1.绘制三维图像

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X = [1, 1, 2, 2]
Y = [3, 4, 4, 3]
Z = [1, 2, 1, 1]
ax.plot_trisurf(X, Y, Z)
plt.show()
1
2
3
4
5
6
7
8
9

2.plt.axes()

我们先来看什么是Figure和Axes对象。在matplotlib中，整个图像为一个Figure对象。在Figure对象中可以包含一个，或者多个Axes对象。每个Axes对象都是一个拥有自己坐标系统的绘图区域。其逻辑关系如下：

import matplotlib.pyplot as plt
import numpy as np

# create some data to use for the plot
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t[:1000] / 0.05)  # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)] * dt  # colored noise

# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1 * np.amin(s), 2 * np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Gaussian colored noise')

# this is an inset axes over the main axes
a = plt.axes([.65, .6, .2, .2], axisbg='y')
n, bins, patches = plt.hist(s, 400, normed=1)
plt.title('Probability')
plt.xticks([])
plt.yticks([])

# this is another inset axes over the main axes
a = plt.axes([0.2, 0.6, .2, .2], axisbg='y')
plt.plot(t[:len(r)], r)
plt.title('Impulse response')
plt.xlim(0, 0.2)
plt.xticks([])
plt.yticks([])

plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33