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<p class="admonition-title">Note</p>
<p><a class="reference internal" href="#sphx-glr-download-tutorials-colors-colormaps-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code</p>
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<section class="sphx-glr-example-title" id="choosing-colormaps-in-matplotlib">
<span id="sphx-glr-tutorials-colors-colormaps-py"></span><h1>Choosing Colormaps in Matplotlib<a class="headerlink" href="#choosing-colormaps-in-matplotlib" title="Link to this heading">#</a></h1>
<p>Matplotlib has a number of built-in colormaps accessible via
<a class="reference internal" href="../../api/matplotlib_configuration_api.html#matplotlib.colormaps" title="matplotlib.colormaps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.colormaps</span></code></a>. There are also external libraries that
have many extra colormaps, which can be viewed in the
<a class="reference external" href="https://matplotlib.org/mpl-third-party/#colormaps-and-styles">Third-party colormaps</a> section of the Matplotlib documentation.
Here we briefly discuss how to choose between the many options. For
help on creating your own colormaps, see
<a class="reference internal" href="colormap-manipulation.html"><span class="doc">Creating Colormaps in Matplotlib</span></a>.</p>
<section id="overview">
<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
<p>The idea behind choosing a good colormap is to find a good representation in 3D
colorspace for your data set. The best colormap for any given data set depends
on many things including:</p>
<ul class="simple">
<li><p>Whether representing form or metric data (<a class="reference internal" href="#ware" id="id1"><span>[Ware]</span></a>)</p></li>
<li><p>Your knowledge of the data set (<em>e.g.</em>, is there a critical value
from which the other values deviate?)</p></li>
<li><p>If there is an intuitive color scheme for the parameter you are plotting</p></li>
<li><p>If there is a standard in the field the audience may be expecting</p></li>
</ul>
<p>For many applications, a perceptually uniform colormap is the best choice;
i.e. a colormap in which equal steps in data are perceived as equal
steps in the color space. Researchers have found that the human brain
perceives changes in the lightness parameter as changes in the data
much better than, for example, changes in hue. Therefore, colormaps
which have monotonically increasing lightness through the colormap
will be better interpreted by the viewer. Wonderful examples of
perceptually uniform colormaps can be found in the
<a class="reference external" href="https://matplotlib.org/mpl-third-party/#colormaps-and-styles">Third-party colormaps</a> section as well.</p>
<p>Color can be represented in 3D space in various ways. One way to represent color
is using CIELAB. In CIELAB, color space is represented by lightness,
<span class="math notranslate nohighlight">\(L^*\)</span>; red-green, <span class="math notranslate nohighlight">\(a^*\)</span>; and yellow-blue, <span class="math notranslate nohighlight">\(b^*\)</span>. The lightness
parameter <span class="math notranslate nohighlight">\(L^*\)</span> can then be used to learn more about how the matplotlib
colormaps will be perceived by viewers.</p>
<p>An excellent starting resource for learning about human perception of colormaps
is from <a class="reference internal" href="#ibm" id="id2"><span>[IBM]</span></a>.</p>
</section>
<section id="classes-of-colormaps">
<span id="color-colormaps-reference"></span><h2>Classes of colormaps<a class="headerlink" href="#classes-of-colormaps" title="Link to this heading">#</a></h2>
<p>Colormaps are often split into several categories based on their function (see,
<em>e.g.</em>, <a class="reference internal" href="#moreland" id="id3"><span>[Moreland]</span></a>):</p>
<ol class="arabic simple">
<li><p>Sequential: change in lightness and often saturation of color
incrementally, often using a single hue; should be used for
representing information that has ordering.</p></li>
<li><p>Diverging: change in lightness and possibly saturation of two
different colors that meet in the middle at an unsaturated color;
should be used when the information being plotted has a critical
middle value, such as topography or when the data deviates around
zero.</p></li>
<li><p>Cyclic: change in lightness of two different colors that meet in
the middle and beginning/end at an unsaturated color; should be
used for values that wrap around at the endpoints, such as phase
angle, wind direction, or time of day.</p></li>
