类似于stackoverflow上的许多其他研究人员，他们试图用4D数据绘制等高线图（即X，Y，Z和它们对应的值C），我试图用我的数据绘制一个4D等值线图。我已经尝试了堆栈流中的许多建议的解决方案。从所有建议的this图中，和this最接近我想要的，但在数据解释方面还不完全是我需要的。
Here是数据的一个子集。我把它放在dropbox上。一旦这些数据下载到python文件的目录中，下面的代码就会工作。我已经从this post修改了这个脚本。

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

#####Importing the data
df = pd.read_csv('Data_4D_plot.csv')

do_random_pt_example = False;

index_x = 0; index_y = 1; index_z = 2; index_c = 3;
list_name_variables = ['x', 'y', 'z', 'c'];
name_color_map = 'seismic';

if do_random_pt_example:
    number_of_points = 200;
    x = np.random.rand(number_of_points);
    y = np.random.rand(number_of_points);
    z = np.random.rand(number_of_points);
    c = np.random.rand(number_of_points);
else:
    x = df['X'].to_numpy(); 
    y = df['Y'].to_numpy();
    z = df['Z'].to_numpy();
    c = df['C'].to_numpy();
#end
#-----

# We create triangles that join 3 pt at a time and where their colors will be
# determined by the values of their 4th dimension. Each triangle contains 3
# indexes corresponding to the line number of the points to be grouped. 
# Therefore, different methods can be used to define the value that 
# will represent the 3 grouped points and I put some examples.
triangles = mtri.Triangulation(x, y).triangles;

choice_calcuation_colors = 2;
if choice_calcuation_colors == 1: # Mean of the "c" values of the 3 pt of the triangle
    colors = np.mean( [c[triangles[:,0]], c[triangles[:,1]], c[triangles[:,2]]], axis = 0);
elif choice_calcuation_colors == 2: # Mediane of the "c" values of the 3 pt of the triangle
    colors = np.median( [c[triangles[:,0]], c[triangles[:,1]], c[triangles[:,2]]], axis = 0);
elif choice_calcuation_colors == 3: # Max of the "c" values of the 3 pt of the triangle
    colors = np.max( [c[triangles[:,0]], c[triangles[:,1]], c[triangles[:,2]]], axis = 0);
#end
#----------

###=====adjust this part for the labeling of the graph

list_name_variables[index_x] = 'X (m)'
list_name_variables[index_y] = 'Y (m)'
list_name_variables[index_z] = 'Z (m)'
list_name_variables[index_c] = 'C values'

# Displays the 4D graphic.
fig = plt.figure(figsize = (15,15));
ax = fig.gca(projection='3d');
triang = mtri.Triangulation(x, y, triangles);
surf = ax.plot_trisurf(triang, z, cmap = name_color_map, shade=False, linewidth=0.2);
surf.set_array(colors); surf.autoscale();

#Add a color bar with a title to explain which variable is represented by the color.
cbar = fig.colorbar(surf, shrink=0.5, aspect=5);
cbar.ax.get_yaxis().labelpad = 15; cbar.ax.set_ylabel(list_name_variables[index_c], rotation = 270);

# Add titles to the axes and a title in the figure.
ax.set_xlabel(list_name_variables[index_x]); ax.set_ylabel(list_name_variables[index_y]);
ax.set_zlabel(list_name_variables[index_z]);
ax.view_init(elev=15., azim=45)

plt.show()

输出如下：

虽然它看起来很棒，但它并不是我想要的（上面的等高线图示例）。我已经从this post修改了下面的脚本，希望达到所需的图表，但是，图表看起来与我所期望的完全不同（类似于前面的输出图表）。警告：以下代码可能需要一些时间才能运行。

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

df = pd.read_csv('Data_4D_plot.csv')

x = df['X'].to_numpy(); 
y = df['Y'].to_numpy();
z = df['Z'].to_numpy();
cc = df['C'].to_numpy();


# convert to 2d matrices
Z = np.outer(z.T, z)       
X, Y = np.meshgrid(x, y)   
C = np.outer(cc.T,cc)
# fourth dimention - colormap
# create colormap according to cc-value 
color_dimension = C # 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')
plt.show()

现在，我想知道我们的社区和Maven是否可以帮助我绘制一个类似于示例图（本文中的图一）的等值线图，其中等值线基于C范围内的值？