球体上的颜色来描绘价值

I have a data set such that for discrete values of theta and phi, I have some value. I want to represent this on a sphere such that at the point on the sphere given by the polar angle theta and the azimuthal angle phi, the color shows that particular value.

我有一个数据集，对于theta和phi的离散值，我有一些价值。我想在球体上表示这一点，使得在由极角θ和方位角φ给出的球体上的点处，颜色显示出该特定值。

How can I do it in python?

我怎么能在python中做到这一点？

1 个解决方案

#1

I think this spherical harmonics example is what you need.

我认为这个球谐函数的例子就是你所需要的。

Minimal example:

最小的例子：

from mayavi import mlab
import numpy as np

# Make sphere, choose colors
phi, theta = np.mgrid[0:np.pi:101j, 0:2*np.pi:101j]
x, y, z = np.sin(phi) * np.cos(theta), np.sin(phi) * np.sin(theta), np.cos(phi)
s = x*y  # <-- colors

# Display
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(600, 500))
mlab.mesh(x, y, z, scalars=s, colormap='Spectral')
mlab.view()
mlab.show()

You will need python2, not python3. See this thread from 2015.
你需要python2，而不是python3。从2015年看这个主题。

In Ubuntu 14.04 I said:

在Ubuntu 14.04中我说：

sudo apt-get install python-vtk python-scipy python-numpy
sudo pip install mayavi
python main.py  # After saving the code below as main.py

Here's the full code:

这是完整的代码：

# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Copyright (c) 2008, Enthought, Inc.
# License: BSD Style.

from mayavi import mlab
import numpy as np
from scipy.special import sph_harm

# Create a sphere
r = 0.3
pi = np.pi
cos = np.cos
sin = np.sin
phi, theta = np.mgrid[0:pi:101j, 0:2 * pi:101j]

x = r * sin(phi) * cos(theta)
y = r * sin(phi) * sin(theta)
z = r * cos(phi)

mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
# Represent spherical harmonics on the surface of the sphere
for n in range(1, 6):
    for m in range(n):
        s = sph_harm(m, n, theta, phi).real

        mlab.mesh(x - m, y - n, z, scalars=s, colormap='jet')

        s[s < 0] *= 0.97

        s /= s.max()
        mlab.mesh(s * x - m, s * y - n, s * z + 1.3,
                  scalars=s, colormap='Spectral')

mlab.view(90, 70, 6.2, (-1.3, -2.9, 0.25))
mlab.show()

You can expect about 20 seconds of loading time for this example if you have a slow computer.
如果您的计算机速度较慢，则此示例的加载时间约为20秒。
You can use your mouse to rotate and zoom the image.
您可以使用鼠标旋转和缩放图像。

1 个解决方案

#1

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