public static void ComputePSD(List <List <double> > data, int fs, int N, double Nw, double Nover, int Nzp, DetectorWindowType windowtype, out List <List <double> > dataInFreqDomain, out List <double> freqAll)
{
double[] win;
switch (windowtype)
{
case Core.Models.DetectorWindowType.hann:
win = Window.Hann((int)Nw);
break;
case Core.Models.DetectorWindowType.rectwin:
win = _rectwin((int)Nw);
break;
case Core.Models.DetectorWindowType.bartlett:
win = Window.Bartlett((int)Nw);
break;
case Core.Models.DetectorWindowType.hamming:
win = Window.Hamming((int)Nw);
break;
case Core.Models.DetectorWindowType.blackman:
win = Window.Blackman((int)Nw);
break;
default:
throw new Exception(windowtype.ToString() + " is not an acceptable window type.");
}
var U = win.Sum(x => Math.Pow(x, 2));
dataInFreqDomain = new List <List <double> >();
//find the frequency vector which should be the x axis of the spectrum
freqAll = new List <double>();
for (int i = 0; i < Nzp; i++)
{
freqAll.Add((double)fs * i / Nzp);
}
//comput the number of segments: (signalLength - overlap)/(windowLength - overlap)
//round towards zero
var M = Math.Floor((N - Nover) / (Nw - Nover));
foreach (var sig in data)
{
var newSig = new List <double>();
var dat = sig.GetRange(0, N);
var mean = dat.Average();
foreach (var p in dat)
{
var pp = p - mean;
newSig.Add(pp);
}
double[] Pxx = null;
for (int i = 0; i < M; i++)
{
var start = (int)(i * (Nw - Nover));
var end = (int)(i * (Nw - Nover) + Nw);
//Console.WriteLine("start from " + start.ToString() + ", end at " + end.ToString());
//to match InspectionSpectral where mean is removed before calling pwelch
var frag = newSig.GetRange(start, (int)Nw);
//to match CalcPSD_OmegaB.m where mean is not removed from the data before calling fft
//var frag = dat.GetRange(start, (int)Nw);
//Console.WriteLine("number of point " + frag.Count());
//double[] real = null;
var fragcount = frag.Count;
//if (fragcount % 2 == 0)
//{
// real = new double[fragcount + 2];
// real[fragcount] = 0d;
// real[fragcount + 1] = 0d;
//}
//else
//{
// real = new double[fragcount + 1];
// real[fragcount] = 0d;
//}
var complex = new Complex[Nzp];
//multiply with filter win first
for (int ii = 0; ii < frag.Count; ii++)
{
//real[ii] = frag[ii] * win[ii];
complex[ii] = new Complex(frag[ii] * win[ii], 0);
}
//might need an empty vector to hold results, or pass by ref?
//Console.WriteLine(frag[0]);
//var fragarray = frag.ToArray();
//Console.WriteLine("[{0}]", string.Join(", ", real));
//Fourier.ForwardReal(real, fragcount, FourierOptions.Matlab);
//Console.WriteLine("[{0}]", string.Join(", ", real));
//Console.WriteLine("[{0}]", string.Join(", ", complex));
Fourier.Forward(complex, FourierOptions.Matlab);
//Console.WriteLine("[{0}]", string.Join(", ", complex));
//Console.WriteLine("real: {0}, imaginary: {1}, magnitude: {2}", complex[0].Real, complex[0].Imaginary, complex[0].Magnitude);
if (Pxx == null)
{
Pxx = new double[complex.Length];
}
for (int ii = 0; ii < complex.Length; ii++)
{
Pxx[ii] = Pxx[ii] + (Math.Pow(complex[ii].Magnitude, 2) / (U * M));
}
}
//Pxx[0] = Pxx[0] / 2;
dataInFreqDomain.Add(Pxx.ToList());
}
}
public void Transform()
{
Fourier.Forward(_data, FourierOptions.Default);
Fourier.Inverse(_data, FourierOptions.Default);
}
public static SpectralDecomp Decompose(
IBGCStream stream,
int windowCount = 400,
int windowOrder = 12,
int targetChannel = 0,
double minFreq = 0.0,
double maxFreq = double.PositiveInfinity)
{
//WindowSize is 2 ^ windowOrder
int windowSize = 1 << windowOrder;
if (stream.Channels <= targetChannel)
{
throw new ArgumentException(
$"TargetChannel ({targetChannel}) exceeded stream channels ({stream.Channels})",
nameof(targetChannel));
}
float[] samples = stream.IsolateChannel(targetChannel).Cache().Samples;
int sampleOffset = (int)((samples.Length - windowSize) / (double)(windowCount - 1));
//Adjust windowSize to conform to sample size and requirements
while (sampleOffset <= 0 && windowOrder > 4)
{
--windowOrder;
windowSize = 1 << windowOrder;
windowCount /= 2;
sampleOffset = (int)((samples.Length - windowSize) / (double)(windowCount - 1));
}
if (windowOrder == 4)
{
throw new ArgumentException("Clip too short to evaluate");
}
if (maxFreq == double.PositiveInfinity)
{
//Default MaxFrequency to be determined by the size of the window
maxFreq = FrequencyDomain.GetComplexSampleFrequency(windowSize, windowSize / 2);
}
//Limit Max Frquency by the size of the window
maxFreq = Math.Min(maxFreq, FrequencyDomain.GetComplexSampleFrequency(windowSize, windowSize / 2));
//Limit Min Frequency by the
minFreq = Math.Max(minFreq, FrequencyDomain.GetComplexSampleFrequency(windowSize, 1));
//Our output will be just the real-valued amplitudes
SpectralDecomp decomp = new SpectralDecomp(minFreq, maxFreq, windowSize, windowCount);
Complex64[] fftBuffer = new Complex64[windowSize];
IBGCEnvelopeStream hammingWindow = new EnvelopeConcatenator(
CosineEnvelope.HammingWindow(windowSize / 2, true),
CosineEnvelope.HammingWindow(windowSize / 2, false));
for (int window = 0; window < windowCount; window++)
{
int specificOffset = sampleOffset * window;
hammingWindow.Reset();
//Copy samples into buffer
for (int i = 0; i < windowSize; i++)
{
//Set real value
fftBuffer[i] = samples[specificOffset + i] * hammingWindow.ReadNextSample();
}
Fourier.Forward(fftBuffer);
decomp.Add(window, fftBuffer);
}
return(decomp);
}
private void button1_Click(object sender, EventArgs e) // Refresh
{
// Init Graphs
double intervals = 315; // "samples"
double mag = 0;
double phase = 0;
double w = 0;
double scale = 11;
Complex i1 = new Complex(1, 0);
Complex i2 = new Complex(0, 0);
GlobalVar.refresh = 1;
//Analog Plottage
GAMag.Series[0].Points.Clear();
GAPhase.Series[0].Points.Clear();
// Analog Magnitude
GAMag.ChartAreas[0].AxisX.Title = "Frequency (kHz)";
GAMag.ChartAreas[0].AxisY.Title = "Magnitude (dB)";
//GAMag.ChartAreas[0].AxisX.IsLogarithmic = true;
GAMag.Series[0].LegendText = "Magnitude";
GAMag.Series[0].IsVisibleInLegend = false;
// Analog Phase
GAPhase.ChartAreas[0].AxisX.Title = "Frequency (kHz)";
GAPhase.ChartAreas[0].AxisY.Title = "Phase";
GAPhase.Series[0].IsVisibleInLegend = false;
for (double n = 0; n < intervals; n = ++n)
{
w = (2 * Math.PI * GlobalVar.samplef / intervals) * n;
i2 = new Complex(0, w); // i2 = 0 + jw
// Mag = 20log(|Bandalog|)
// Phase = Atan(Im(bandalog)/Re(bandalog)
i1 = Bandalog(i2); // s = jw
mag = 20 * Math.Log10(Complex.Abs(i1)); // Log10!!!
