Python中的标量字段可视化 [英] Scalar fields visualisation in Python

问题描述

我需要在Python中可视化几个重叠的标量字段。我发现 mayavi 库可以进行这种绘图。问题是我不了解如何为标量字段自定义颜色图。我的想法是每个字段都有一种颜色的阴影。我尝试采用一个示例，但是不起作用。这是我的代码，使用红色阴影将标量字段可视化：

I need to visualize several overlapping scalar fields in Python. I found mayavi library to do this kind of plots. The problem is that I don't understand how to customize a color map for scalar fields. My idea is to have shades of one color for each field. I tried to adopt an example, but it doesn't work. Here there is my code to visualize a scalar field using shades of red:

import numpy as np
from mayavi import mlab

x, y, z = np.ogrid[-10:10:20j, -10:10:20j, -10:10:20j]
s = np.sin(x*y*z)/(x*y*z)

src = mlab.pipeline.scalar_field(s)
volume = mlab.pipeline.volume(src)

lut = np.zeros((256, 4), np.uint8)
lut[:,-1] = 255
lut[:, 0] = np.linspace(0, 255, 256)

volume.module_manager.scalar_lut_manager.lut.table = lut

mlab.draw()
mlab.view(40, 85)

mlab.show()

但是，输出图始终带有标准的蓝色-红色查找表。

However, the output plot is always with a standard blue-red look-up table.

推荐答案

我找不到使用 lut_manager ，但是下面的解决方案，遵循此github答复为我工作。

I couldn't find a solution using the lut_manager, however the solution below, following this github reply works for me.

import numpy as np
from mayavi import mlab
# import color transfer function from vtk
from tvtk.util import ctf
# import matlab colormaps
from matplotlib.pyplot import cm

x, y, z = np.ogrid[-10:10:20j, -10:10:20j, -10:10:20j]
s = np.sin(x*y*z)/(x*y*z)

src = mlab.pipeline.scalar_field(s)
volume = mlab.pipeline.volume(src)

# save the color transfer function of the current volume
c = ctf.save_ctfs(volume._volume_property)
# change the alpha channel as needed
c['alpha'][1][1] = 0.5
# change the color points to another color scheme
# in this case 'magma'
c['rgb']=[[a[0],a[1],a[2],cm.magma.colors.index(a)/255] for a in cm.magma.colors]
# load the new color transfer function
ctf.load_ctfs(c, volume._volume_property)
# signal for update
volume.update_ctf = True

mlab.show()

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