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  <h3><a href="../../contents.html">Table of Contents</a></h3>
<li><a class="reference internal" href="#">Usage Guide</a><ul>
<li><a class="reference internal" href="#general-concepts">General Concepts</a></li>
<li><a class="reference internal" href="#parts-of-a-figure">Parts of a Figure</a><ul>
<li><a class="reference internal" href="#figure"><code class="docutils literal notranslate"><span class="pre">Figure</span></code></a></li>
<li><a class="reference internal" href="#axes"><code class="docutils literal notranslate"><span class="pre">Axes</span></code></a></li>
<li><a class="reference internal" href="#axis"><code class="docutils literal notranslate"><span class="pre">Axis</span></code></a></li>
<li><a class="reference internal" href="#artist"><code class="docutils literal notranslate"><span class="pre">Artist</span></code></a></li>
<li><a class="reference internal" href="#types-of-inputs-to-plotting-functions">Types of inputs to plotting functions</a></li>
<li><a class="reference internal" href="#matplotlib-pyplot-and-pylab-how-are-they-related">Matplotlib, pyplot and pylab: how are they related?</a></li>
<li><a class="reference internal" href="#coding-styles">Coding Styles</a></li>
<li><a class="reference internal" href="#backends">Backends</a><ul>
<li><a class="reference internal" href="#what-is-a-backend">What is a backend?</a></li>
<li><a class="reference internal" href="#ipympl">ipympl</a></li>
<li><a class="reference internal" href="#gtk-and-cairo">GTK and Cairo</a></li>
<li><a class="reference internal" href="#how-do-i-select-pyqt4-or-pyside">How do I select PyQt4 or PySide?</a></li>
<li><a class="reference internal" href="#what-is-interactive-mode">What is interactive mode?</a><ul>
<li><a class="reference internal" href="#interactive-example">Interactive example</a></li>
<li><a class="reference internal" href="#non-interactive-example">Non-interactive example</a></li>
<li><a class="reference internal" href="#summary">Summary</a></li>
<li><a class="reference internal" href="#performance">Performance</a><ul>
<li><a class="reference internal" href="#line-segment-simplification">Line segment simplification</a></li>
<li><a class="reference internal" href="#marker-simplification">Marker simplification</a></li>
<li><a class="reference internal" href="#splitting-lines-into-smaller-chunks">Splitting lines into smaller chunks</a></li>
<li><a class="reference internal" href="#legends">Legends</a></li>
<li><a class="reference internal" href="#using-the-fast-style">Using the <em>fast</em> style</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-tutorials-introductory-usage-py"><span class="std std-ref">here</span></a> to download the full example code</p>
<div class="sphx-glr-example-title section" id="usage-guide">
<span id="sphx-glr-tutorials-introductory-usage-py"></span><h1>Usage Guide<a class="headerlink" href="#usage-guide" title="Permalink to this headline">¶</a></h1>
<p>This tutorial covers some basic usage patterns and best-practices to
help you get started with Matplotlib.</p>
<div class="section" id="general-concepts">
<span id="id1"></span><h2>General Concepts<a class="headerlink" href="#general-concepts" title="Permalink to this headline">¶</a></h2>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib</span></code> has an extensive codebase that can be daunting to many
new users. However, most of matplotlib can be understood with a fairly
simple conceptual framework and knowledge of a few important points.</p>
<p>Plotting requires action on a range of levels, from the most general
(e.g., 'contour this 2-D array') to the most specific (e.g., 'color
this screen pixel red'). The purpose of a plotting package is to assist
you in visualizing your data as easily as possible, with all the necessary
control -- that is, by using relatively high-level commands most of
the time, and still have the ability to use the low-level commands when
needed.</p>
<p>Therefore, everything in matplotlib is organized in a hierarchy. At the top
of the hierarchy is the matplotlib &quot;state-machine environment&quot; which is
provided by the <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib.pyplot</span></code></a> module. At this level, simple
functions are used to add plot elements (lines, images, text, etc.) to
the current axes in the current figure.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Pyplot's state-machine environment behaves similarly to MATLAB and
should be most familiar to users with MATLAB experience.</p>
<p>The next level down in the hierarchy is the first level of the object-oriented
interface, in which pyplot is used only for a few functions such as figure
creation, and the user explicitly creates and keeps track of the figure
and axes objects. At this level, the user uses pyplot to create figures,
and through those figures, one or more axes objects can be created. These
axes objects are then used for most plotting actions.</p>
<p>For even more control -- which is essential for things like embedding
matplotlib plots in GUI applications -- the pyplot level may be dropped
completely, leaving a purely object-oriented approach.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># sphinx_gallery_thumbnail_number = 3</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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
<div class="section" id="parts-of-a-figure">
<span id="figure-parts"></span><h2>Parts of a Figure<a class="headerlink" href="#parts-of-a-figure" title="Permalink to this headline">¶</a></h2>
<img alt="../../_images/anatomy.png" src="../../_images/anatomy.png" />
<div class="section" id="figure">
<h3><a class="reference internal" href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal notranslate"><span class="pre">Figure</span></code></a><a class="headerlink" href="#figure" title="Permalink to this headline">¶</a></h3>
<p>The <strong>whole</strong> figure.  The figure keeps
track of all the child <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code></a>, a smattering of
'special' artists (titles, figure legends, etc), and the <strong>canvas</strong>.
