Matplotlib 类似于 matlab 的 trisurf [英] Matplotlib like matlab's trisurf

问题描述

长话短说，我想在 python 中绘制一个通用的 3D 三角形网格.Matplotlib 似乎是理想的候选者，但我会选择任何可以完成我将要描述的内容的 3D 渲染.

To make a long story short, I'd like to plot a generic 3D triangle mesh in python. Matplotlib seems to be the ideal candidate, but I'd go with any 3D rendering that can do what I'm about to describe.

假设我有一个由 X、Y 和 Z 定义的三角形网格，点云的 3D 坐标，每个都是一个长度为 n 的向量，以及 UVW，一个 2D mx-3 矩阵，其中每一行是索引到点云中.这个三元组代表一个单独的三角形.换句话说，我在 n 个点上有 m 个三角形.在 Matlab 中，要生成 3D 图，我只需要:

Suppose I have a triangle mesh defined by X, Y, and Z, the 3D coordinates of a point cloud, each a vector of length n, and UVW, a 2D m-x-3 matrix in which each row is a triplet of indices into the point cloud. This triplet represents an individual triangle. In other words, I have m triangles over n points. In Matlab, to generated a 3D plot, I just do:

trisurf(UVW, X, Y, Z)

有人有这方面的经验吗?特别是，mplots trisurf 可以硬着头皮工作吗?

Does anyone have any experience with this? In particular, can mplots trisurf be shoehorned to work?

推荐答案

根据您的性能需求，mayavi 可能最适合于此 - 根据 Davis 的评论.

Depending on your performance needs, mayavi is likely to be best suited for this - as per Davis comment.

但是，matplotlib 带有 plot_trisurf 您可以完美地将通用的 UVW、X、Y、Z 传递给您所描述的.

However, matplotlib comes with plot_trisurf to which you can perfectly pass generic UVW, X, Y , Z as you describe.

具有圆环网格的示例:

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.tri as mtri

R = 1.
r = 0.8
n = 50
m = 50

def torus_triangles(n, m):
    """ Returns triangles to mesh a (n, m) torus """
    tri = []
    for i in range(n):
        for j in range(m):
            a = i + j*(n)
            b = ((i+1) % n) + j*n
            d = i + ((j+1) % m) * n
            c = ((i+1) % n) + ((j+1) % m) * n
            tri += [[a, b, d], [b, c, d]]
    return np.array(tri, dtype=np.int32)

theta0 = np.linspace(0, (2*np.pi), n, endpoint=False)
phi0 = np.linspace(0, (2*np.pi), m, endpoint=False)
theta, phi = np.meshgrid(theta0, phi0)

x = (R + r * np.sin(phi)) * np.cos(theta)
y = (R + r * np.sin(phi)) * np.sin(theta)
z = r * np.cos(phi)

triangles = torus_triangles(n , m)
triang = mtri.Triangulation(x.ravel(), y.ravel(), triangles)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(triang, z.ravel(), lw=0.2, edgecolor="black", color="grey",
                alpha=0.5)

plt.show()

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