Python 作图实现坐标轴截断(打断)的效果

 import matplotlib.pyplot as plt from brokenaxes import brokenaxes import numpy as np fig = plt.figure(figsize=(5,2)) bax = brokenaxes(xlims=((0, .1), (.4, .7)), ylims=((-1, .7), (.79, 1)), hspace=.05, despine=False) x = np.linspace(0, 1, 100) bax.plot(x, np.sin(10 * x), label='sin') bax.plot(x, np.cos(10 * x), label='cos') bax.legend(loc=3) bax.set_xlabel('time') bax.set_ylabel('value') 

 """ Broken axis example, where the y-axis will have a portion cut out. """ import matplotlib.pyplot as plt import numpy as np # 30 points between [0, 0.2) originally made using np.random.rand(30)*.2 pts = np.array([ 0.015, 0.166, 0.133, 0.159, 0.041, 0.024, 0.195, 0.039, 0.161, 0.018, 0.143, 0.056, 0.125, 0.096, 0.094, 0.051, 0.043, 0.021, 0.138, 0.075, 0.109, 0.195, 0.050, 0.074, 0.079, 0.155, 0.020, 0.010, 0.061, 0.008]) # Now let's make two outlier points which are far away from everything. pts[[3, 14]] += .8 # If we were to simply plot pts, we'd lose most of the interesting # details due to the outliers. So let's 'break' or 'cut-out' the y-axis # into two portions - use the top (ax) for the outliers, and the bottom # (ax2) for the details of the majority of our data f, (ax, ax2) = plt.subplots(2, 1, sharex=True) # plot the same data on both axes ax.plot(pts) ax2.plot(pts) # zoom-in / limit the view to different portions of the data ax.set_ylim(.78, 1.)  # outliers only ax2.set_ylim(0, .22)  # most of the data # hide the spines between ax and ax2 ax.spines['bottom'].set_visible(False) ax2.spines['top'].set_visible(False) ax.xaxis.tick_top() ax.tick_params(labeltop='off')  # don't put tick labels at the top ax2.xaxis.tick_bottom() # This looks pretty good, and was fairly painless, but you can get that # cut-out diagonal lines look with just a bit more work. The important # thing to know here is that in axes coordinates, which are always # between 0-1, spine endpoints are at these locations (0,0), (0,1), # (1,0), and (1,1).  Thus, we just need to put the diagonals in the # appropriate corners of each of our axes, and so long as we use the # right transform and disable clipping. d = .015  # how big to make the diagonal lines in axes coordinates # arguments to pass to plot, just so we don't keep repeating them kwargs = dict(transform=ax.transAxes, color='k', clip_on=False) ax.plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal ax.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal # What's cool about this is that now if we vary the distance between # ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(), # the diagonal lines will move accordingly, and stay right at the tips # of the spines they are 'breaking' plt.show() 

 # -*- coding: utf-8 -*- """ Created on Wed Dec  4 21:50:38 2019 @author: muli """ import matplotlib.pyplot as plt from pylab import * mpl.rcParams['font.sans-serif'] = ['SimHei'] #支持中文 names = ["1","2","3","4","5"]  # 刻度值命名 x = [1,2,3,4,5]    # 横坐标 y3= [2,3,1,4,5]    # 纵坐标 y4= [4,6,8,5,9]    # 纵坐标 y5=[24,27,22,26,28]     # 纵坐标 f, (ax3, ax) = plt.subplots(2, 1, sharex=False)  # 绘制两个子图 plt.subplots_adjust(wspace=0,hspace=0.08) # 设置 子图间距 ax.plot(x, y3, color='red', marker='o', linestyle='solid',label=u'1')   # 绘制折线 ax.plot(x, y4, color='g', marker='o', linestyle='solid',label=u'2')  # 绘制折线 plt.xticks(x, names, rotation=45) # 刻度值 ax3.xaxis.set_major_locator(plt.NullLocator()) # 删除坐标轴的刻度显示 ax3.plot(x, y5, color='blue', marker='o', linestyle='solid',label=u'3')  # 绘制折线 ax3.plot(x, y3, color='red', marker='o', linestyle='solid',label=u'1') # 起图例作用 ax3.plot(x, y4, color='g', marker='o', linestyle='solid',label=u'2') # 起图例作用 ax3.set_ylim(21, 30) # 设置纵坐标范围 ax.set_ylim(0, 10)  # 设置纵坐标范围 ax3.grid(axis='both',linestyle='-.') # 打开网格线 ax.grid(axis='y',linestyle='-.')   # 打开网格线 ax3.legend() # 让图例生效 plt.xlabel(u"λ") #X轴标签 plt.ylabel("mAP") #Y轴标签 ax.spines['top'].set_visible(False)    # 边框控制 ax.spines['bottom'].set_visible(True) # 边框控制 ax.spines['right'].set_visible(False)  # 边框控制 ax3.spines['top'].set_visible(False)   # 边框控制 ax3.spines['bottom'].set_visible(False) # 边框控制 ax3.spines['right'].set_visible(False)  # 边框控制 ax.tick_params(labeltop='off') # 绘制断层线 d = 0.01  # 断层线的大小 kwargs = dict(transform=ax3.transAxes, color='k', clip_on=False) ax3.plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal kwargs.update(transform=ax.transAxes, color='k')  # switch to the bottom axes ax.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal plt.show()