【4.2】matplotlib-多图合并

一、多合一显示

1、subplot方法:设置行、列和起始点

plt.subplot(2,1,1) # 分成两行一列,起始点为1

2、代码

# -*- coding: utf-8 -*-  
""" 
Created on Sun Sep 24 15:02:51 2017 

@author: ryoyun 
"""  

# subplot 多合一显示  
import matplotlib.pyplot as plt  

plt.figure()  

plt.subplot(2,1,1)      # 分成两行一列,起始点为1  
plt.plot([0,1],[0,1])   # 设置xy轴范围  

plt.subplot(2,3,4)      # 分成两行三列,起始点位4  
plt.plot([0,1],[0,2])  

plt.subplot(2,3,5)  
plt.plot([0,1],[0,3])  

plt.subplot(2,3,6)  
plt.plot([0,1],[0,4])  

plt.show()  

3、效果

二、分格显示

1、代码

# -*- coding: utf-8 -*-  
""" 
Created on Sun Sep 24 15:11:01 2017 

@author: ryoyun 
"""  

# 分格显示  
import matplotlib.pyplot as plt  
import matplotlib.gridspec as gridspec  

# method 1: subplot2grid  
############################  
#plt.figure()  
#ax1 = plt.subplot2grid((3,3),(0,0),colspan = 3,rowspan = 1)  
#ax1.plot([1,2],[1,2])  
#ax1.set_title('ax1_title')  

#plt.figure()  
#ax2 = plt.subplot2grid((3,3),(1,0),colspan = 2)  
#ax3 = plt.subplot2grid((3,3),(1,2),rowspan = 2)  
#ax4 = plt.subplot2grid((3,3),(2,0))  
#ax5 = plt.subplot2grid((3,3),(2,1))  

# method 2: gridspec  
###########################  
#plt.figure()  
#gs = gridspec.GridSpec(3,3)  
#ax1 = plt.subplot(gs[0,:])  
#ax2 = plt.subplot(gs[1,:2])  
#ax3 = plt.subplot(gs[1:,2])  
#ax4 = plt.subplot(gs[-1,0])  
#ax5 = plt.subplot(gs[-1,-2])  


# method 3: easy to define structure  
###########################  
f,((ax11,ax12),(ax21,ax22)) = plt.subplots(2,2,sharex=True,sharey=True)  
ax11.scatter([1,2],[1,2])  


# plt.tick_layout()  
plt.show() 

2.效果:

三、图中图

1、每个图的位置设定很重要

left,bottom,width,height = 0.1,0.1,0.8,0.8
ax1 = fig.add_axes([left,bottom,width,height])# 设置位置

2、代码

# -*- coding: utf-8 -*-  
""" 
Created on Sun Sep 24 15:32:13 2017 

@author: ryoyun 
"""  

# 图中图  
import matplotlib.pyplot as plt  
import numpy as np    

fig = plt.figure()  
x = [1,2,3,4,5,6,7]  
y = [1,3,4,2,5,8,6]  

left,bottom,width,height = 0.1,0.1,1.5,1.5  
ax1 = fig.add_axes([left,bottom,width,height])# 设置位置  
ax1.plot(x,y,'r')  
ax1.set_xlabel('x')  
ax1.set_ylabel('y')  
ax1.set_title('title')  


left,bottom,width,height = 0.2,0.6,0.25,0.25  
ax2 = fig.add_axes([left,bottom,width,height])  
ax2.plot(y,x,'b')  
ax2.set_xlabel('x')  
ax2.set_ylabel('y')  
ax2.set_title('title inside 1')  

plt.axes([.6,0.2,0.25,0.25])  
plt.plot(y[::-1],x,'g')  
plt.xlabel('x')  
plt.ylabel('y')  
plt.title('title inside 2')  


plt.show()  

2.效果:

四、次坐标轴(两个Y轴)