<li><p>Qualitative: often are miscellaneous colors; should be used to
represent information which does not have ordering or
relationships.</p></li>
</ol>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">colorspacious</span> <span class="kn">import</span> <span class="n">cspace_converter</span>
</pre></div>
</div>
<p>First, we'll show the range of each colormap. Note that some seem
to change more "quickly" than others.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmaps</span></a> <span class="o">=</span> <span class="p">{}</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">256</span><span class="p">)</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.vstack.html#numpy.vstack" title="numpy.vstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">vstack</span></a><span class="p">((</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a><span class="p">))</span>
<span class="k">def</span> <span class="nf">plot_color_gradients</span><span class="p">(</span><span class="n">category</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">):</span>
<span class="c1"># Create figure and adjust figure height to number of colormaps</span>
<span class="n">nrows</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">)</span>
<span class="n">figh</span> <span class="o">=</span> <span class="mf">0.35</span> <span class="o">+</span> <span class="mf">0.15</span> <span class="o">+</span> <span class="p">(</span><span class="n">nrows</span> <span class="o">+</span> <span class="p">(</span><span class="n">nrows</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.1</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.22</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">nrows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">6.4</span><span class="p">,</span> <span class="n">figh</span><span class="p">))</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure.subplots_adjust" title="matplotlib.figure.Figure.subplots_adjust" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">1</span> <span class="o">-</span> <span class="mf">0.35</span> <span class="o">/</span> <span class="n">figh</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="mf">0.15</span> <span class="o">/</span> <span class="n">figh</span><span class="p">,</span>
<span class="n">left</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mf">0.99</span><span class="p">)</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">category</span><span class="si">}</span><span class="s1"> colormaps'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="k">for</span> <a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">):</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow" title="matplotlib.axes.Axes.imshow" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap</span></a><span class="o">=</span><a href="../../api/cm_api.html#matplotlib.cm.ColormapRegistry" title="matplotlib.cm.ColormapRegistry" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mpl</span><span class="o">.</span><span class="n">colormaps</span></a><span class="p">[</span><span class="n">name</span><span class="p">])</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text" title="matplotlib.axes.Axes.text" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">text</span></a><span class="p">(</span><span class="o">-</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'right'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">transform</span><span class="o">=</span><a href="../../api/transformations.html#matplotlib.transforms.BboxTransformTo" title="matplotlib.transforms.BboxTransformTo" class="sphx-glr-backref-module-matplotlib-transforms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span></a><span class="p">)</span>
<span class="c1"># Turn off *all* ticks & spines, not just the ones with colormaps.</span>
<span class="k">for</span> <a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a><span class="p">:</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_axis_off.html#matplotlib.axes.Axes.set_axis_off" title="matplotlib.axes.Axes.set_axis_off" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span></a><span class="p">()</span>
<span class="c1"># Save colormap list for later.</span>
<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmaps</span></a><span class="p">[</span><span class="n">category</span><span class="p">]</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a>
</pre></div>
</div>
<section id="sequential">
<h3>Sequential<a class="headerlink" href="#sequential" title="Link to this heading">#</a></h3>
<p>For the Sequential plots, the lightness value increases monotonically through
