一组噪声点的闭曲线拟合
一组噪声点的闭曲线拟合
提问于 2021-01-05 14:10:07
这是我的数据集，我想在这里拟合一条封闭曲线，就像this post一样
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]])以下是可视化数据集：
然而，无论我如何排序我的数组，这些都是我得到的结果。
我列举了一些关于我的数据集的问题，但不知道如何处理它们：
- 许多x和y值不是一对一的
- 点没有按相邻的顺序
排序。
因此，如果我的假设是正确的，那么主要的问题将是如何排序数组的顺序，使splprep方法工作？如果没有，我真的很感激任何帮助我解决问题的解决方案！
感谢@michael-szczesny的回复，我得到了满意的结果。
回答 1
Stack Overflow用户
回答已采纳
发布于 2021-01-05 15:14:53
您可以根据复杂的角度将数据转换为原点。
设置数据
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import 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()]# plot from https://stackoverflow.com/a/31466013/14277722 as mentioned in the question
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.figure(figsize=(10,10))
plt.plot(x_sort[:,0], x_sort[:,1], 'ro')
plt.plot(x_new, y_new, 'b--');退出：
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/65580488
复制相关文章
相似问题
腾讯云开发者
Copyright © 2013 - 2026 Tencent Cloud. All Rights Reserved. 腾讯云 版权所有
深圳市腾讯计算机系统有限公司 ICP备案/许可证号：粤B2-20090059 
粤公网安备44030502008569号
腾讯云计算（北京）有限责任公司 京ICP证150476号 | 京ICP备11018762号