Python 将闭合曲线拟合到一组噪声点

这是我的数据集，我想拟合一条闭合曲线，就像

以下是可视化数据集：

然而，无论我如何对数组排序，这些都是我得到的结果。

关于我的数据集，我发现了一些问题，但不知道如何处理：

许多x和y值不是一对一

点不按相邻顺序排序

因此，如果我的假设是正确的，那么主要的问题是如何按照splprep方法工作的顺序对数组进行排序？如果没有，我将非常感谢任何能帮助我解决问题的解决方案

[更新]感谢您的回复，我得到了一个令人满意的结果

---

您可以将数据转换为原点，并按复数角度排序

设置数据

将numpy导入为np
将matplotlib.pyplot作为plt导入
从scipy.interpolate导入splprep，splev
x=np.array(
[[-0.50, -1.20],
[-0.50, -1.15],
[-0.50, -1.10],
[-0.50, -1.05],
[-0.50, -1.00],
[-0.50, -0.95],
[-0.50, -0.90],
[-0.50, -0.85],
[-0.45, -1.90],
[-0.45, -1.85],
[-0.45, -1.70],
[-0.45, -1.65],
[-0.45, -1.60],
[-0.45, -1.55],
[-0.45, -1.50],
[-0.45, -1.45],
[-0.45, -1.40],
[-0.45, -1.35],
[-0.45, -1.30],
[-0.45, -1.25],
[-0.45, -1.20],
[-0.45, -1.15],
[-0.45, -1.10],
[-0.45, -1.05],
[-0.40, -2.25],
[-0.40, -2.20],
[-0.40, -2.05],
[-0.40, -2.00],
[-0.40, -1.95],
[-0.40, -1.90],
[-0.40, -1.85],
[-0.40, -1.80],
[-0.40, -1.75],
[-0.40, -1.70],
[-0.40, -1.65],
[-0.40, -1.60],
[-0.40, -1.55],
[-0.40, -1.50],
[-0.40, -1.45],
[-0.40, -0.70],
[-0.40, -0.65],
[-0.35, -2.30],
[-0.35, -2.25],
[-0.35, -2.20],
[-0.35, -2.15],
[-0.35, -2.10],
[-0.35, -2.05],
[-0.35, -2.00],
[-0.30, -2.45],
[-0.30, -2.40],
[-0.30, -2.35],
[-0.25, -0.60],
[-0.20, -2.60],
[-0.20, -0.60],
[-0.15, -2.70],
[-0.15, -0.45],
[-0.15, -0.40],
[-0.05, -2.80],
[-0.05, -2.75],
[0.00, -2.80],
[0.00, -2.75],
[0.00, -0.20],
[0.00, -0.15],
[0.00, -0.10],
[0.05, -2.80],
[0.05, -2.75],
[0.05, -0.10],
[0.10, -2.80],
[0.10, -2.75],
[0.15, -2.75],
[0.30, -0.05],
[0.35, -0.05],
[0.40, -0.05],
[0.45, -0.05],
[0.50, -0.05],
[0.55, -0.05],
[0.60, -0.05],
[0.65, -0.05],
[0.70, -2.85],
[0.70, -0.05],
[0.75, -2.85],
[0.75, -0.05],
[0.80, -2.85],
[0.80, -0.05],
[0.85, -2.85],
[0.85, -0.05],
[0.90, -2.85],
[0.90, -0.05],
[0.95, -2.85],
[0.95, -0.05],
[1.00, -2.85],
[1.00, -0.05],
[1.05, -2.85],
[1.05, -0.05],
[1.10, -2.80],
[1.10, -0.05],
[1.15, -2.80],
[1.15, -0.05],
[1.20, -2.80],
[1.20, -0.05],
[1.25, -2.80],
[1.25, -0.05],
[1.30, -2.80],
[1.30, -0.05],
[1.35, -2.80],
[1.35, -0.05],
[1.40, -2.80],
[1.40, -0.05],
[1.45, -2.80],
[1.45, -0.05],
[1.50, -2.80],
[1.50, -0.05],
[1.55, -2.80],
[1.55, -0.05],
[1.60, -2.80],
[1.60, -0.05],
[1.65, -2.80],
[1.65, -0.05],
[1.70, -2.80],
[1.70, -0.05],
[1.75, -2.80],
[1.75, -0.05],
[1.80, -2.80],
[1.80, -0.05],
[2.05, -2.60],
[2.10, -2.65],
[2.10, -0.20],
[2.10, -0.15],
[2.15, -0.20],
[2.15, -0.15],
[2.20, -2.60],
[2.20, -0.25],
[2.20, -0.20],
[2.25, -2.60],
[2.35, -0.50],
[2.35, -0.45],
[2.35, -0.40],
[2.35, -0.35],
[2.40, -0.60],
[2.40, -0.55],
[2.40, -0.50],
[2.40, -0.45],
[2.45, -2.35],
[2.45, -2.30],
[2.45, -0.90],
[2.45, -0.85],
[2.45, -0.80],
[2.45, -0.75],
[2.45, -0.70],
[2.45, -0.65],
[2.45, -0.60],
[2.50, -2.20],
[2.50, -2.15],
[2.50, -2.10],
[2.50, -2.05],
[2.50, -1.20],
[2.50, -1.15],
[2.50, -1.10],
[2.50, -1.05],
[2.50, -1.00],
[2.50, -0.95],
[2.50, -0.90],
[2.50, -0.85],
[2.50, -0.70],
[2.55, -2.15],
[2.55, -2.10],
[2.55, -2.05],
[2.55, -1.85],
[2.55, -1.80],
[2.55, -1.75],
[2.55, -1.45],
[2.55, -1.40],
[2.55, -1.35],
[2.55, -1.30],
[2.55, -1.25],
[2.55, -1.20],
[2.55, -1.15],
[2.55, -1.10],
[2.60, -1.70],
[2.60, -1.65],
[2.60, -1.60],
[2.60, -1.55],
[2.60, -1.50]])

