private void CalRMS()
{
Complex32[] at = new Complex32[_sampleLength]; //原始信号
float[] freq = new float[_sampleLength];
for (int i = 0; i < _sampleLength; i++)
{
at[i] = new Complex32(_xval[i], _yval[i]);
freq[i] = i * _sampleRate / _sampleLength;
}
Fourier.Forward(at); //FFT之后的结果保留在at序列中
for (int i = _sampleLength / 2 + 1; i < _sampleLength; i++) //去掉大于fs/2的成分，之后幅值再乘以2/N
{
at[i] = 0;
}
Complex32[] weight = new Complex32[_sampleLength];
Weight weigh = new Weight(_weightType, freq, out weight);
Complex32[] aw = new Complex32[_sampleLength]; //aw为加权后的信号
for (int i = 0; i < _sampleLength; i++)
{
aw[i] = 2 * at[i] * weight[i] / _sampleLength; //恢复幅值，幅值乘以2/N
}
Fourier.Inverse(aw);
double[] awAbs = new double[_sampleLength];
for (int i = 0; i < _sampleLength; i++)
{
awAbs[i] = Complex32.Abs(aw[i]);
}
_aw = awAbs;
_RMS = _aw.RootMeanSquare();
}
/// <summary>
/// 计算峰值系数和加权加速度时间历程
/// </summary>
private void CalCF()
{
Complex32[] at = new Complex32[_sampleLength]; //原始信号
float[] freq = new float[_sampleLength];
for (int i = 0; i < _sampleLength; i++)
{
at[i] = new Complex32(_xval[i], _yval[i]);
freq[i] = i * _sampleRate / _sampleLength;
}
Fourier.Forward(at); //FFT之后的结果保留在at序列中
Complex32[] weight = new Complex32[_sampleLength];
Weight weigh = new Weight(_weightType, freq, out weight);
Complex32[] aw = new Complex32[_sampleLength]; //aw为加权后的信号
int halfNfft;
if (_sampleLength % 2 == 1)
{
halfNfft = (int)Math.Floor((double)(_sampleLength / 2)) + 1;
}
else
{
halfNfft = (int)Math.Floor((double)(_sampleLength / 2));
}
for (int i = 0; i < halfNfft; i++)
{
aw[i] = at[i] * weight[i];
aw[_sampleLength - i - 1] = aw[i];
}
Fourier.Inverse(aw);
double[] awAbs = new double[_sampleLength];
for (int i = 0; i < _sampleLength; i++)
{
awAbs[i] = Complex32.Abs(aw[i]);
}
_aw = awAbs;
_rms = _aw.RootMeanSquare();
_Peak = awAbs.Max();
double tempAp = awAbs.Max() - awAbs.Min();
_Ap = Math.Abs(tempAp);
_CF = _Peak / _rms;
}
//Method has optional bool argument removeAverage, because in other methods it accepts already averaged vector.
public static FftResult GetAmpSpectrumAndMax(double fs, List <double> vector, bool removeAverage = true)
{
if (vector == null || !vector.Any())
{
return(null);
}
if (vector.Count == 1)
{
throw new ArgumentException("Vector must have more than 1 value", nameof(vector));
}
if (fs <= 0)
{
throw new ArgumentException("Fs cannot be less than or equal to zero", nameof(fs));
}
Complex32[] vec = new Complex32[vector.Count];
int i = 0;
double vecAvg = removeAverage ? vector.Average():0;//don't compute avg if not needed
foreach (var ve in vector)
{
vec[i] = removeAverage ? new Complex32((float)(ve - vecAvg), 0) : new Complex32((float)ve, 0);
i++;
}
Fourier.Forward(vec, FourierOptions.Matlab);
FftResult res = new FftResult();
res.Frequencies = new List <double>();
res.Values = new List <double>();
int l = (int)Math.Round((double)vec.Length / 2, MidpointRounding.AwayFromZero);
double konec = fs / 2;
var k = Generate.LinearSpaced(l, 0, konec);
for (int p = 0; p < l; p++)
{
k[p] = Math.Round(k[p], 10);
res.Frequencies.Add(k[p]);
res.Values.Add(Complex32.Abs(vec[p]));
}
res.MaxIndex = res.Values.FindIndex(n => n == (res.Values.Max()));
return(res);
}
private void FFTDisplay(AccelerometerAxis axis)
{
// Clear points
s4.Points.Clear();
List <DataPoint> pointsList;
List <DataPoint> pointsList2; //Additional for Imaginary , Added by JP
switch (axis)
{
case AccelerometerAxis.X_Axis:
pointsList = s1.Points;
pointsList2 = s1.Points; //Additional for Imaginary , Added by JP
break;
case AccelerometerAxis.Y_Axis:
pointsList = s2.Points;
pointsList2 = s2.Points; //Additional for Imaginary , Added by JP
break;
case AccelerometerAxis.Z_Axis:
pointsList = s3.Points;
pointsList2 = s3.Points; //Additional for Imaginary , Added by JP
break;
default:
// This should not happen
pointsList = null;
pointsList2 = null;
break;
}
//var pointsList = s1.Points;
var pointsRealList = new List <double>();
var pointsImagList = new List <double>(); //Additional for Imaginary , Added by JP
foreach (var dataPoint in pointsList)
{
pointsRealList.Add(dataPoint.Y);
}
foreach (var dataPoint in pointsList2) //Additional for Imaginary , Added by JP
{
pointsImagList.Add(dataPoint.Y * 0); // Multiply by 0, all imaginary values are 0.
}
int numSamples = pointsRealList.Count;
int numSamples2 = pointsImagList.Count; //Additional for Imaginary , Added by JP
if (pointsRealList.Count.IsEven())
{
pointsRealList.Add(0);
pointsRealList.Add(0);
}
else
{
pointsRealList.Add(0);
}
if (pointsImagList.Count.IsEven()) //Additional for Imaginary , Added by JP
{
pointsImagList.Add(0);
pointsImagList.Add(0);
}
else
{
pointsImagList.Add(0);
}
var pointsRealArray = pointsRealList.ToArray();
var pointsImagArray = pointsImagList.ToArray(); //Additional for Imaginary , Added by JP
var FFT_Count = pointsRealArray.Count();
int ctr = 0; //Additional for Imaginary , Added by JP
Complex32[] For_FFTval = new Complex32[FFT_Count]; //Additional for Imaginary , Added by JP
foreach (var data_points in pointsRealArray) //Additional for Imaginary , Added by JP
{
For_FFTval[ctr] = new Complex32((float)pointsRealArray[ctr], (float)pointsImagArray[ctr]);
ctr = ctr + 1;
}
Fourier.Forward(For_FFTval); //Additional for Imaginary , Added by JP
ctr = 0;
double[] FFT_plot = new double[FFT_Count]; //Additional for Imaginary , Added by JP
foreach (var data_points in For_FFTval)
{
FFT_plot[ctr] = Complex32.Abs(For_FFTval[ctr]); //Additional for Imaginary , Added by JP
ctr = ctr + 1;
}
var x = 0;
foreach (var realPoint in FFT_plot)
{
var count = (double)s4.Points.Count;
s4.Points.Add(new DataPoint(count, realPoint));
}
FFTPlotModel = fFTPlot;
FFTPlotModel.InvalidatePlot(true);
}