//Vector2 magpoints = [n / 2 * Math.PI, mag];
GAMag.Series[0].Points.AddXY(Math.Round((n / (2 * Math.PI)) / scale, 2) + 1, mag); //Add x,y to chart
//GAMag.ChartAreas[0].RecalculateAxesScale();
phase = Math.Atan(i1.Imaginary / i1.Real);
GAPhase.Series[0].Points.AddXY(Math.Round((n / (2 * Math.PI)) / scale, 2) + 1, phase); //Add x,y to chart
}
// Digital Plottage
GDMag.Series[0].Points.Clear();
GDPhase.Series[0].Points.Clear();
// Init Graphs
double intervalz = 1000; // "samples"
double magz = 0;
double phasez = 0;
Complex i3 = new Complex(1, 0);
Complex i4 = new Complex(0, 0);
// Digital Magnitude
GDMag.ChartAreas[0].AxisX.Title = "Frequency (kHz)";
GDMag.ChartAreas[0].AxisY.Title = "Magnitude (dB)";
GDMag.Series[0].LegendText = "Magnitude";
GDMag.Series[0].IsVisibleInLegend = false; // Saves space
// Digital Phase
GDPhase.ChartAreas[0].AxisX.Title = "Frequency (kHz)";
GDPhase.ChartAreas[0].AxisY.Title = "Phase";
GDPhase.Series[0].IsVisibleInLegend = false; // Saves space
Complex i5 = new Complex(0, 0);
for (double j = 1; j < intervalz; ++j) // j = w from 0 -> pi
{
double dw = (Math.PI / (intervalz)) * j;
i4 = new Complex(0, dw); // i4 = 0 + jw
i5 = Complex.Exp(i4); // i5 = e^jw = s
// Mag = 20log(|Bandigital|)
// Phase = Atan(Im(bandigital)/Re(bandigital)
i3 = Bandigital(i5);
magz = 20 * Math.Log10(Complex.Abs(i3)); // Log10!!!
GDMag.Series[0].Points.Add(new DataPoint(Math.Round(GlobalVar.samplef * dw / (2 * Math.PI) / 1000, 3), magz)); // add x,y values
phasez = Math.Atan(i3.Imaginary / i3.Real);
GDPhase.Series[0].Points.Add(new DataPoint(Math.Round(GlobalVar.samplef * dw / (2 * Math.PI) / 1000, 3), phasez)); // add x,y values
}
// Realisations
GIn1.Series[0].Points.Clear();
GIn2.Series[0].Points.Clear();
GOut1.Series[0].Points.Clear();
GOut2.Series[0].Points.Clear();
GFFT1.Series[0].Points.Clear();
GFFT2.Series[0].Points.Clear();
// Direct Realisation
int dintervals = 1000;
double sineinputA = 0;
//double[] inputA;
//double[] outputA1;
//double[] outputA2;
double[] num = { 0.298534, 0.348534, 0.952554, 0.68258, 0.952554, 0.348534, 0.298534 };
double[] den = { 1, 0.748427, 0.876976, 0.544886, 0.690464, 0.086335, -0.065264 };
GIn1.ChartAreas[0].AxisX.Title = "Time (s)";
GIn1.ChartAreas[0].AxisY.Title = "Amplitude";
GIn1.Series[0].IsVisibleInLegend = false; // Saves space
GOut1.ChartAreas[0].AxisX.Title = "Time (s)";
GOut1.ChartAreas[0].AxisY.Title = "Amplitude";
GOut1.Series[0].IsVisibleInLegend = false; // Saves space
GIn2.ChartAreas[0].AxisX.Title = "Time (s)";
GIn2.ChartAreas[0].AxisY.Title = "Amplitude";
GIn2.Series[0].IsVisibleInLegend = false; // Saves space
for (int oloop = 1; oloop < dintervals; oloop++)
{
GlobalVar.outputA1[oloop] = 0;
sineinputA = findinputA(oloop);
GlobalVar.inputA[oloop] = sineinputA;
for (int iloop = 1; oloop - iloop > 1 && iloop <= 7; iloop++) //Realisation 1
{
GlobalVar.outputA1[oloop] += num[iloop - 1] * GlobalVar.inputA[oloop - iloop] - den[iloop - 1] * GlobalVar.inputA[oloop];
}
GlobalVar.compinputA1[oloop] = new Complex(GlobalVar.inputA[oloop], 0);
GlobalVar.compoutputA1[oloop] = new Complex(GlobalVar.outputA1[oloop], 0);
GIn1.Series[0].Points.Add(new DataPoint(oloop, GlobalVar.inputA[oloop]));
GOut1.Series[0].Points.Add(new DataPoint(oloop, GlobalVar.outputA1[oloop]));
GIn2.Series[0].Points.Add(new DataPoint(oloop, GlobalVar.inputA[oloop]));
GOut2.Series[0].Points.Add(new DataPoint(oloop, GlobalVar.outputA1[oloop]));
}
GFFT1.Series[0].Points.Clear();
Fourier.Forward(GlobalVar.compinputA1);
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 (20 * Math.Log10(GlobalVar.compoutputA1[q].Magnitude) > 0)
{
GFFT1.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 20 * Math.Log10(GlobalVar.compinputA1[q].Magnitude)));
GFFT2.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 20 * Math.Log10(GlobalVar.compinputA1[q].Magnitude)));
}
else
{
GFFT1.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 0));
GFFT2.Series[0].Points.Add(new DataPoint(GlobalVar.samplef / dintervals * q, 0));
}
}
// Cascade Realisation
GOut2.ChartAreas[0].AxisX.Title = "Time (s)";
GOut2.ChartAreas[0].AxisY.Title = "Amplitude";
GOut2.Series[0].IsVisibleInLegend = false; // Saves space
double[] cnum1 = { -0.133277, 1 };
double[] cden1 = { 0.184704, -0.0944 };
double[] cnum2 = { 0.407766, 1 };
double[] cden2 = { 1.217628, 0.848231 };
double[] cnum3 = { 0.893, 1 };
double[] cden3 = { -0.653845, 0.815172 };
double nmult = 2.629;
double temp = 0;
double temp1 = 0;
double temp2 = 0;
double temp3 = 0;
for (int jloop = 1; jloop < dintervals; jloop++)
{
GlobalVar.outputA1[jloop] = 0;
sineinputA = findinputA(jloop);
GlobalVar.inputA[jloop] = sineinputA;
temp = 0;
for (int kloop = 2; jloop - kloop > 1 && kloop >= 0; kloop--)
{
//temp1 += cnum1[kloop] * GlobalVar.inputA[jloop - kloop] - cden1[kloop] * GlobalVar.inputA[jloop];
}
temp = temp1;
for (int lloop = 2; jloop - lloop > 1 && lloop >= 0; lloop--)
{
//temp3 += cnum2[lloop] * GlobalVar.inputA[jloop] - cden2[jloop-lloop] * GlobalVar.inputA[jloop-1];
}
temp += temp2;
for (int mloop = 2; jloop - mloop > 1 && mloop >= 0; mloop--)
{
//temp3 += cnum3[mloop] * GlobalVar.inputA[jloop] - cden3[jloop - mloop] * GlobalVar.inputA[jloop-1];
GlobalVar.outputA2[jloop] += 0 * temp3;
temp = temp3;
}
temp += temp3;
GlobalVar.outputA2[jloop] += nmult * temp;
GOut1.Series[0].Points.Add(new DataPoint(jloop, GlobalVar.outputA2[jloop]));
}
}
public override int Read(float[] data, int offset, int count)
{
if (!initialized)
{
Initialize();
}
int samplesWritten = ReadBody(data, offset, count);
while (samplesWritten < count)
{
int read = stream.Read(inputBuffer, 0, inputBuffer.Length);
if (read <= 0)
{
//Done, No samples left to work with
break;
}
//Slide output samples over to accumulate on the remainder
Array.Copy(
sourceArray: outputAccumulation,
sourceIndex: bufferCount,
destinationArray: outputAccumulation,
destinationIndex: 0,
length: outputAccumulation.Length - bufferCount);
Array.Clear(
array: outputAccumulation,
index: outputAccumulation.Length - bufferCount,
length: bufferCount);
bufferIndex = 0;
bufferCount = Channels * samplesPerOverlap;
if (read < inputBuffer.Length)
{
//Copy all the remaining samples
