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 signal

def 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/4
    b,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