如何在python中使用matplotlib制作3D数据曲面图

I am trying to make a 3d surface plot of experimental data using matplotlib. I would like to plot different Z values against the same X and Y axes. When I try the simple code below, I get the error

我正在尝试使用matplotlib制作实验数据的三维表面图。我想在相同的X和Y轴上绘制不同的Z值。当我尝试下面的简单代码时，我得到了错误

"plot_surface() missing 1 required positional argument: 'Z' ".

“plot_surface（）缺少1个必需的位置参数：'Z'”。

It seems that the Axes3D package only work if Z is given as a function of X and Y, rather than an actual data matrix. Does anybody know a way around this?

似乎Axes3D软件包仅在Z作为X和Y的函数给出时才起作用，而不是实际的数据矩阵。有人知道解决这个问题吗？

Please note that the Zmatrix that I need is actual data, but I just used a random matrix for illustration here.

请注意，我需要的Zmatrix是实际数据，但我在这里只是使用随机矩阵进行说明。

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()

X=[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
Y= [0,2500,5000,7500,10000,15000,20000,25000,30000,35000,40000,45000,50000,55000,60000,65000,70000]

Zmatrix=np.random.rand(len(X),len(Y))


Axes3D.plot_surface(X,Y,Zmatrix)

2 个解决方案

#1

There were sone issues with your code: First you have to get a mashgrid of X and Y (all combinations as matrices). Next swap len(X) and len(Y) inside the Zmatrix. And first define ax = Axes3D(plt.gcf()) and plot_surface afterwards on ax.

您的代码存在问题：首先，您必须获得X和Y的mashgrid（所有组合都作为矩阵）。接下来在Zmatrix中交换len（X）和len（Y）。首先在ax上定义ax = Axes3D（plt.gcf（））和plot_surface。

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()

X=[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
Y= [0,2500,5000,7500,10000,15000,20000,25000,30000,35000,40000,45000,50000,55000,60000,65000,70000]
Xm, Ym = np.meshgrid(X, Y)
Zmatrix=np.random.rand(len(Y),len(X))

ax = Axes3D(plt.gcf())
ax.plot_surface(Xm, Ym, Zmatrix)

#2

Here is an example of surface plot.

这是表面图的一个例子。

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

  def fun(x, y):
      return x**2 + y

  fig = plt.figure()
  ax = fig.add_subplot(111, projection='3d')
  x = y = np.arange(-3.0, 3.0, 0.05)
  X, Y = np.meshgrid(x, y)
  zs = np.array([fun(x,y) for x,y in zip(np.ravel(X), np.ravel(Y))])
  Z = zs.reshape(X.shape)

  ax.plot_surface(X, Y, Z)

  ax.set_xlabel('X Label')
  ax.set_ylabel('Y Label')
  ax.set_zlabel('Z Label')

  plt.show()