[Python] vibration analysis

分析如震动、音频等资讯时，时间序列的资料往往分析不出太多资讯
以下以快速傅立叶转换(Fast Fourier Transform, FFT)
可以绘製出
* 原始资讯波形
* RMS波形
* 时频图
* 时频图(log Data)

vibra_ana(10240,df_vib.DateTime,[df_vib.X,df_vib.Y,df_vib.Z])

def vibra_ana(Fs,t,x):        print('from {} to {}'.format(t[0],t[len(t)-1]))        l=len(x)    figsize = (10 * l, 20 )    row = 5    col = l    plt.figure(figsize=figsize)     #Determine variables    N = np.int(np.prod(t.shape))#length of the array    T = 1/Fs    T = (t[1]-t[0]).microseconds*0.000001#timedelta(seconds=0.2).total_seconds()    Fs = 1/T #sample rate (Hz)    print("Fs :{} , T :{}".format(Fs,T))    print("# Samples:",N)     for index_ele , ele in enumerate(x) :        index = 0 * l + index_ele + 1                 #Plot Data        tic = time.clock()        plt.subplot(row,col,index)           plt.plot(t, ele)        plt.xlabel('Time (seconds)')        plt.ylabel('Accel (g)')        plt.title('RAW Data - ' )        plt.grid()        toc = time.clock()        print("Plot Time:",toc-tic)                index = 1 * l + index_ele + 1         #Compute RMS and Plot        tic = time.clock()        w = np.int(np.floor(Fs/10)); #width of the window for computing RMS        print('w :{}'.format(w))        steps = np.int_(np.floor(N/w)) #Number of steps for RMS        t_RMS = np.zeros((steps,1)); #Create array for RMS time values        x_RMS = np.zeros((steps,1)); #Create array for RMS values        for i in range (0, steps):#             t_RMS[i] = np.mean([(t[(i*w)]-t[0]).microseconds,(t[((i+1)*w-1)]-t[0]).microseconds])            t_RMS[i] = np.mean([(t[(i*w)]-t[0]).seconds,(t[((i+1)*w-1)]-t[0]).seconds])    #         t_RMS[i] = np.mean(t[(i*w):((i+1)*w)]);            x_RMS[i] = np.sqrt(np.mean(ele[(i*w):((i+1)*w)]**2));          plt.subplot(row,col,index)           plt.plot(t_RMS, x_RMS)        plt.xlabel('Time (seconds)')        plt.ylabel('RMS Accel (g)')        plt.title('RMS - ' )        plt.grid()        toc = time.clock()        print("RMS Time:",toc-tic)        index = 2 * l + index_ele + 1         #Compute and Plot FFT        tic = time.clock()        plt.subplot(row,col,index)           xf = np.linspace(0.0, 1.0/(2.0*T), N/2)        yf = fft(ele)        plt.plot(xf, 2.0/N * np.abs(yf[0:np.int(N/2)]))        plt.grid()        plt.xlabel('Frequency (Hz)')        plt.ylabel('Accel (g)')        plt.title('FFT - ' )        toc = time.clock()        print("FFT Time:",toc-tic)        index = 3 * l + index_ele + 1                 # Compute and Plot Spectrogram        tic = time.clock()        plt.subplot(row,col,index)           f, t2, Sxx = signal.spectrogram(ele, Fs )#, nperseg = Fs/4)        ax=plt.pcolormesh(t2, f, np.log(Sxx))#         plt.colorbar(ax)        plt.ylabel('Frequency [Hz]')        plt.xlabel('Time [sec]')        plt.title('Spectrogram(log) - ' )        toc = time.clock()        print("Spectrogram Time:",toc-tic)        index = 4 * l + index_ele + 1     # Compute and Plot Spectrogram        tic = time.clock()        plt.subplot(row,col,index)           plt.pcolormesh(t2, f, (Sxx))        plt.ylabel('Frequency [Hz]')        plt.xlabel('Time [sec]')        plt.title('Spectrogram - ' )        toc = time.clock()        print("Spectrogram Time:",toc-tic)        plt.tight_layout()    plt.show()