(Don't worry too much about the canvas, it is crucial as it is the
object that actually does the drawing to get you your plot, but as the
user it is more-or-less invisible to you).  A figure can have any
number of <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code></a>, but to be useful should have
at least one.</p>
<p>The easiest way to create a new figure is with pyplot:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="View documentation for matplotlib.figure.Figure"><span class="n">fig</span></a> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="View documentation for matplotlib.pyplot.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>  <span class="c1"># an empty figure with no axes</span>
<a href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="View documentation for matplotlib.figure.Figure"><span class="n">fig</span></a><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">&#39;No axes on this figure&#39;</span><span class="p">)</span>  <span class="c1"># Add a title so we know which it is</span>
<a href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="View documentation for matplotlib.figure.Figure"><span class="n">fig</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">ax_lst</span></a> <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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>  <span class="c1"># a figure with a 2x2 grid of Axes</span>
</pre></div>
<ul class="sphx-glr-horizontal">
<li><img alt="../../_images/sphx_glr_usage_001.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_usage_001.png" />
<li><img alt="../../_images/sphx_glr_usage_002.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_usage_002.png" />
<div class="section" id="axes">
<h3><a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code></a><a class="headerlink" href="#axes" title="Permalink to this headline">¶</a></h3>
<p>This is what you think of as 'a plot', it is the region of the image
with the data space. A given figure
can contain many Axes, but a given <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code></a>
object can only be in one <a class="reference internal" href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal notranslate"><span class="pre">Figure</span></code></a>.  The
Axes contains two (or three in the case of 3D)
<a class="reference internal" href="../../api/axis_api.html#matplotlib.axis.Axis" title="matplotlib.axis.Axis"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axis</span></code></a> objects (be aware of the difference
between <strong>Axes</strong> and <strong>Axis</strong>) which take care of the data limits (the
data limits can also be controlled via set via the
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_xlim()</span></code></a> and
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_ylim()</span></code></a> <code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code> methods).  Each
<code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code> has a title (set via
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_title()</span></code></a>), an x-label (set via
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_xlabel()</span></code></a>), and a y-label set via
<a class="reference internal" href="../../api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_ylabel()</span></code></a>).</p>
<p>The <code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code> class and its member functions are the primary entry
point to working with the OO interface.</p>
<div class="section" id="axis">
<h3><a class="reference internal" href="../../api/axis_api.html#matplotlib.axis.Axis" title="matplotlib.axis.Axis"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axis</span></code></a><a class="headerlink" href="#axis" title="Permalink to this headline">¶</a></h3>
<p>These are the number-line-like objects. They take
care of setting the graph limits and generating the ticks (the marks
on the axis) and ticklabels (strings labeling the ticks).  The
location of the ticks is determined by a
<a class="reference internal" href="../../api/ticker_api.html#matplotlib.ticker.Locator" title="matplotlib.ticker.Locator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Locator</span></code></a> object and the ticklabel strings
are formatted by a <a class="reference internal" href="../../api/ticker_api.html#matplotlib.ticker.Formatter" title="matplotlib.ticker.Formatter"><code class="xref py py-class docutils literal notranslate"><span class="pre">Formatter</span></code></a>.  The
combination of the correct <code class="xref py py-class docutils literal notranslate"><span class="pre">Locator</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">Formatter</span></code> gives
very fine control over the tick locations and labels.</p>
<div class="section" id="artist">
<h3><a class="reference internal" href="../../api/artist_api.html#matplotlib.artist.Artist" title="matplotlib.artist.Artist"><code class="xref py py-class docutils literal notranslate"><span class="pre">Artist</span></code></a><a class="headerlink" href="#artist" title="Permalink to this headline">¶</a></h3>
<p>Basically everything you can see on the figure is an artist (even the
<code class="xref py py-class docutils literal notranslate"><span class="pre">Figure</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code>, and <code class="xref py py-class docutils literal notranslate"><span class="pre">Axis</span></code> objects).  This
includes <code class="xref py py-class docutils literal notranslate"><span class="pre">Text</span></code> objects, <code class="xref py py-class docutils literal notranslate"><span class="pre">Line2D</span></code> objects,
<code class="xref py py-class docutils literal notranslate"><span class="pre">collection</span></code> objects, <code class="xref py py-class docutils literal notranslate"><span class="pre">Patch</span></code> objects ... (you get the
idea).  When the figure is rendered, all of the artists are drawn to
the <strong>canvas</strong>.  Most Artists are tied to an Axes; such an Artist
cannot be shared by multiple Axes, or moved from one to another.</p>
<div class="section" id="types-of-inputs-to-plotting-functions">
<span id="input-types"></span><h2>Types of inputs to plotting functions<a class="headerlink" href="#types-of-inputs-to-plotting-functions" title="Permalink to this headline">¶</a></h2>
<p>All of plotting functions expect <code class="xref py py-obj docutils literal notranslate"><span class="pre">np.array</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">np.ma.masked_array</span></code> as
input.  Classes that are 'array-like' such as <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/index.html#module-pandas" title="(in pandas v1.0.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas</span></code></a> data objects
and <code class="xref py py-obj docutils literal notranslate"><span class="pre">np.matrix</span></code> may or may not work as intended.  It is best to
convert these to <code class="xref py py-obj docutils literal notranslate"><span class="pre">np.array</span></code> objects prior to plotting.</p>
<p>For example, to convert a <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.0.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</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">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcde&#39;</span><span class="p">))</span>
<span class="n">a_asarray</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">values</span>
</pre></div>
<p>and to convert a <code class="xref py py-obj docutils literal notranslate"><span class="pre">np.matrix</span></code></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matrix</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span>
<span class="n">b_asarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
</pre></div>
<div class="section" id="matplotlib-pyplot-and-pylab-how-are-they-related">
<span id="pylab"></span><h2>Matplotlib, pyplot and pylab: how are they related?<a class="headerlink" href="#matplotlib-pyplot-and-pylab-how-are-they-related" title="Permalink to this headline">¶</a></h2>
<p>Matplotlib is the whole package and <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib.pyplot</span></code></a> is a module in
Matplotlib.</p>
<p>For functions in the pyplot module, there is always a &quot;current&quot; figure and
axes (which is created automatically on request).  For example, in the
following example, the first call to <code class="docutils literal notranslate"><span class="pre">plt.plot</span></code> creates the axes, then
subsequent calls to <code class="docutils literal notranslate"><span class="pre">plt.plot</span></code> add additional lines on the same axes, and
<code class="docutils literal notranslate"><span class="pre">plt.xlabel</span></code>, <code class="docutils literal notranslate"><span class="pre">plt.ylabel</span></code>, <code class="docutils literal notranslate"><span class="pre">plt.title</span></code> and <code class="docutils literal notranslate"><span class="pre">plt.legend</span></code> set the
