10.Matplotlib绘图--子图

缺乏、安全感 2022-05-14 14:54 282阅读 0赞

本篇有参考如下博客，对原作者表示十分感谢：

[https://blog.csdn.net/claroja/article/details/70841382][https_blog.csdn.net_claroja_article_details_70841382]

[https://www.jianshu.com/p/de223a79217a][https_www.jianshu.com_p_de223a79217a]

很多时候需要把多幅图放一起观测更加直观,  Matplotlib可以实现在一个大图(figure)中存放多个子图(axes)，主要方法有subplot(), subplots().

pylot中，所有的绘图都是对当前对象作用，包括当前图形(figure)和当前坐标轴(axes)，可以通过gca()获得当前axes, 通过gcf()获得当前figure.

如果不指定figure()，figure(1)会默认建立，同样如果不指定subplot(numrows,numcols,fignum),subplot(1,1,1)自动被建立；

# 1.创建多个figure图 #

默认创建一个子图；

t2 = np.arange(0.0,2*np.pi,0.02)
    
    plt.figure()
    
    plt.plot(t2,np.sin(t2),'k')
    
    plt.show()

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创建两个figure

import matplotlib.pyplot as plt
    import numpy as np
    
    def f(t):
        return np.exp(-t)*np.cos(2*np.pi*t)
    
    t1 = np.arange(0.0,2*np.pi,0.1)
    t2 = np.arange(0.0,2*np.pi,0.02)
    
    fig1 = plt.figure()
    plt.plot(t2,np.sin(t2),'k')
    plt.title(r'$figure1$',fontsize=30)
    
    fig2 = plt.figure(2)
    plt.plot(t1,f(t1),'b')
    plt.title(r'$figure2$',fontsize=30)
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x1dGVyZXNh_size_27_color_FFFFFF_t_70 1][]

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# 2. subplot(): 在一个大图(Figure)里创建多个子图(Axes): #

plt.plot(t1,f(t1),'bo',t2,f(t2),'g')
    
    plt.subplot(212)
    #plt.plot(t2,np.cos(2*np.pi*t2),'r--')
    
    fig = plt.gcf()
    fig.set_size_inches(3,3) #改变整个figure大小
    
    ax = plt.gca() #获取当前axes
    ax.plot(t2,np.cos(2*np.pi*t2),'r--')
    
    plt.show()

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# 3.subplots():一次性创建多个子图(Axes) #

t = np.arange(0,2.2,0.1)
    s = np.sin(t*np.pi)
    
    #直接一次性创建多个subplot
    fig,ax = plt.subplots(2,2)
    
    ax[0][0].plot(t,s,'r*')
    
    plt.show()

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t = np.arange(0,2.2,0.1)
    s = np.sin(t*np.pi)
    
    fig,ax = plt.subplots(2,2)
    
    ax[0][0].plot(t,s,'r*')
    ax[1][1].plot(t*2,s,'b--')
    
    plt.show()

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# 4.在一个大图(一个figure只包含一个axes)中实现多个小图 #

先创建一个大图，只包含一个axes

plt.axes((left,bottom,width,height), facecolor='w)

(left,bottom,width,height):分别为子图的左下坐标，以及相对整个大图的宽高(范围0～1)

import matplotlib.pyplot as plt
    import numpy as np
    
    dt = 0.001
    t = np.arange(0.0,10.0,dt)
    r = np.exp(-t[:len(t)]/0.05)
    x = np.random.randn(len(t))
    s = np.convolve(x,r)[:len(x)]*dt
    
    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')
    
    plt.show()

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添加一个新子图

a = plt.axes([0.65,.6,0.2,0.2],facecolor='y')
    n,bins,patches = plt.hist(s,400,normed=1)
    plt.title('Probability')

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再添加一个子图

b = plt.axes([0.2,0.6,.2,.2],facecolor='b')
    plt.plot(t[:len(r)],r)
    plt.title('Impulse response')
    plt.xlim(0,0.2)
    
    #去掉坐标轴刻度
    plt.xticks([])
    plt.yticks([])
    plt.show()

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[https_blog.csdn.net_claroja_article_details_70841382]: https://blog.csdn.net/claroja/article/details/70841382
[https_www.jianshu.com_p_de223a79217a]: https://www.jianshu.com/p/de223a79217a
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