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<div class="section" id="fill-between-and-alpha">
<span id="sphx-glr-gallery-recipes-fill-between-alpha-py"></span><h1>Fill Between and Alpha<a class="headerlink" href="#fill-between-and-alpha" title="Permalink to this headline">¶</a></h1>
<p>The <a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.fill_between.html#matplotlib.axes.Axes.fill_between" title="matplotlib.axes.Axes.fill_between"><code class="xref py py-meth docutils literal"><span class="pre">fill_between()</span></code></a> function generates a
shaded region between a min and max boundary that is useful for
illustrating ranges. It has a very handy <code class="docutils literal"><span class="pre">where</span></code> argument to
combine filling with logical ranges, e.g., to just fill in a curve over
some threshold value.</p>
<p>At its most basic level, <code class="docutils literal"><span class="pre">fill_between</span></code> can be use to enhance a
graphs visual appearance. Let’s compare two graphs of a financial
times with a simple line plot on the left and a filled line on the
right.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</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="kn">import</span> <span class="nn">matplotlib.cbook</span> <span class="kn">as</span> <span class="nn">cbook</span>
<span class="c1"># load up some sample financial data</span>
<span class="k">with</span> <a href="../../api/cbook_api.html#matplotlib.cbook.get_sample_data" title="View documentation for matplotlib.cbook.get_sample_data"><span class="n">cbook</span><span class="o">.</span><span class="n">get_sample_data</span></a><span class="p">(</span><span class="s1">'goog.npz'</span><span class="p">)</span> <span class="k">as</span> <span class="n">datafile</span><span class="p">:</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">datafile</span><span class="p">)[</span><span class="s1">'price_data'</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">recarray</span><span class="p">)</span>
<span class="c1"># Matplotlib prefers datetime instead of np.datetime64.</span>
<span class="n">date</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">date</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'O'</span><span class="p">)</span>
<span class="c1"># create two subplots with the shared x and y axes</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</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="n">sharey</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">pricemin</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">close</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">close</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">pricemin</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">close</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'price'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">ax2</span><span class="o">.</span><span class="n">get_yticklabels</span><span class="p">():</span>
<span class="n">label</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">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Google (GOOG) daily closing price'</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
</pre></div>
</div>
<img alt="../../_images/sphx_glr_fill_between_alpha_001.png" class="align-center" src="../../_images/sphx_glr_fill_between_alpha_001.png" />
<p>The alpha channel is not necessary here, but it can be used to soften
colors for more visually appealing plots. In other examples, as we’ll
see below, the alpha channel is functionally useful as the shaded
regions can overlap and alpha allows you to see both. Note that the
postscript format does not support alpha (this is a postscript
limitation, not a matplotlib limitation), so when using alpha save
your figures in PNG, PDF or SVG.</p>
<p>Our next example computes two populations of random walkers with a
different mean and standard deviation of the normal distributions from
which the steps are drawn. We use shared regions to plot +/- one
standard deviation of the mean position of the population. Here the
alpha channel is useful, not just aesthetic.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">Nsteps</span><span class="p">,</span> <span class="n">Nwalkers</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">250</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">Nsteps</span><span class="p">)</span>
<span class="c1"># an (Nsteps x Nwalkers) array of random walk steps</span>
<span class="n">S1</span> <span class="o">=</span> <span class="mf">0.002</span> <span class="o">+</span> <span class="mf">0.01</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">Nsteps</span><span class="p">,</span> <span class="n">Nwalkers</span><span class="p">)</span>
<span class="n">S2</span> <span class="o">=</span> <span class="mf">0.004</span> <span class="o">+</span> <span class="mf">0.02</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">Nsteps</span><span class="p">,</span> <span class="n">Nwalkers</span><span class="p">)</span>
<span class="c1"># an (Nsteps x Nwalkers) array of random walker positions</span>
<span class="n">X1</span> <span class="o">=</span> <span class="n">S1</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">X2</span> <span class="o">=</span> <span class="n">S2</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Nsteps length arrays empirical means and standard deviations of both</span>
<span class="c1"># populations over time</span>
