带通、低通和高通滤波python代码

import numpy as np
import matplotlib.pyplot as plt
import scipy.signal

def butter_filt(data, fs=100., Wn=[0.5, 1], btype='bandpass', N=5, axis=-1):
    nqy = 0.5 * fs

    if btype not in ['bandpass', 'lowpass', 'highpass']:
        raise ValueError("Invalid filter type. Use 'bandpass', 'lowpass', or 'highpass'.")

    wn = [wn / nqy if btype == 'bandpass' else wn / fs for wn in Wn]

    b, a = scipy.signal.butter(N, wn, btype)
    filt_data = scipy.signal.filtfilt(b, a, data, padtype='odd', axis=axis)

    return filt_data

# 生成示例数据
fs = 200.0  # 采样率
t = np.arange(0, 5, 1/fs)  # 时间数组
data = np.sin(2 * np.pi * 10 * t) + 0.5 * np.random.randn(len(t))  # 包含噪声的正弦信号

# 使用带通滤波器过滤数据
filtered_data = butter_filt(data, fs=fs, Wn=[8, 12], btype='bandpass', N=4)

# 绘制原始数据和滤波后的数据
plt.figure(figsize=(10, 6))
plt.plot(t, data, label='Original Data', alpha=0.7)
plt.plot(t, filtered_data, label='Filtered Data', linewidth=2)
plt.title('Original and Filtered Data')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True)
plt.show()

# 使用高通滤波器过滤数据
filtered_data = butter_filt(data, fs=fs, Wn=5, btype='highpass', N=4)

# 绘制原始数据和滤波后的数据
plt.figure(figsize=(10, 6))
plt.plot(t, data, label='Original Data', alpha=0.7)
plt.plot(t, filtered_data, label='Filtered Data', linewidth=2)
plt.title('Original and High-pass Filtered Data')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True)
plt.show()