axes labels and title and add a legend.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html#numpy.linspace" title="View documentation for numpy.linspace"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><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">&#39;quadratic&#39;</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="o">**</span><span class="mi">3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="View documentation for matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s1">&#39;x label&#39;</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="View documentation for matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s1">&#39;y label&#39;</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">&quot;Simple Plot&quot;</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="View documentation for matplotlib.pyplot.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
<img alt="../../_images/sphx_glr_usage_003.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_003.png" />
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">pylab</span></code> is a convenience module that bulk imports
<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib.pyplot</span></code></a> (for plotting) and <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/index.html#module-numpy" title="(in NumPy v1.17)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code></a>
(for mathematics and working with arrays) in a single namespace.
pylab is deprecated and its use is strongly discouraged because
of namespace pollution. Use pyplot instead.</p>
<p>For non-interactive plotting it is suggested
to use pyplot to create the figures and then the OO interface for
plotting.</p>
<div class="section" id="coding-styles">
<span id="id2"></span><h2>Coding Styles<a class="headerlink" href="#coding-styles" title="Permalink to this headline">¶</a></h2>
<p>When viewing this documentation and examples, you will find different
coding styles and usage patterns. These styles are perfectly valid
and have their pros and cons. Just about all of the examples can be
converted into another style and achieve the same results.
The only caveat is to avoid mixing the coding styles for your own code.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Developers for matplotlib have to follow a specific style and guidelines.
See <a class="reference internal" href="../../devel/index.html#developers-guide-index"><span class="std std-ref">The Matplotlib Developers' Guide</span></a>.</p>
<p>Of the different styles, there are two that are officially supported.
Therefore, these are the preferred ways to use matplotlib.</p>
<p>For the pyplot style, the imports at the top of your
scripts will typically be:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
<p>Then one calls, for example, np.arange, np.zeros, np.pi, plt.figure,
plt.plot, plt.show, etc.  Use the pyplot interface
for creating figures, and then use the object methods for the rest:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange" title="View documentation for numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)</span>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.sin.html#numpy.sin" title="View documentation for numpy.sin"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="View documentation for matplotlib.figure.Figure"><span class="n">fig</span></a><span class="p">,</span> <a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a> <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>
<a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
<img alt="../../_images/sphx_glr_usage_004.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_004.png" />
<p>So, why all the extra typing instead of the MATLAB-style (which relies
on global state and a flat namespace)?  For very simple things like
this example, the only advantage is academic: the wordier styles are
more explicit, more clear as to where things come from and what is
going on.  For more complicated applications, this explicitness and
clarity becomes increasingly valuable, and the richer and more
complete object-oriented interface will likely make the program easier
to write and maintain.</p>
<p>Typically one finds oneself making the same plots over and over
again, but with different data sets, which leads to needing to write
specialized functions to do the plotting.  The recommended function
signature is something like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">my_plotter</span><span class="p">(</span><a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data2</span></a><span class="p">,</span> <span class="n">param_dict</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    A helper function to make a graph</span>
<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    ax : Axes</span>
<span class="sd">        The axes to draw to</span>
<span class="sd">    data1 : array</span>
<span class="sd">       The x data</span>
<span class="sd">    data2 : array</span>
<span class="sd">       The y data</span>
<span class="sd">    param_dict : dict</span>
<span class="sd">       Dictionary of kwargs to pass to ax.plot</span>
<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    out : list</span>
<span class="sd">        list of artists added</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">out</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data2</span></a><span class="p">,</span> <span class="o">**</span><span class="n">param_dict</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">out</span>
<span class="c1"># which you would then use as:</span>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data2</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data3</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data4</span></a> <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="mi">4</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="View documentation for matplotlib.figure.Figure"><span class="n">fig</span></a><span class="p">,</span> <a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a> <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">1</span><span class="p">)</span>
<a href="https://docs.python.org/3/library/functions.html#object" title="View documentation for builtins.object"><span class="n">my_plotter</span></a><span class="p">(</span><a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data2</span></a><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span> <span class="s1">&#39;x&#39;</span><span class="p">})</span>
</pre></div>
<img alt="../../_images/sphx_glr_usage_005.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_005.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7f1889198850&gt;]
</pre></div>
<p>or if you wanted to have 2 sub-plots:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="View documentation for matplotlib.figure.Figure"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax1</span></a><span class="p">,</span> <a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax2</span></a><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>
<a href="https://docs.python.org/3/library/functions.html#object" title="View documentation for builtins.object"><span class="n">my_plotter</span></a><span class="p">(</span><a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data1</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data2</span></a><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span> <span class="s1">&#39;x&#39;</span><span class="p">})</span>
<a href="https://docs.python.org/3/library/functions.html#object" title="View documentation for builtins.object"><span class="n">my_plotter</span></a><span class="p">(</span><a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax2</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data3</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">data4</span></a><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span> <span class="s1">&#39;o&#39;</span><span class="p">})</span>
</pre></div>
<img alt="../../_images/sphx_glr_usage_006.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_usage_006.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7f188c2c78e0&gt;]
</pre></div>
<p>Again, for these simple examples this style seems like overkill, however
once the graphs get slightly more complex it pays off.</p>
<div class="section" id="backends">
<span id="id3"></span><h2>Backends<a class="headerlink" href="#backends" title="Permalink to this headline">¶</a></h2>
<div class="section" id="what-is-a-backend">
<span id="id4"></span><h3>What is a backend?<a class="headerlink" href="#what-is-a-backend" title="Permalink to this headline">¶</a></h3>
<p>A lot of documentation on the website and in the mailing lists refers
to the &quot;backend&quot; and many new users are confused by this term.
