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<div class="section" id="cm-colormap">
<h1>cm (colormap)<a class="headerlink" href="#cm-colormap" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-matplotlib.cm">
<span id="matplotlib-cm"></span><h2><a class="reference internal" href="#module-matplotlib.cm" title="matplotlib.cm"><code class="xref py py-mod docutils literal"><span class="pre">matplotlib.cm</span></code></a><a class="headerlink" href="#module-matplotlib.cm" title="Permalink to this headline">¶</a></h2>
<p>This module provides a large set of colormaps, functions for
registering new colormaps and for getting a colormap by name,
and a mixin class for adding color mapping functionality.</p>
<dl class="class">
<dt id="matplotlib.cm.ScalarMappable">
<em class="property">class </em><code class="descclassname">matplotlib.cm.</code><code class="descname">ScalarMappable</code><span class="sig-paren">(</span><em>norm=None</em>, <em>cmap=None</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
<p>This is a mixin class to support scalar data to RGBA mapping.
The ScalarMappable makes use of data normalization before returning
RGBA colors from the given colormap.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>norm</strong> : <a class="reference internal" href="colors_api.html#matplotlib.colors.Normalize" title="matplotlib.colors.Normalize"><code class="xref py py-class docutils literal"><span class="pre">matplotlib.colors.Normalize</span></code></a> instance</p>
<blockquote>
<div><p>The normalizing object which scales data, typically into the
interval <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">1]</span></code>.
If <em>None</em>, <em>norm</em> defaults to a <em>colors.Normalize</em> object which
initializes its scaling based on the first data processed.</p>
</div></blockquote>
<p><strong>cmap</strong> : str or <a class="reference internal" href="colors_api.html#matplotlib.colors.Colormap" title="matplotlib.colors.Colormap"><code class="xref py py-class docutils literal"><span class="pre">Colormap</span></code></a> instance</p>
<blockquote class="last">
<div><p>The colormap used to map normalized data values to RGBA colors.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.add_checker">
<code class="descname">add_checker</code><span class="sig-paren">(</span><em>checker</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.add_checker" title="Permalink to this definition">¶</a></dt>
<dd><p>Add an entry to a dictionary of boolean flags
that are set to True when the mappable is changed.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.autoscale">
<code class="descname">autoscale</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.autoscale" title="Permalink to this definition">¶</a></dt>
<dd><p>Autoscale the scalar limits on the norm instance using the
current array</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.autoscale_None">
<code class="descname">autoscale_None</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.autoscale_None" title="Permalink to this definition">¶</a></dt>
<dd><p>Autoscale the scalar limits on the norm instance using the
current array, changing only limits that are None</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.changed">
<code class="descname">changed</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.changed" title="Permalink to this definition">¶</a></dt>
<dd><p>Call this whenever the mappable is changed to notify all the
callbackSM listeners to the ‘changed’ signal</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.check_update">
<code class="descname">check_update</code><span class="sig-paren">(</span><em>checker</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.check_update" title="Permalink to this definition">¶</a></dt>
<dd><p>If mappable has changed since the last check,
return True; else return False</p>
</dd></dl>
<dl class="attribute">
<dt id="matplotlib.cm.ScalarMappable.cmap">
<code class="descname">cmap</code><em class="property"> = None</em><a class="headerlink" href="#matplotlib.cm.ScalarMappable.cmap" title="Permalink to this definition">¶</a></dt>
<dd><p>The Colormap instance of this ScalarMappable.</p>
</dd></dl>
<dl class="attribute">
<dt id="matplotlib.cm.ScalarMappable.colorbar">
<code class="descname">colorbar</code><em class="property"> = None</em><a class="headerlink" href="#matplotlib.cm.ScalarMappable.colorbar" title="Permalink to this definition">¶</a></dt>
<dd><p>The last colorbar associated with this ScalarMappable. May be None.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.get_array">
<code class="descname">get_array</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.get_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the array</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.get_clim">
<code class="descname">get_clim</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.get_clim" title="Permalink to this definition">¶</a></dt>
<dd><p>return the min, max of the color limits for image scaling</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.get_cmap">
<code class="descname">get_cmap</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.get_cmap" title="Permalink to this definition">¶</a></dt>
<dd><p>return the colormap</p>
</dd></dl>
<dl class="attribute">
<dt id="matplotlib.cm.ScalarMappable.norm">
<code class="descname">norm</code><em class="property"> = None</em><a class="headerlink" href="#matplotlib.cm.ScalarMappable.norm" title="Permalink to this definition">¶</a></dt>
<dd><p>The Normalization instance of this ScalarMappable.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.set_array">
<code class="descname">set_array</code><span class="sig-paren">(</span><em>A</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.set_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the image array from numpy array <em>A</em></p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.set_clim">
<code class="descname">set_clim</code><span class="sig-paren">(</span><em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.set_clim" title="Permalink to this definition">¶</a></dt>
<dd><p>set the norm limits for image scaling; if <em>vmin</em> is a length2
sequence, interpret it as <code class="docutils literal"><span class="pre">(vmin,</span> <span class="pre">vmax)</span></code> which is used to
support setp</p>
<p>ACCEPTS: a length 2 sequence of floats</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.set_cmap">
<code class="descname">set_cmap</code><span class="sig-paren">(</span><em>cmap</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.set_cmap" title="Permalink to this definition">¶</a></dt>
<dd><p>set the colormap for luminance data</p>
<p>ACCEPTS: a colormap or registered colormap name</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.set_norm">
<code class="descname">set_norm</code><span class="sig-paren">(</span><em>norm</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.set_norm" title="Permalink to this definition">¶</a></dt>
<dd><p>set the normalization instance</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.cm.ScalarMappable.to_rgba">
<code class="descname">to_rgba</code><span class="sig-paren">(</span><em>x</em>, <em>alpha=None</em>, <em>bytes=False</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.ScalarMappable.to_rgba" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a normalized rgba array corresponding to <em>x</em>.</p>
<p>In the normal case, <em>x</em> is a 1-D or 2-D sequence of scalars, and
the corresponding ndarray of rgba values will be returned,
based on the norm and colormap set for this ScalarMappable.</p>
<p>There is one special case, for handling images that are already
rgb or rgba, such as might have been read from an image file.