//We are guaranteed to have enough room because the output buffer's
//length is Channels * (inputBuffer.Length + fftLength)
bufferCount = Channels * (read + filterLength - 1);
//Set rest of inputBuffer to zero
Array.Clear(inputBuffer, read, inputBuffer.Length - read);
}
for (int i = 0; i < samplesPerOverlap; i++)
{
fftBuffer[i] = inputBuffer[i];
}
Array.Clear(fftBuffer, samplesPerOverlap, fftLength - samplesPerOverlap);
//FFT
Fourier.Forward(fftBuffer);
for (int channel = 0; channel < Channels; channel++)
{
for (int i = 0; i < fftLength; i++)
{
ifftBuffer[i] = fftBuffer[i] * filterFD[channel][i];
}
//IFFT
Fourier.Inverse(ifftBuffer);
//Accumualte the window samples
for (int i = 0; i < fftLength; i++)
{
outputAccumulation[Channels * i + channel] += (float)ifftBuffer[i].Real;
}
}
samplesWritten += ReadBody(data, offset + samplesWritten, count - samplesWritten);
}
return(samplesWritten);
}
private void showChart(object sender, EventArgs e)
{
var complexData = new Complex[N];
try
{
string[] strArrayData = message.Split(',');// カンマで分割
if (strArrayData.Length == 5)
{
//ゼロ点調整
if (checkBox_zeroset.Checked && flag_zeroset == true)
{
for (int i = 1; i < strArrayData.Length; i++)
{
ZeroData[i] = Math.Round(Convert.ToDouble(strArrayData[i]), order);
}
//label_Free.Text = "Zero pos.: " + ZeroData[1] + ", " + ZeroData[2] + ", " + ZeroData[3] + ", " + ZeroData[4];
if (ZeroData[4] < 20)// ゼロ点調整を終わらせる条件
{
flag_zeroset = false;
}
}
for (int i = 1; i < strArrayData.Length; i++)
{
originalData[i] = Math.Round(Convert.ToDouble(strArrayData[i]), order); // 小数第５位までに四捨五入
data[i] = originalData[i] - ZeroData[i]; // ゼロ点からの差分をデータとする
// 複素数データに変換し、追加
complexData[dataPointNum] = new Complex(Convert.ToDouble(strArrayData[1]), 0);
}
// データの個数カウント
dataPointNum++;
label_Free.Text = "point num:" + dataPointNum;
z_old = z;//自動ゼロ点更新用
//４次元データを三次元データに変換
x_raw = data[3] + data[4] - data[1] - data[2];
y_raw = data[1] + data[4] - data[2] - data[3];
z_raw = data[1] + data[2] + data[3] + data[4];// 押下度合いの計算
x = (int)(x_raw * amp);
y = (int)(y_raw * amp);
z = (int)(z_raw * amp);// 押下度合いの計算
//解像度設定に合わせる
x = (int)(Convert.ToDouble(x_reso - 1 - x_origin) * x / (x_max * amp));
y = (int)(Convert.ToDouble(y_reso - 1 - y_origin) * y / (y_max * amp));
z = (int)(Convert.ToDouble(z_reso - 1 - z_origin) * z / (z_max * amp));
//overflow check
if (x < 0 - x_origin)
{
x = 0 - x_origin;
}
if (x > x_reso - 1)
{
x = x_reso - 1;
}
if (y < 0 - y_origin)
{
y = 0 - y_origin;
}
if (y > y_reso - 1)
{
y = y_reso - 1;
}
if (z < 0 - z_origin)
{
z = 0 - z_origin;
}
if (z > z_reso - 1)
{
z = z_reso - 1;
}
/***
* //自動ゼロ点更新
* if (checkBox_zeroset.Checked)
* {
* if (Math.Abs(z_old - z) < reset_diff)// 前回差分が規定値未満の状況が…
* {
* resetcounter++;
* }
* else
* {
* resetcounter = 0;
* }
*
* if (resetcounter > reset_count)// 規定ステップ連続すればゼロ点調整のフラグをオンにする
* {
* flag_zeroset = true;
* resetcounter = 0;
* }
* }***/
double CH0 = Convert.ToDouble(data[1]);
//double y = Convert.ToDouble(strArrayData[1]);// CH0のインダクタンス値
double time = Convert.ToDouble(strArrayData[0]);// 時間
chart1.Series[legend1].Points.AddXY(time, CH0);
// グラフの横軸の表示範囲設定
chart1.ChartAreas[0].AxisX.Maximum = time;
chart1.ChartAreas[0].AxisX.Minimum = time - displayTime;// 何秒前のデータまで表示するか
// logの作成
if (flag_log)
{
string logmsg = strArrayData[1] + "," + strArrayData[2] + "," + strArrayData[3] + "," + strArrayData[4];// CSVファイルに書き込み
logging.write(logmsg);
}
}
//if (dataPointNum+1 == Math.Pow(Math.Floor(Math.Sqrt(dataPointNum+1)), 2))// 平方数になっているかどうかの確認
if (dataPointNum == N) // データ数がNならFFT実行
{
Fourier.Forward(complexData, FourierOptions.Default); // FFT実行
dataPointNum = 0; // データの個数のカウントのリセット
}
// グラフの描画設定
chart1.Series[legend1].IsVisibleInLegend = false; // 凡例表示設定
chart1.Series[legend1].IsValueShownAsLabel = false; // データラベル表示設定
chart1.Series[legend1].ChartType = SeriesChartType.Line; // 折れ線グラフを指定
chart1.Series[legend1].BorderWidth = 2; // 折れ線グラフの幅を指定
chart1.Series[legend1].Color = Color.FromArgb(243, 152, 0); // RGBでグラフの色を指定
chart1.ChartAreas[0].AxisY.Maximum = 0.1; // Y軸の最大値指定
chart1.ChartAreas[0].AxisY.Minimum = -0.05; // Y軸の最小値指定
}
catch { }
}
private void GetRMS()
{
int len1 = _sample.Length; //获取数据个数，默认应为3个
_rms = new double[len1]; //每个位置的加权加速度均方根值
//double _ratio = _sampleFreq / Nfft;//频域分辨率
for (int i = 0; i < len1; i++)
{
_rms[i] = 0;
double[] aj = new double[23];
double[] aw = new double[23];
double[] sampleCopy = _sample[i];
int len = _sample[i].Length;
double _ratio = (double)len / Nfft;
if (len > 0)
{
double[] data;
double[] imag;
data = _sample[i];
imag = new double[len];
Fourier.Forward(data, imag, FourierOptions.Default);//FFT结果也保存在data里
double[] wj = new double[23];
if (_rank == 1)
{
switch (i)
{
case 1:
case 2:
wj = wd;
break;
case 3:
wj = wk;
break;
default:
wj = wd;
break;
}
}
else if (_rank == 2)
{
switch (i)
{
case 4:
wj = wc;
break;
case 5:
case 6:
wj = wd;
break;
default:
wj = wd;
break;
}
}
else
{
wj = wk;
}
for (int j = 0; j < aj.Length; j++)
{
aj[j] = 0;
aw[j] = 0;
int _freqDown = (int)Math.Floor(freqlimit[j] / _ratio);
int _freqUp = (int)Math.Floor(freqlimit[j + 1] / _ratio);
if (_freqDown < data.Length && _freqDown < _freqUp)
{
if (_freqUp > data.Length)
{
_freqUp = data.Length;
}
for (int k = _freqDown; k < _freqUp; k++)
{
aj[j] += Math.Pow(data[k], 2); //周期法自功率谱计算包括在内
}
aj[j] = Math.Pow((aj[j] * _ratio), 0.5); //计算中心频率为fj的1/3倍频程加速度均方根值
}
aw[j] = Math.Pow(wj[j] * aj[j] / 1000, 2);
}
_rms[i] = Math.Pow(aw.Sum(), 0.5);//单轴向加权加速度均方根值
}
}
_rmsN = _rms;
_xRms = _rms[0];
_yRms = _rms[1];
_zRms = _rms[2];
}
protected override void _Initialize()
{
int fftBufferSize = _channelSamples.CeilingToPowerOfTwo();
fftBuffer = new Complex64[fftBufferSize];
double effectiveDuration = fftBufferSize / SamplingRate;
double t_step = effectiveDuration / (2.0 * Math.Round(maxTemporalVelocity * effectiveDuration));
int t_env_size = 2 * (int)Math.Round(effectiveDuration / (2.0 * t_step));
double[] carrierFrequencies;
switch (componentSpacing)
{
case ComponentSpacing.Harmonic:
{
int componentLB = (int)Math.Round(lowestFrequency / frequencySpacing);
int componentUB = (int)Math.Round(lowestFrequency * Math.Pow(2, componentBandwidth) / frequencySpacing) + 1;
