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<h1>Broken Axis<a class="headerlink" href="#broken-axis" title="Permalink to this headline">¶</a></h1>

Broken axis example, where the y-axis will have a portion cut out.

<div class="highlight-python"><div class="highlight"><pre>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# 30 points between [0, 0.2) originally made using np.random.rand(30)*.2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) = <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots">plt.subplots</a>(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'

<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show">plt.show</a>()

</pre></div>

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