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<div class="section" id="pylab-examples-example-code-broken-axis-py">
<span id="pylab-examples-broken-axis"></span><h1>pylab_examples example code: broken_axis.py<a class="headerlink" href="#pylab-examples-example-code-broken-axis-py" title="Permalink to this headline">ΒΆ</a></h1>
<p>(<a class="reference external" href="../../mpl_examples/pylab_examples/broken_axis.py">Source code</a>, <a class="reference external" href="../../mpl_examples/pylab_examples/broken_axis.png">png</a>, <a class="reference external" href="../../mpl_examples/pylab_examples/broken_axis.hires.png">hires.png</a>, <a class="reference external" href="../../mpl_examples/pylab_examples/broken_axis.pdf">pdf</a>)</p>
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<img alt="../../_images/broken_axis.png" src="../../_images/broken_axis.png" />
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<div class="highlight-python"><div class="highlight"><pre><span class="sd">"""</span>
<span class="sd">Broken axis example, where the y-axis will have a portion cut out.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="c"># 30 points between 0 0.2] originally made using np.random.rand(30)*.2</span>
<span class="n">pts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.015</span><span class="p">,</span> <span class="mf">0.166</span><span class="p">,</span> <span class="mf">0.133</span><span class="p">,</span> <span class="mf">0.159</span><span class="p">,</span> <span class="mf">0.041</span><span class="p">,</span> <span class="mf">0.024</span><span class="p">,</span> <span class="mf">0.195</span><span class="p">,</span>
<span class="mf">0.039</span><span class="p">,</span> <span class="mf">0.161</span><span class="p">,</span> <span class="mf">0.018</span><span class="p">,</span> <span class="mf">0.143</span><span class="p">,</span> <span class="mf">0.056</span><span class="p">,</span> <span class="mf">0.125</span><span class="p">,</span> <span class="mf">0.096</span><span class="p">,</span> <span class="mf">0.094</span><span class="p">,</span> <span class="mf">0.051</span><span class="p">,</span>
<span class="mf">0.043</span><span class="p">,</span> <span class="mf">0.021</span><span class="p">,</span> <span class="mf">0.138</span><span class="p">,</span> <span class="mf">0.075</span><span class="p">,</span> <span class="mf">0.109</span><span class="p">,</span> <span class="mf">0.195</span><span class="p">,</span> <span class="mf">0.05</span> <span class="p">,</span> <span class="mf">0.074</span><span class="p">,</span> <span class="mf">0.079</span><span class="p">,</span>
<span class="mf">0.155</span><span class="p">,</span> <span class="mf">0.02</span> <span class="p">,</span> <span class="mf">0.01</span> <span class="p">,</span> <span class="mf">0.061</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">])</span>
<span class="c"># Now let's make two outlier points which are far away from everything.</span>
<span class="n">pts</span><span class="p">[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">14</span><span class="p">]]</span> <span class="o">+=</span> <span class="o">.</span><span class="mi">8</span>
<span class="c"># If we were to simply plot pts, we'd lose most of the interesting</span>
<span class="c"># details due to the outliers. So let's 'break' or 'cut-out' the y-axis</span>
<span class="c"># into two portions - use the top (ax) for the outliers, and the bottom</span>
<span class="c"># (ax2) for the details of the majority of our data</span>
<span class="n">f</span><span class="p">,(</span><span class="n">ax</span><span class="p">,</span><span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">sharex</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="c"># plot the same data on both axes</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
<span class="c"># zoom-in / limit the view to different portions of the data</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">.</span><span class="mi">78</span><span class="p">,</span><span class="mf">1.</span><span class="p">)</span> <span class="c"># outliers only</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">.</span><span class="mi">22</span><span class="p">)</span> <span class="c"># most of the data</span>
<span class="c"># hide the spines between ax and ax2</span>
<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">tick_top</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labeltop</span><span class="o">=</span><span class="s">'off'</span><span class="p">)</span> <span class="c"># don't put tick labels at the top</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">tick_bottom</span><span class="p">()</span>
<span class="c"># This looks pretty good, and was fairly painless, but you can get that</span>
<span class="c"># cut-out diagonal lines look with just a bit more work. The important</span>
<span class="c"># thing to know here is that in axes coordinates, which are always</span>
<span class="c"># between 0-1, spine endpoints are at these locations (0,0), (0,1),</span>
<span class="c"># (1,0), and (1,1). Thus, we just need to put the diagonals in the</span>
<span class="c"># appropriate corners of each of our axes, and so long as we use the</span>
<span class="c"># right transform and disable clipping.</span>
<span class="n">d</span> <span class="o">=</span> <span class="o">.</span><span class="mo">015</span> <span class="c"># how big to make the diagonal lines in axes coordinates</span>
<span class="c"># arguments to pass plot, just so we don't keep repeating them</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">transform</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'k'</span><span class="p">,</span> <span class="n">clip_on</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">((</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="o">+</span><span class="n">d</span><span class="p">),(</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="o">+</span><span class="n">d</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c"># top-left diagonal</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">((</span><span class="mi">1</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="mi">1</span><span class="o">+</span><span class="n">d</span><span class="p">),(</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="o">+</span><span class="n">d</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c"># top-right diagonal</span>
<span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">transform</span><span class="o">=</span><span class="n">ax2</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span> <span class="c"># switch to the bottom axes</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">((</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="o">+</span><span class="n">d</span><span class="p">),(</span><span class="mi">1</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="mi">1</span><span class="o">+</span><span class="n">d</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c"># bottom-left diagonal</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">((</span><span class="mi">1</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="mi">1</span><span class="o">+</span><span class="n">d</span><span class="p">),(</span><span class="mi">1</span><span class="o">-</span><span class="n">d</span><span class="p">,</span><span class="mi">1</span><span class="o">+</span><span class="n">d</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c"># bottom-right diagonal</span>
<span class="c"># What's cool about this is that now if we vary the distance between</span>
<span class="c"># ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),</span>
<span class="c"># the diagonal lines will move accordingly, and stay right at the tips</span>
<span class="c"># of the spines they are 'breaking'</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<p>Keywords: python, matplotlib, pylab, example, codex (see <a class="reference internal" href="../../faq/howto_faq.html#how-to-search-examples"><em>Search examples</em></a>)</p>
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