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<section id="performance">
<span id="id1"></span><h1>Performance<a class="headerlink" href="#performance" title="Permalink to this heading">#</a></h1>
<p>Whether exploring data in interactive mode or programmatically
saving lots of plots, rendering performance can be a challenging
bottleneck in your pipeline. Matplotlib provides multiple
ways to greatly reduce rendering time at the cost of a slight
change (to a settable tolerance) in your plot's appearance.
The methods available to reduce rendering time depend on the
type of plot that is being created.</p>
<section id="line-segment-simplification">
<h2>Line segment simplification<a class="headerlink" href="#line-segment-simplification" title="Permalink to this heading">#</a></h2>
<p>For plots that have line segments (e.g. typical line plots, outlines
of polygons, etc.), rendering performance can be controlled by
<code class="docutils literal notranslate"><a class="reference internal" href="../../tutorials/introductory/customizing.html?highlight=path.simplify#matplotlibrc-sample"><span class="std std-ref"><span class="pre">rcParams["path.simplify"]</span></span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) and <code class="docutils literal notranslate"><a class="reference internal" href="../../tutorials/introductory/customizing.html?highlight=path.simplify_threshold#matplotlibrc-sample"><span class="std std-ref"><span class="pre">rcParams["path.simplify_threshold"]</span></span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">0.111111111111</span></code>), which
can be defined e.g. in the <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file (see
<a class="reference internal" href="../../tutorials/introductory/customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a> for more information about
the <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file). <code class="docutils literal notranslate"><a class="reference internal" href="../../tutorials/introductory/customizing.html?highlight=path.simplify#matplotlibrc-sample"><span class="std std-ref"><span class="pre">rcParams["path.simplify"]</span></span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">True</span></code>) is a Boolean
indicating whether or not line segments are simplified at all.
<code class="docutils literal notranslate"><a class="reference internal" href="../../tutorials/introductory/customizing.html?highlight=path.simplify_threshold#matplotlibrc-sample"><span class="std std-ref"><span class="pre">rcParams["path.simplify_threshold"]</span></span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">0.111111111111</span></code>) controls how much line segments are simplified;
higher thresholds result in quicker rendering.</p>
<p>The following script will first display the data without any
simplification, and then display the same data with simplification.
Try interacting with both of them:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="c1"># Setup, and create the data to plot</span>
<span class="n">y</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">rand</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
<span class="n">y</span><span class="p">[</span><span class="mi">50000</span><span class="p">:]</span> <span class="o">*=</span> <span class="mi">2</span>
<span class="n">y</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify_threshold'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify_threshold'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p>Matplotlib currently defaults to a conservative simplification
threshold of <code class="docutils literal notranslate"><span class="pre">1/9</span></code>. To change default settings to use a different
value, change the <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file. Alternatively, users
can create a new style for interactive plotting (with maximal
simplification) and another style for publication quality plotting
(with minimal simplification) and activate them as necessary. See
<a class="reference internal" href="../../tutorials/introductory/customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a> for instructions on
how to perform these actions.</p>
<p>The simplification works by iteratively merging line segments
into a single vector until the next line segment's perpendicular
distance to the vector (measured in display-coordinate space)
is greater than the <code class="docutils literal notranslate"><span class="pre">path.simplify_threshold</span></code> parameter.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Changes related to how line segments are simplified were made
in version 2.1. Rendering time will still be improved by these
parameters prior to 2.1, but rendering time for some kinds of
data will be vastly improved in versions 2.1 and greater.</p>
</div>
</section>
<section id="marker-subsampling">
<h2>Marker subsampling<a class="headerlink" href="#marker-subsampling" title="Permalink to this heading">#</a></h2>
<p>Markers can also be simplified, albeit less robustly than line
segments. Marker subsampling is only available to <a class="reference internal" href="../../api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Line2D</span></code></a> objects
