C# pythonnet(3)_Butter-worth低通滤波

Python代码如下

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import signaldef lowpass_Butterworth(sig,fs,filter_cutoff=None,order=8,axis=0):'''Butter-worth低通滤波Inputs:sig --- numpy array, 输入时间序列数据fs --- int, 采样率filter_cutoff --- float, 截断频率，滤掉高于此频率的成分order --- int, 滤波器阶次axis --- int, 在sig的哪个轴上施加滤波'''if not filter_cutoff:filter_cutoff=fs/4b,a = signal.butter(order,filter_cutoff / (fs/2),'low')  sig = signal.filtfilt(b,a,sig,axis=axis)return sig# 读取数据
data = pd.read_csv('clean_data_row6.csv')
data = np.array(data)
dataLen = len(data)
# Butter-worth低通滤波
fs=50 #采样频率
filter_cutoff=10 #截断频率
axis=0 #时间轴所在维度
# use_downsamping=True #是否降采样
calcData = lowpass_Butterworth(data, fs, filter_cutoff) 
calcData = np.array(calcData)
calcDataLen = len(calcData)# 绘制原始数据图和Butter-worth低通滤波图
# 原始数据图
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.plot(np.arange(dataLen), np.abs(data))
plt.title('Original Datas')# Butter-worth低通滤波图
plt.subplot(1, 2, 2)
plt.plot(np.arange(calcDataLen), np.abs(calcData))
plt.title('Lowpass Butterworth Datas')plt.tight_layout()
plt.show()

下面我们修改成C#代码

创建控制台程序，Nuget安装 CsvHelper 和 pythonnet

public class Program
{const string PathToPythonDir = "D:\\Python311";const string DllOfPython = "python311.dll";static void Main(string[] args){// Butter-worth低通滤波
        ButterworthLowpass();}/// <summary>/// Butter-worth低通滤波/// </summary>static void ButterworthLowpass(int fs = 250, double filterCutoff = 50, int axis = 0, int order = 8){try{Runtime.PythonDLL = Path.Combine(PathToPythonDir, DllOfPython);PythonEngine.Initialize();using (Py.GIL()){dynamic pd = Py.Import("pandas");dynamic np = Py.Import("numpy");dynamic plt = Py.Import("matplotlib.pyplot");dynamic signal = Py.Import("scipy.signal");dynamic data = pd.read_csv("clean_data_row.csv");int listLength = data.__len__();double wn = filterCutoff / (fs / 2.0);PyTuple baTuple = signal.butter(order, wn, "low");dynamic b = baTuple[0];dynamic a = baTuple[1];PyObject calcData = signal.filtfilt(b, a, data, axis: axis, padlen: listLength - 1);int calcLength = data.__len__();double[][] calcDataArray = calcData.As<dynamic[]>().Select(s => (double[])s.As<double[]>()).ToArray();plt.figure(figsize: new dynamic[] { 12, 6 });// 原始数据plt.subplot(1, 2, 1);plt.plot(np.arange(listLength), data);plt.title("Original Datas");// 低通滤波plt.subplot(1, 2, 2);plt.plot(np.arange(calcLength), calcData);plt.title("Butterworth Lowpas Datas");// 布局调整，防止重叠
                plt.tight_layout();// 显示图表
                plt.show();}}catch (Exception e){Console.WriteLine("报错了：" + e.Message + "\r\n" + e.StackTrace);}}/// <summary>/// 读取CSV数据/// </summary>/// <param name="filePath">文件路径</param>/// <returns>文件中数据集合，都是double类型</returns>static List<double[]> ReadCsvWithCsvHelper(string filePath){using (var reader = new StreamReader(filePath))using (var csv = new CsvReader(reader, CultureInfo.InvariantCulture)){var result = new List<double[]>();// 如果你的CSV文件有标题行，可以调用ReadHeader来读取它们
            csv.Read();csv.ReadHeader();while (csv.Read()){result.Add(new double[] {csv.GetField<double>(0),csv.GetField<double>(1),csv.GetField<double>(2),});}return result;}}
}

以下是运行后结果，左边是原始数据折线图，右边是执行Butter-worth低通滤波后数据折线图

 源代码：https://gitee.com/Karl_Albright/csharp-demo/tree/master/PythonnetDemo/PythonnetButterworth