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<li><a class="reference internal" href="#">Usage</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"><span class="pre">Figure</span></code></a></li>
<li><a class="reference internal" href="#axes"><code class="docutils literal"><span class="pre">Axes</span></code></a></li>
<li><a class="reference internal" href="#axis"><code class="docutils literal"><span class="pre">Axis</span></code></a></li>
<li><a class="reference internal" href="#artist"><code class="docutils literal"><span class="pre">Artist</span></code></a></li>
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<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>
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<li><a class="reference internal" href="#what-is-a-backend">What is a backend?</a></li>
<li><a class="reference internal" href="#how-do-i-select-pyqt4-or-pyside">How do I select PyQt4 or PySide?</a></li>
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<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>
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<div class="section" id="usage">
<span id="usage-faq"></span><h1>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">¶</a></h1>
<div class="contents topic" id="contents">
<p class="topic-title first">Contents</p>
<ul class="simple">
<li><a class="reference internal" href="#usage" id="id4">Usage</a><ul>
<li><a class="reference internal" href="#general-concepts" id="id5">General Concepts</a></li>
<li><a class="reference internal" href="#parts-of-a-figure" id="id6">Parts of a Figure</a><ul>
<li><a class="reference internal" href="#figure" id="id7"><a class="reference internal" href="../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal"><span class="pre">Figure</span></code></a></a></li>
<li><a class="reference internal" href="#axes" id="id8"><a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes"><code class="xref py py-class docutils literal"><span class="pre">Axes</span></code></a></a></li>
<li><a class="reference internal" href="#axis" id="id9"><a class="reference internal" href="../api/axis_api.html#matplotlib.axis.Axis" title="matplotlib.axis.Axis"><code class="xref py py-class docutils literal"><span class="pre">Axis</span></code></a></a></li>
<li><a class="reference internal" href="#artist" id="id10"><a class="reference internal" href="../api/artist_api.html#matplotlib.artist.Artist" title="matplotlib.artist.Artist"><code class="xref py py-class docutils literal"><span class="pre">Artist</span></code></a></a></li>
</ul>
</li>
<li><a class="reference internal" href="#types-of-inputs-to-plotting-functions" id="id11">Types of inputs to plotting functions</a></li>
<li><a class="reference internal" href="#matplotlib-pyplot-and-pylab-how-are-they-related" id="id12">Matplotlib, pyplot and pylab: how are they related?</a></li>
<li><a class="reference internal" href="#coding-styles" id="id13">Coding Styles</a></li>
<li><a class="reference internal" href="#what-is-a-backend" id="id14">What is a backend?</a></li>
<li><a class="reference internal" href="#how-do-i-select-pyqt4-or-pyside" id="id15">How do I select PyQt4 or PySide?</a></li>
<li><a class="reference internal" href="#what-is-interactive-mode" id="id16">What is interactive mode?</a><ul>
<li><a class="reference internal" href="#interactive-example" id="id17">Interactive example</a></li>
<li><a class="reference internal" href="#non-interactive-example" id="id18">Non-interactive example</a></li>
<li><a class="reference internal" href="#summary" id="id19">Summary</a></li>
</ul>
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<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"><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 “state-machine environment” which is
provided by the <a class="reference internal" href="../api/pyplot_api.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal"><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>
</div>
<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>
<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/fig_map.png" src="../_images/fig_map.png" />
<div class="section" id="figure">
<h3><a class="reference internal" href="../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal"><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 (marked as the outer red box). 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"><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"><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-python"><div class="highlight"><pre><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span> <span class="c"># an empty figure with no axes</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax_lst</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c"># a figure with a 2x2 grid of Axes</span>
</pre></div>
</div>
</div>
<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"><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 (marked as the inner blue box). 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"><span class="pre">Axes</span></code></a>
object can only be in one <a class="reference internal" href="../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-class docutils literal"><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"><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/axes_api.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim"><code class="xref py py-meth docutils literal"><span class="pre">set_xlim()</span></code></a> and
<a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim"><code class="xref py py-meth docutils literal"><span class="pre">set_ylim()</span></code></a> <code class="xref py py-class docutils literal"><span class="pre">Axes</span></code> methods). Each