the colormaps. This is good. Some of the <span class="math notranslate nohighlight">\(L^*\)</span> values in the colormaps
span from 0 to 100 (binary and the other grayscale), and others start around
<span class="math notranslate nohighlight">\(L^*=20\)</span>. Those that have a smaller range of <span class="math notranslate nohighlight">\(L^*\)</span> will accordingly
have a smaller perceptual range. Note also that the <span class="math notranslate nohighlight">\(L^*\)</span> function varies
amongst the colormaps: some are approximately linear in <span class="math notranslate nohighlight">\(L^*\)</span> and others
are more curved.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plot_color_gradients</span><span class="p">(</span><span class="s1">'Perceptually Uniform Sequential'</span><span class="p">,</span>
<span class="p">[</span><span class="s1">'viridis'</span><span class="p">,</span> <span class="s1">'plasma'</span><span class="p">,</span> <span class="s1">'inferno'</span><span class="p">,</span> <span class="s1">'magma'</span><span class="p">,</span> <span class="s1">'cividis'</span><span class="p">])</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_colormaps_001.png" srcset="../../_images/sphx_glr_colormaps_001.png, ../../_images/sphx_glr_colormaps_001_2_00x.png 2.00x" alt="Perceptually Uniform Sequential colormaps" class = "sphx-glr-single-img"/><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plot_color_gradients</span><span class="p">(</span><span class="s1">'Sequential'</span><span class="p">,</span>
<span class="p">[</span><span class="s1">'Greys'</span><span class="p">,</span> <span class="s1">'Purples'</span><span class="p">,</span> <span class="s1">'Blues'</span><span class="p">,</span> <span class="s1">'Greens'</span><span class="p">,</span> <span class="s1">'Oranges'</span><span class="p">,</span> <span class="s1">'Reds'</span><span class="p">,</span>
<span class="s1">'YlOrBr'</span><span class="p">,</span> <span class="s1">'YlOrRd'</span><span class="p">,</span> <span class="s1">'OrRd'</span><span class="p">,</span> <span class="s1">'PuRd'</span><span class="p">,</span> <span class="s1">'RdPu'</span><span class="p">,</span> <span class="s1">'BuPu'</span><span class="p">,</span>
<span class="s1">'GnBu'</span><span class="p">,</span> <span class="s1">'PuBu'</span><span class="p">,</span> <span class="s1">'YlGnBu'</span><span class="p">,</span> <span class="s1">'PuBuGn'</span><span class="p">,</span> <span class="s1">'BuGn'</span><span class="p">,</span> <span class="s1">'YlGn'</span><span class="p">])</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_colormaps_002.png" srcset="../../_images/sphx_glr_colormaps_002.png, ../../_images/sphx_glr_colormaps_002_2_00x.png 2.00x" alt="Sequential colormaps" class = "sphx-glr-single-img"/></section>
<section id="sequential2">
<h3>Sequential2<a class="headerlink" href="#sequential2" title="Link to this heading">#</a></h3>
<p>Many of the <span class="math notranslate nohighlight">\(L^*\)</span> values from the Sequential2 plots are monotonically
increasing, but some (autumn, cool, spring, and winter) plateau or even go both
up and down in <span class="math notranslate nohighlight">\(L^*\)</span> space. Others (afmhot, copper, gist_heat, and hot)
have kinks in the <span class="math notranslate nohighlight">\(L^*\)</span> functions. Data that is being represented in a
region of the colormap that is at a plateau or kink will lead to a perception of
banding of the data in those values in the colormap (see <a class="reference internal" href="#mycarta-banding" id="id4"><span>[mycarta-banding]</span></a> for
an excellent example of this).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plot_color_gradients</span><span class="p">(</span><span class="s1">'Sequential (2)'</span><span class="p">,</span>
<span class="p">[</span><span class="s1">'binary'</span><span class="p">,</span> <span class="s1">'gist_yarg'</span><span class="p">,</span> <span class="s1">'gist_gray'</span><span class="p">,</span> <span class="s1">'gray'</span><span class="p">,</span> <span class="s1">'bone'</span><span class="p">,</span>
<span class="s1">'pink'</span><span class="p">,</span> <span class="s1">'spring'</span><span class="p">,</span> <span class="s1">'summer'</span><span class="p">,</span> <span class="s1">'autumn'</span><span class="p">,</span> <span class="s1">'winter'</span><span class="p">,</span> <span class="s1">'cool'</span><span class="p">,</span>
