python 之FFT

痛定思痛。 2023-06-12 06:24 9阅读 0赞

获取信号的频域信息，FFT是可行性方案，本文有两种求FFT 的方法，个人觉得第一种方法比较好，简单一点。第二种方法比较复杂。

方法1：

from scipy.fftpack import fft
    import matplotlib.pyplot as plt
    import numpy as np
    # Number of sample points
    N =600
    # sample spacing
    fs=800
    T = 1.0 / 800.0
    x = np.linspace(0.0, N*T, N)# (0.0   ，采样点*周期数   ，   采样点)
    y = np.sin(50.0 *x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)
    yf = fft(y)
    print("yf:",yf)
    xf = np.linspace(0.0, 1.0/(2.0*T), N//2)#  目的是获取频率  print("N//2",N//2) 保证是整数，频谱图的频率一般是不会高于采样率的。 
    #plt.plot(xf, 2.0/N * np.abs(yf[0:N//2]))
    plt.plot(xf, np.abs(yf[0:N//2])*2.0/N)#   因为幅值有正负， 所以有abs
    plt.grid()
    plt.show()

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方法2：

from scipy.fftpack import fft
    import matplotlib.pyplot as plt
    import numpy as np
    
    import pylab as pl
    
    sampling_rate = 8000
    fft_size = 512
    t = np.arange(0, 1.0, 1.0/sampling_rate)
    x = np.sin(2*np.pi*156.25*t)  + 2*np.sin(2*np.pi*234.375*t)
    xs = x[:fft_size]
    xf = np.fft.rfft(xs)/fft_size
    freqs = np.linspace(0, sampling_rate/2, fft_size/2+1)
    xfp = 20*np.log10(np.clip(np.abs(xf), 1e-20, 1e100))
    pl.figure(figsize=(8,4))
    pl.subplot(211)
    pl.plot(t[:fft_size], xs)
    pl.xlabel(u"时间(秒)")
    pl.title(u"156.25Hz和234.375Hz的波形和频谱")
    pl.subplot(212)
    pl.plot(freqs, xfp)
    pl.xlabel(u"频率(Hz)")
    pl.subplots_adjust(hspace=0.4)
    pl.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjUyODA4OQ_size_16_color_FFFFFF_t_70 1][]

转载：[https://docs.huihoo.com/scipy/scipy-zh-cn/frequency\_process.html][https_docs.huihoo.com_scipy_scipy-zh-cn_frequency_process.html]

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjUyODA4OQ_size_16_color_FFFFFF_t_70 1]: /images/20230531/7458f42aab8b456bbd3073f7ffa9bfe0.png
[https_docs.huihoo.com_scipy_scipy-zh-cn_frequency_process.html]: https://docs.huihoo.com/scipy/scipy-zh-cn/frequency_process.html