int componentCount = componentUB - componentLB;
carrierFrequencies = new double[componentCount];
for (int n = 0; n < componentCount; n++)
{
carrierFrequencies[n] = frequencySpacing * (n + componentLB);
}
}
break;
case ComponentSpacing.Log:
{
int componentUB = (int)(Math.Round(2 * componentBandwidth / frequencySpacing) / 2);
carrierFrequencies = new double[componentUB];
for (int n = 0; n < componentUB; n++)
{
carrierFrequencies[n] = lowestFrequency * Math.Pow(2.0, n * frequencySpacing);
}
}
break;
default:
UnityEngine.Debug.LogError($"Unexpected ComponentSpacing: {componentSpacing}");
goto case ComponentSpacing.Log;
}
int f_env_size = carrierFrequencies.Length;
double[] t_env_phases_sin = new double[t_env_size];
double[] t_env_phases_cos = new double[t_env_size];
double t_env_phase;
double t_env_phase_factor = 2.0 * Math.PI * temporalModulationRate;
for (int t = 0; t < t_env_size; t++)
{
t_env_phase = t * t_env_phase_factor * t_step;
t_env_phases_sin[t] = Math.Sin(t_env_phase);
t_env_phases_cos[t] = Math.Cos(t_env_phase);
}
double value;
double f_env_phase;
double f_env_phase_factor = 2.0 * Math.PI * spectralModulationRate;
double f_env_phase_offset = 0.5 * Math.PI;
double f_env_phase_sin;
double f_env_phase_cos;
Complex64[] complexProfile = new Complex64[t_env_size];
for (int c = 0; c < f_env_size; c++)
{
int f_index = (int)Math.Round(carrierFrequencies[c] * effectiveDuration);
f_env_phase = Math.Log(carrierFrequencies[c] / lowestFrequency, 2.0) * f_env_phase_factor + f_env_phase_offset;
f_env_phase_sin = Math.Sin(f_env_phase);
f_env_phase_cos = Math.Cos(f_env_phase);
for (int t = 0; t < t_env_size; t++)
{
value = f_env_phase_sin * t_env_phases_cos[t] + f_env_phase_cos * t_env_phases_sin[t];
complexProfile[t] = Math.Pow(10.0, modulationDepth * value / 20.0);
}
Fourier.Forward(complexProfile);
FFTShift(complexProfile);
double componentPhase = 2.0 * Math.PI * randomizer.NextDouble();
for (int t = 0; t < t_env_size; t++)
{
complexProfile[t] *= Complex64.FromPolarCoordinates(
magnitude: fftBufferSize / (2.0 * t_env_size),
phase: componentPhase);
}
int leftPad = f_index - (t_env_size / 2) - 1;
int rightPad = (fftBufferSize / 2) - f_index - (t_env_size / 2);
if (leftPad >= 0 && rightPad >= 0)
{
for (int i = 0; i < t_env_size; i++)
{
int index = i + leftPad + 1;
fftBuffer[index] += complexProfile[i];
fftBuffer[fftBufferSize - index] += complexProfile[i].Conjugate();
}
}
else if (leftPad < 0 && rightPad > 0)
{
for (int i = -leftPad; i < t_env_size; i++)
{
int index = i + leftPad + 1;
fftBuffer[index] += complexProfile[i];
fftBuffer[fftBufferSize - index] += complexProfile[i].Conjugate();
}
}
else if (leftPad > 0 && rightPad < 0)
{
for (int i = 0; i < t_env_size + rightPad; i++)
{
int index = i + leftPad + 1;
fftBuffer[index] += complexProfile[i];
fftBuffer[fftBufferSize - index] += complexProfile[i].Conjugate();
}
}
}
Fourier.Inverse(fftBuffer);
}
/// <summary>
/// The internal core method for calculating the autocorrelation.
/// </summary>
/// <param name="x">The data array to calculate auto correlation for</param>
/// <param name="k_low">Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length</param>
/// <param name="k_high">Max lag (EXCLUSIVE) to calculate ACF for must be positive and smaller than x.Length</param>
/// <returns>An array with the ACF as a function of the lags k.</returns>
private static double[] AutoCorrelationFft(IEnumerable <double> x, int k_low, int k_high)
{
if (x == null)
{
throw new ArgumentNullException(nameof(x));
}
int N = x.Count(); // Sample size
if (k_low < 0 || k_low >= N)
{
throw new ArgumentOutOfRangeException(nameof(k_low), "kMin must be zero or positive and smaller than x.Length");
}
if (k_high < 0 || k_high >= N)
{
throw new ArgumentOutOfRangeException(nameof(k_high), "kMax must be positive and smaller than x.Length");
}
if (N < 1)
{
return(new double[0]);
}
int nFFT = Euclid.CeilingToPowerOfTwo(N) * 2;
Complex[] x_fft = new Complex[nFFT];
Complex[] x_fft2 = new Complex[nFFT];
double x_dash = Statistics.Mean(x);
double xArrNow = 0.0d;
using (IEnumerator <double> iex = x.GetEnumerator())
{
for (int ii = 0; ii < nFFT; ii++)
{
if (ii < N)
{
if (!iex.MoveNext())
{
throw new ArgumentOutOfRangeException(nameof(x));
}
xArrNow = iex.Current;
x_fft[ii] = new Complex(xArrNow - x_dash, 0.0); // copy values in range and substract mean
}
else
{
x_fft[ii] = new Complex(0.0, 0.0); // pad all remaining points
}
}
}
Fourier.Forward(x_fft, FourierOptions.Matlab);
// maybe a Vector<Complex> implementation here would be faster
for (int ii = 0; ii < x_fft.Length; ii++)
{
x_fft2[ii] = Complex.Multiply(x_fft[ii], Complex.Conjugate(x_fft[ii]));
}
Fourier.Inverse(x_fft2, FourierOptions.Matlab);
double acf_Val1 = x_fft2[0].Real;
double[] acf_Vec = new double[k_high - k_low + 1];
// normalize such that acf[0] would be 1.0
for (int ii = 0; ii < (k_high - k_low + 1); ii++)
{
acf_Vec[ii] = x_fft2[k_low + ii].Real / acf_Val1;
}
return(acf_Vec);
}
public DigitalAudio Embed(DigitalAudio cover, byte[] message)
{
BitArray embeddingMessage = CreateEmbeddedMessage(message);
var msgLength = embeddingMessage.Length;
// Create embeddingMessage
var value = (float)(Math.PI / 2.0);
float[] embeddingData = new float[msgLength];
for (int i = 0; i < msgLength; i++)
{
if (embeddingMessage[i])
{
embeddingData[i] = -value;
}
else
{
embeddingData[i] = value;
}
}
// Signal
var coverSignal = cover.GetSignal();
var ComplexSignal = coverSignal.Select(x => new Complex32(x, 0)).ToArray();
double ratio = cover.SamplesInChannel / segmentLength;
int N = (int)Math.Floor(ratio); //Segmnets count
//---------------------------------------------
List <Complex32[]> signalSegments = new List <Complex32[]>(N);
List <float[]> phases = new List <float[]>(N);
List <float[]> magnitude = new List <float[]>(N);
List <float[]> deltaPhases = new List <float[]>(N);
for (int seg = 0; seg < N; seg++)
{
//----------Create-----
signalSegments.Add(new Complex32[segmentLength]);
phases.Add(new float[segmentLength]);
magnitude.Add(new float[segmentLength]);
deltaPhases.Add(new float[segmentLength]);
//---------------------
//---Segments init---