1、画几个散点一点都不难

ax2 = ax1.twinx()           # 做镜像处理,ax2是ax1的镜像

2、代码

# -*- coding: utf-8 -*-  
""" 
Created on Sun Sep 24 15:44:29 2017 

@author: ryoyun 
"""  

# 次坐标轴  
import matplotlib.pyplot as plt  
import numpy as np  

x = np.arange(0,10,0.1)  
y1= 0.05*x**2  
y2= -1*y1  

fig,ax1 = plt.subplots()  
ax2 = ax1.twinx()           # 做镜像处理  
ax1.plot(x,y1,'g-')  
ax2.plot(x,y2,'b--')  

ax1.set_xlabel('X data')    #设置x轴标题  
ax1.set_ylabel('Y1',color = 'g')   #设置Y1轴标题  
ax2.set_ylabel('Y2',color = 'b')   #设置Y2轴标题  

plt.show()  

3.效果:

五、Animation动画

1、代码

# -*- coding: utf-8 -*-  
""" 
Created on Sun Sep 24 15:51:25 2017 

@author: ryoyun 
"""  

# animation 动画  
from matplotlib import animation  
import matplotlib.pyplot as plt  
import numpy as np  

fig, ax = plt.subplots()  

x = np.arange(0,2*np.pi,0.01)  
line, = ax.plot(x,np.sin(x))  

def animate(i):  
    line.set_ydata(np.sin(x+i/100))  
    return line,  

def init():  
    line.set_ydata(np.sin(x))  
    return line,  

ani = animation.FuncAnimation(fig =fig,func=animate,frames= 100,init_func=init,  
                              interval=20,blit=False,)  


plt.show()  

3.效果:

其他

#组合图
import numpy as np
from matplotlib import pyplot as plt
# the random data
x = np.random.randn(1000)
y = np.random.randn(1000)
#自定义子图区域,需要先构建一个list,由左下角坐标,宽,高组成,来生成子图[x,y,w,h]
left_x,left_y=0.1,0.1
width,height=0.65,0.65
left_xh=left_x+width+0.02
left_yh=left_y+height+0.02

scatter_area=[left_x,left_y,width,height]
hist_x=[left_x,left_yh,width,0.2]
hist_y=[left_xh,left_y,0.2,height]


plt.figure(1, figsize=(8, 8))
#生成子图的方法用到plt.axes
area_scatter=plt.axes(scatter_area)
area_histx=plt.axes(hist_x)
area_histy=plt.axes(hist_y)

#画散点图
area_scatter.scatter(x, y)

#统计散点图,画概率分布图
#设置概率分布图的bins的宽度
binwidth=0.25
#统计最大的x值,最大的y值
#np.fabs()返回绝对值
xymax=np.max([np.max(np.fabs(x)),np.max(np.fabs(y))])
#bin的数量
N_bins=int(xymax/binwidth)+1
#最大坐标
lim=N_bins*binwidth
#最小坐标
nlim=-lim
#坐标轴的分布
bins=np.arange(nlim,lim+binwidth,binwidth)
#根据取得的坐标分布,将散点图的坐标轴与此对应
area_scatter.set_xlim(nlim,lim)
area_scatter.set_ylim(nlim,lim)
#画出概率分布图
area_histx.hist(x, bins=bins)
area_histy.hist(y, bins=bins, orientation='horizontal')
#设置概率分布图的坐标
area_histx.set_xlim(area_scatter.get_xlim())
area_histy.set_ylim(area_scatter.get_ylim())
plt.show()

参考资料:

https://morvanzhou.github.io/tutorials/

https://blog.csdn.net/ruoyunliufeng/article/details/78077444

https://www.jianshu.com/p/e1f5474576c6

个人公众号,比较懒,很少更新,可以在上面提问题,如果回复不及时,可发邮件给我: tiehan@sina.cn

Sam avatar
About Sam
专注生物信息 专注转化医学