<code class="xref py py-class docutils literal"><span class="pre">Axes</span></code> has a title (set via
<a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title"><code class="xref py py-meth docutils literal"><span class="pre">set_title()</span></code></a>), an x-label (set via
<a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel"><code class="xref py py-meth docutils literal"><span class="pre">set_xlabel()</span></code></a>), and a y-label set via
<a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel"><code class="xref py py-meth docutils literal"><span class="pre">set_ylabel()</span></code></a>).</p>
<p>The <code class="xref py py-class docutils literal"><span class="pre">Axes</span></code> class and it’s member functions are the primary entry
point to working with the OO interface.</p>
</div>
<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"><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 (circled in green). 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"><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"><span class="pre">Formatter</span></code></a>. The
combination of the correct <code class="xref py py-class docutils literal"><span class="pre">Locator</span></code> and <code class="xref py py-class docutils literal"><span class="pre">Formatter</span></code> gives
very fine control over the tick locations and labels.</p>
</div>
<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"><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"><span class="pre">Figure</span></code>, <code class="xref py py-class docutils literal"><span class="pre">Axes</span></code>, and <code class="xref py py-class docutils literal"><span class="pre">Axis</span></code> objects). This
includes <code class="xref py py-class docutils literal"><span class="pre">Text</span></code> objects, <code class="xref py py-class docutils literal"><span class="pre">Line2D</span></code> objects,
<code class="xref py py-class docutils literal"><span class="pre">collection</span></code> objects, <code class="xref py py-class docutils literal"><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>
</div>
<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"><span class="pre">np.array</span></code> or <code class="xref py py-obj docutils literal"><span class="pre">np.ma.masked_array</span></code> as
input. Classes that are ‘array-like’ such as <code class="xref py py-obj docutils literal"><span class="pre">pandas</span></code> data objects
and <code class="xref py py-obj docutils literal"><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"><span class="pre">np.array</span></code> objects prior to plotting.</p>
<p>For example, to covert a <code class="xref py py-obj docutils literal"><span class="pre">pandas.DataFrame</span></code></p>
<div class="highlight-python"><div class="highlight"><pre><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="s">'abcde'</span><span class="p">))</span>
<span class="n">a_asndarray</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">values</span>
</pre></div>
</div>
<p>and to covert a <code class="xref py py-obj docutils literal"><span class="pre">np.matrix</span></code></p>
<div class="highlight-python"><div class="highlight"><pre><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>
</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; <a class="reference internal" href="../api/pyplot_api.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.pyplot</span></code></a>
is a module in matplotlib; and <code class="xref py py-mod docutils literal"><span class="pre">pylab</span></code> is a module
that gets installed alongside <code class="xref py py-mod docutils literal"><span class="pre">matplotlib</span></code>.</p>
<p>Pyplot provides the state-machine interface to the underlying
object-oriented plotting library. The state-machine implicitly and
automatically creates figures and axes to achieve the desired
plot. For example:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><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>
<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">x</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">'linear'</span><span class="p">)</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">x</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">'quadratic'</span><span class="p">)</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">x</span><span class="o">**</span><span class="mi">3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">'cubic'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">'x label'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'y label'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">"Simple Plot"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</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>The first call to <code class="docutils literal"><span class="pre">plt.plot</span></code> will automatically create the necessary
figure and axes to achieve the desired plot. Subsequent calls to
<code class="docutils literal"><span class="pre">plt.plot</span></code> re-use the current axes and each add another line.
Setting the title, legend, and axis labels also automatically use the
current axes and set the title, create the legend, and label the axis
respectively.</p>
<p><code class="xref py py-mod docutils literal"><span class="pre">pylab</span></code> is a convenience module that bulk imports
<a class="reference internal" href="../api/pyplot_api.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.pyplot</span></code></a> (for plotting) and <code class="xref py py-mod docutils literal"><span class="pre">numpy</span></code>
(for mathematics and working with arrays) in a single name space.