Array.Copy(ComplexSignal, seg * segmentLength, signalSegments[seg], 0, segmentLength); // Signal copy
//---------------------
//-------FFT----------
Fourier.Forward(signalSegments[seg], FourierOptions.Matlab); // Signal transform for each segment
//--------------------
for (int j = 0; j < segmentLength; j++)
{
phases[seg][j] = signalSegments[seg][j].Phase; //Phases for each segment
magnitude[seg][j] = signalSegments[seg][j].Magnitude; //Magnitude for each segment
}
}
var spectrumMiddle = segmentLength / 2;
// Delta phases
for (int seg = 1; seg < N; seg++)
{
for (int j = 0; j < segmentLength; j++)
{
deltaPhases[seg][j] = phases[seg][j] - phases[seg - 1][j];
}
}
int startIndex = spectrumMiddle - 1;
int startSymmetryIndex = spectrumMiddle + 1;
for (int i = 0; i < msgLength; i++)
{
phases[0][startIndex - i] = embeddingData[i];
phases[0][startSymmetryIndex + i] = -embeddingData[i]; // symmetry
}
// New phases
for (int seg = 1; seg < N; seg++)
{
for (int j = 0; j < segmentLength; j++)
{
phases[seg][j] = phases[seg - 1][j] + deltaPhases[seg][j];
}
}
//Restore signal
for (int seg = 0; seg < N; seg++)
{
for (int j = 0; j < segmentLength; j++)
{
var A = magnitude[seg][j];
var phase = phases[seg][j];
signalSegments[seg][j] = Complex32.FromPolarCoordinates(A, phase);
}
Fourier.Inverse(signalSegments[seg], FourierOptions.Matlab);
}
for (int i = 0; i < N; i++)
{
for (int j = 0; j < segmentLength; j++)
{
coverSignal[segmentLength * i + j] = (int)signalSegments[i][j].Real;
}
}
return(cover);
}
public static double[] FFT(double[] data)
{
Complex[] samples = (from x in data select new Complex(x, 0)).ToArray();
Fourier.Forward(samples);
return((from x in samples select x.Magnitude).ToArray());
}
public static Complex[] Transform(Complex[] functionPoints, string transformate)
{
var copyofFunctionsPoints = functionPoints;
var copyofFunctionsPoints2 = new Complex[functionPoints.Length];
var multidimensialArray = new double[functionPoints.Length, 2];
var jaggedArray = new double[functionPoints.Length][];
for (var i = 0; i < functionPoints.Length; i++)
{
jaggedArray[i] = new double[2];
}
for (var i = 0; i < functionPoints.Length; i++)
{
jaggedArray[i][0] = multidimensialArray[i, 0] = functionPoints[i].Real;
jaggedArray[i][1] =
multidimensialArray[i, 1] = functionPoints[i].Imaginary;
copyofFunctionsPoints2[i] = new Complex(functionPoints[i].Real,
copyofFunctionsPoints2[i].Imaginary);
}
switch (transformate)
{
case "FFT":
Fourier.Forward(copyofFunctionsPoints);
break;
case "IFFT":
Fourier.Inverse(copyofFunctionsPoints);
break;
case "DST":
SineTransform.DST(jaggedArray);
copyofFunctionsPoints = jaggedToComplex(jaggedArray);
break;
case "IDST":
SineTransform.IDST(jaggedArray);
copyofFunctionsPoints = jaggedToComplex(jaggedArray);
break;
case "DCT":
CosineTransform.DCT(multidimensialArray);
copyofFunctionsPoints = multidimensialToComplex(multidimensialArray);
break;
case "IDCT":
CosineTransform.IDCT(multidimensialArray);
copyofFunctionsPoints = multidimensialToComplex(multidimensialArray);
break;
case "DHT":
HartleyTransform.DHT(multidimensialArray);
copyofFunctionsPoints = multidimensialToComplex(multidimensialArray);
break;
case "FHT":
HilbertTransform.FHT(copyofFunctionsPoints2,
FourierTransform.Direction.Forward);
copyofFunctionsPoints = copyofFunctionsPoints2;
break;
case "IFHT":
HilbertTransform.FHT(copyofFunctionsPoints2,
FourierTransform.Direction.Backward);
copyofFunctionsPoints = copyofFunctionsPoints2;
break;
default:
throw new ArgumentException("Unknown transformation!");
}
return(copyofFunctionsPoints); //athenia programuje//dididididi//di/kocham PaciA// JJKAKAKK K
}
private void btnProcessAccelerationTimeSeries_Click(object sender, EventArgs e)
{
if ((bgwCalculate != null) && (bgwCalculate.IsBusy))
{
bgwCalculate.CancelAsync();
return;
}
//simpleMultipleImagesShow imagesRepresentingForm = new simpleMultipleImagesShow();
//imagesRepresentingForm.Show();
DoWorkEventHandler bgwCalculate_DoWorkHandler = delegate(object currBGWsender, DoWorkEventArgs args)
{
BackgroundWorker selfWorker = currBGWsender as BackgroundWorker;
//simpleMultipleImagesShow multImagesRepresentingForm = (simpleMultipleImagesShow)((args.Argument as object[])[0]);
//Type theShowImagesType = multImagesRepresentingForm.GetType();
//MethodInfo thePicturePlacingMethodInfo = theShowImagesType.GetMethod("PlaceAPicture");
int imageRepresentingCounter = 0;
DateTime dbgDT = new DateTime(2014, 7, 9, 9, 0, 0);
dbgDT = dbgDT.AddMinutes(33);
DateTime dtSeriesStart = accSubseries[0].StartTime;
double tsOverallSeriesDurationMillisec = (accSubseries[accSubseries.Count - 1].EndTime - accSubseries[0].StartTime).TotalMilliseconds;
string strToWrite = " fileName ; lat ; lon ; date ; time ; time(s) since start ; period(s) ; spectrum amplitude";
ServiceTools.logToTextFile(strOutputDirectory + "\\100sData-spectra-maximums.dat", strToWrite + Environment.NewLine, true);
foreach (TimeSeries <double> accSubseria in accSubseries)
{
if (selfWorker.CancellationPending)
{
break;
}
int startindex = 0;
while (true)
{
int endIndex;
if (selfWorker.CancellationPending)
{
break;
}
TimeSeries <double> currTimeSeria = accSubseria.SubSeria(startindex, new TimeSpan(1000000000),
out endIndex); //100s
currTimeSeria = currTimeSeria.InterpolateSeria(new TimeSpan(500000));
currTimeSeria = currTimeSeria.ExtractDataDeviationValues();
//обработать и оценить наличие выраженных периодов
Complex[] sourceSignalArray = currTimeSeria.DataRealValuesComplexArray();
Fourier.Forward(sourceSignalArray);
//Transform.FourierForward(sourceSignalArray);
List <Complex> FourierTransformedSignal = new List <Complex>(sourceSignalArray);
List <double> FourierTransformedSignalAmplitudes =
FourierTransformedSignal.ConvertAll <double>(
cVal =>
((double.IsNaN(cVal.Magnitude)) || (double.IsInfinity(cVal.Magnitude)))
? (0.0d)
: (cVal.Magnitude));
List <double> FourierTransformedSignalPeriods = new List <double>();
for (int ind = 0; ind < FourierTransformedSignalAmplitudes.Count; ind++)
{
FourierTransformedSignalPeriods.Add(currTimeSeria.TotalSeriaDuration.TotalSeconds /
(double)ind);
}
FourierTransformedSignalAmplitudes =
new List <double>(
FourierTransformedSignalAmplitudes.Zip <double, double, double>(