<span class="n">mu1</span> <span class="o">=</span> <span class="n">X1</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">sigma1</span> <span class="o">=</span> <span class="n">X1</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">mu2</span> <span class="o">=</span> <span class="n">X2</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">sigma2</span> <span class="o">=</span> <span class="n">X2</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># plot it!</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</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="n">t</span><span class="p">,</span> <span class="n">mu1</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'mean population 1'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</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="n">t</span><span class="p">,</span> <span class="n">mu2</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'mean population 2'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'yellow'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">mu1</span><span class="o">+</span><span class="n">sigma1</span><span class="p">,</span> <span class="n">mu1</span><span class="o">-</span><span class="n">sigma1</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">mu2</span><span class="o">+</span><span class="n">sigma2</span><span class="p">,</span> <span class="n">mu2</span><span class="o">-</span><span class="n">sigma2</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'yellow'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'random walkers empirical $\mu$ and $\pm \sigma$ interval'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'num steps'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'position'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
</pre></div>
</div>
<img alt="../../_images/sphx_glr_fill_between_alpha_002.png" class="align-center" src="../../_images/sphx_glr_fill_between_alpha_002.png" />
<p>The <code class="docutils literal"><span class="pre">where</span></code> keyword argument is very handy for highlighting certain
regions of the graph. <code class="docutils literal"><span class="pre">where</span></code> takes a boolean mask the same length
as the x, ymin and ymax arguments, and only fills in the region where
the boolean mask is True. In the example below, we simulate a single
random walker and compute the analytic mean and standard deviation of
the population positions. The population mean is shown as the black
dashed line, and the plus/minus one sigma deviation from the mean is
shown as the yellow filled region. We use the where mask
<code class="docutils literal"><span class="pre">X</span> <span class="pre">></span> <span class="pre">upper_bound</span></code> to find the region where the walker is above the one
sigma boundary, and shade that region blue.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">Nsteps</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">Nsteps</span><span class="p">)</span>
<span class="n">mu</span> <span class="o">=</span> <span class="mf">0.002</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="mf">0.01</span>
<span class="c1"># the steps and position</span>
<span class="n">S</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">Nsteps</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">S</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
<span class="c1"># the 1 sigma upper and lower analytic population bounds</span>
<span class="n">lower_bound</span> <span class="o">=</span> <span class="n">mu</span><span class="o">*</span><span class="n">t</span> <span class="o">-</span> <span class="n">sigma</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="n">upper_bound</span> <span class="o">=</span> <span class="n">mu</span><span class="o">*</span><span class="n">t</span> <span class="o">+</span> <span class="n">sigma</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</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="n">t</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'walker position'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</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="n">t</span><span class="p">,</span> <span class="n">mu</span><span class="o">*</span><span class="n">t</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'population mean'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">upper_bound</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'yellow'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'1 sigma range'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">)</span>
<span class="c1"># here we use the where argument to only fill the region where the</span>
<span class="c1"># walker is above the population 1 sigma boundary</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">upper_bound</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="n">X</span> <span class="o">></span> <span class="n">upper_bound</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'num steps'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'position'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
</pre></div>
</div>
<img alt="../../_images/sphx_glr_fill_between_alpha_003.png" class="align-center" src="../../_images/sphx_glr_fill_between_alpha_003.png" />
<p>Another handy use of filled regions is to highlight horizontal or
vertical spans of an axes – for that matplotlib has some helper
functions <a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.axhspan.html#matplotlib.axes.Axes.axhspan" title="matplotlib.axes.Axes.axhspan"><code class="xref py py-meth docutils literal"><span class="pre">axhspan()</span></code></a> and
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.axvspan.html#matplotlib.axes.Axes.axvspan" title="matplotlib.axes.Axes.axvspan"><code class="xref py py-meth docutils literal"><span class="pre">axvspan()</span></code></a> and example
<a class="reference internal" href="../pylab_examples/axhspan_demo.html#sphx-glr-gallery-pylab-examples-axhspan-demo-py"><span class="std std-ref">Axhspan Demo</span></a>.</p>
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