Although many examples use <code class="xref py py-mod docutils literal"><span class="pre">pylab</span></code>, it is no longer recommended.</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>
<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"><em>The Matplotlib Developers’ Guide</em></a>.</p>
</div>
<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-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
</pre></div>
</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-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="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>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</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>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-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">my_plotter</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="n">param_dict</span><span class="p">):</span>
<span class="sd">"""</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"> """</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><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>
</pre></div>
</div>
<p>which you would then use as:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">my_plotter</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="p">{</span><span class="s">'marker'</span><span class="p">:</span><span class="s">'x'</span><span class="p">})</span>
</pre></div>
</div>
<p>or if you wanted to have 2 sub-plots:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</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="n">my_plotter</span><span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="p">{</span><span class="s">'marker'</span><span class="p">:</span><span class="s">'x'</span><span class="p">})</span>
<span class="n">my_plotter</span><span class="p">(</span><span class="n">ax2</span><span class="p">,</span> <span class="n">data3</span><span class="p">,</span> <span class="n">data4</span><span class="p">,</span> <span class="p">{</span><span class="s">'marker'</span><span class="p">:</span><span class="s">'o'</span><span class="p">})</span>
</pre></div>
</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>
<div class="section" id="what-is-a-backend">
<span id="id3"></span><h2>What is a backend?<a class="headerlink" href="#what-is-a-backend" title="Permalink to this headline">¶</a></h2>
<p>A lot of documentation on the website and in the mailing lists refers
to the “backend” 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 embed
matplotlib into graphical user interfaces like wxpython or pygtk to
build rich applications. Others use matplotlib in batch scripts to
generate postscript images from some numerical simulations, and still
others in 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
“frontend” is the user facing code, ie the plotting code, whereas the
“backend” 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
“interactive backends”) and hardcopy backends to make image files
(PNG, SVG, PDF, PS; also referred to as “non-interactive backends”).</p>
<p>There are a two primary ways to configure your backend. One is to set
the <code class="docutils literal"><span class="pre">backend</span></code> parameter in your <code class="docutils literal"><span class="pre">matplotlibrc</span></code> file (see
<a class="reference internal" href="../users/customizing.html#customizing-matplotlib"><em>Customizing matplotlib</em></a>):</p>
<div class="highlight-python"><div class="highlight"><pre>backend : WXAgg # use wxpython with antigrain (agg) rendering
</pre></div>
</div>
<p>The other is to use the matplotlib <a class="reference internal" href="../api/matplotlib_configuration_api.html#matplotlib.use" title="matplotlib.use"><code class="xref py py-func docutils literal"><span class="pre">use()</span></code></a> directive:</p>
<div class="highlight-python"><div class="highlight"><pre><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="s">'PS'</span><span class="p">)</span> <span class="c"># generate postscript output by default</span>
</pre></div>
</div>
<p>If you use the <code class="docutils literal"><span class="pre">use</span></code> directive, this must be done before importing
<a class="reference internal" href="../api/pyplot_api.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.pyplot</span></code></a> or <code class="xref py py-mod docutils literal"><span class="pre">matplotlib.pylab</span></code>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Backend name specifications are not case-sensitive; e.g., ‘GTKAgg’
and ‘gtkagg’ are equivalent.</p>
</div>
<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 either of the
two methods given above.</p>
<p>If, however, you want to write graphical user interfaces, or a web
application server (<a class="reference internal" href="howto_faq.html#howto-webapp"><em>Matplotlib in a web application server</em></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"><span class="pre">Agg</span></code> which uses the <a class="reference external" href="http://agg.sourceforge.net/antigrain.com/index.html">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"><span class="pre">macosx</span></code> can be used with
agg rendering, eg
<code class="docutils literal"><span class="pre">WXAgg</span></code>, <code class="docutils literal"><span class="pre">GTKAgg</span></code>, <code class="docutils literal"><span class="pre">QT4Agg</span></code>, <code class="docutils literal"><span class="pre">TkAgg</span></code>. In
addition, some of the user interfaces support other rendering engines.