<span class="s1">'Wistia'</span><span class="p">,</span> <span class="s1">'hot'</span><span class="p">,</span> <span class="s1">'afmhot'</span><span class="p">,</span> <span class="s1">'gist_heat'</span><span class="p">,</span> <span class="s1">'copper'</span><span class="p">])</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_colormaps_003.png" srcset="../../_images/sphx_glr_colormaps_003.png, ../../_images/sphx_glr_colormaps_003_2_00x.png 2.00x" alt="Sequential (2) colormaps" class = "sphx-glr-single-img"/></section>
<section id="diverging">
<h3>Diverging<a class="headerlink" href="#diverging" title="Link to this heading">#</a></h3>
<p>For the Diverging maps, we want to have monotonically increasing <span class="math notranslate nohighlight">\(L^*\)</span>
values up to a maximum, which should be close to <span class="math notranslate nohighlight">\(L^*=100\)</span>, followed by
monotonically decreasing <span class="math notranslate nohighlight">\(L^*\)</span> values. We are looking for approximately
equal minimum <span class="math notranslate nohighlight">\(L^*\)</span> values at opposite ends of the colormap. By these
measures, BrBG and RdBu are good options. coolwarm is a good option, but it
doesn't span a wide range of <span class="math notranslate nohighlight">\(L^*\)</span> values (see grayscale section below).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plot_color_gradients</span><span class="p">(</span><span class="s1">'Diverging'</span><span class="p">,</span>
<span class="p">[</span><span class="s1">'PiYG'</span><span class="p">,</span> <span class="s1">'PRGn'</span><span class="p">,</span> <span class="s1">'BrBG'</span><span class="p">,</span> <span class="s1">'PuOr'</span><span class="p">,</span> <span class="s1">'RdGy'</span><span class="p">,</span> <span class="s1">'RdBu'</span><span class="p">,</span> <span class="s1">'RdYlBu'</span><span class="p">,</span>
<span class="s1">'RdYlGn'</span><span class="p">,</span> <span class="s1">'Spectral'</span><span class="p">,</span> <span class="s1">'coolwarm'</span><span class="p">,</span> <span class="s1">'bwr'</span><span class="p">,</span> <span class="s1">'seismic'</span><span class="p">])</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_colormaps_004.png" srcset="../../_images/sphx_glr_colormaps_004.png, ../../_images/sphx_glr_colormaps_004_2_00x.png 2.00x" alt="Diverging colormaps" class = "sphx-glr-single-img"/></section>
<section id="cyclic">
<h3>Cyclic<a class="headerlink" href="#cyclic" title="Link to this heading">#</a></h3>
<p>For Cyclic maps, we want to start and end on the same color, and meet a
symmetric center point in the middle. <span class="math notranslate nohighlight">\(L^*\)</span> should change monotonically
from start to middle, and inversely from middle to end. It should be symmetric
on the increasing and decreasing side, and only differ in hue. At the ends and
middle, <span class="math notranslate nohighlight">\(L^*\)</span> will reverse direction, which should be smoothed in
<span class="math notranslate nohighlight">\(L^*\)</span> space to reduce artifacts. See <a class="reference internal" href="#kovesi-colormaps" id="id5"><span>[kovesi-colormaps]</span></a> for more
information on the design of cyclic maps.</p>
<p>The often-used HSV colormap is included in this set of colormaps, although it
is not symmetric to a center point. Additionally, the <span class="math notranslate nohighlight">\(L^*\)</span> values vary
widely throughout the colormap, making it a poor choice for representing data
for viewers to see perceptually. See an extension on this idea at
<a class="reference internal" href="#mycarta-jet" id="id6"><span>[mycarta-jet]</span></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plot_color_gradients</span><span class="p">(</span><span class="s1">'Cyclic'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'twilight'</span><span class="p">,</span> <span class="s1">'twilight_shifted'</span><span class="p">,</span> <span class="s1">'hsv'</span><span class="p">])</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_colormaps_005.png" srcset="../../_images/sphx_glr_colormaps_005.png, ../../_images/sphx_glr_colormaps_005_2_00x.png 2.00x" alt="Cyclic colormaps" class = "sphx-glr-single-img"/></section>
<section id="qualitative">
<h3>Qualitative<a class="headerlink" href="#qualitative" title="Link to this heading">#</a></h3>
<p>Qualitative colormaps are not aimed at being perceptual maps, but looking at the
lightness parameter can verify that for us. The <span class="math notranslate nohighlight">\(L^*\)</span> values move all over
the place throughout the colormap, and are clearly not monotonically increasing.
