(python)使用colormap作为第4维绘制xd，y，z函数的3d曲面 [英] (python) plot 3d surface with colormap as 4th dimension, function of x,y,z

问题描述

我正在尝试绘制3d曲面，其中三个维度中的每个维度都在一个单独的值数组中，并且每个坐标处的曲面颜色是x，y，z的函数.一种numpy.pcolormesh，但是是4D而不是3D的. 3D图由下式给出:

I'm trying to plot a 3d surface where each of the three dimensions in a separate array of values and the colouring of the surface at each coordinate is a function of x,y,z. A sort of numpy.pcolormesh but in 4D, rather than 3D. The 3D plot is given by:

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.logspace(-1.,np.log10(5),50)
y = np.linspace(6,9,50)
z = np.linspace(-1,1,50)
colors = LikeBeta(y,range(50),range(50))
ax.plot_trisurf(x,y,z,cmap=colors,linewidth=0.2)

其中

def LikeBeta(rho0,r0,beta):
    M0 = 10**rho0*r0_array[r0]**3
    I = cst*M0*sigma_los_beta[beta,:,r0]
    S = dv**2+I
    res = (np.log(S) + (v-u)**2/S).sum()
    return res/2.

cmap=colors可能是错误的，但是问题出在其他地方.我收到以下错误:

Probably the cmap=colors is wrong, but the problem lies elsewhere. I get the following error:

----> 8 colors = LikeBeta(y,range(50),range(50))
----> 4     I = cst*M0*sigma_los_beta[beta,:,r0]
    ValueError: operands could not be broadcast together with shapes (50,) (50,353)

实际上，sigma_los_beta是我单独求值的数组，形状为(50,353,50)，而那些353是我必须拥有的数据.

Indeed sigma_los_beta is an array that I evaluate separately and has shape (50,353,50) and those 353 are data that I must have.

如何将该函数转换为与plot_trisurf的其他条目兼容的形式?

How can I cast this function into a form that is compatible with the other entries of plot_trisurf?

对不起，但是我无法提供最少的工作代码，因为dv，v和u是数据. 非常感谢您的帮助.干杯

Sorry, but I can't supply a minimal working code, because dv,v and u are data. Thank you very much for your help. Cheers

推荐答案

此答案解决了4d表面图问题.它使用matplotlib的plot_surface函数而不是plot_trisurf.

This answer addresses the 4d surface plot problem. It uses matplotlib's plot_surface function instead of plot_trisurf.

基本上，您希望将x，y和z变量重塑为相同尺寸的2d数组.要将第四维添加为颜色图，必须提供另一个二维数组，其尺寸与轴变量相同.

Basically you want to reshape your x, y and z variables into 2d arrays of the same dimension. To add the fourth dimension as a colormap, you must supply another 2d array of the same dimension as your axes variables.

下面是3d图的示例代码，其色图与x值相对应. facecolors参数用于根据您的喜好更改颜色图.请注意，它的值是从matplotlib.cm.ScalarMappable类中的to_rgba()函数获取的.

Below is example code for a 3d plot with the colormap corresponding to the x values. The facecolors argument is used to alter the colormap to your liking. Note that its value is acquired from the to_rgba() function in the matplotlib.cm.ScalarMappable class.

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

# domains
x = np.logspace(-1.,np.log10(5),50) # [0.1, 5]
y = np.linspace(6,9,50)             # [6, 9]
z = np.linspace(-1,1,50)            # [-1, 1]

# convert to 2d matrices
Z = np.outer(z.T, z)        # 50x50
X, Y = np.meshgrid(x, y)    # 50x50

# fourth dimention - colormap
# create colormap according to x-value (can use any 50x50 array)
color_dimension = X # change to desired fourth dimension
minn, maxx = color_dimension.min(), color_dimension.max()
norm = matplotlib.colors.Normalize(minn, maxx)
m = plt.cm.ScalarMappable(norm=norm, cmap='jet')
m.set_array([])
fcolors = m.to_rgba(color_dimension)

# plot
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X,Y,Z, rstride=1, cstride=1, facecolors=fcolors, vmin=minn, vmax=maxx, shade=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
fig.canvas.show()

我参考的答案(和其他答案)提到您应该对第四维数据进行规范化.似乎可以像我在代码示例中所做的那样，通过显式设置颜色图的限制来避免这种情况.

The answer I referenced (and others) mentions that you should normalize your fourth dimension data. It seems that this may be avoided by explicitly setting the limits of the colormap as I did in the code sample.

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