scipy中signal模块之IIR滤波器(低通)的使用

东方耀

scipy中signal模块之IIR滤波器(低通)的使用

1. # -*- coding: utf-8 -*-
   
2. __author__ = u'东方耀 微信：dfy_88888'
   
3. __date__ = '2021/9/17 上午11:41'
   
4. __product__ = 'PyCharm'
   
5. __filename__ = '00_重新理解一下复信号'
   
6. 
   
7. 
   
8. from scipy.fftpack import fft, ifft, fftshift
   
9. import numpy as np
   
10. from scipy import signal
    
11. import random
    
12. import matplotlib.pyplot as plt
    
13. import matplotlib.ticker as mticker
    
14. from matplotlib.font_manager import FontProperties
    
15. 
    
16. font_fname = "/usr/share/fonts/wps-office/simfang.ttf"
    
17. font = FontProperties(fname=font_fname)
    
18. # plt.rcParams['font.sans-serif']=['simfang']#设置作图中文显示
    
19. 
    
20. # scipy中signal模块之IIR滤波器(低通)的使用
    
21. 
    
22. continue_time = 2e-6
    
23. fs = 100e6
    
24. f = [10e6, 30e6, 40e6]
    
25. sample_num = int(np.ceil(continue_time * fs))
    
26. print("每个子脉冲的采样点数(采样速率有关)=", sample_num)
    
27. print("多个频率分量的信号=", f)
    
28. x_time = np.linspace(0, continue_time, sample_num, endpoint=True)
    
29. 
    
30. 
    
31. RCS = 1
    
32. Rx_signal_1 = RCS * np.cos(2 * np.pi * f[0] * x_time)
    
33. Rx_signal_2 = RCS * np.cos(2 * np.pi * f[1] * x_time)
    
34. Rx_signal_3 = RCS * np.cos(2 * np.pi * f[2] * x_time)
    
35. Rx_signal_all = Rx_signal_1 + Rx_signal_2 + Rx_signal_3
    
36. 
    
37. 
    
38. Rx_y_fft_1 = fftshift(fft(Rx_signal_1))
    
39. Rx_y_fft_2 = fftshift(fft(Rx_signal_2))
    
40. Rx_y_fft_3 = fftshift(fft(Rx_signal_3))
    
41. Rx_y_fft_all = fftshift(fft(Rx_signal_all))
    
42. x_f = np.linspace(0, int(fs), sample_num, endpoint=True) - int(fs/2)
    
43. 
    
44. # 开始滤波   何为0相位滤波器？
    
45. # Wn：归一化截止频率。计算公式Wn=2*截止频率/采样频率  截止频率=35e6  20e6
    
46. # b，a: IIR滤波器的分子（b）和分母（a）多项式系数向量。output='ba'
    
47. b, a = signal.butter(N=7, Wn=0.7, btype="lowpass")   # N表示滤波器的阶数  7 9 11阶
    
48. print("IIR滤波器的分子b:", len(b), b)
    
49. print("IIR滤波器的分母a:", len(a), a)
    
50. filtedData = signal.filtfilt(b, a, Rx_signal_all)  #data为要过滤的信号
    
51. print("滤波之后的数据：", filtedData.shape, filtedData[:3])
    
52. Rx_y_fft_filtedData = fftshift(fft(filtedData))
    
53. 
    
54. 
    
55. # 画图展示
    
56. plt.figure(figsize=(14, 12))
    
57. plt.figure(1)
    
58. plt.subplot(5, 1, 1)
    
59. # plt.plot(np.arange(sample_num), Rx_signal_1, marker='o', markersize=6, markerfacecolor='r')
    
60. # plt.title('信号_时域', fontproperties=font, fontsize=16)
    
61. # plt.xlabel('time/sample_num')
    
62. plt.plot(x_f, np.abs(Rx_y_fft_filtedData), marker='o', markersize=6, markerfacecolor='r')
    
63. plt.title('滤波后_幅度谱', fontproperties=font, fontsize=16)
    
64. plt.xlabel('freq')
    
65. 
    
66. plt.subplot(5, 1, 2)
    
67. plt.plot(x_f, np.abs(Rx_y_fft_1), marker='o', markersize=6, markerfacecolor='b')
    
68. plt.title('信号1_幅度谱', fontproperties=font, fontsize=16)
    
69. # plt.axis([0, 500, -3, 3])
    
70. plt.xlabel('freq')
    
71. 
    
72. plt.subplot(5, 1, 3)
    
73. plt.plot(x_f, np.abs(Rx_y_fft_2), marker='o', markersize=6, markerfacecolor='r')
    
74. plt.title('信号2_幅度谱', fontproperties=font, fontsize=16)
    
75. # plt.axis([0, 500, -3, 3])
    
76. plt.xlabel('freq')
    
77. 
    
78. plt.subplot(5, 1, 4)
    
79. plt.plot(x_f, np.abs(Rx_y_fft_3), marker='o', markersize=6, markerfacecolor='b')
    
80. plt.title('信号3_幅度谱', fontproperties=font, fontsize=16)
    
81. # plt.axis([0, 500, -3, 3])
    
82. plt.xlabel('freq')
    
83. 
    
84. plt.subplot(5, 1, 5)
    
85. plt.plot(x_f, np.abs(Rx_y_fft_all), marker='o', markersize=6, markerfacecolor='r')
    
86. plt.title('信号all_幅度谱', fontproperties=font, fontsize=16)
    
87. # plt.axis([0, 500, -3, 3])
    
88. plt.xlabel('freq')
    
89. 
    
90. 
    
91. plt.show()
    
92. 
    

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