plt.title('Amplitude Spectrum') plt.xlabel('Frequency (Hz)') plt.ylabel('Amplitude') 绘制相位谱 plt.subplot(2, 1, 2) plt.plot(freqs[:n//2], phase_spectrum[:n//2]) plt.title('Phase Spectrum') plt.xlabel('Frequency (Hz)') plt.ylabel('Phase (radians)') plt.tight_layout() plt....
python # xf = np.linspace(0.0, li_fsam/2, li_N//2) fig, ax = plt.subplots() ## nexttile ax.plot(xf, 2.0/li_N * np.abs(yf[:li_N//2])) plt.xlabel('Frequency [Hz]'); plt.ylabel('Amplitude [Peak]') plt.grid() # plt.grid() # plt.show() ## title('Plot 1') #...
fft_image = np.fft.fft2(gray_image) ``` 这将得到一个复数数组,其中每个元素代表了对应频率分量的幅度和相位信息。 如果要可视化频率分量,可以通过计算幅度谱和相位谱来实现。可以使用以下代码来计算幅度谱和相位谱,并调用Matplotlib的imshow(函数显示图像: ```python amplitude = np.abs(fft_image) phase =...
y(ndarray) − The transformed 2D array in the frequency domain. The output is complex, containing the amplitude and phase information of the frequency components. Example 1 This is a basic example of calculating the 2D FFT of a basic 2x2 array usingfft2()method and then transforming this ...
floor(rows/2) # fft for the input image imf = fftshift(fft2(phase)) # calculate distance distfact = 1.0e3 # to convert into meters [xx, yy] = np.meshgrid(range(cols), range(rows)) xx = (xx - cx) * x_size # these are in meters as x_size in meters yy = (yy - cy) *...
□ Extracting instantaneous amplitude, phase, frequency □ Phase demodulation using Hilbert transform● Choosing a filter : FIR or IIR : understanding the design perspective □ Design specification □ General considerations in design No data available in table Showing 0 to 0 of 0 entrie...