FourierTransformedSignalPeriods,
(amp, periodSec) =>
((double.IsNaN(periodSec)) || (double.IsInfinity(periodSec))) ? (0.0d) : (amp)));
FourierTransformedSignalPeriods =
FourierTransformedSignalPeriods.ConvertAll <double>(
dval => ((double.IsNaN(dval)) || (double.IsInfinity(dval))) ? (0.0d) : (dval));
//проанализируем этот участок - есть ли выраженные пики по амплитуде конкретных частот
//найти максимум в спектре и выдать данные об этом максимуме в файл
// сначала отфильтруем периоды меньше 1с - для данных по динамике судна они несущественны
FourierTransformedSignalAmplitudes =
new List <double>(
FourierTransformedSignalAmplitudes.Zip <double, double, double>(
FourierTransformedSignalPeriods,
(amp, periodSec) => (periodSec <= 1.0d) ? (0.0d) : (amp)));
FourierTransformedSignalPeriods =
FourierTransformedSignalPeriods.ConvertAll <double>(dVal => (dVal <= 1.0d) ? (0.0d) : (dVal));
DescriptiveStatistics currAmpsStat =
new DescriptiveStatistics(FourierTransformedSignalAmplitudes);
List <double> lAmpsOutstanding = FourierTransformedSignalAmplitudes.ConvertAll <double>(dVal =>
{
if (dVal / currAmpsStat.Mean >= 100.0d)
{
return(dVal);
}
return(0.0d);
});
List <double> lPeriodsOutstanding =
new List <double>(FourierTransformedSignalPeriods.Zip <double, double, double>(lAmpsOutstanding,
(per, amp) => (amp == 0.0d) ? (0.0d) : (per)));
if (lAmpsOutstanding.Sum() > 0.0d)
{
MultipleScatterAndFunctionsRepresentation renderer =
new MultipleScatterAndFunctionsRepresentation(2048, 1536);
renderer.dvScatterXSpace.Add(currTimeSeria.TimeStampsValuesSeconds);
renderer.dvScatterFuncValues.Add(currTimeSeria.dvDoubleDataValues);
renderer.scatterLineColors.Add(new Bgr(255, 50, 50));
renderer.scatterDrawingVariants.Add(SequencesDrawingVariants.polyline);
renderer.dvScatterXSpace.Add(DenseVector.OfEnumerable(FourierTransformedSignalPeriods));
renderer.dvScatterFuncValues.Add(DenseVector.OfEnumerable(FourierTransformedSignalAmplitudes));
renderer.scatterLineColors.Add(new Bgr(50, 255, 50));
renderer.scatterDrawingVariants.Add(SequencesDrawingVariants.squares);
renderer.dvScatterXSpace.Add(DenseVector.OfEnumerable(lPeriodsOutstanding));
renderer.dvScatterFuncValues.Add(DenseVector.OfEnumerable(lAmpsOutstanding));
renderer.scatterLineColors.Add(new Bgr(50, 50, 255));
renderer.scatterDrawingVariants.Add(SequencesDrawingVariants.circles);
renderer.Represent();
if (strOutputDirectory != "")
{
double maxAmp = lAmpsOutstanding.Max();
int idx = lAmpsOutstanding.FindIndex(dval => dval == maxAmp);
double maxAmpPeriod = lPeriodsOutstanding[idx];
GPSdata gpsMark = gpsSeriaData.GetMostClose(currTimeSeria.StartTime).Item2;
string fName = currTimeSeria.StartTime.ToString("s").Replace(":", "-") +
"-100sData-spectrum.jpg";
renderer.SaveToImage(strOutputDirectory + "\\" + fName, true);
strToWrite = "" + fName + " ; ";
strToWrite += gpsMark.LatDec + " ; ";
strToWrite += gpsMark.LonDec + " ; ";
strToWrite += currTimeSeria.StartTime.Date.ToString("yyyy-MM-dd") + " ; ";
strToWrite += currTimeSeria.StartTime.ToString("HH-mm-ss") + " ; ";
strToWrite += (currTimeSeria.StartTime - dtSeriesStart).TotalSeconds + " ; ";
strToWrite += maxAmpPeriod.ToString() + " ; ";
strToWrite += maxAmp.ToString() + " ; ";
ServiceTools.logToTextFile(strOutputDirectory + "\\100sData-spectra-maximums.dat", strToWrite + Environment.NewLine, true);
ThreadSafeOperations.SetText(lblStatusString, "processing: " + currTimeSeria.StartTime.ToString("s"), false);
}
}
if ((currTimeSeria.StartTime >= dbgDT) || (currTimeSeria.EndTime >= dbgDT))
{
startindex++;
startindex--;
}
if (endIndex == accSubseria.Count - 1)
{
break;
}
Application.DoEvents();
selfWorker.ReportProgress(Convert.ToInt32(100.0d * (currTimeSeria.EndTime - dtSeriesStart).TotalMilliseconds / tsOverallSeriesDurationMillisec));
startindex += Convert.ToInt32((endIndex - startindex) / 2.0d);
}
}
};
RunWorkerCompletedEventHandler bgwCalculate_CompletedHandler = delegate(object currBGWCompletedSender, RunWorkerCompletedEventArgs args)
{
ThreadSafeOperations.ToggleButtonState(btnProcessAccelerationTimeSeries, true, "Process acceleration timeseries", false);
};
ProgressChangedEventHandler bgwCalculate_ProgressChanged = delegate(object bgwDataReaderSender, ProgressChangedEventArgs args)
{
ThreadSafeOperations.UpdateProgressBar(prbReadingProcessingData, args.ProgressPercentage);
};
ThreadSafeOperations.ToggleButtonState(btnProcessAccelerationTimeSeries, true, "STOP", true);
bgwCalculate = new BackgroundWorker();
bgwCalculate.WorkerSupportsCancellation = true;
bgwCalculate.WorkerReportsProgress = true;
bgwCalculate.DoWork += bgwCalculate_DoWorkHandler;
bgwCalculate.RunWorkerCompleted += bgwCalculate_CompletedHandler;
bgwCalculate.ProgressChanged += bgwCalculate_ProgressChanged;
//object[] bgwCalculateArgs = new object[] { imagesRepresentingForm };
object[] bgwCalculateArgs = new object[] { };
bgwCalculate.RunWorkerAsync(bgwCalculateArgs);
}
public override int Read(float[] data, int offset, int count)
{
if (!initialized)
{
Initialize();
}
int samplesWritten = ReadBody(data, offset, count);
while (samplesWritten < count)
{
//Slide over noise samples
Array.Copy(
sourceArray: noiseBuffer,
sourceIndex: stepSize,
destinationArray: noiseBuffer,
destinationIndex: 0,
length: overlapSize);
int read = stream.Read(inputBuffer, overlapSize, stepSize);
if (read <= 0 && samplesHandled <= 0)
{
//Done, No samples left to work with
break;
}
else if (read <= 0)
{
//We are in buffer-dumping window
//Set rest of inputBuffer to zero
Array.Clear(inputBuffer, overlapSize, stepSize);
}
else if (read < stepSize)
{
//Near or at the end
//Set rest of inputBuffer to zero
Array.Clear(inputBuffer, overlapSize + read, inputBuffer.Length - overlapSize - read);
}
//Generate new noise
for (int i = 0; i < stepSize; i++)
{
noiseBuffer[overlapSize + i] = noiseScalarA - noiseScalarB * randomizer.NextDouble();
}
//Copy in the input data
for (int i = 0; i < fftSize; i++)
{
signalFFTBuffer[i] = inputBuffer[i] * window[i];
noiseFFTBuffer[i] = noiseBuffer[i];
}
//FFT
Task.WaitAll(
Task.Run(() => Fourier.Forward(signalFFTBuffer)),
Task.Run(() => Fourier.Forward(noiseFFTBuffer)));
//For each band...
