一、在一个大图上做若干子图:
fig.add_subplot(numrows, numcols, fignum) ####三个参数,分别代表子图的行数,列数,图索引号。
可以写成:
ax = fig.add_subplot(1, 1, 1)或者,ax = fig.add_subplot(111)
A simple example can clarify a bit:
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax1.plot([1, 2, 3], [1, 2, 3]);
ax2 = fig.add_subplot(212)
ax2.plot([1, 2, 3], [3, 2, 1]);
plt.show()
二、作几个大图:
import matplotlib.pyplot as plt
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot([1, 2, 3], [1, 2, 3]);
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.plot([1, 2, 3], [3, 2, 1]);
plt.show()
三、一个图上作不同的函数:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0., np.e, 0.01)
y1 = np.exp(-x)
y2 = np.log(x)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(x, y1);
ax1.set_ylabel('Y values for exp(-x)');
ax2 = ax1.twinx()
# this is the important function
ax2.plot(x, y2, 'r');
ax2.set_xlim([0, np.e]);
ax2.set_ylabel('Y values for ln(x)');
ax2.set_xlabel('Same X for both exp(-x) and ln(x)');
plt.show()