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<h1>Source code for matplotlib.cm</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">Builtin colormaps, colormap handling utilities, and the `ScalarMappable` mixin.</span>
<span class="sd">.. seealso::</span>
<span class="sd"> :doc:`/gallery/color/colormap_reference` for a list of builtin colormaps.</span>
<span class="sd"> :doc:`/tutorials/colors/colormap-manipulation` for examples of how to</span>
<span class="sd"> make colormaps.</span>
<span class="sd"> :doc:`/tutorials/colors/colormaps` an in-depth discussion of</span>
<span class="sd"> choosing colormaps.</span>
<span class="sd"> :doc:`/tutorials/colors/colormapnorms` for more details about data</span>
<span class="sd"> normalization.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">functools</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">ma</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">colors</span>
<span class="kn">import</span> <span class="nn">matplotlib.cbook</span> <span class="k">as</span> <span class="nn">cbook</span>
<span class="kn">from</span> <span class="nn">matplotlib._cm</span> <span class="kn">import</span> <span class="n">datad</span>
<span class="kn">from</span> <span class="nn">matplotlib._cm_listed</span> <span class="kn">import</span> <span class="n">cmaps</span> <span class="k">as</span> <span class="n">cmaps_listed</span>
<span class="k">def</span> <span class="nf">_reverser</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> <span class="c1"># Deprecated, remove this at the same time as revcmap.</span>
<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="p">)</span> <span class="c1"># Toplevel helper for revcmap ensuring cmap picklability.</span>
<div class="viewcode-block" id="revcmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.revcmap">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.2"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"Colormap.reversed()"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">revcmap</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
<span class="sd">"""Can only handle specification *data* in dictionary format."""</span>
<span class="n">data_r</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
<span class="c1"># Return a partial object so that the result is picklable.</span>
<span class="n">valnew</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span><span class="n">_reverser</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Flip x and exchange the y values facing x = 0 and x = 1.</span>
<span class="n">valnew</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">x</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="n">y0</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">y1</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">val</span><span class="p">)]</span>
<span class="n">data_r</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">valnew</span>
<span class="k">return</span> <span class="n">data_r</span></div>
<span class="n">LUTSIZE</span> <span class="o">=</span> <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'image.lut'</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_gen_cmap_d</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> Generate a dict mapping standard colormap names to standard colormaps, as</span>
<span class="sd"> well as the reversed colormaps.</span>
<span class="sd"> """</span>
<span class="n">cmap_d</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">cmaps_listed</span><span class="p">}</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">spec</span> <span class="ow">in</span> <span class="n">datad</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">cmap_d</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span> <span class="c1"># Precache the cmaps at a fixed lutsize..</span>
<span class="n">colors</span><span class="o">.</span><span class="n">LinearSegmentedColormap</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">spec</span><span class="p">,</span> <span class="n">LUTSIZE</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'red'</span> <span class="ow">in</span> <span class="n">spec</span> <span class="k">else</span>
<span class="n">colors</span><span class="o">.</span><span class="n">ListedColormap</span><span class="p">(</span><span class="n">spec</span><span class="p">[</span><span class="s1">'listed'</span><span class="p">],</span> <span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'listed'</span> <span class="ow">in</span> <span class="n">spec</span> <span class="k">else</span>
<span class="n">colors</span><span class="o">.</span><span class="n">LinearSegmentedColormap</span><span class="o">.</span><span class="n">from_list</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">spec</span><span class="p">,</span> <span class="n">LUTSIZE</span><span class="p">))</span>
<span class="c1"># Generate reversed cmaps.</span>
<span class="k">for</span> <span class="n">cmap</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">cmap_d</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span>
<span class="n">rmap</span> <span class="o">=</span> <span class="n">cmap</span><span class="o">.</span><span class="n">reversed</span><span class="p">()</span>
<span class="n">cmap_d</span><span class="p">[</span><span class="n">rmap</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">rmap</span>
<span class="k">return</span> <span class="n">cmap_d</span>
<span class="n">cmap_d</span> <span class="o">=</span> <span class="n">_gen_cmap_d</span><span class="p">()</span>
<span class="nb">locals</span><span class="p">()</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">cmap_d</span><span class="p">)</span>
<span class="c1"># Continue with definitions ...</span>
<div class="viewcode-block" id="register_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.register_cmap">[docs]</a><span class="k">def</span> <span class="nf">register_cmap</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lut</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Add a colormap to the set recognized by :func:`get_cmap`.</span>
<span class="sd"> It can be used in two ways::</span>
<span class="sd"> register_cmap(name='swirly', cmap=swirly_cmap)</span>
<span class="sd"> register_cmap(name='choppy', data=choppydata, lut=128)</span>
<span class="sd"> In the first case, *cmap* must be a :class:`matplotlib.colors.Colormap`</span>
<span class="sd"> instance. The *name* is optional; if absent, the name will</span>
<span class="sd"> be the :attr:`~matplotlib.colors.Colormap.name` attribute of the *cmap*.</span>