matplotlib targets many different use cases and output formats.  Some
people use matplotlib interactively from the python shell and have
plotting windows pop up when they type commands.  Some people run
<a class="reference external" href="https://jupyter.org">Jupyter</a> notebooks and draw inline plots for
quick data analysis. Others embed matplotlib into graphical user
interfaces like wxpython or pygtk to build rich applications.  Some
people use matplotlib in batch scripts to generate postscript images
from numerical simulations, and still others run web application
servers to dynamically serve up graphs.</p>
<p>To support all of these use cases, matplotlib can target different
outputs, and each of these capabilities is called a backend; the
&quot;frontend&quot; is the user facing code, i.e., the plotting code, whereas the
&quot;backend&quot; does all the hard work behind-the-scenes to make the figure.
There are two types of backends: user interface backends (for use in
pygtk, wxpython, tkinter, qt4, or macosx; also referred to as
&quot;interactive backends&quot;) and hardcopy backends to make image files
(PNG, SVG, PDF, PS; also referred to as &quot;non-interactive backends&quot;).</p>
<p>There are four ways to configure your backend. If they conflict each other,
the method mentioned last in the following list will be used, e.g. calling
<a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">use()</span></code></a> will override the setting in your <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code>.</p>
<ol class="arabic">
<li><p class="first">The <code class="docutils literal notranslate"><span class="pre">backend</span></code> parameter in your <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file (see
<a class="reference internal" href="customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a>):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">backend</span> <span class="p">:</span> <span class="n">WXAgg</span>   <span class="c1"># use wxpython with antigrain (agg) rendering</span>
</pre></div>
<li><p class="first">Setting the <span class="target" id="index-0"></span><a class="reference internal" href="../../faq/environment_variables_faq.html#envvar-MPLBACKEND"><code class="xref std std-envvar docutils literal notranslate"><span class="pre">MPLBACKEND</span></code></a> environment variable, either for your
current shell or for a single script.  On Unix:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&gt;</span> <span class="n">export</span> <span class="n">MPLBACKEND</span><span class="o">=</span><span class="n">module</span><span class="p">:</span><span class="o">//</span><span class="n">my_backend</span>
<span class="o">&gt;</span> <span class="n">python</span> <span class="n">simple_plot</span><span class="o">.</span><span class="n">py</span>
<span class="o">&gt;</span> <span class="n">MPLBACKEND</span><span class="o">=</span><span class="s2">&quot;module://my_backend&quot;</span> <span class="n">python</span> <span class="n">simple_plot</span><span class="o">.</span><span class="n">py</span>
</pre></div>
<p>On Windows, only the former is possible:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&gt;</span> <span class="nb">set</span> <span class="n">MPLBACKEND</span><span class="o">=</span><span class="n">module</span><span class="p">:</span><span class="o">//</span><span class="n">my_backend</span>
<span class="o">&gt;</span> <span class="n">python</span> <span class="n">simple_plot</span><span class="o">.</span><span class="n">py</span>
</pre></div>
<p>Setting this environment variable will override the <code class="docutils literal notranslate"><span class="pre">backend</span></code> parameter
in <em>any</em> <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code>, even if there is a <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code> in your
current working directory. Therefore setting <span class="target" id="index-1"></span><a class="reference internal" href="../../faq/environment_variables_faq.html#envvar-MPLBACKEND"><code class="xref std std-envvar docutils literal notranslate"><span class="pre">MPLBACKEND</span></code></a>
globally, e.g. in your <code class="docutils literal notranslate"><span class="pre">.bashrc</span></code> or <code class="docutils literal notranslate"><span class="pre">.profile</span></code>, is discouraged as it
might lead to counter-intuitive behavior.</p>
<li><p class="first">If your script depends on a specific backend you can use the
<a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">use()</span></code></a> function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>
<span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;PS&#39;</span><span class="p">)</span>   <span class="c1"># generate postscript output by default</span>
</pre></div>
<p>If you use the <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">use()</span></code></a> function, this must be done before
importing <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib.pyplot</span></code></a>. Calling <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">use()</span></code></a> after
pyplot has been imported will have no effect.  Using
<a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">use()</span></code></a> will require changes in your code if users want to
use a different backend.  Therefore, you should avoid explicitly calling
<a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal notranslate"><span class="pre">use()</span></code></a> unless absolutely necessary.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Backend name specifications are not case-sensitive; e.g., 'GTK3Agg'
and 'gtk3agg' are equivalent.</p>
<p>With a typical installation of matplotlib, such as from a
binary installer or a linux distribution package, a good default
backend will already be set, allowing both interactive work and
plotting from scripts, with output to the screen and/or to
a file, so at least initially you will not need to use any of the
methods given above.</p>
<p>If, however, you want to write graphical user interfaces, or a web
application server (<a class="reference internal" href="../../faq/howto_faq.html#howto-webapp"><span class="std std-ref">How to use Matplotlib in a web application server</span></a>), or need a better
understanding of what is going on, read on. To make things a little
more customizable for graphical user interfaces, matplotlib separates
the concept of the renderer (the thing that actually does the drawing)
from the canvas (the place where the drawing goes).  The canonical
renderer for user interfaces is <code class="docutils literal notranslate"><span class="pre">Agg</span></code> which uses the <a class="reference external" href="http://antigrain.com/">Anti-Grain
Geometry</a> C++ library to make a raster (pixel) image of the figure.
