Introduction to the method of drawing 3D graphs using Matplotlib in python

This article mainly introduces the example code of Matplotlib in python to draw 3D images. It has certain reference value. Those who are interested can learn about it.

Matplotlib can also draw 3D images, which is different from two-dimensional images. What is interesting is that drawing three-dimensional images is mainly achieved through the mplot3d module. However, using Matplotlib to draw three-dimensional images is actually displayed on a two-dimensional canvas, so generally when drawing three-dimensional images, you also need to load the pyplot module.
mplot3d module mainly contains 4 major categories, namely:

• mpl_toolkits.mplot3d.axes3d()

• mpl_toolkits.mplot3d .axis3d()

• mpl_toolkits.mplot3d.art3d()

• mpl_toolkits.mplot3d.proj3d()

Among them, axes3d() mainly includes various classes and methods for implementing drawing. axis3d() mainly includes classes and methods related to coordinate axes. art3d() contains classes and methods that convert 2D images and use them for 3D drawing. proj3d() contains some fragmented classes and methods, such as calculating the length of three-dimensional vectors.

Generally, what we use most is the mpl_toolkits.mplot3d.axes3d.Axes3D() class in mpl_toolkits.mplot3d.axes3d(), and there are different types of 3D drawing under Axes3D() diagram method. You can import Axes3D() in the following way.

from mpl_toolkits.mplot3d.axes3d import Axes3D or from mpl_toolkits.mplot3d import Axes3D

Three-dimensional scatter plot

First, we import Numpy randomly generates a set of data.

import numpy as np

# x, y, z 均为 0 到 1 之间的 100 个随机数
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)
z = np.random.normal(0, 1, 100)

Next, start drawing. The first step is to load the 2D and 3D drawing modules.

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

The second step is to use Axes3D() to create a 3D graphics object.

fig = plt.figure()
ax = Axes3D(fig)

Finally, call the scatter plot drawing method to draw and display it.

ax.scatter(x, y, z)
plt.show()

Three-dimensional line chart

The line chart is similar to the scatter chart and needs to be passed in x, y, z The numerical value of the coordinate. The detailed code is as follows.

# 载入模块
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

# 生成数据
x = np.linspace(-6 * np.pi, 6 * np.pi, 1000)
y = np.sin(x)
z = np.cos(x)

# 创建 3D 图形对象
fig = plt.figure()
ax = Axes3D(fig)

# 绘制线型图
ax.plot(x, y, z)

# 显示图
plt.show()

Three-dimensional bar chart

After drawing the line chart, we continue to try to draw the three-dimensional bar chart. In fact, its drawing steps Again very similar to above.

# 载入模块
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

# 创建 3D 图形对象
fig = plt.figure()
ax = Axes3D(fig)

# 生成数据并绘图
x = [0, 1, 2, 3, 4, 5, 6]
for i in x:
  y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
  z = abs(np.random.normal(1, 10, 10))
  ax.bar(y, z, i, zdir='y', color=['r', 'g', 'b', 'y'])
plt.show()

Three-dimensional surface graph

The next three-dimensional surface graph that needs to be drawn is a little more troublesome. We need to matrix the data. deal with. In fact, it is very similar to drawing a two-dimensional contour map, except that one more dimension is added.

# 载入模块
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 创建 3D 图形对象
fig = plt.figure()
ax = Axes3D(fig)

# 生成数据
X = np.arange(-2, 2, 0.1)
Y = np.arange(-2, 2, 0.1)
X, Y = np.meshgrid(X, Y)
Z = np.sqrt(X ** 2 + Y ** 2)

# 绘制曲面图，并使用 cmap 着色
ax.plot_surface(X, Y, Z, cmap=plt.cm.winter)

plt.show()

cmap=plt.cm.winter indicates that the winter color scheme is used, which is the gradient color in the picture below.

Mixed graph drawing

Mixed graph is to draw two different types of graphs in one graph. There is generally a prerequisite for drawing a mixed diagram, that is, the range of the two different types of diagrams is roughly the same, otherwise there will be serious proportional disharmony, making the mixed diagram meaningless.

# -*- coding: utf-8 -*
# 载入模块
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt

# 创建 3D 图形对象
fig = plt.figure()
ax = Axes3D(fig)

# 生成数据并绘制图 1
x1 = np.linspace(-3 * np.pi, 3 * np.pi, 500)
y1 = np.sin(x1)
ax.plot(x1, y1, zs=0, c='red')

# 生成数据并绘制图 2
x2 = np.random.normal(0, 1, 100)
y2 = np.random.normal(0, 1, 100)
z2 = np.random.normal(0, 1, 100)
ax.scatter(x2, y2, z2)

# 显示图
plt.show()

Subgraph drawing

##

# -*- coding: utf-8 -*
# 载入模块
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

# 创建 1 张画布
fig = plt.figure()

#===============

# 向画布添加子图 1 
ax1 = fig.add_subplot(1, 2, 1, projection='3d')

# 生成子图 1 数据
x = np.linspace(-6 * np.pi, 6 * np.pi, 1000)
y = np.sin(x)
z = np.cos(x)

# 绘制第 1 张图
ax1.plot(x, y, z)

#===============

# 向画布添加子图 2
ax2 = fig.add_subplot(1, 2, 2, projection='3d')

# 生成子图 2 数据
X = np.arange(-2, 2, 0.1)
Y = np.arange(-2, 2, 0.1)
X, Y = np.meshgrid(X, Y)
Z = np.sqrt(X ** 2 + Y ** 2)

# 绘制第 2 张图
ax2.plot_surface(X, Y, Z, cmap=plt.cm.winter)

# 显示图
plt.show()

We can take a look at these codes. Since the two sub-pictures are drawn on one canvas, a canvas needs to be created in advance. Then add a subplot through .add_subplot(). The subplot serial number is similar to that of two-dimensional plotting. Just note that the projection='3d' parameter must be added when doing 3D plotting.

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