<span class="sd"> In the second case, the three arguments are passed to</span>
<span class="sd"> the :class:`~matplotlib.colors.LinearSegmentedColormap` initializer,</span>
<span class="sd"> and the resulting colormap is registered.</span>
<span class="sd"> """</span>
<span class="n">cbook</span><span class="o">.</span><span class="n">_check_isinstance</span><span class="p">((</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">cmap</span><span class="o">.</span><span class="n">name</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Arguments must include a name or a Colormap"</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cmap</span><span class="p">,</span> <span class="n">colors</span><span class="o">.</span><span class="n">Colormap</span><span class="p">):</span>
<span class="n">cmap_d</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">cmap</span>
<span class="k">return</span>
<span class="c1"># For the remainder, let exceptions propagate.</span>
<span class="k">if</span> <span class="n">lut</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">lut</span> <span class="o">=</span> <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'image.lut'</span><span class="p">]</span>
<span class="n">cmap</span> <span class="o">=</span> <span class="n">colors</span><span class="o">.</span><span class="n">LinearSegmentedColormap</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lut</span><span class="p">)</span>
<span class="n">cmap_d</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">cmap</span></div>
<div class="viewcode-block" id="get_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.get_cmap">[docs]</a><span class="k">def</span> <span class="nf">get_cmap</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lut</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Get a colormap instance, defaulting to rc values if *name* is None.</span>
<span class="sd"> Colormaps added with :func:`register_cmap` take precedence over</span>
<span class="sd"> built-in colormaps.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : `matplotlib.colors.Colormap` or str or None, default: None</span>
<span class="sd"> If a `Colormap` instance, it will be returned. Otherwise, the name of</span>
<span class="sd"> a colormap known to Matplotlib, which will be resampled by *lut*. The</span>
<span class="sd"> default, None, means :rc:`image.cmap`.</span>
<span class="sd"> lut : int or None, default: None</span>
<span class="sd"> If *name* is not already a Colormap instance and *lut* is not None, the</span>
<span class="sd"> colormap will be resampled to have *lut* entries in the lookup table.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'image.cmap'</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">colors</span><span class="o">.</span><span class="n">Colormap</span><span class="p">):</span>
<span class="k">return</span> <span class="n">name</span>
<span class="n">cbook</span><span class="o">.</span><span class="n">_check_in_list</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">cmap_d</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="n">lut</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">cmap_d</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">cmap_d</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">_resample</span><span class="p">(</span><span class="n">lut</span><span class="p">)</span></div>
<div class="viewcode-block" id="ScalarMappable"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable">[docs]</a><span class="k">class</span> <span class="nc">ScalarMappable</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> This is a mixin class to support scalar data to RGBA mapping.</span>
<span class="sd"> The ScalarMappable makes use of data normalization before returning</span>
<span class="sd"> RGBA colors from the given colormap.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> norm : :class:`matplotlib.colors.Normalize` instance</span>
<span class="sd"> The normalizing object which scales data, typically into the</span>
<span class="sd"> interval ``[0, 1]``.</span>
<span class="sd"> If *None*, *norm* defaults to a *colors.Normalize* object which</span>
<span class="sd"> initializes its scaling based on the first data processed.</span>
<span class="sd"> cmap : str or :class:`~matplotlib.colors.Colormap` instance</span>
<span class="sd"> The colormap used to map normalized data values to RGBA colors.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">callbacksSM</span> <span class="o">=</span> <span class="n">cbook</span><span class="o">.</span><span class="n">CallbackRegistry</span><span class="p">()</span>
<span class="k">if</span> <span class="n">cmap</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">cmap</span> <span class="o">=</span> <span class="n">get_cmap</span><span class="p">()</span>
<span class="k">if</span> <span class="n">norm</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">norm</span> <span class="o">=</span> <span class="n">colors</span><span class="o">.</span><span class="n">Normalize</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_A</span> <span class="o">=</span> <span class="kc">None</span>
<span class="c1">#: The Normalization instance of this ScalarMappable.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span>
<span class="c1">#: The Colormap instance of this ScalarMappable.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cmap</span> <span class="o">=</span> <span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
<span class="c1">#: The last colorbar associated with this ScalarMappable. May be None.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">colorbar</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'array'</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
<div class="viewcode-block" id="ScalarMappable.to_rgba"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.to_rgba">[docs]</a> <span class="k">def</span> <span class="nf">to_rgba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">bytes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Return a normalized rgba array corresponding to *x*.</span>
<span class="sd"> In the normal case, *x* is a 1-D or 2-D sequence of scalars, and</span>
<span class="sd"> the corresponding ndarray of rgba values will be returned,</span>
<span class="sd"> based on the norm and colormap set for this ScalarMappable.</span>
<span class="sd"> There is one special case, for handling images that are already</span>
<span class="sd"> rgb or rgba, such as might have been read from an image file.</span>
<span class="sd"> If *x* is an ndarray with 3 dimensions,</span>