使用

np.angle（（xs[：，0]+1j*xs[：，1]）将数据转换为复杂坐标

，并使用它对数据进行排序

xs=（x-x.mean（0））
x_sort=xs[np.angle（（xs[：，0]+1j*xs[：，1]）。argsort（）]

现在您可以按正确的顺序对数据进行编码

#从https://stackoverflow.com/a/31466013/14277722 如问题所述
tck，u=splprep（x_sort.T，u=None，s=0.0，per=1）
u_new=np.linspace（u.min（），u.max（），1000）
x_new，y_new=splev（u_new，tck，der=0）
plt.图（figsize=（10,10））
plt.plot（x_排序[：，0]，x_排序[：，1]，'ro'）
plt.plot（x_new，y_new，'b--'）；

输出：

---

您能否将数据发布为有效的python（使用适当的逗号，例如使用

print（array.\uu repr\uuu（））

）和用于绘制数据的代码？很抱歉，我不知道我会马上编辑我的帖子hi@michael szczesny谢谢你的回复（还有我的错误数据）

array([[ 0.3 , -0.05],
       [ 0.35, -0.05],
       [ 0.4 , -0.05],
       [ 0.45, -0.05],
       [ 0.5 , -0.05],
       [ 0.55, -0.05],
       [ 0.6 , -0.05],
       [ 0.65, -0.05],
       [ 0.7 , -0.05],
       [ 0.75, -0.05],
       [ 0.8 , -0.05],
       [ 0.85, -0.05],
       [ 0.9 , -0.05],
       [ 0.95, -0.05],
       [ 1.  , -0.05],
       [ 1.05, -0.05],
       [ 1.1 , -0.05],
       [ 1.15, -0.05],
       [ 1.2 , -0.05],
       [ 1.25, -0.05],
       [ 1.3 , -0.05],
       [ 1.35, -0.05],
       [ 1.4 , -0.05],
       [ 1.45, -0.05],
       [ 1.5 , -0.05],
       [ 1.55, -0.05],
       [ 1.6 , -0.05],
       [ 1.65, -0.05],
       [ 1.7 , -0.05],
       [ 1.75, -0.05],
       [ 1.8 , -0.05],
       [ 0.  , -0.1 ],
       [ 0.05, -0.1 ],
       [ 0.  , -0.15],
       [ 2.1 , -0.15],
       [ 2.15, -0.15],
       [ 0.  , -0.2 ],
       [ 2.1 , -0.2 ],
       [ 2.15, -0.2 ],
       [ 2.2 , -0.2 ],
       [ 2.2 , -0.25],
       [ 2.35, -0.35],
       [-0.15, -0.4 ],
       [ 2.35, -0.4 ],
       [-0.15, -0.45],
       [ 2.35, -0.45],
       [ 2.4 , -0.45],
       [ 2.35, -0.5 ],
       [ 2.4 , -0.5 ],
       [ 2.4 , -0.55],
       [-0.25, -0.6 ],
       [-0.2 , -0.6 ],
       [ 2.4 , -0.6 ],
       [ 2.45, -0.6 ],
       [-0.4 , -0.65],
       [ 2.45, -0.65],
       [-0.4 , -0.7 ],
       [ 2.45, -0.7 ],
       [ 2.5 , -0.7 ],
       [ 2.45, -0.75],
       [ 2.45, -0.8 ],
       [-0.5 , -0.85],
       [ 2.45, -0.85],
       [ 2.5 , -0.85],
       [-0.5 , -0.9 ],
       [ 2.45, -0.9 ],
       [ 2.5 , -0.9 ],
       [-0.5 , -0.95],
       [ 2.5 , -0.95],
       [-0.5 , -1.  ],
       [ 2.5 , -1.  ],
       [-0.5 , -1.05],
       [-0.45, -1.05],
       [ 2.5 , -1.05],
       [-0.5 , -1.1 ],
       [-0.45, -1.1 ],
       [ 2.5 , -1.1 ],
       [ 2.55, -1.1 ],
       [-0.5 , -1.15],
       [-0.45, -1.15],