If <em>x</em> is an ndarray with 3 dimensions,
and the last dimension is either 3 or 4, then it will be
treated as an rgb or rgba array, and no mapping will be done.
If the last dimension is 3, the <em>alpha</em> kwarg (defaulting to 1)
will be used to fill in the transparency. If the last dimension
is 4, the <em>alpha</em> kwarg is ignored; it does not
replace the pre-existing alpha. A ValueError will be raised
if the third dimension is other than 3 or 4.</p>
<p>In either case, if <em>bytes</em> is <em>False</em> (default), the rgba
array will be floats in the 0-1 range; if it is <em>True</em>,
the returned rgba array will be uint8 in the 0 to 255 range.</p>
<p>Note: this method assumes the input is well-behaved; it does
not check for anomalies such as <em>x</em> being a masked rgba
array, or being an integer type other than uint8, or being
a floating point rgba array with values outside the 0-1 range.</p>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="matplotlib.cm.get_cmap">
<code class="descclassname">matplotlib.cm.</code><code class="descname">get_cmap</code><span class="sig-paren">(</span><em>name=None</em>, <em>lut=None</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.get_cmap" title="Permalink to this definition">¶</a></dt>
<dd><p>Get a colormap instance, defaulting to rc values if <em>name</em> is None.</p>
<p>Colormaps added with <a class="reference internal" href="#matplotlib.cm.register_cmap" title="matplotlib.cm.register_cmap"><code class="xref py py-func docutils literal"><span class="pre">register_cmap()</span></code></a> take precedence over
built-in colormaps.</p>
<p>If <em>name</em> is a <a class="reference internal" href="colors_api.html#matplotlib.colors.Colormap" title="matplotlib.colors.Colormap"><code class="xref py py-class docutils literal"><span class="pre">matplotlib.colors.Colormap</span></code></a> instance, it will be
returned.</p>
<p>If <em>lut</em> is not None it must be an integer giving the number of
entries desired in the lookup table, and <em>name</em> must be a standard
mpl colormap name.</p>
</dd></dl>
<dl class="function">
<dt id="matplotlib.cm.register_cmap">
<code class="descclassname">matplotlib.cm.</code><code class="descname">register_cmap</code><span class="sig-paren">(</span><em>name=None</em>, <em>cmap=None</em>, <em>data=None</em>, <em>lut=None</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.register_cmap" title="Permalink to this definition">¶</a></dt>
<dd><p>Add a colormap to the set recognized by <a class="reference internal" href="#matplotlib.cm.get_cmap" title="matplotlib.cm.get_cmap"><code class="xref py py-func docutils literal"><span class="pre">get_cmap()</span></code></a>.</p>
<p>It can be used in two ways:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">register_cmap</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s">'swirly'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">swirly_cmap</span><span class="p">)</span>
<span class="n">register_cmap</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s">'choppy'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">choppydata</span><span class="p">,</span> <span class="n">lut</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
</pre></div>
</div>
<p>In the first case, <em>cmap</em> must be a <a class="reference internal" href="colors_api.html#matplotlib.colors.Colormap" title="matplotlib.colors.Colormap"><code class="xref py py-class docutils literal"><span class="pre">matplotlib.colors.Colormap</span></code></a>
instance. The <em>name</em> is optional; if absent, the name will
be the <code class="xref py py-attr docutils literal"><span class="pre">name</span></code> attribute of the <em>cmap</em>.</p>
<p>In the second case, the three arguments are passed to
the <a class="reference internal" href="colors_api.html#matplotlib.colors.LinearSegmentedColormap" title="matplotlib.colors.LinearSegmentedColormap"><code class="xref py py-class docutils literal"><span class="pre">LinearSegmentedColormap</span></code></a> initializer,
and the resulting colormap is registered.</p>
</dd></dl>
<dl class="function">
<dt id="matplotlib.cm.revcmap">
<code class="descclassname">matplotlib.cm.</code><code class="descname">revcmap</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.cm.revcmap" title="Permalink to this definition">¶</a></dt>
<dd><p>Can only handle specification <em>data</em> in dictionary format.</p>
</dd></dl>
</div>
</div>
</div>
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