<span class="sd"> and the last dimension is either 3 or 4, then it will be</span>
<span class="sd"> treated as an rgb or rgba array, and no mapping will be done.</span>
<span class="sd"> The array can be uint8, or it can be floating point with</span>
<span class="sd"> values in the 0-1 range; otherwise a ValueError will be raised.</span>
<span class="sd"> If it is a masked array, the mask will be ignored.</span>
<span class="sd"> If the last dimension is 3, the *alpha* kwarg (defaulting to 1)</span>
<span class="sd"> will be used to fill in the transparency. If the last dimension</span>
<span class="sd"> is 4, the *alpha* kwarg is ignored; it does not</span>
<span class="sd"> replace the pre-existing alpha. A ValueError will be raised</span>
<span class="sd"> if the third dimension is other than 3 or 4.</span>
<span class="sd"> In either case, if *bytes* is *False* (default), the rgba</span>
<span class="sd"> array will be floats in the 0-1 range; if it is *True*,</span>
<span class="sd"> the returned rgba array will be uint8 in the 0 to 255 range.</span>
<span class="sd"> If norm is False, no normalization of the input data is</span>
<span class="sd"> performed, and it is assumed to be in the range (0-1).</span>
<span class="sd"> """</span>
<span class="c1"># First check for special case, image input:</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">alpha</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">:</span>
<span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="mi">255</span><span class="p">)</span>
<span class="n">m</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">xx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">xx</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span>
<span class="n">xx</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">alpha</span>
<span class="k">elif</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">xx</span> <span class="o">=</span> <span class="n">x</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"third dimension must be 3 or 4"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">xx</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">kind</span> <span class="o">==</span> <span class="s1">'f'</span><span class="p">:</span>
<span class="k">if</span> <span class="n">norm</span> <span class="ow">and</span> <span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o"><</span> <span class="mi">0</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Floating point image RGB values "</span>
<span class="s2">"must be in the 0..1 range."</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">bytes</span><span class="p">:</span>
<span class="n">xx</span> <span class="o">=</span> <span class="p">(</span><span class="n">xx</span> <span class="o">*</span> <span class="mi">255</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">xx</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">bytes</span><span class="p">:</span>
<span class="n">xx</span> <span class="o">=</span> <span class="n">xx</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">/</span> <span class="mi">255</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Image RGB array must be uint8 or "</span>
<span class="s2">"floating point; found </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">xx</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">return</span> <span class="n">xx</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="c1"># e.g., x is not an ndarray; so try mapping it</span>
<span class="k">pass</span>
<span class="c1"># This is the normal case, mapping a scalar array:</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">ma</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="n">norm</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">rgba</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cmap</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="nb">bytes</span><span class="o">=</span><span class="nb">bytes</span><span class="p">)</span>
<span class="k">return</span> <span class="n">rgba</span></div>
<div class="viewcode-block" id="ScalarMappable.set_array"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_array">[docs]</a> <span class="k">def</span> <span class="nf">set_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">):</span>
<span class="sd">"""Set the image array from numpy array *A*.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> A : ndarray</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_A</span> <span class="o">=</span> <span class="n">A</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_dict</span><span class="p">[</span><span class="s1">'array'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span></div>
<div class="viewcode-block" id="ScalarMappable.get_array"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_array">[docs]</a> <span class="k">def</span> <span class="nf">get_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s1">'Return the array'</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_A</span></div>
<div class="viewcode-block" id="ScalarMappable.get_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_cmap">[docs]</a> <span class="k">def</span> <span class="nf">get_cmap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s1">'return the colormap'</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">cmap</span></div>
<div class="viewcode-block" id="ScalarMappable.get_clim"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_clim">[docs]</a> <span class="k">def</span> <span class="nf">get_clim</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s1">'return the min, max of the color limits for image scaling'</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">vmin</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">vmax</span></div>
<div class="viewcode-block" id="ScalarMappable.set_clim"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_clim">[docs]</a> <span class="k">def</span> <span class="nf">set_clim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Set the norm limits for image scaling.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> vmin, vmax : float</span>
<span class="sd"> The limits.</span>
<span class="sd"> The limits may also be passed as a tuple (*vmin*, *vmax*) as a</span>
<span class="sd"> single positional argument.</span>
<span class="sd"> .. ACCEPTS: (vmin: float, vmax: float)</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span> <span class="o">=</span> <span class="n">vmin</span>
