/// <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(); }