These would not be good options for use as perceptual colormaps.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plot_color_gradients</span><span class="p">(</span><span class="s1">'Qualitative'</span><span class="p">,</span>
<span class="p">[</span><span class="s1">'Pastel1'</span><span class="p">,</span> <span class="s1">'Pastel2'</span><span class="p">,</span> <span class="s1">'Paired'</span><span class="p">,</span> <span class="s1">'Accent'</span><span class="p">,</span> <span class="s1">'Dark2'</span><span class="p">,</span>
<span class="s1">'Set1'</span><span class="p">,</span> <span class="s1">'Set2'</span><span class="p">,</span> <span class="s1">'Set3'</span><span class="p">,</span> <span class="s1">'tab10'</span><span class="p">,</span> <span class="s1">'tab20'</span><span class="p">,</span> <span class="s1">'tab20b'</span><span class="p">,</span>
<span class="s1">'tab20c'</span><span class="p">])</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_colormaps_006.png" srcset="../../_images/sphx_glr_colormaps_006.png, ../../_images/sphx_glr_colormaps_006_2_00x.png 2.00x" alt="Qualitative colormaps" class = "sphx-glr-single-img"/></section>
<section id="miscellaneous">
<h3>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Link to this heading">#</a></h3>
<p>Some of the miscellaneous colormaps have particular uses for which
they have been created. For example, gist_earth, ocean, and terrain
all seem to be created for plotting topography (green/brown) and water
depths (blue) together. We would expect to see a divergence in these
colormaps, then, but multiple kinks may not be ideal, such as in
gist_earth and terrain. CMRmap was created to convert well to
grayscale, though it does appear to have some small kinks in
<span class="math notranslate nohighlight">\(L^*\)</span>. cubehelix was created to vary smoothly in both lightness
and hue, but appears to have a small hump in the green hue area. turbo
was created to display depth and disparity data.</p>
<p>The often-used jet colormap is included in this set of colormaps. We can see
that the <span class="math notranslate nohighlight">\(L^*\)</span> values vary widely throughout the colormap, making it a
poor choice for representing data for viewers to see perceptually. See an
extension on this idea at <a class="reference internal" href="#mycarta-jet" id="id7"><span>[mycarta-jet]</span></a> and <a class="reference internal" href="#turbo" id="id8"><span>[turbo]</span></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plot_color_gradients</span><span class="p">(</span><span class="s1">'Miscellaneous'</span><span class="p">,</span>
<span class="p">[</span><span class="s1">'flag'</span><span class="p">,</span> <span class="s1">'prism'</span><span class="p">,</span> <span class="s1">'ocean'</span><span class="p">,</span> <span class="s1">'gist_earth'</span><span class="p">,</span> <span class="s1">'terrain'</span><span class="p">,</span>
<span class="s1">'gist_stern'</span><span class="p">,</span> <span class="s1">'gnuplot'</span><span class="p">,</span> <span class="s1">'gnuplot2'</span><span class="p">,</span> <span class="s1">'CMRmap'</span><span class="p">,</span>
<span class="s1">'cubehelix'</span><span class="p">,</span> <span class="s1">'brg'</span><span class="p">,</span> <span class="s1">'gist_rainbow'</span><span class="p">,</span> <span class="s1">'rainbow'</span><span class="p">,</span> <span class="s1">'jet'</span><span class="p">,</span>
<span class="s1">'turbo'</span><span class="p">,</span> <span class="s1">'nipy_spectral'</span><span class="p">,</span> <span class="s1">'gist_ncar'</span><span class="p">])</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_colormaps_007.png" srcset="../../_images/sphx_glr_colormaps_007.png, ../../_images/sphx_glr_colormaps_007_2_00x.png 2.00x" alt="Miscellaneous colormaps" class = "sphx-glr-single-img"/></section>
</section>
<section id="lightness-of-matplotlib-colormaps">
<h2>Lightness of Matplotlib colormaps<a class="headerlink" href="#lightness-of-matplotlib-colormaps" title="Link to this heading">#</a></h2>
<p>Here we examine the lightness values of the matplotlib colormaps.