Parallel.For(
fromInclusive: 0,
toExclusive: bandFrequencies.Length - 1,
body: (int band) =>
{
int lowerBound = FrequencyDomain.GetComplexFrequencyBin(fftSize, bandFrequencies[band], SamplingRate);
int upperBound = FrequencyDomain.GetComplexFrequencyBin(fftSize, bandFrequencies[band + 1], SamplingRate);
Complex64[] amplitudeBuffer = amplitudeBuffers[band];
Complex64[] noiseBandBuffer = noiseBandBuffers[band];
//Copy over band just the relevant frequency band
for (int i = lowerBound; i < upperBound; i++)
{
amplitudeBuffer[i] = 2.0 * signalFFTBuffer[i];
noiseBandBuffer[i] = 2.0 * noiseFFTBuffer[i];
}
Complex64 zero = Complex64.Zero;
//Clear rest of buffers
for (int i = 0; i < lowerBound; i++)
{
amplitudeBuffer[i] = zero;
noiseBandBuffer[i] = zero;
}
for (int i = upperBound; i < amplitudeBuffer.Length; i++)
{
amplitudeBuffer[i] = zero;
noiseBandBuffer[i] = zero;
}
//IFFT
Task.WaitAll(
Task.Run(() => Fourier.Inverse(amplitudeBuffer)),
Task.Run(() => Fourier.Inverse(noiseBandBuffer)));
for (int i = 0; i < amplitudeBuffer.Length; i++)
{
outputAccumulation[i] += outputFactor * window[i] * noiseBandBuffer[i].Real * amplitudeBuffer[i].Magnitude;
}
});
samplesHandled += read;
if (--frameLag <= 0)
{
bufferIndex = 0;
bufferCount = Math.Min(stepSize, samplesHandled);
samplesHandled -= bufferCount;
//Copy output samples to output buffer
for (int sample = 0; sample < bufferCount; sample++)
{
cachedSampleBuffer[sample] = (float)outputAccumulation[sample];
}
}
//Slide over input samples
Array.Copy(
sourceArray: inputBuffer,
sourceIndex: stepSize,
destinationArray: inputBuffer,
destinationIndex: 0,
length: overlapSize);
//Slide output samples
Array.Copy(
sourceArray: outputAccumulation,
sourceIndex: stepSize,
destinationArray: outputAccumulation,
destinationIndex: 0,
length: overlapSize);
//Clear empty output accumulation region
Array.Clear(outputAccumulation, overlapSize, stepSize);
samplesWritten += ReadBody(data, offset + samplesWritten, count - samplesWritten);
}
return(samplesWritten);
}
public void Transform()
{
Fourier.Forward(_data, FourierOptions.NoScaling);
Fourier.Inverse(_data, FourierOptions.NoScaling);
}
private void Capture(AudioCapturedEventArgs e)
{
DataUtils.Normalize(e.Left.Buffer);
DataUtils.Normalize(e.Right.Buffer);
var a = new Complex32[e.Left.Buffer.Length];
var b = new Complex32[e.Right.Buffer.Length];
var sp1 = new Complex32[a.Length];
var sp2 = new Complex32[b.Length];
var window = Window.Hamming(a.Length);
// Составляем комплексный массив и умножаем на окно Хэмминга
for (int i = 0; i < a.Length; i++)
{
a[i] = new Complex32(e.Left.Buffer[i] * (float)window[i], 0);
b[i] = new Complex32(e.Left.Buffer[i] * (float)window[i], 0);
}
// Преобразование фурье
Fourier.Forward(a);
Fourier.Forward(b);
Array.Copy(a, sp1, a.Length);
Array.Copy(b, sp2, b.Length);
// Поэлементно умножаем первое на сопряженное ко второму
for (int i = 0; i < a.Length; i++)
{
a[i] = a[i] * b[i].Conjugate();
}
// Поэлементно делим на модуль самого себя
//for (int i = 0; i < a.Length; i++)
// a[i] = Complex32.Divide(a[i], a[i].Magnitude);
//Fourier.Inverse(a);
// Поиск максимума
/*int maxI = 0;
* float max = a[0].Imaginary;
* for (int i = 0; i < a.Length; i++)
* {
* if (Math.Abs(a[i].Imaginary) > max)
* {
* maxI = i;
* max = Math.Abs(a[i].Imaginary);
* }
* }*/
sgraphWave.Clear();
sgraphSpectrum.Clear();
for (int i = 0; i < e.Left.Buffer.Length; i++)
{
sgraphWave.AddData(i, e.Left.Buffer[i], e.Right.Buffer[i]);
}
for (int i = 0; i < a.Length / 10; i++)
{
float hz = i * e.Left.Source.WaveFormat.SampleRate / (float)a.Length;
//sgraphSpectrum.AddData(hz, sp1[i].Magnitude, sp2[i].Magnitude);
sgraphSpectrum.AddData(hz, 0, a[i].Imaginary);
}
sgraphWave.UpdateGraph();
sgraphSpectrum.UpdateGraph();
}
public override int Read(float[] data, int offset, int count)
{
int samplesWritten = ReadBody(data, offset, count);
while (samplesWritten < count)
{
int read = stream.Read(localSampleBuffer, 0, localSampleBuffer.Length);
if (read <= 0)
{
//Done
break;
}
else if (read < localSampleBuffer.Length)
{
//Set rest to zero
Array.Clear(localSampleBuffer, read, localSampleBuffer.Length - read);
}
bufferIndex = 0;
bufferCount = Channels * stepSize;
for (int channel = 0; channel < Channels; channel++)
{
//Slide input samples over
Array.Copy(
sourceArray: inputBuffers[channel],
sourceIndex: stepSize,
destinationArray: inputBuffers[channel],
destinationIndex: 0,
length: overlap);
//Copy new samples into buffer
for (int i = 0; i < stepSize; i++)
{
inputBuffers[channel][overlap + i] = localSampleBuffer[Channels * i + channel];
}
//Copy and Window into fftbuffer
for (int i = 0; i < frameSize; i++)
{
fftBuffer[i] = inputBuffers[channel][i] * windowInput[i];
}
//FFT
Fourier.Forward(fftBuffer);
//Copy values into IFFT Buffer
for (int i = 0; i < halfFrameSize; i++)
{
fftBuffer[i] *= phasors[channel][i];
}
Array.Clear(
array: fftBuffer,
index: halfFrameSize + 1,
length: halfFrameSize - 1);
//IFFT
Fourier.Inverse(fftBuffer);
//Accumualte the window samples
for (int i = 0; i < frameSize; i++)
{
outputAccumulation[Channels * i + channel] += (float)(outputScalar * fftBuffer[i].Real);
}
}
//Copy output samples to output buffer
Array.Copy(
sourceArray: outputAccumulation,
destinationArray: cachedSampleBuffer,
length: bufferCount);
//Slide down output accumulation
Array.Copy(
sourceArray: outputAccumulation,
sourceIndex: bufferCount,
destinationArray: outputAccumulation,
destinationIndex: 0,
length: outputAccumulation.Length - bufferCount);
//Clear empty output accumulation region
Array.Clear(outputAccumulation, outputAccumulation.Length - bufferCount, bufferCount);
samplesWritten += ReadBody(data, offset + samplesWritten, count - samplesWritten);
}
return(samplesWritten);
}
/// <summary>
/// Called to process the audio input once the required number of samples are available.
/// </summary>
public void ProcessSample(Complex[] samples)
{
//Console.WriteLine($"process sample {samples[0]},{samples[1]},{samples[2]},{samples[3]},{samples[4]},{samples[5]},{samples[6]},{samples[7]}");
//for (int i = 0; i < samples.Length; i++)
//{
// Complex mono = new Complex(GAIN * samples[i], 0.0f);
// _timeRingBuffer[_timeIndex + i] = mono; // Left.
// _timeRingBuffer[_timeIndex + FFT_SIZE + i] = mono; // right
//}
//Array.Copy(samples, 0, _timeRingBuffer, _timeIndex, samples.Length);
//Array.Copy(samples, 0, _timeRingBuffer, _timeIndex + FFT_SIZE, samples.Length);
Array.Copy(samples, 0, _timeRingBuffer, _timeIndex, samples.Length > FFT_SIZE ? FFT_SIZE : samples.Length);
Array.Copy(samples, 0, _timeRingBuffer, _timeIndex + FFT_SIZE, samples.Length > (_timeRingBuffer.Length / 2 - _timeIndex) ? _timeRingBuffer.Length / 2 - _timeIndex : samples.Length);
_timeIndex = (_timeIndex + samples.Length) % FFT_SIZE;
var freqBuffer = _timeRingBuffer.Skip(_timeIndex).Take(FFT_SIZE).ToArray();
Fourier.Forward(freqBuffer, FourierOptions.NoScaling);
for (int j = 0; j < freqBuffer.Length; j++)
{
freqBuffer[j] = freqBuffer[j] * _analytic[j];
}
Fourier.Inverse(freqBuffer, FourierOptions.NoScaling);
float scale = (float)FFT_SIZE;
var complexAnalyticBuffer = freqBuffer.Skip(FFT_SIZE - BUFFER_SIZE).Take(BUFFER_SIZE).ToArray();
var data = new float[BUFFER_SIZE * DISPLAY_ARRAY_STRIDE + PREVIOUS_SAMPLES_LENGTH];
//Complex prevInput = new Complex(_previousResults[_previousResults.Length - DISPLAY_ARRAY_STRIDE], _previousResults[_previousResults.Length - DISPLAY_ARRAY_STRIDE + 1]);
//Complex secondPrevInput = new Complex(_previousResults[_previousResults.Length - DISPLAY_ARRAY_STRIDE * 2], _previousResults[_previousResults.Length - DISPLAY_ARRAY_STRIDE * 2 + 1]);
//Complex prevDiff = prevInput - secondPrevInput;
Console.WriteLine($"Output sample {_outputSampleCount}:");
for (int k = 0; k < complexAnalyticBuffer.Length; k++)
{
var diff = complexAnalyticBuffer[k] - _prevInput;
_prevInput = complexAnalyticBuffer[k];
var angle = (float)Math.Max(Math.Log(Math.Abs(GetAngle(diff, _prevDiff)), 2.0f), -1.0e12);
_prevDiff = diff;
var output = _angleLowPass.Apply(angle);
//Console.WriteLine($"angle {angle}.");
data[k * DISPLAY_ARRAY_STRIDE] = (float)(complexAnalyticBuffer[k].Real / scale);
data[k * DISPLAY_ARRAY_STRIDE + 1] = (float)(complexAnalyticBuffer[k].Imaginary / scale);
data[k * DISPLAY_ARRAY_STRIDE + 2] = (float)Math.Pow(2, output); // Smoothed angular velocity.