All of the user interfaces except <code class="docutils literal notranslate"><span class="pre">macosx</span></code> can be used with
agg rendering, e.g., <code class="docutils literal notranslate"><span class="pre">WXAgg</span></code>, <code class="docutils literal notranslate"><span class="pre">GTK3Agg</span></code>, <code class="docutils literal notranslate"><span class="pre">QT4Agg</span></code>, <code class="docutils literal notranslate"><span class="pre">QT5Agg</span></code>,
<code class="docutils literal notranslate"><span class="pre">TkAgg</span></code>.  In addition, some of the user interfaces support other rendering
engines.  For example, with GTK+ 3, you can also select Cairo rendering
(backend <code class="docutils literal notranslate"><span class="pre">GTK3Cairo</span></code>).</p>
<p>For the rendering engines, one can also distinguish between <a class="reference external" href="https://en.wikipedia.org/wiki/Vector_graphics">vector</a> or <a class="reference external" href="https://en.wikipedia.org/wiki/Raster_graphics">raster</a> renderers.  Vector
graphics languages issue drawing commands like &quot;draw a line from this
point to this point&quot; and hence are scale free, and raster backends
generate a pixel representation of the line whose accuracy depends on a
DPI setting.</p>
<p>Here is a summary of the matplotlib renderers (there is an eponymous
backend for each; these are <em>non-interactive backends</em>, capable of
writing to a file):</p>
<table border="1" class="docutils">
<col width="18%" />
<col width="16%" />
<col width="66%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Renderer</th>
<th class="head">Filetypes</th>
<th class="head">Description</th>
<tbody valign="top">
<tr class="row-even"><td><a class="reference internal" href="../../glossary/index.html#term-agg"><span class="xref std std-term">AGG</span></a></td>
<td><a class="reference internal" href="../../glossary/index.html#term-png"><span class="xref std std-term">png</span></a></td>
<td><a class="reference internal" href="../../glossary/index.html#term-raster-graphics"><span class="xref std std-term">raster graphics</span></a> -- high quality images
using the <a class="reference external" href="http://antigrain.com/">Anti-Grain Geometry</a> engine</td>
<tr class="row-odd"><td>PS</td>
<td><a class="reference internal" href="../../glossary/index.html#term-ps"><span class="xref std std-term">ps</span></a>
<a class="reference internal" href="../../glossary/index.html#term-eps"><span class="xref std std-term">eps</span></a></td>
<td><a class="reference internal" href="../../glossary/index.html#term-vector-graphics"><span class="xref std std-term">vector graphics</span></a> -- <a class="reference external" href="https://en.wikipedia.org/wiki/PostScript">Postscript</a> output</td>
<tr class="row-even"><td>PDF</td>
<td><a class="reference internal" href="../../glossary/index.html#term-pdf"><span class="xref std std-term">pdf</span></a></td>
<td><a class="reference internal" href="../../glossary/index.html#term-vector-graphics"><span class="xref std std-term">vector graphics</span></a> --
<a class="reference external" href="https://en.wikipedia.org/wiki/Portable_Document_Format">Portable Document Format</a></td>
<tr class="row-odd"><td>SVG</td>
<td><a class="reference internal" href="../../glossary/index.html#term-svg"><span class="xref std std-term">svg</span></a></td>
<td><a class="reference internal" href="../../glossary/index.html#term-vector-graphics"><span class="xref std std-term">vector graphics</span></a> --
<a class="reference external" href="https://en.wikipedia.org/wiki/Scalable_Vector_Graphics">Scalable Vector Graphics</a></td>
<tr class="row-even"><td><a class="reference internal" href="../../glossary/index.html#term-cairo"><span class="xref std std-term">Cairo</span></a></td>
<td><a class="reference internal" href="../../glossary/index.html#term-png"><span class="xref std std-term">png</span></a>
<a class="reference internal" href="../../glossary/index.html#term-ps"><span class="xref std std-term">ps</span></a>
<a class="reference internal" href="../../glossary/index.html#term-pdf"><span class="xref std std-term">pdf</span></a>
<a class="reference internal" href="../../glossary/index.html#term-svg"><span class="xref std std-term">svg</span></a></td>
<td><a class="reference internal" href="../../glossary/index.html#term-raster-graphics"><span class="xref std std-term">raster graphics</span></a> and
<a class="reference internal" href="../../glossary/index.html#term-vector-graphics"><span class="xref std std-term">vector graphics</span></a> -- using the
<a class="reference external" href="https://wwW.cairographics.org">Cairo graphics</a> library</td>
<p>To save plots using the non-interactive backends, use the
<code class="docutils literal notranslate"><span class="pre">matplotlib.pyplot.savefig('filename')</span></code> method.</p>
<p>And here are the user interfaces and renderer combinations supported;
these are <em>interactive backends</em>, capable of displaying to the screen
and of using appropriate renderers from the table above to write to
a file:</p>
<table border="1" class="docutils">
<col width="12%" />
<col width="88%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Backend</th>
<th class="head">Description</th>
<tbody valign="top">
<tr class="row-even"><td>Qt5Agg</td>
<td>Agg rendering in a <a class="reference internal" href="../../glossary/index.html#term-qt5"><span class="xref std std-term">Qt5</span></a> canvas (requires <a class="reference external" href="https://riverbankcomputing.com/software/pyqt/intro">PyQt5</a>).  This
backend can be activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">qt5</span></code>.</td>
<tr class="row-odd"><td>ipympl</td>
<td>Agg rendering embedded in a Jupyter widget.  (requires ipympl).