Note that some documentation on the colormaps is available
(<a class="reference internal" href="#list-colormaps" id="id9"><span>[list-colormaps]</span></a>).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict.update" title="builtins.dict.update" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-method"><span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span></a><span class="p">({</span><span class="s1">'font.size'</span><span class="p">:</span> <span class="mi">12</span><span class="p">})</span>
<span class="c1"># Number of colormap per subplot for particular cmap categories</span>
<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_DSUBS</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">'Perceptually Uniform Sequential'</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s1">'Sequential'</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span>
<span class="s1">'Sequential (2)'</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span> <span class="s1">'Diverging'</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span> <span class="s1">'Cyclic'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s1">'Qualitative'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">'Miscellaneous'</span><span class="p">:</span> <span class="mi">6</span><span class="p">}</span>
<span class="c1"># Spacing between the colormaps of a subplot</span>
<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_DC</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">'Perceptually Uniform Sequential'</span><span class="p">:</span> <span class="mf">1.4</span><span class="p">,</span> <span class="s1">'Sequential'</span><span class="p">:</span> <span class="mf">0.7</span><span class="p">,</span>
<span class="s1">'Sequential (2)'</span><span class="p">:</span> <span class="mf">1.4</span><span class="p">,</span> <span class="s1">'Diverging'</span><span class="p">:</span> <span class="mf">1.4</span><span class="p">,</span> <span class="s1">'Cyclic'</span><span class="p">:</span> <span class="mf">1.4</span><span class="p">,</span>
<span class="s1">'Qualitative'</span><span class="p">:</span> <span class="mf">1.4</span><span class="p">,</span> <span class="s1">'Miscellaneous'</span><span class="p">:</span> <span class="mf">1.4</span><span class="p">}</span>
<span class="c1"># Indices to step through colormap</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="c1"># Do plot</span>
<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmaps</span></a><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="c1"># Do subplots so that colormaps have enough space.</span>
<span class="c1"># Default is 6 colormaps per subplot.</span>
<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dsub</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_DSUBS</span></a><span class="o">.</span><span class="n">get</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">nsubplots</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">ceil</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">)</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dsub</span></a><span class="p">))</span>
<span class="c1"># squeeze=False to handle similarly the case of a single subplot</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">nsubplots</span></a><span class="p">,</span> <span class="n">squeeze</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mf">2.6</span><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">nsubplots</span></a><span class="p">))</span>
<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">,</span> <a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.flatiter.html#numpy.flatiter" title="numpy.flatiter" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span><span class="o">.</span><span class="n">flat</span></a><span class="p">):</span>
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">locs</span></a> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># locations for text labels</span>
<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">j</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap</span></a> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dsub</span></a><span class="p">:(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dsub</span></a><span class="p">]):</span>
<span class="c1"># Get RGB values for colormap and convert the colormap in</span>
<span class="c1"># CAM02-UCS colorspace. lab[0, :, 0] is the lightness.</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rgb</span></a> <span class="o">=</span> <a href="../../api/cm_api.html#matplotlib.cm.ColormapRegistry" title="matplotlib.cm.ColormapRegistry" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mpl</span><span class="o">.</span><span class="n">colormaps</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap</span></a><span class="p">](</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">)[</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.newaxis" title="numpy.newaxis" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span></a><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lab</span></a> <span class="o">=</span> <span class="n">cspace_converter</span><span class="p">(</span><span class="s2">"sRGB1"</span><span class="p">,</span> <span class="s2">"CAM02-UCS"</span><span class="p">)(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rgb</span></a><span class="p">)</span>
<span class="c1"># Plot colormap L values. Do separately for each category</span>
<span class="c1"># so each plot can be pretty. To make scatter markers change</span>
<span class="c1"># color along plot:</span>
<span class="c1"># https://stackoverflow.com/q/8202605/</span>
<span class="k">if</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a> <span class="o">==</span> <span class="s1">'Sequential'</span><span class="p">:</span>
