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python - matplotlib 中的子图创建循环

转载 作者:太空宇宙 更新时间:2023-11-04 01:05:38 37 4
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我是 python 的新手,我正在尝试创建一系列子图,唯一更改的参数是每个图的 fill_between 参数。目前我重复整个代码并更改每个子图的 fill_between 。是否有更有效的方法为子图创建循环,其中唯一改变的是 fill_between?下面是我试图制作的情节类型的示例。

import numpy as np
import matplotlib.pyplot as plt
#data
data1 = [11,20,25,80]
data2 = [15,35,50,90]
data3 =[25,36,58,63]
data4=[30,40,68,78]
element = np.arange(4)
fig = plt.figure()
#first plot
ax1 = fig.add_subplot(2,2,1)
phase1 = ax1.plot(data1,color='blue', lw=2)
phase2 = ax1.plot(data2,color='blue', lw=2)
phase3 = ax1.plot(data3,color='green', lw=2)
phase4 = ax1.plot(data4,color='green', lw=2)
plt.xticks(element,('La','Ce','Pr','Nd'))
ax1.fill_between(element,data1,data2,color='grey')
ax1.set_yscale('log')
fig.set_size_inches(10,5)
#second plot
ax2 = fig.add_subplot(2,2,2,sharex=ax1,sharey=ax1)
phase1 = ax2.plot(data1,color='blue', lw=2)
phase2 = ax2.plot(data2,color='blue', lw=2)
phase3 = ax2.plot(data3,color='green', lw=2)
phase4 = ax2.plot(data4,color='green', lw=2)
plt.xticks(element,('La','Ce','Pr','Nd'))
#the fill is the ONLY thing to change
ax2.fill_between(element,data3,data4,color='red')
ax2.set_yscale('log')

plt.show()

最佳答案

希望对你有帮助

import numpy as np
import matplotlib.pyplot as plt

#data
data1 = [11,20,25,80]
data2 = [15,35,50,90]
data3 = [25,36,58,63]
data4 = [30,40,68,78]
element = np.arange(4)

# data is used to automatize the fill_between argument
data = [[data1,data2],[data3,data4]]

# creating an list of the colors used
cols = ['grey','red']

# creating the figure including all axes
fig,ax = plt.subplots(1,2)

for i,a in enumerate(ax):
phase1 = a.plot(data1,color='blue', lw=2)
phase2 = a.plot(data2,color='blue', lw=2)
phase3 = a.plot(data3,color='green', lw=2)
phase4 = a.plot(data4,color='green', lw=2)
a.set_xticks(element)
a.set_xticklabels(['La','Ce','Pr','Nd'])
a.fill_between(element,data[i][0],data[i][1],color=cols[i])
a.set_yscale('log')

fig.set_size_inches(10,5)

关于python - matplotlib 中的子图创建循环,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/30625963/

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