data[k * DISPLAY_ARRAY_STRIDE + 3] = _noiseLowPass.Apply((float)Math.Abs(angle - output)); // Average angular noise.
//Console.WriteLine($"{ data[k * DISPLAY_ARRAY_STRIDE]},{data[k * DISPLAY_ARRAY_STRIDE + 1] },angle={angle},{data[k * DISPLAY_ARRAY_STRIDE + 2] },{data[k * DISPLAY_ARRAY_STRIDE + 3] }");
}
Array.Copy(_previousResults, 0, data, 0, PREVIOUS_SAMPLES_LENGTH);
_lastSample = data;
_outputSampleCount++;
_previousResults = data.Skip(data.Length - PREVIOUS_SAMPLES_LENGTH).ToArray();
}
private void CreateFreqGraph(ZedGraphControl zgc, double[] sectionBuffers)
{
try
{
int FreqMin = int.Parse(FreqMintextBox.Text);
int FreqMax = int.Parse(FreqMaxtextBox.Text);
convertClkRate = int.Parse(SamplingTextbox.Text);
sectionLength = int.Parse(DataLengthtextBox.Text);
Freqrange = int.Parse(FreqrangetextBox.Text);
Complex[] fftsamples = new Complex[sectionLength];
for (int i = 0; i < sectionLength; i++)
{
fftsamples[i] = sectionBuffers[i];
}
Fourier.Forward(fftsamples, FourierOptions.NoScaling);
double[] hzsample = new double[sectionBuffers.Length / Freqrange];
double[] mag = new double[sectionBuffers.Length / Freqrange];
for (int i = 0; i < fftsamples.Length / Freqrange; i++)
{
mag[i] = 2000 * (2.0 / sectionLength) * (Math.Abs(Math.Sqrt(Math.Pow(fftsamples[i].Real, 2) + Math.Pow(fftsamples[i].Imaginary, 2))));
hzsample[i] = convertClkRate / sectionLength * i;
}
int Minxlength = int.Parse(FreqMintextBox.Text);
int Maxxlength = int.Parse(FreqMaxtextBox.Text);
double[] x = new double[Minxlength];
double[] y = new double[Minxlength];
double[] x1 = new double[Maxxlength];
double[] y1 = new double[Maxxlength];
for (int i = 0; i < x.Length; i++)
{
x[i] = i;
}
for (int i = 0; i < x1.Length; i++)
{
x1[i] = i;
}
y[Minxlength - 1] = 10;
y1[Maxxlength - 1] = 10;
zgc.GraphPane.CurveList.Clear();
GraphPane myPane = zgc.GraphPane;
// Set the titles and axis labels
// Make up some data points from the Sine function
LineItem myCurve;
LineItem Mincurve;
LineItem Maxcurve;
PointPairList list = new PointPairList();
list.Add(hzsample, mag);
// Generate a blue curve with circle symbols, and "My Curve 2" in the legend
myCurve = zedGraphControl2.GraphPane.AddCurve("Channel 0", list, Color.Red, SymbolType.None);
//myCurve = zedGraphControl2.GraphPane.AddCurve("Channel 0", hzsample, mag, Color.Red, SymbolType.None);
Mincurve = zedGraphControl2.GraphPane.AddCurve("FreqMin", x, y, Color.DarkBlue, SymbolType.None);
Maxcurve = zedGraphControl2.GraphPane.AddCurve("FreqMax", x1, y1, Color.DarkOrange, SymbolType.None);
Mincurve.Line.Style = DashStyle.Custom;
Mincurve.Line.Width = 2;
Mincurve.Line.DashOn = 5;
Mincurve.Line.DashOff = 5;
Maxcurve.Line.Style = DashStyle.Custom;
Maxcurve.Line.Width = 2;
Maxcurve.Line.DashOn = 5;
Maxcurve.Line.DashOff = 5;
// Make the symbols opaque by filling them with white
myCurve.Line.Fill = new Fill(Color.White, Color.Red, 45F);
myCurve.Symbol.Fill = new Fill(Color.White);
// Fill the axis background with a color gradient
zgc.AxisChange();
zgc.Refresh();
}
catch (Exception err)
{
_log.Error("Frequency domain calculate or drawing Error message is---" + err.Message);
MessageBox.Show(err.Message);
}
}
public void RecalcSpectrum()
{
if (_buffer.Count > BUFFER_SIZE)
{
_buffer.RemoveRange(0, _buffer.Count - BUFFER_SIZE);
}
UNIT_TIME = (_buffer.Count * 1.0f / SAMPLE_RATE);
int _buffer_len = _buffer.Count;
var windowfunc = Window.Hann(_buffer_len);
float ScaleFactor = 1.0f;
var fourier_v = new Complex32[_buffer_len];
for (int i = 0; i < _buffer_len; i++)
{
fourier_v[i] = new Complex32(ScaleFactor * _buffer[i].Real * (float)windowfunc[i], 0);
}
Fourier.Forward(fourier_v);
spectrum = fourier_v.Take(fourier_v.Length / 2).Select(x => (float)x.Norm()).ToArray();
/*
* Complex32[] cepstrum = fourier_v.Take(fourier_v.Length / 2).Select(x => Complex32.Log(x) ).ToArray();
* Fourier.Inverse(cepstrum);
* for (int i = 120; i < cepstrum.Length; i++) {
* cepstrum[i] = 0;
* }
* Fourier.Forward(cepstrum);
* test_graph = cepstrum.Take(cepstrum.Length / 2).Select(x => (float) x.Norm() ).ToArray(); //cepstrum.Select(x => (float)x.Norm()).ToArray();
*/
float prev = -1.0f;
bool prev_up = true;
float max_fq = 0.0f;
float max_v = 0.0f;
peaks.Clear();
for (int i = 0; i < spectrum.Length; i++)
{
float v = spectrum[i];
float fq = i / UNIT_TIME;
if (fq < CUTOFF)
{
continue;
}
bool up = false;
if (v > prev)
{
up = true;
}
if ((!up) && prev_up && prev > THRESHOLD)
{
peaks.Add(new KeyValuePair <int, float>(i - 1, prev));
}
if (max_v < v)
{
max_fq = fq;
max_v = v;
}
prev = v;
prev_up = up;
}
float freq = float.NaN;
if (peaks.Count > 0)
{
freq = peaks.Select(x => x.Key).Min() / UNIT_TIME;
}
freq_graph[freq_graph_pos++] = freq;
//Console.WriteLine(String.Format("{0,12:F4},{1,12:F4}", max_fq, max_v));
/* if (max_v > THRESHOLD) {
* freq_graph[freq_graph_pos++] = max_fq;
* } else {
* freq_graph[freq_graph_pos++] = float.NaN;
* } */
if (freq_graph_pos >= FREQ_GRAPH_SIZE)
{
freq_graph_pos = 0;
}
}