This backend can be enabled in a Jupyter notebook with
<code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">ipympl</span></code>.</td>
<tr class="row-even"><td>GTK3Agg</td>
<td>Agg rendering to a <a class="reference internal" href="../../glossary/index.html#term-gtk"><span class="xref std std-term">GTK</span></a> 3.x canvas (requires <a class="reference external" href="https://wiki.gnome.org/action/show/Projects/PyGObject">PyGObject</a>,
and <a class="reference external" href="https://www.cairographics.org/pycairo/">pycairo</a> or <a class="reference external" href="https://pythonhosted.org/cairocffi/">cairocffi</a>).  This backend can be activated in
IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">gtk3</span></code>.</td>
<tr class="row-odd"><td>macosx</td>
<td>Agg rendering into a Cocoa canvas in OSX.  This backend can be
activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">osx</span></code>.</td>
<tr class="row-even"><td>TkAgg</td>
<td>Agg rendering to a <a class="reference internal" href="../../glossary/index.html#term-tk"><span class="xref std std-term">Tk</span></a> canvas (requires <a class="reference external" href="https://wiki.python.org/moin/TkInter">TkInter</a>). This
backend can be activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">tk</span></code>.</td>
<tr class="row-odd"><td>nbAgg</td>
<td>Embed an interactive figure in a Jupyter classic notebook.  This
backend can be enabled in Jupyter notebooks via
<code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">notebook</span></code>.</td>
<tr class="row-even"><td>WebAgg</td>
<td>On <code class="docutils literal notranslate"><span class="pre">show()</span></code> will start a tornado server with an interactive
figure.</td>
<tr class="row-odd"><td>GTK3Cairo</td>
<td>Cairo rendering to a <a class="reference internal" href="../../glossary/index.html#term-gtk"><span class="xref std std-term">GTK</span></a> 3.x canvas (requires <a class="reference external" href="https://wiki.gnome.org/action/show/Projects/PyGObject">PyGObject</a>,
and <a class="reference external" href="https://www.cairographics.org/pycairo/">pycairo</a> or <a class="reference external" href="https://pythonhosted.org/cairocffi/">cairocffi</a>).</td>
<tr class="row-even"><td>Qt4Agg</td>
<td>Agg rendering to a <a class="reference internal" href="../../glossary/index.html#term-qt4"><span class="xref std std-term">Qt4</span></a> canvas (requires <a class="reference external" href="https://riverbankcomputing.com/software/pyqt/intro">PyQt4</a> or
<code class="docutils literal notranslate"><span class="pre">pyside</span></code>).  This backend can be activated in IPython with
<code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">qt4</span></code>.</td>
<tr class="row-odd"><td>WXAgg</td>
<td>Agg rendering to a <a class="reference internal" href="../../glossary/index.html#term-wxwidgets"><span class="xref std std-term">wxWidgets</span></a> canvas (requires <a class="reference external" href="https://www.wxpython.org/">wxPython</a> 4).