For example, with GTK, you can also select GDK rendering (backend
<code class="docutils literal"><span class="pre">GTK</span></code>) or Cairo rendering (backend <code class="docutils literal"><span class="pre">GTKCairo</span></code>).</p>
<p>For the rendering engines, one can also distinguish between <a class="reference external" href="http://en.wikipedia.org/wiki/Vector_graphics">vector</a> or <a class="reference external" href="http://en.wikipedia.org/wiki/Raster_graphics">raster</a> renderers. Vector
graphics languages issue drawing commands like “draw a line from this
point to this point” 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
backed for each; these are <em>non-interactive backends</em>, capable of
writing to a file):</p>
<table border="1" class="docutils">
<colgroup>
<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>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><a class="reference internal" href="../glossary/index.html#term-agg"><em class="xref std std-term">AGG</em></a></td>
<td><a class="reference internal" href="../glossary/index.html#term-png"><em class="xref std std-term">png</em></a></td>
<td><a class="reference internal" href="../glossary/index.html#term-raster-graphics"><em class="xref std std-term">raster graphics</em></a> – high quality images
using the <a class="reference external" href="http://agg.sourceforge.net/antigrain.com/index.html">Anti-Grain Geometry</a> engine</td>
</tr>
<tr class="row-odd"><td>PS</td>
<td><a class="reference internal" href="../glossary/index.html#term-ps"><em class="xref std std-term">ps</em></a>
<a class="reference internal" href="../glossary/index.html#term-eps"><em class="xref std std-term">eps</em></a></td>
<td><a class="reference internal" href="../glossary/index.html#term-vector-graphics"><em class="xref std std-term">vector graphics</em></a> – <a class="reference external" href="http://en.wikipedia.org/wiki/PostScript">Postscript</a> output</td>
</tr>
<tr class="row-even"><td>PDF</td>
<td><a class="reference internal" href="../glossary/index.html#term-pdf"><em class="xref std std-term">pdf</em></a></td>
<td><a class="reference internal" href="../glossary/index.html#term-vector-graphics"><em class="xref std std-term">vector graphics</em></a> –
<a class="reference external" href="http://en.wikipedia.org/wiki/Portable_Document_Format">Portable Document Format</a></td>
</tr>
<tr class="row-odd"><td>SVG</td>
<td><a class="reference internal" href="../glossary/index.html#term-svg"><em class="xref std std-term">svg</em></a></td>
<td><a class="reference internal" href="../glossary/index.html#term-vector-graphics"><em class="xref std std-term">vector graphics</em></a> –
<a class="reference external" href="http://en.wikipedia.org/wiki/Scalable_Vector_Graphics">Scalable Vector Graphics</a></td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="../glossary/index.html#term-cairo"><em class="xref std std-term">Cairo</em></a></td>
<td><a class="reference internal" href="../glossary/index.html#term-png"><em class="xref std std-term">png</em></a>
<a class="reference internal" href="../glossary/index.html#term-ps"><em class="xref std std-term">ps</em></a>
<a class="reference internal" href="../glossary/index.html#term-pdf"><em class="xref std std-term">pdf</em></a>
<a class="reference internal" href="../glossary/index.html#term-svg"><em class="xref std std-term">svg</em></a>
...</td>
<td><a class="reference internal" href="../glossary/index.html#term-vector-graphics"><em class="xref std std-term">vector graphics</em></a> –
<a class="reference external" href="http://en.wikipedia.org/wiki/Cairo_(graphics)">Cairo graphics</a></td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="../glossary/index.html#term-gdk"><em class="xref std std-term">GDK</em></a></td>
<td><a class="reference internal" href="../glossary/index.html#term-png"><em class="xref std std-term">png</em></a>
<a class="reference internal" href="../glossary/index.html#term-jpg"><em class="xref std std-term">jpg</em></a>
<a class="reference internal" href="../glossary/index.html#term-tiff"><em class="xref std std-term">tiff</em></a>
...</td>
<td><a class="reference internal" href="../glossary/index.html#term-raster-graphics"><em class="xref std std-term">raster graphics</em></a> –
the <a class="reference external" href="http://en.wikipedia.org/wiki/GDK">Gimp Drawing Kit</a></td>
</tr>
</tbody>
</table>
<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">
<colgroup>
<col width="16%" />
<col width="84%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Backend</th>
<th class="head">Description</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>GTKAgg</td>
