使用Python中的带阻滤波滤除频率范围，并使用傅里叶变换FFT进行确认

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

def band_stop_filter(data, fs, f_range, order):
    nyquist = 0.5 * fs
    low = f_range[0] / nyquist
    high = f_range[1] / nyquist
    b, a = signal.butter(order, [low, high], btype='bandstop')
    filtered_data = signal.lfilter(b, a, data)
    return filtered_data

# 生成示例信号
fs = 1000  # 采样率
t = np.arange(0, 1, 1/fs)  # 时间序列
f1 = 50  # 需要滤除的频率范围起始频率
f2 = 150  # 需要滤除的频率范围结束频率
data = np.sin(2*np.pi*50*t) + np.sin(2*np.pi*150*t) + 0.5*np.random.randn(len(t))

# 带阻滤波
filtered_data = band_stop_filter(data, fs, [f1, f2], order=4)

# 傅里叶变换
fft_data = np.fft.fft(filtered_data)
freq = np.fft.fftfreq(len(filtered_data), 1/fs)

# 绘制原始信号和滤波后信号的时域和频域图像
fig, axs = plt.subplots(2, 2, figsize=(10, 6))
axs[0, 0].plot(t, data)
axs[0, 0].set_title('Original Signal')
axs[0, 0].set_xlabel('Time')
axs[0, 0].set_ylabel('Amplitude')

axs[0, 1].plot(t, filtered_data)
axs[0, 1].set_title('Filtered Signal')
axs[0, 1].set_xlabel('Time')
axs[0, 1].set_ylabel('Amplitude')

axs[1, 0].plot(freq, np.abs(fft_data))
axs[1, 0].set_title('Frequency Spectrum')
axs[1, 0].set_xlabel('Frequency')
axs[1, 0].set_ylabel('Magnitude')

axs[1, 1].plot(freq, np.abs(fft_data))
axs[1, 1].set_xlim([0, 200])
axs[1, 1].set_title('Zoomed Frequency Spectrum')
axs[1, 1].set_xlabel('Frequency')
axs[1, 1].set_ylabel('Magnitude')

plt.tight_layout()
plt.show()