这只是此示例的简单修改，具有不连续的x轴而不是y轴。（这就是为什么我要将此帖子设为CW）
基本上，你只需要执行以下操作：
import matplotlib.pylab as pltimport numpy as np# If you're not familiar with np.r_, don't worry too much about this. It's just # a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.x = np.r_[0:1:0.1, 9:10:0.1]y = np.sin(x)fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)# plot the same data on both axesax.plot(x, y, 'bo')ax2.plot(x, y, 'bo')# zoom-in / limit the view to different portions of the dataax.set_xlim(0,1) # most of the dataax2.set_xlim(9,10) # outliers only# hide the spines between ax and ax2ax.spines['right'].set_visible(False)ax2.spines['left'].set_visible(False)ax.yaxis.tick_left()ax.tick_params(labeltop='off') # don't put tick labels at the topax2.yaxis.tick_right()# Make the spacing between the two axes a bit smallerplt.subplots_adjust(wspace=0.15)plt.show()
要添加折断的轴线//效果，我们可以这样做（同样，从Paul Ivanov的示例进行了修改）：
import matplotlib.pylab as pltimport numpy as np# If you're not familiar with np.r_, don't worry too much about this. It's just # a series with points from 0 to 1 spaced at 0.1, and 9 to 10 with the same spacing.x = np.r_[0:1:0.1, 9:10:0.1]y = np.sin(x)fig,(ax,ax2) = plt.subplots(1, 2, sharey=True)# plot the same data on both axesax.plot(x, y, 'bo')ax2.plot(x, y, 'bo')# zoom-in / limit the view to different portions of the dataax.set_xlim(0,1) # most of the dataax2.set_xlim(9,10) # outliers only# hide the spines between ax and ax2ax.spines['right'].set_visible(False)ax2.spines['left'].set_visible(False)ax.yaxis.tick_left()ax.tick_params(labeltop='off') # don't put tick labels at the topax2.yaxis.tick_right()# Make the spacing between the two axes a bit smallerplt.subplots_adjust(wspace=0.15)# 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 plot, just so we don't keep repeating themkwargs = dict(transform=ax.transAxes, color='k', clip_on=False)ax.plot((1-d,1+d),(-d,+d), **kwargs) # top-left diagonalax.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-left diagonalkwargs.update(transform=ax2.transAxes) # switch to the bottom axesax2.plot((-d,d),(-d,+d), **kwargs) # top-right diagonalax2.plot((-d,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()
