使用bandpass Butterworth filter对信号数据进行滤波去噪

在信号处理中，有些信号会包含大量的噪声，需要用一些滤波器去噪，本文介绍使用bandpass Butterworth filter进行去噪。
直接使用sciPypython库可以实现噪声滤波

步骤1：导入所需python模块

from scipy.signal import filtfilt
from scipy import stats
import CSV
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy

步骤2：读取数据及可视化数据

def plot():
    data = pd.read_csv(filePath)
    signal_data = data[['data']]
    signal_data = np.array(data)
    time = np.linspace(0, 0.02, 52)
    plt.plot(time, signal_data)
    plt.show()

数据的样子如下：

而执行plot()函数得到的图形为：

步骤3：执行滤波器滤波

def bandPass_filter(signal):
    fs = 4000.0
    lowcut = 20.0
    highcut = 50.0
    nyqs = 0.5* fs
    low = lowcut / nyqs
    high = highcut/ nyqs
    
    order=2
    
    b, a = scipy.signal.butter(order, [low, high], 'bandpass', analog = False) # b: 滤波器的分子系数向量，a: 滤波器的分母系数向量
    y = scipy.signal.filtfilt(b, a, signal, axis=0)  # 滤波函数
    return(y)

data = pd.read_csv(filePath)
signal_data = data[['data']]
signal_data = np.array(data)
time = np.linspace(0, 0.02, 52)
filtered_signal= bandPass_filter(signal_data)
plt.plot(time, filtered_signal)
plt.show()

结果显示：

完整的代码以及将两种效果放入一张图中显示

from scipy.signal import filtfilt
from scipy import stats
import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy
def plot():
    data = pd.read_csv(filePath)
    signal_data = data[['data']]
    signal_data = np.array(signal_data)
    time = np.linspace(0, 0.2, 52)
    plt.plot(time, signal_data)
    plt.show()
    filtered_signal= bandPass_filter(signal_data)
    plt.plot(time, filtered_signal)
    plt.show()
#syntax for bandpass filter
def bandPass_filter(signal):
    fs = 4000.0
    lowcut = 20.0
    highcut = 50.0
    nyqs = 0.5* fs
    low = lowcut / nyqs
    high = highcut/ nyqs
    
    order=2
    
    b, a = scipy.signal.butter(order, [low, high], 'bandpass', analog = False)
    y = scipy.signal.filtfilt(b, a, signal, axis=0)
    return(y)
plot()

结果显示：