<td>Agg rendering to a <a class="reference internal" href="../glossary/index.html#term-gtk"><em class="xref std std-term">GTK</em></a> 2.x canvas (requires <a class="reference external" href="http://www.pygtk.org">PyGTK</a>)</td>
</tr>
<tr class="row-odd"><td>GTK3Agg</td>
<td>Agg rendering to a <a class="reference internal" href="../glossary/index.html#term-gtk"><em class="xref std std-term">GTK</em></a> 3.x canvas (requires <a class="reference external" href="https://live.gnome.org/PyGObject">PyGObject</a>)</td>
</tr>
<tr class="row-even"><td>GTK</td>
<td>GDK rendering to a <a class="reference internal" href="../glossary/index.html#term-gtk"><em class="xref std std-term">GTK</em></a> 2.x canvas (not recommended)
(requires <a class="reference external" href="http://www.pygtk.org">PyGTK</a>)</td>
</tr>
<tr class="row-odd"><td>GTKCairo</td>
<td>Cairo rendering to a <a class="reference internal" href="../glossary/index.html#term-gtk"><em class="xref std std-term">GTK</em></a> 2.x canvas (requires <a class="reference external" href="http://www.pygtk.org">PyGTK</a>
and <a class="reference external" href="http://www.cairographics.org/pycairo/">pycairo</a>)</td>
</tr>
<tr class="row-even"><td>GTK3Cairo</td>
<td>Cairo rendering to a <a class="reference internal" href="../glossary/index.html#term-gtk"><em class="xref std std-term">GTK</em></a> 3.x canvas (requires <a class="reference external" href="https://live.gnome.org/PyGObject">PyGObject</a>
and <a class="reference external" href="http://www.cairographics.org/pycairo/">pycairo</a>)</td>
</tr>
<tr class="row-odd"><td>WXAgg</td>
<td>Agg rendering to to a <a class="reference internal" href="../glossary/index.html#term-wxwidgets"><em class="xref std std-term">wxWidgets</em></a> canvas
(requires <a class="reference external" href="http://www.wxpython.org/">wxPython</a>)</td>
</tr>
<tr class="row-even"><td>WX</td>
<td>Native <a class="reference internal" href="../glossary/index.html#term-wxwidgets"><em class="xref std std-term">wxWidgets</em></a> drawing to a <a class="reference internal" href="../glossary/index.html#term-wxwidgets"><em class="xref std std-term">wxWidgets</em></a> Canvas
(not recommended) (requires <a class="reference external" href="http://www.wxpython.org/">wxPython</a>)</td>
</tr>
<tr class="row-odd"><td>TkAgg</td>
<td>Agg rendering to a <a class="reference internal" href="../glossary/index.html#term-tk"><em class="xref std std-term">Tk</em></a> canvas (requires <a class="reference external" href="http://wiki.python.org/moin/TkInter">TkInter</a>)</td>
</tr>
<tr class="row-even"><td>Qt4Agg</td>
<td>Agg rendering to a <a class="reference internal" href="../glossary/index.html#term-qt4"><em class="xref std std-term">Qt4</em></a> canvas (requires <a class="reference external" href="http://www.riverbankcomputing.co.uk/software/pyqt/intro">PyQt4</a>)</td>
</tr>
<tr class="row-odd"><td>macosx</td>
<td>Cocoa rendering in OSX windows
(presently lacks blocking show() behavior when matplotlib
is in non-interactive mode)</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="how-do-i-select-pyqt4-or-pyside">
<h2>How do I select PyQt4 or PySide?<a class="headerlink" href="#how-do-i-select-pyqt4-or-pyside" title="Permalink to this headline">¶</a></h2>
<p>You can choose either PyQt4 or PySide when using the <code class="xref py py-obj docutils literal"><span class="pre">qt4</span></code> backend by setting
the appropriate value for <code class="xref py py-obj docutils literal"><span class="pre">backend.qt4</span></code> in your <code class="file docutils literal"><span class="pre">matplotlibrc</span></code> file. The
default value is <code class="xref py py-obj docutils literal"><span class="pre">PyQt4</span></code>.</p>
<p>The setting in your <code class="file docutils literal"><span class="pre">matplotlibrc</span></code> file can be overridden by setting the
<code class="xref py py-obj docutils literal"><span class="pre">QT_API</span></code> environment variable to either <code class="xref py py-obj docutils literal"><span class="pre">pyqt</span></code> or <code class="xref py py-obj docutils literal"><span class="pre">pyside</span></code> to use <code class="xref py py-obj docutils literal"><span class="pre">PyQt4</span></code> or
<code class="xref py py-obj docutils literal"><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"><span class="pre">PyQt4</span></code>,
<code class="xref py py-mod docutils literal"><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"><span class="pre">PySide</span></code>.</p>
</div>