This backend can be activated in IPython with <code class="docutils literal notranslate"><span class="pre">%matplotlib</span> <span class="pre">wx</span></code>.</td>
<div class="section" id="ipympl">
<h3>ipympl<a class="headerlink" href="#ipympl" title="Permalink to this headline">¶</a></h3>
<p>The Jupyter widget ecosystem is moving too fast to support directly in
Matplotlib.  To install ipympl</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install ipympl
jupyter nbextension <span class="nb">enable</span> --py --sys-prefix ipympl
</pre></div>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda install ipympl -c conda-forge
</pre></div>
<p>See <a class="reference external" href="https://github.com/matplotlib/jupyter-matplotlib">jupyter-matplotlib</a>
for more details.</p>
<div class="section" id="gtk-and-cairo">
<h3>GTK and Cairo<a class="headerlink" href="#gtk-and-cairo" title="Permalink to this headline">¶</a></h3>
<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">GTK3</span></code> backends (<em>both</em> <code class="xref py py-obj docutils literal notranslate"><span class="pre">GTK3Agg</span></code> and <code class="xref py py-obj docutils literal notranslate"><span class="pre">GTK3Cairo</span></code>) depend on Cairo
(pycairo&gt;=1.11.0 or cairocffi).</p>
<div class="section" id="how-do-i-select-pyqt4-or-pyside">
<h3>How do I select PyQt4 or PySide?<a class="headerlink" href="#how-do-i-select-pyqt4-or-pyside" title="Permalink to this headline">¶</a></h3>
<p>The <code class="xref py py-obj docutils literal notranslate"><span class="pre">QT_API</span></code> environment variable can be set to either <code class="xref py py-obj docutils literal notranslate"><span class="pre">pyqt</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">pyside</span></code>
to use <code class="xref py py-obj docutils literal notranslate"><span class="pre">PyQt4</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">PySide</span></code>, respectively.</p>
<p>Since the default value for the bindings to be used is <code class="xref py py-obj docutils literal notranslate"><span class="pre">PyQt4</span></code>,
<code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib</span></code> first tries to import it, if the import fails, it tries to
import <code class="xref py py-obj docutils literal notranslate"><span class="pre">PySide</span></code>.</p>
<div class="section" id="what-is-interactive-mode">
<span id="interactive-mode"></span><h2>What is interactive mode?<a class="headerlink" href="#what-is-interactive-mode" title="Permalink to this headline">¶</a></h2>
<p>Use of an interactive backend (see <a class="reference internal" href="#what-is-a-backend"><span class="std std-ref">What is a backend?</span></a>)
permits--but does not by itself require or ensure--plotting
to the screen.  Whether and when plotting to the screen occurs,
and whether a script or shell session continues after a plot
is drawn on the screen, depends on the functions and methods
that are called, and on a state variable that determines whether
matplotlib is in &quot;interactive mode&quot;.  The default Boolean value is set
by the <code class="file docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file, and may be customized like any other
configuration parameter (see <a class="reference internal" href="customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a>).  It
may also be set via <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.interactive" title="matplotlib.interactive"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.interactive()</span></code></a>, and its
value may be queried via <a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.is_interactive" title="matplotlib.is_interactive"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.is_interactive()</span></code></a>.  Turning
interactive mode on and off in the middle of a stream of plotting
commands, whether in a script or in a shell, is rarely needed
and potentially confusing, so in the following we will assume all
plotting is done with interactive mode either on or off.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Major changes related to interactivity, and in particular the
role and behavior of <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-func docutils literal notranslate"><span class="pre">show()</span></code></a>, were made in the
transition to matplotlib version 1.0, and bugs were fixed in
1.0.1.  Here we describe the version 1.0.1 behavior for the
primary interactive backends, with the partial exception of
<em>macosx</em>.</p>
<p>Interactive mode may also be turned on via <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.ion.html#matplotlib.pyplot.ion" title="matplotlib.pyplot.ion"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.ion()</span></code></a>,
and turned off via <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.ioff.html#matplotlib.pyplot.ioff" title="matplotlib.pyplot.ioff"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.ioff()</span></code></a>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Interactive mode works with suitable backends in ipython and in
the ordinary python shell, but it does <em>not</em> work in the IDLE IDE.
If the default backend does not support interactivity, an interactive
backend can be explicitly activated using any of the methods discussed in <a class="reference internal" href="#id4">What is a backend?</a>.</p>
<div class="section" id="interactive-example">
<h3>Interactive example<a class="headerlink" href="#interactive-example" title="Permalink to this headline">¶</a></h3>
<p>From an ordinary python prompt, or after invoking ipython with no options,
try this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">([</span><span class="mf">1.6</span><span class="p">,</span> <span class="mf">2.7</span><span class="p">])</span>
</pre></div>
<p>Assuming you are running version 1.0.1 or higher, and you have
an interactive backend installed and selected by default, you should
see a plot, and your terminal prompt should also be active; you
can type additional commands such as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">&quot;interactive test&quot;</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="View documentation for matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>
</pre></div>
<p>and you will see the plot being updated after each line.  Since version 1.5,
modifying the plot by other means <em>should</em> also automatically
update the display on most backends. Get a reference to the <a class="reference internal" href="../../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal notranslate"><span class="pre">Axes</span></code></a> instance,
and call a method of that instance:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.axes.SubplotBase.html#matplotlib.axes.SubplotBase" title="View documentation for matplotlib.axes.SubplotBase"><span class="n">ax</span></a><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mf">3.1</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">])</span>
</pre></div>
<p>If you are using certain backends (like <code class="xref py py-obj docutils literal notranslate"><span class="pre">macosx</span></code>), or an older version
of matplotlib, you may not see the new line added to the plot immediately.
In this case, you need to explicitly call <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.draw.html#matplotlib.pyplot.draw" title="matplotlib.pyplot.draw"><code class="xref py py-func docutils literal notranslate"><span class="pre">draw()</span></code></a>
in order to update the plot:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span>
</pre></div>
<div class="section" id="non-interactive-example">
<h3>Non-interactive example<a class="headerlink" href="#non-interactive-example" title="Permalink to this headline">¶</a></h3>
<p>Start a fresh session as in the previous example, but now
turn interactive mode off:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">([</span><span class="mf">1.6</span><span class="p">,</span> <span class="mf">2.7</span><span class="p">])</span>
</pre></div>
<p>Nothing happened--or at least nothing has shown up on the
screen (unless you are using <em>macosx</em> backend, which is
anomalous).  To make the plot appear, you need to do this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
<p>Now you see the plot, but your terminal command line is
unresponsive; the <code class="xref py py-func docutils literal notranslate"><span class="pre">show()</span></code> command <em>blocks</em> the input
of additional commands until you manually kill the plot
window.</p>
<p>What good is this--being forced to use a blocking function?
