fea: python build sucess

.gitignore

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+dist/

README.md

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+
+# polyfit 一次/二次拟合工具
+
+
+## 说明
+
+本程序主要功能为读取待拟合的数据文件，文件格式可以为.txt 或者.csv格式，以空格分隔，无头部。
+通过命令行 `polyfit` [filename] 执行,如果未指定文件名，则默认寻找当前目录下的 `data.txt`
+
+数据样本为N行两列的数据，第一列为x, 第二列为Y,
+实例数据如下：
+
+```text
+
+   0.134	2.79
+   0.160	3.39
+   0.195	4.09
+   0.244	5.19
+   0.313	6.59
+```
+
+## 命令行执行
+调用 `polyfit [data.txt]`
+
+执行结果如下：
+
+![拟合图形](images/fitting.png)

data.txt

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+0.134	2.79
+0.160	3.39
+0.195	4.09
+0.244	5.19
+0.313	6.59

pyproject.toml

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+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+description = "polyfit data use linear and Quadratic"
+name = "fy-polyfit"
+dynamic = ["version"]
+authors = [
+  {name = "fengyang", email = "[email protected]"}
+]
+readme = "README.md"
+requires-python = ">=3.8"
+classifiers = [
+  # How mature is this project? Common values are
+  #   3 - Alpha
+  #   4 - Beta
+  #   5 - Production/Stable
+  "Development Status :: 4 - Beta",
+
+  # Indicate who your project is intended for
+  "Intended Audience :: Developers",
+  "Topic :: Software Development :: Build Tools",
+
+  # Pick your license as you wish (see also "license" above)
+  "License :: OSI Approved :: MIT License",
+  # Specify the Python versions you support here.
+  "Programming Language :: Python :: 3.8",
+]
+[tool.hatch.version]
+path = "src/fy_polyfit/polyfit.py"
+
+[project.scripts]
+polyfit = "fy_polyfit.polyfit:main"
+
+[project.urls]
+Documentation = "https://readthedocs.org"
+Repository = "https://github.com/me/spam.git"
+Issues = "https://github.com/me/spam/issues"
+Changelog = "https://github.com/me/spam/blob/master/CHANGELOG.md"

src/fy_polyfit/polyfit.py

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+"""
+本文件通过简单的例子展示了numpy的多项式曲线拟合功能
+和matplotlib的绘图功能，主要内容如下：
+1. 通过 numpy.loadtxt() 读取data.txt 文件（包含待拟合数据）
+    0.134	2.79
+    0.160	3.39
+    0.195	4.09
+    0.244	5.19
+    0.313	6.59
+2. 通过 numpy.polyfit() 分别进行一次/二次曲线拟合
+
+3. 分别计算均方根值误差
+
+4. 分别绘制 两个图，并显示公式和均方根误差
+
+"""
+import os
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+
+__version__ = '1.0.0'
+
+# 计算均方根误差函数
+def  cal_rmse(y_true, y_pred):
+    return np.sqrt(np.mean((y_pred - y_true) **2))
+
+
+def main():
+    parser = argparse.ArgumentParser(description='从指定文件中读取数据,默认为data.txt, 无头部,空格分隔,两列')
+    parser.add_argument('filename', nargs='?', default='data.txt', 
+                        help='数据文件名')
+    args = parser.parse_args()
+    filename = args.filename
+    # 读取 数据
+    try:
+        # 检查文件是否存在
+        if not os.path.isfile(filename):
+            raise FileNotFoundError(f"文件'{filename}'不存在!")
+
+        # 尝试读取数据
+        df = np.loadtxt(filename)
+        print(f"数据文件'{filename}'读取成功：")
+        print(df)
+        x = df[: , 0]
+        y = df[:, 1]
+
+        # 二次曲线拟合
+        coefficients2 = np.polyfit(x, y, 2)
+
+        #一次曲线拟合
+        coefficients1 = np.polyfit(x, y, 1)
+
+        #打印参数
+        print(f"linear coefficients: {coefficients1}")
+        print(f"Quadratic coefficients: {coefficients2}")
+        # 获取拟合的多项式系数
+        a1, b1 = coefficients1
+        a2, b2, c2 = coefficients2
+
+        polynomial1 = np.poly1d(coefficients1)
+        polynomial2 = np.poly1d(coefficients2)
+
+
+
+        x_fit = np.linspace(min(x), max(x), 100)
+        y_fit1 = polynomial1(x_fit)
+        y_fit2 = polynomial2(x_fit)
+
+        rmse1 = cal_rmse(y, polynomial1(x))
+        rmse2 = cal_rmse(y, polynomial2(x))
+
+
+
+        formula1 = f'y = {"-" if a1 < 0 else ""} {abs(a1):.2f}x'\
+                f' {"+" if b1>=0 else "-"} {abs(b1):.2f}'
+        formula2 = f'y = {a2:.2f}x^2 {"+" if b2>=0 else "-"} {abs(b2):.2f}x'\
+                f' {"+" if c2>=0 else "-"} {abs(c2):.2f}'
+
+
+        # 创建2个子图
+        """
+        创建1行2列的子图，figsize 控制整个图的大小
+        """
+        fig, ax = plt.subplots(1,2, figsize=(10,5))
+
+
+
+        #绘制原始数据点和一次拟合曲线
+        ax[0].scatter(x, y, label='source points')
+        ax[0].plot(x_fit, y_fit1, color='red',label=f" Linear Fitted Curve, rmse = {rmse1 *100:.2f}%")
+        ax[0].set_title('Linear Curve Fitting')
+        ax[0].set_xlabel('x')
+        ax[0].set_ylabel('y')
+        ax[0].legend()
+        ax[0].grid(True)
+        ax[0].text(0.5, 0.8, formula1,color='red',fontsize=16, transform=ax[0].transAxes, verticalalignment='top', horizontalalignment='center')
+
+        #绘制原始数据点和二次拟和曲线
+        ax[1].scatter(x, y, label='source points')
+        ax[1].plot(x_fit, y_fit2, color='blue',label=f" Quadratic Fitted Curve, rmse = {rmse2 * 100:.2f}%")
+        ax[1].set_title('Quadratic Curve Fitting')
+        ax[1].set_xlabel('x')
+        ax[1].set_ylabel('y')
+        ax[1].legend()
+        ax[1].grid(True)
+        # transform 变换到[0,1] 的坐标系下，方便定位，左下角0,0, 右上角1，1
+        ax[1].text(0.5, 0.8, formula2,color='blue',fontsize=16, transform=ax[1].transAxes, verticalalignment='top', horizontalalignment='center')
+
+        plt.tight_layout()
+        plt.show()
+
+    except FileNotFoundError as e:
+        print(e)
+
+    except ValueError as e:
+        print(f"发生了意外错误：{e}")
+
+
+
+if __name__ == '__main__':
+    main()