<span class="k">except</span> <span class="p">(</span><span class="ne">TypeError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">vmin</span> <span class="o">=</span> <span class="n">colors</span><span class="o">.</span><span class="n">_sanitize_extrema</span><span class="p">(</span><span class="n">vmin</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">vmax</span> <span class="o">=</span> <span class="n">colors</span><span class="o">.</span><span class="n">_sanitize_extrema</span><span class="p">(</span><span class="n">vmax</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">changed</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScalarMappable.get_alpha"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_alpha">[docs]</a> <span class="k">def</span> <span class="nf">get_alpha</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> alpha : float</span>
<span class="sd"> Always returns 1.</span>
<span class="sd"> """</span>
<span class="c1"># This method is intended to be overridden by Artist sub-classes</span>
<span class="k">return</span> <span class="mf">1.</span></div>
<div class="viewcode-block" id="ScalarMappable.set_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_cmap">[docs]</a> <span class="k">def</span> <span class="nf">set_cmap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cmap</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> set the colormap for luminance data</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cmap : colormap or registered colormap name</span>
<span class="sd"> """</span>
<span class="n">cmap</span> <span class="o">=</span> <span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap</span>
<span class="bp">self</span><span class="o">.</span><span class="n">changed</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScalarMappable.set_norm"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_norm">[docs]</a> <span class="k">def</span> <span class="nf">set_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norm</span><span class="p">):</span>
<span class="sd">"""Set the normalization instance.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> norm : `.Normalize`</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> If there are any colorbars using the mappable for this norm, setting</span>
<span class="sd"> the norm of the mappable will reset the norm, locator, and formatters</span>
<span class="sd"> on the colorbar to default.</span>
<span class="sd"> """</span>
<span class="n">cbook</span><span class="o">.</span><span class="n">_check_isinstance</span><span class="p">((</span><span class="n">colors</span><span class="o">.</span><span class="n">Normalize</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">)</span>
<span class="k">if</span> <span class="n">norm</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">norm</span> <span class="o">=</span> <span class="n">colors</span><span class="o">.</span><span class="n">Normalize</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span>
<span class="bp">self</span><span class="o">.</span><span class="n">changed</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScalarMappable.autoscale"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.autoscale">[docs]</a> <span class="k">def</span> <span class="nf">autoscale</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Autoscale the scalar limits on the norm instance using the</span>
<span class="sd"> current array</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_A</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'You must first set_array for mappable'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">autoscale</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_A</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">changed</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScalarMappable.autoscale_None"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.autoscale_None">[docs]</a> <span class="k">def</span> <span class="nf">autoscale_None</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Autoscale the scalar limits on the norm instance using the</span>
<span class="sd"> current array, changing only limits that are None</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_A</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'You must first set_array for mappable'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">autoscale_None</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_A</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">changed</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScalarMappable.add_checker"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.add_checker">[docs]</a> <span class="k">def</span> <span class="nf">add_checker</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">checker</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Add an entry to a dictionary of boolean flags</span>
<span class="sd"> that are set to True when the mappable is changed.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_dict</span><span class="p">[</span><span class="n">checker</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span></div>
<div class="viewcode-block" id="ScalarMappable.check_update"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.check_update">[docs]</a> <span class="k">def</span> <span class="nf">check_update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">checker</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> If mappable has changed since the last check,</span>
<span class="sd"> return True; else return False</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_dict</span><span class="p">[</span><span class="n">checker</span><span class="p">]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_dict</span><span class="p">[</span><span class="n">checker</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">return</span> <span class="kc">False</span></div>
<div class="viewcode-block" id="ScalarMappable.changed"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.changed">[docs]</a> <span class="k">def</span> <span class="nf">changed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Call this whenever the mappable is changed to notify all the</span>
<span class="sd"> callbackSM listeners to the 'changed' signal</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">callbacksSM</span><span class="o">.</span><span class="n">process</span><span class="p">(</span><span class="s1">'changed'</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_dict</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stale</span> <span class="o">=</span> <span class="kc">True</span></div></div>
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