<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"><em>What is a backend?</em></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 “interactive mode”. The default Boolean value is set
by the <code class="file docutils literal"><span class="pre">matplotlibrc</span></code> file, and may be customized like any other
configuration parameter (see <a class="reference internal" href="../users/customizing.html#customizing-matplotlib"><em>Customizing matplotlib</em></a>). It
may also be set via <code class="xref py py-func docutils literal"><span class="pre">matplotlib.interactive()</span></code>, and its
value may be queried via <code class="xref py py-func docutils literal"><span class="pre">matplotlib.is_interactive()</span></code>. 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/pyplot_api.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-func docutils literal"><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>
</div>
<p>Interactive mode may also be turned on via <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.ion" title="matplotlib.pyplot.ion"><code class="xref py py-func docutils literal"><span class="pre">matplotlib.pyplot.ion()</span></code></a>,
and turned off via <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.ioff" title="matplotlib.pyplot.ioff"><code class="xref py py-func docutils literal"><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.</p>
</div>
<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-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">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>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><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>
</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-python"><div class="highlight"><pre><span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">"interactive test"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">"index"</span><span class="p">)</span>
</pre></div>
</div>
<p>and you will see the plot being updated after each line. This is
because you are in interactive mode <em>and</em> you are using pyplot
functions. Now try an alternative method of modifying the
plot. 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"><span class="pre">Axes</span></code></a> instance, and
call a method of that instance:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mf">3.1</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">])</span>
</pre></div>
</div>
<p>Nothing changed, because the Axes methods do not include an
automatic call to <code class="xref py py-func docutils literal"><span class="pre">draw_if_interactive()</span></code>;
that call is added by the pyplot functions. If you are using
methods, then when you want to update the plot on the screen,
you need to call <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.draw" title="matplotlib.pyplot.draw"><code class="xref py py-func docutils literal"><span class="pre">draw()</span></code></a>:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">plt</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span>
</pre></div>
</div>
<p>Now you should see the new line added to the plot.</p>
</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-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">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="n">plt</span><span class="o">.</span><span class="n">plot</span><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>
</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-python"><div class="highlight"><pre><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p>Now you see the plot, but your terminal command line is
unresponsive; the <code class="xref py py-func docutils literal"><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-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">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>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</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">10</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>which makes three plots, one at a time.</p>
</div>
<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/pyplot_api.html#matplotlib.pyplot.draw" title="matplotlib.pyplot.draw"><code class="xref py py-func docutils literal"><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/pyplot_api.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-func docutils literal"><span class="pre">show()</span></code></a> to display the figure(s) and
to block execution until you have manually destroyed them.</p>
</div>
</div>
</div>
</div>
</div>
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Last updated on Oct 25, 2014.
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