       [ 2.5 , -1.15],
       [ 2.55, -1.15],
       [-0.5 , -1.2 ],
       [-0.45, -1.2 ],
       [ 2.5 , -1.2 ],
       [ 2.55, -1.2 ],
       [-0.45, -1.25],
       [ 2.55, -1.25],
       [-0.45, -1.3 ],
       [ 2.55, -1.3 ],
       [-0.45, -1.35],
       [ 2.55, -1.35],
       [-0.45, -1.4 ],
       [ 2.55, -1.4 ],
       [-0.45, -1.45],
       [-0.4 , -1.45],
       [ 2.55, -1.45],
       [-0.45, -1.5 ],
       [-0.4 , -1.5 ],
       [ 2.6 , -1.5 ],
       [-0.45, -1.55],
       [-0.4 , -1.55],
       [ 2.6 , -1.55],
       [-0.45, -1.6 ],
       [-0.4 , -1.6 ],
       [ 2.6 , -1.6 ],
       [-0.45, -1.65],
       [-0.4 , -1.65],
       [ 2.6 , -1.65],
       [-0.45, -1.7 ],
       [-0.4 , -1.7 ],
       [ 2.6 , -1.7 ],
       [-0.4 , -1.75],
       [ 2.55, -1.75],
       [-0.4 , -1.8 ],
       [ 2.55, -1.8 ],
       [-0.45, -1.85],
       [-0.4 , -1.85],
       [ 2.55, -1.85],
       [-0.45, -1.9 ],
       [-0.4 , -1.9 ],
       [-0.4 , -1.95],
       [-0.4 , -2.  ],
       [-0.35, -2.  ],
       [-0.4 , -2.05],
       [-0.35, -2.05],
       [ 2.5 , -2.05],
       [ 2.55, -2.05],
       [-0.35, -2.1 ],
       [ 2.5 , -2.1 ],
       [ 2.55, -2.1 ],
       [-0.35, -2.15],
       [ 2.5 , -2.15],
       [ 2.55, -2.15],
       [-0.4 , -2.2 ],
       [-0.35, -2.2 ],
       [ 2.5 , -2.2 ],
       [-0.4 , -2.25],
       [-0.35, -2.25],
       [-0.35, -2.3 ],
       [ 2.45, -2.3 ],
       [-0.3 , -2.35],
       [ 2.45, -2.35],
       [-0.3 , -2.4 ],
       [-0.3 , -2.45],
       [-0.2 , -2.6 ],
       [ 2.05, -2.6 ],
       [ 2.2 , -2.6 ],
       [ 2.25, -2.6 ],
       [ 2.1 , -2.65],
       [-0.15, -2.7 ],
       [-0.05, -2.75],
       [ 0.  , -2.75],
       [ 0.05, -2.75],
       [ 0.1 , -2.75],
       [ 0.15, -2.75],
       [-0.05, -2.8 ],
       [ 0.  , -2.8 ],
       [ 0.05, -2.8 ],
       [ 0.1 , -2.8 ],
       [ 1.1 , -2.8 ],
       [ 1.15, -2.8 ],
       [ 1.2 , -2.8 ],
       [ 1.25, -2.8 ],
       [ 1.3 , -2.8 ],
       [ 1.35, -2.8 ],
       [ 1.4 , -2.8 ],
       [ 1.45, -2.8 ],
       [ 1.5 , -2.8 ],
       [ 1.55, -2.8 ],
       [ 1.6 , -2.8 ],
       [ 1.65, -2.8 ],
       [ 1.7 , -2.8 ],
       [ 1.75, -2.8 ],
       [ 1.8 , -2.8 ],
       [ 0.7 , -2.85],
       [ 0.75, -2.85],
       [ 0.8 , -2.85],
       [ 0.85, -2.85],
       [ 0.9 , -2.85],
       [ 0.95, -2.85],
       [ 1.  , -2.85],
       [ 1.05, -2.85]])