使用Mayavi/Python从数据中提取3D轮廓图 [英] 3D Contour plot from data using Mayavi / Python

问题描述

我想使用Mayavi进行3D等高线图绘制，其方式与本页第三幅图(氢电子云模型)完全相同:

I would like to do a 3D contour plot using Mayavi in exactly the same way as the third figure on this page (a hydrogen electron cloud model) :

http://www.sethanil.com/python-for-reseach/5

我有一组数据点，这些数据点是使用我想使用的自己的模型创建的.数据点存储在多维numpy数组中，如下所示:

I have a set of data points which I created using my own model which I would like to use. The data points are stored in a multi-dimensional numpy array like so:

XYZV = [[1, 2, 3, 4],
        [6, 7, 8, 9],
        ...
        [4, 5, 6, 7]]

数据点在XYZ空间中分布不均匀，并且没有以任何特定顺序存储.我认为该示例使用了网格网格来生成数据点-我已经看过了，但完全不理解.任何帮助将不胜感激?

The data points are not uniformly spread in XYZ space and not stored in any particular order. I think the example uses a meshgrid to generate the data points - I have looked this up but totally don't understand it. Any help would be much appreciated?

_{(来源: sethanil. com )}

_{(source: sethanil.com)}

推荐答案

诀窍是在绘制之前在网格上进行插值-为此，我将使用scipy.在R下面是一个(500,3)XYZ值数组，而V是每个XYZ点处的幅值".

The trick is to interpolate over a grid before you plot - I'd use scipy for this. Below R is a (500,3) array of XYZ values and V is the "magnitude" at each XYZ point.

from scipy.interpolate import griddata
import numpy as np

# Create some test data, 3D gaussian, 200 points
dx, pts = 2, 100j

N = 500
R = np.random.random((N,3))*2*dx - dx
V = np.exp(-( (R**2).sum(axis=1)) )

# Create the grid to interpolate on
X,Y,Z = np.mgrid[-dx:dx:pts, -dx:dx:pts, -dx:dx:pts]

# Interpolate the data
F = griddata(R, V, (X,Y,Z))

从这里可以轻松显示我们的数据:

From here it's a snap to display our data:

from mayavi.mlab import *
contour3d(F,contours=8,opacity=.2 )

这给出了一个很好的(块状)高斯.

This gives a nice (lumpy) Gaussian.

看看 griddata 的文档

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