(through the <code class="docutils literal notranslate"><span class="pre">markevery</span></code> property). Wherever <a class="reference internal" href="../../api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Line2D</span></code></a> construction
parameters are passed through, such as <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyplot.plot</span></code></a> and <a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Axes.plot</span></code></a>,
the <code class="docutils literal notranslate"><span class="pre">markevery</span></code> parameter can be used:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">markevery</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">markevery</span></code> argument allows for naive subsampling, or an
attempt at evenly spaced (along the <em>x</em> axis) sampling. See the
<a class="reference internal" href="../../gallery/lines_bars_and_markers/markevery_demo.html"><span class="doc">Markevery Demo</span></a>
for more information.</p>
</section>
<section id="splitting-lines-into-smaller-chunks">
<h2>Splitting lines into smaller chunks<a class="headerlink" href="#splitting-lines-into-smaller-chunks" title="Permalink to this heading">#</a></h2>
<p>If you are using the Agg backend (see <a class="reference internal" href="backends.html#what-is-a-backend"><span class="std std-ref">What is a backend?</span></a>),
then you can make use of <code class="docutils literal notranslate"><a class="reference internal" href="../../tutorials/introductory/customizing.html?highlight=agg.path.chunksize#matplotlibrc-sample"><span class="std std-ref"><span class="pre">rcParams["agg.path.chunksize"]</span></span></a></code> (default: <code class="docutils literal notranslate"><span class="pre">0</span></code>)
This allows users to specify a chunk size, and any lines with
greater than that many vertices will be split into multiple
lines, each of which has no more than <code class="docutils literal notranslate"><span class="pre">agg.path.chunksize</span></code>
many vertices. (Unless <code class="docutils literal notranslate"><span class="pre">agg.path.chunksize</span></code> is zero, in
which case there is no chunking.) For some kind of data,
chunking the line up into reasonable sizes can greatly
decrease rendering time.</p>
<p>The following script will first display the data without any
chunk size restriction, and then display the same data with
a chunk size of 10,000. The difference can best be seen when
the figures are large, try maximizing the GUI and then
interacting with them:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify_threshold'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="c1"># Setup, and create the data to plot</span>
<span class="n">y</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">rand</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
<span class="n">y</span><span class="p">[</span><span class="mi">50000</span><span class="p">:]</span> <span class="o">*=</span> <span class="mi">2</span>
<span class="n">y</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">geomspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'path.simplify'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'agg.path.chunksize'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'agg.path.chunksize'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</section>
<section id="legends">
<h2>Legends<a class="headerlink" href="#legends" title="Permalink to this heading">#</a></h2>
<p>The default legend behavior for axes attempts to find the location
that covers the fewest data points (<code class="docutils literal notranslate"><span class="pre">loc='best'</span></code>). This can be a
very expensive computation if there are lots of data points. In
this case, you may want to provide a specific location.</p>
</section>
<section id="using-the-fast-style">
<h2>Using the <em>fast</em> style<a class="headerlink" href="#using-the-fast-style" title="Permalink to this heading">#</a></h2>
<p>The <em>fast</em> style can be used to automatically set
simplification and chunking parameters to reasonable
settings to speed up plotting large amounts of data.
The following code runs it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.style</span> <span class="k">as</span> <span class="nn">mplstyle</span>
<span class="n">mplstyle</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'fast'</span><span class="p">)</span>
</pre></div>
</div>
<p>It is very lightweight, so it works well with other
styles. Be sure the fast style is applied last
so that other styles do not overwrite the settings:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mplstyle</span><span class="o">.</span><span class="n">use</span><span class="p">([</span><span class="s1">'dark_background'</span><span class="p">,</span> <span class="s1">'ggplot'</span><span class="p">,</span> <span class="s1">'fast'</span><span class="p">])</span>
</pre></div>
</div>
</section>
</section>
</article>
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<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#line-segment-simplification">
Line segment simplification
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Marker subsampling
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<a class="reference internal nav-link" href="#splitting-lines-into-smaller-chunks">
Splitting lines into smaller chunks
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Legends
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