0; foreach (var iteration in Benchmark.Iterations) { using (iteration.StartMeasurement()) { for (int i = 0; i < FourierIterations; i++) { t.Run(); } } } }