50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import pywt
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# 生成示例信号：正弦波信号加上高斯白噪声
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np.random.seed(0)
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t = np.linspace(0, 1, 1000, endpoint=False) # 时间序列，长度为1000
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signal = np.sin(2 * np.pi * 20 * t) # 20 Hz 的正弦波信号
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noise = np.random.normal(0, 0.5, len(t)) # 高斯白噪声
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signal_noisy = signal + noise # 加噪声后的信号
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# 进行小波变换
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wavelet = 'db4' # 选择小波基函数
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levels = 5 # 分解的层数
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coeffs = pywt.wavedec(signal_noisy, wavelet, level=levels)
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# 对小波系数进行软阈值处理
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threshold = 0.5 # 阈值
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coeffs_thresh = [pywt.threshold(c, threshold, mode='soft') for c in coeffs]
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# 重构信号
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signal_denoised = pywt.waverec(coeffs_thresh, wavelet)
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# 绘制结果
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plt.figure(figsize=(10, 6))
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plt.subplot(3, 1, 1)
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plt.plot(t, signal, 'b', label='Original Signal')
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plt.title('Original Signal')
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plt.xlabel('Time')
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plt.ylabel('Amplitude')
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plt.legend()
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plt.subplot(3, 1, 2)
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plt.plot(t, signal_noisy, 'g', label='Noisy Signal')
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plt.title('Noisy Signal')
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plt.xlabel('Time')
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plt.ylabel('Amplitude')
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plt.legend()
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plt.subplot(3, 1, 3)
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plt.plot(t, signal_denoised, 'r', label='Denoised Signal')
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plt.title('Denoised Signal')
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plt.xlabel('Time')
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plt.ylabel('Amplitude')
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plt.legend()
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plt.tight_layout()
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plt.show()
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