/// <summary>
/// <para>Computes the power spectrum of input time-domain signal.</para>
/// <para>Chinese Simplified: 计算输入信号的功率频谱。</para>
/// </summary>
/// <param name="x">
/// <para>input time-domain signal.</para>
/// <para>Chinese Simplified: 输入的时域波形。</para>
/// </param>
/// <param name="samplingRate">
/// <para>sampling rate of the input time-domain signal, in samples per second.</para>
/// <para>Chinese Simplified: 输入信号的采样率，以S/s为单位。</para>
/// </param>
/// <param name="spectrum">
/// <para>output sequence containing the power spectrum.</para>
/// <para>Chinese Simplified: 输出功率谱。</para>
/// </param>
/// <param name="df">
/// <para>the frequency resolution of the spectrum, in hertz.</para>
/// <para>Chinese Simplified: 功率谱的频谱间隔，以Hz为单位。</para>
/// </param>
/// <param name="unitSettings">
/// <para>unit settings of the output power spectrum</para>
/// <para>Chinese Simplified: 设置功率谱的单位。</para>
/// </param>
/// <param name="windowType">
/// <para>the time-domain window to apply to the time signal.</para>
/// <para>Chinese Simplified: 窗类型。</para>
/// </param>
/// <param name="windowPara">
/// <para>parameter for a Kaiser/Gaussian/Dolph-Chebyshev window, If window is any other window, this parameter is ignored</para>
/// <para>Chinese Simplified: 窗调整系数，仅用于Kaiser/Gaussian/Dolph-Chebyshev窗。</para>
/// </param>
public static void PowerSpectrum(double[] x, double samplingRate, ref double[] spectrum, out double df,
UnitConvSetting unitSettings, WindowType windowType, double windowPara)
{
int spectralLines = spectrum.Length; //谱线数是输出数组的大小
SpectralInfo spectralInfo = new SpectralInfo();
AdvanceRealFFT(x, spectralLines, windowType, spectrum, ref spectralInfo);
double scale = 1.0 / spectralInfo.FFTSize;
//CBLASNative.cblas_dscal(spectralLines, scale, spectrum, 1);
for (int i = 0; i < spectrum.Length; i++)
{
spectrum[i] = spectrum[i] * scale;
}
df = 0.5 * samplingRate / spectralInfo.spectralLines; //计算频率间隔
//Unit Conversion
UnitConversion(spectrum, df, SpectrumType.Amplitude, unitSettings, Window.WindowENBWFactor[(int)windowType]);
}
/// <summary>
/// <para>Computes the power spectrum of input time-domain signal.</para>
/// <para>Chinese Simplified: 计算输入信号的功率频谱。</para>
/// </summary>
/// <param name="x">
/// <para>input time-domain signal.</para>
/// <para>Chinese Simplified: 输入的时域波形。</para>
/// </param>
/// <param name="samplingRate">
/// <para>sampling rate of the input time-domain signal, in samples per second.</para>
/// <para>Chinese Simplified: 输入信号的采样率，以S/s为单位。</para>
/// </param>
/// <param name="spectrum">
/// <para>output sequence containing the power spectrum.</para>
/// <para>Chinese Simplified: 输出功率谱。</para>
/// </param>
/// <param name="df">
/// <para>the frequency resolution of the spectrum, in hertz.</para>
/// <para>Chinese Simplified: 功率谱的频谱间隔，以Hz为单位。</para>
/// </param>
/// <param name="unit">
/// <para>unit of the output power spectrum</para>
/// <para>Chinese Simplified: 设置功率谱的单位。</para>
/// </param>
/// <param name="windowType">
/// <para>the time-domain window to apply to the time signal.</para>
/// <para>Chinese Simplified: 窗类型。</para>
/// </param>
/// <param name="windowPara">
/// <para>parameter for a Kaiser/Gaussian/Dolph-Chebyshev window, If window is any other window, this parameter is ignored</para>
/// <para>Chinese Simplified: 窗调整系数，仅用于Kaiser/Gaussian/Dolph-Chebyshev窗。</para>
/// </param>
/// <param name="PSD">
/// <para>specifies whether the output power spectrum is converted to power spectral density.</para>
/// <para>Chinese Simplified: 输出的频谱是否为功率谱密度。</para>
/// </param>
public static void PowerSpectrum(double[] x, double samplingRate, ref double[] spectrum, out double df,
SpectrumUnits unit = SpectrumUnits.V2, WindowType windowType = WindowType.Hann,
double windowPara = double.NaN, bool PSD = false)
