############################## Convert all signal to FFT spectrum############################ Function fot FFT of samples from scipy.fft import fft, fftfreq # This fft perfectly matches with matlab fft, I checkeddef FFT(data, N=800, Fs=40): # N is Number of sample points, Fs is sampling rate in kHz; to show full Hz, set Fs as full # sample spacing T = 1.0 / Fs yf = fft(data) xf = fftfreq(N, T)[:N//2] yf1=2.0/N * np.abs(yf[0:N//2]) # extract the one sided values return (yf1)
# Import or select the sample needs to convert to FFT dt=train_40 # Data source
## Do the FFT in for loopfor i in range(1,(dt.shape[0])): dtf=FFT(dt[i]) dft0=np.vstack((dft0, dtf))print(dft0.shape)# Save datanp.save("4class_db/fft_40k/dtf_tr_1c_40kHz.npy", dft0)
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# Plotting FFT samples# Function fot FFT of samples # Think Train is the input datafrom scipy.fft import fft, fftfreq # This fft perfectly matches with matlab fft, I checkeddef FFT(data, N=800, Fs=40): # N is Number of sample points, Fs is sampling rate in kHz; to show full Hz, set Fs as full # sample spacing T = 1.0 / Fs# x = np.linspace(0.0, N*T, N, endpoint=False)# print("x=" + str(x.shape)) yf = fft(data) xf = fftfreq(N, T)[:N//2]# import matplotlib.pyplot as plt # plt.plot(xf[1:N//2], 2.0/N * np.abs(yf[1:N//2]), '-r') yf1=2.0/N * np.abs(yf[2:N//2]) # extract the one sided values xf1 = xf[2:N//2]# print(xf1)# print(yf1) print(xf1.shape) print(yf1.shape) return (xf1, yf1)
# FFT of all the arc samples and stored valuesi1=17 # Signal numberi2=35 # Signal numberx1, f1=FFT(train_40[i1]) # i1 is Normal, i2 is Arcx2, f2=FFT(train_40[i2])print(train_label[i1])print(train_label[i2])plt.semilogy(x1, f1, "-b")plt.semilogy(x2, f2, "-r")plt.legend([train_label[i1], train_label[i2]])plt.xlabel("Frequency (kHz)")plt.ylabel("FFT Amplitude")plt.show()print(np.max(f1)-np.min(f1))
plt.plot(x1, f1, "-b")plt.plot(x2, f2, "-r")plt.legend([train_label[i1], train_label[i2]])plt.xlabel("Frequency (kHz)")plt.ylabel("FFT Amplitude")plt.show()
plt.plot(train_40[i1])plt.plot(train_40[i2])plt.legend([train_label[i1], train_label[i2]])plt.show()
# FFT of all the arc samples and stored valuesi1=17 # Signal numberi2=35 # Signal numberx1, f1=FFT(train_40[i1]) # i1 is Normal, i2 is Arcx2, f2=FFT(train_40[i2])print(train_label[i1])print(train_label[i2])plt.semilogy(x1, f1, "-b")plt.semilogy(x2, f2, "-r")plt.legend([train_label[i1], train_label[i2]])plt.xlabel("Frequency (kHz)")plt.ylabel("FFT Amplitude")plt.show()print(np.max(f1)-np.min(f1))
plt.plot(x1, f1, "-b")plt.plot(x2, f2, "-r")plt.legend([train_label[i1], train_label[i2]])plt.xlabel("Frequency (kHz)")plt.ylabel("FFT Amplitude")plt.show()
plt.plot(train_40[i1])plt.plot(train_40[i2])plt.legend([train_label[i1], train_label[i2]])plt.show()
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