Suppose you need a script that plots the contents of a file
to the screen.  You want to look at that plot, and then end
the script.  Without some blocking command such as show(), the
script would flash up the plot and then end immediately,
leaving nothing on the screen.</p>
<p>In addition, non-interactive mode delays all drawing until
show() is called; this is more efficient than redrawing
the plot each time a line in the script adds a new feature.</p>
<p>Prior to version 1.0, show() generally could not be called
more than once in a single script (although sometimes one
could get away with it); for version 1.0.1 and above, this
restriction is lifted, so one can write a script like this:</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="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
    <a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</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">10</span><span class="p">))</span>
    <a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
<p>which makes three plots, one at a time. I.e. the second plot will show up,
once the first plot is closed.</p>
<div class="section" id="summary">
<h3>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h3>
<p>In interactive mode, pyplot functions automatically draw
to the screen.</p>
<p>When plotting interactively, if using
object method calls in addition to pyplot functions, then
call <a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.draw.html#matplotlib.pyplot.draw" title="matplotlib.pyplot.draw"><code class="xref py py-func docutils literal notranslate"><span class="pre">draw()</span></code></a> whenever you want to
refresh the plot.</p>
<p>Use non-interactive mode in scripts in which you want to
generate one or more figures and display them before ending
or generating a new set of figures.  In that case, use
<a class="reference internal" href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-func docutils literal notranslate"><span class="pre">show()</span></code></a> to display the figure(s) and
to block execution until you have manually destroyed them.</p>
<div class="section" id="performance">
<span id="id5"></span><h2>Performance<a class="headerlink" href="#performance" title="Permalink to this headline">¶</a></h2>
<p>Whether exploring data in interactive mode or programmatically
saving lots of plots, rendering performance can be a painful
bottleneck in your pipeline. Matplotlib provides a couple
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>
<div class="section" id="line-segment-simplification">
<h3>Line segment simplification<a class="headerlink" href="#line-segment-simplification" title="Permalink to this headline">¶</a></h3>
<p>For plots that have line segments (e.g. typical line plots,
outlines of polygons, etc.), rendering performance can be
controlled by the <code class="docutils literal notranslate"><span class="pre">path.simplify</span></code> and
<code class="docutils literal notranslate"><span class="pre">path.simplify_threshold</span></code> parameters in your
<code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file (see
<a class="reference internal" href="customizing.html"><span class="doc">Customizing Matplotlib with style sheets and rcParams</span></a> for
more information about the <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code> file).
The <code class="docutils literal notranslate"><span class="pre">path.simplify</span></code> parameter is a boolean indicating whether
or not line segments are simplified at all. The
<code class="docutils literal notranslate"><span class="pre">path.simplify_threshold</span></code> parameter 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>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a> <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>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">[</span><span class="mi">50000</span><span class="p">:]</span> <span class="o">*=</span> <span class="mi">2</span>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</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">&#39;path.simplify&#39;</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">&#39;path.simplify_threshold&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><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">&#39;path.simplify_threshold&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
<p>Matplotlib currently defaults to a conservative simplification
threshold of <code class="docutils literal notranslate"><span class="pre">1/9</span></code>. If you want to change your default settings
to use a different value, you can change your <code class="docutils literal notranslate"><span class="pre">matplotlibrc</span></code>
file.  Alternatively, you could 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="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="first admonition-title">Note</p>
<p class="last">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 class="section" id="marker-simplification">
<h3>Marker simplification<a class="headerlink" href="#marker-simplification" title="Permalink to this headline">¶</a></h3>
<p>Markers can also be simplified, albeit less robustly than
line segments. Marker simplification 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-class 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-class 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-func docutils literal notranslate"><span class="pre">matplotlib.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-meth docutils literal notranslate"><span class="pre">matplotlib.axes.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><a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">,</span> <span class="n">markevery</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
<p>The markevery 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>
<div class="section" id="splitting-lines-into-smaller-chunks">
<h3>Splitting lines into smaller chunks<a class="headerlink" href="#splitting-lines-into-smaller-chunks" title="Permalink to this headline">¶</a></h3>
<p>If you are using the Agg backend (see <a class="reference internal" href="#what-is-a-backend"><span class="std std-ref">What is a backend?</span></a>),
then you can make use of the <code class="docutils literal notranslate"><span class="pre">agg.path.chunksize</span></code> rc parameter.
This allows you 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">&#39;path.simplify_threshold&#39;</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>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a> <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>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">[</span><span class="mi">50000</span><span class="p">:]</span> <span class="o">*=</span> <span class="mi">2</span>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">log10</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">&#39;path.simplify&#39;</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">&#39;agg.path.chunksize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><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">&#39;agg.path.chunksize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10000</span>
<a href="../../api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="View documentation for matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="View documentation for numpy.ndarray"><span class="n">y</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
<div class="section" id="legends">
<h3>Legends<a class="headerlink" href="#legends" title="Permalink to this headline">¶</a></h3>
<p>The default legend behavior for axes attempts to find the location
that covers the fewest data points (<code class="xref py py-obj 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>
<div class="section" id="using-the-fast-style">
<h3>Using the <em>fast</em> style<a class="headerlink" href="#using-the-fast-style" title="Permalink to this headline">¶</a></h3>
<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.
It can be used simply by running:</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">&#39;fast&#39;</span><span class="p">)</span>
</pre></div>
<p>It is very light weight, so it plays nicely with other
styles, just make 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">&#39;dark_background&#39;</span><span class="p">,</span> <span class="s1">&#39;ggplot&#39;</span><span class="p">,</span> <span class="s1">&#39;fast&#39;</span><span class="p">])</span>
</pre></div>
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