如何使用 matplotlib 在 Python 中为 Voronoi 镶嵌的多边形创建轮廓？

问题描述

我正在尝试从一个包含 305 个粒子和多帧数据的trajectory.xyz 文件创建一个voronoi 曲面细分。我使用了在 GitHub 上找到的这个脚本： https://gist.github.com/pv/8036995 并重新调整了一些东西以更准确地满足我绘制单帧曲面细分的目标（下面的代码）。然而，我的问题是，每当我运行代码时，生成的任何多边形都没有边框/轮廓。

这让整个事情看起来有点混乱和混乱。我相当有信心它与曲面细分的图形有关，但我不确定如何修复它。即使我完全按照随机点运行 GitHub 脚本，我的多边形也没有轮廓。有什么想法吗？

代码：

import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Voronoi
from curved_analysis import read_xyz,read_nfo,project_frame

def voronoi(traj):
    voronoi_frames = []
    coords = np.array(read_xyz("traj0.xyz"))
    for k in range(coords.shape[0]):
        points = project_frame(coords[k])
        vor = Voronoi(points[:,:2],qhull_options=('Qz'))
        voronoi_frames.append(vor) 
    return points,voronoi_frames


def voronoi_finite_polygons_2d(vor,radius=None):
    if vor.points.shape[1] != 2:
        raise ValueError("Requires 2D input")

    new_regions = []
    new_vertices = vor.vertices.tolist()

    center = vor.points.mean(axis=0)
    if radius is None:
        radius = vor.points.ptp().max()*2

    # Construct a map containing all ridges for a given point
    all_ridges = {}
    for (p1,p2),(v1,v2) in zip(vor.ridge_points,vor.ridge_vertices):
        all_ridges.setdefault(p1,[]).append((p2,v1,v2))
        all_ridges.setdefault(p2,[]).append((p1,v2))

# Reconstruct infinite regions
    for p1,region in enumerate(vor.point_region):
        vertices = vor.regions[region]

        if all(v >= 0 for v in vertices):
            # finite region
            new_regions.append(vertices)
            continue

        # reconstruct a non-finite region
        ridges = all_ridges[p1]
        new_region = [v for v in vertices if v >= 0]

        for p2,v2 in ridges:
            if v2 < 0:
                v1,v2 = v2,v1
            if v1 >= 0:
                # finite ridge: already in the region
                continue

        # Compute the missing endpoint of an infinite ridge

            t = vor.points[p2] - vor.points[p1] # tangent
            t /= np.linalg.norm(t)
            n = np.array([-t[1],t[0]])  # normal

            midpoint = vor.points[[p1,p2]].mean(axis=0)
            direction = np.sign(np.dot(midpoint - center,n)) * n
            far_point = vor.vertices[v2] + direction * radius

            new_region.append(len(new_vertices))
            new_vertices.append(far_point.tolist())

        # sort region counterclockwise
        vs = np.asarray([new_vertices[v] for v in new_region])
        c = vs.mean(axis=0)
        angles = np.arctan2(vs[:,1] - c[1],vs[:,0] - c[0])
        new_region = np.array(new_region)[np.argsort(angles)]

        # finish
        new_regions.append(new_region.tolist())

    return new_regions,np.asarray(new_vertices)

# make up data points


def voronoi_plot(vor,points):
    regions,vertices = voronoi_finite_polygons_2d(vor)


    for region in regions:
        polygon = vertices[region]
        plt.fill(*zip(*polygon),alpha=0.4)

    plt.plot(points[:,0],points[:,1],'ko',marker=".",markersize=2)
    plt.axis('equal')
    plt.xlim(vor.min_bound[0] - 0.1,vor.max_bound[0] + 0.1)
    plt.ylim(vor.min_bound[1] - 0.1,vor.max_bound[1] + 0.1)

    plt.savefig('voro.png')
    plt.show()

if __name__ == "__main__":
    frame_num = 166
    points,vor = voronoi("traj0.xyz")
    voronoi_plot(vor[frame_num],points)

解决方法

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