{
int spectralLines = spectrum.Length; //谱线数是输出数组的大小
SpectralInfo spectralInfo = new SpectralInfo();
AdvanceRealFFT(x, spectralLines, windowType, spectrum, ref spectralInfo);
double scale = 1.0 / spectralInfo.FFTSize;
//CBLASNative.cblas_dscal(spectralLines, scale, spectrum, 1);
for (int i = 0; i < spectrum.Length; i++)
{
spectrum[i] *= scale;
}
df = 0.5 * samplingRate / spectralInfo.spectralLines; //计算频率间隔
//Unit Conversion
UnitConvSetting unitSettings = new UnitConvSetting(unit, PeakScaling.Rms, 50.00, PSD);
UnitConversion(spectrum, df, SpectrumType.Amplitude, unitSettings, Window.WindowENBWFactor[(int)windowType]);
}
/// <summary>
/// Advance Real FFT
/// </summary>
/// <param name="xIn">time domain data</param>
/// <param name="spectralLines">spectralLines</param>
/// <param name="windowType">window type</param>
/// <param name="xOut">spectral out data</param>
/// <param name="spectralInfo">spectral info</param>
public static void AdvanceRealFFT(double[] xIn, int spectralLines, WindowType windowType,
double[] xOut, ref SpectralInfo spectralInfo)
{
int n = xIn.Length, windowsize = 0, fftcnt = 0; //做FFT的次数
int fftsize = 0; //做FFT点数
double cg = 0, enbw = 0, scale = 0.0;
double[] xInTmp = null;
double[] windowData = null;
double[] xOutCTmp = null;
//输入的线数超过最大支持的线数则使用最大支持线数
if (spectralLines > MaxSpectralLine)
{
spectralLines = MaxSpectralLine;
}
//输入的点数小于线数，则窗长度为N，先加窗再补零到2*spectralLines再做FFT
if (n <= 2 * spectralLines)
{
windowsize = n;
fftcnt = 1;
}
else
{
windowsize = 2 * spectralLines;
fftcnt = n / (2 * spectralLines);
}
fftsize = 2 * spectralLines; //不管N与2*spectralLines的关系是怎么样，FFT的点数都应该为 2*spectralLines
xInTmp = new double[fftsize];
//xOutCTmp = new Complex[fftsize / 2 + 1];
if (xInTmp.Length % 2 == 0)
{
xOutCTmp = new double[xInTmp.Length + 2];
}
else
{
xOutCTmp = new double[xInTmp.Length + 1];
}
if (n < (2 * spectralLines))
{
//memset(x_in + N, 0, (fftsize - N) * sizeof(double)); //补零至spectralLines
for (int i = n; i < fftsize; i++)
{
xInTmp[i] = 0;
}
}
//memset(xOut, 0, spectralLines * sizeof(double));
//生成窗函数的数据
windowData = new double[windowsize];
Window.GetWindow(windowType, ref windowData, out cg, out enbw);
for (int i = 0; i < xOut.Length; i++)
{
xOut[i] = 0;
}
//CBLASNative.cblas_dscal(windowsize, 1 / cg, windowData, 1); //窗系数归一化
//CBLASNative.cblas_dscal(xOut.Length, 0, xOut, 1); //将xOut清零
GCHandle gch = GCHandle.Alloc(xIn, GCHandleType.Pinned);
var xInPtr = gch.AddrOfPinnedObject();
for (int i = 0; i < fftcnt; i++)
{
//拷贝数据到临时内存中
//memcpy(x_in, x + i * windowsize, fftsize * sizeof(double));
/*TIME_DOMAIN_WINDOWS(windowType, x_in, &CG, &ENBW, windowsize);*//*(double*)(xIn + i * windowsize)*/
for (int k = 0; k < windowsize; k++)
{
xInTmp[k] = windowData[k] * xIn[i * fftsize + k];
}
Buffer.BlockCopy(xInTmp, 0, xOutCTmp, 0, xInTmp.Length * sizeof(double));
Fourier.ForwardReal(xOutCTmp, xInTmp.Length, FourierOptions.NoScaling);
//VMLNative.vdMul(windowsize, windowData, xInPtr + i * fftsize * sizeof(double), xInTmp);
//BasicFFT.RealFFT(xInTmp, ref xOutCTmp);
////计算FFT结果复数的模,复用x_in做中间存储
//VMLNative.vzAbs(fftsize / 2 + 1, xOutCTmp, xInTmp);
//xOut[0] += xOutCTmp[0];
for (int j = 0; j < spectralLines; j++)
{
xOut[j] += Math.Sqrt(xOutCTmp[2 * j + 1] * xOutCTmp[2 * j + 1] + xOutCTmp[2 * j] * xOutCTmp[2 * j]);
}
////每次计算结果累加起来
//VMLNative.vdAdd(spectralLines, xInTmp, xOut, xOut);
}
scale = 2 * (1.0 / fftcnt) / Sqrt2; //双边到单边有一个二倍关系,输出为Vrms要除以根号2
for (int j = 0; j < spectralLines; j++)
{
xOut[j] *= scale;
}
////fftcnt次的频谱做平均
//CBLASNative.cblas_dscal(spectralLines, scale, xOut, 1);
xOut[0] = xOut[0] / Sqrt2; //上一步零频上多除了根号2，这里乘回来（Rms在零频上不用除根号2，单双边到单边还是要乘2 ?）
spectralInfo.spectralLines = spectralLines;
spectralInfo.FFTSize = fftsize;
spectralInfo.windowSize = windowsize;
spectralInfo.windowType = windowType;
gch.Free();
}