<span class="c1"># These colormaps all start at high lightness, but we want them</span>
<span class="c1"># reversed to look nice in the plot, so reverse the order.</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lab</span></a><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">::</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">c_</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lab</span></a><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">c_</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a>
<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dc</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_DC</span></a><span class="o">.</span><span class="n">get</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a><span class="p">,</span> <span class="mf">1.4</span><span class="p">)</span> <span class="c1"># cmaps horizontal spacing</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">j</span></a><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dc</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_</span></a><span class="p">,</span> <span class="n">c</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">c_</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap</span></a><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
<span class="c1"># Store locations for colormap labels</span>
<span class="k">if</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a> <span class="ow">in</span> <span class="p">(</span><span class="s1">'Perceptually Uniform Sequential'</span><span class="p">,</span>
<span class="s1">'Sequential'</span><span class="p">):</span>
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">locs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">j</span></a><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dc</span></a><span class="p">)</span>
<span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a> <span class="ow">in</span> <span class="p">(</span><span class="s1">'Diverging'</span><span class="p">,</span> <span class="s1">'Qualitative'</span><span class="p">,</span> <span class="s1">'Cyclic'</span><span class="p">,</span>
<span class="s1">'Miscellaneous'</span><span class="p">,</span> <span class="s1">'Sequential (2)'</span><span class="p">):</span>
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">locs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">[</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">size</span></a><span class="o">/</span><span class="mf">2.</span><span class="p">)]</span> <span class="o">+</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">j</span></a><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dc</span></a><span class="p">)</span>
<span class="c1"># Set up the axis limits:</span>
<span class="c1"># * the 1st subplot is used as a reference for the x-axis limits</span>
<span class="c1"># * lightness values goes from 0 to 100 (y-axis limits)</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">())</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">100.0</span><span class="p">)</span>
<span class="c1"># Set up labels for colormaps</span>
<a href="../../api/_as_gen/matplotlib.axis.XAxis.set_ticks_position.html#matplotlib.axis.XAxis.set_ticks_position" title="matplotlib.axis.XAxis.set_ticks_position" class="sphx-glr-backref-module-matplotlib-axis sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span></a><span class="p">(</span><span class="s1">'top'</span><span class="p">)</span>
<a href="../../api/ticker_api.html#matplotlib.ticker.FixedLocator" title="matplotlib.ticker.FixedLocator" class="sphx-glr-backref-module-matplotlib-ticker sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ticker</span></a> <span class="o">=</span> <a href="../../api/ticker_api.html#matplotlib.ticker.FixedLocator" title="matplotlib.ticker.FixedLocator" class="sphx-glr-backref-module-matplotlib-ticker sphx-glr-backref-type-py-class"><span class="n">mpl</span><span class="o">.</span><span class="n">ticker</span><span class="o">.</span><span class="n">FixedLocator</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">locs</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.axis.Axis.set_major_locator.html#matplotlib.axis.Axis.set_major_locator" title="matplotlib.axis.Axis.set_major_locator" class="sphx-glr-backref-module-matplotlib-axis sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span></a><span class="p">(</span><a href="../../api/ticker_api.html#matplotlib.ticker.FixedLocator" title="matplotlib.ticker.FixedLocator" class="sphx-glr-backref-module-matplotlib-ticker sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ticker</span></a><span class="p">)</span>
<a href="../../api/ticker_api.html#matplotlib.ticker.FixedFormatter" title="matplotlib.ticker.FixedFormatter" class="sphx-glr-backref-module-matplotlib-ticker sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">formatter</span></a> <span class="o">=</span> <a href="../../api/ticker_api.html#matplotlib.ticker.FixedFormatter" title="matplotlib.ticker.FixedFormatter" class="sphx-glr-backref-module-matplotlib-ticker sphx-glr-backref-type-py-class"><span class="n">mpl</span><span class="o">.</span><span class="n">ticker</span><span class="o">.</span><span class="n">FixedFormatter</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dsub</span></a><span class="p">:(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dsub</span></a><span class="p">])</span>
<a href="../../api/_as_gen/matplotlib.axis.Axis.set_major_formatter.html#matplotlib.axis.Axis.set_major_formatter" title="matplotlib.axis.Axis.set_major_formatter" class="sphx-glr-backref-module-matplotlib-axis sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span></a><span class="p">(</span><a href="../../api/ticker_api.html#matplotlib.ticker.FixedFormatter" title="matplotlib.ticker.FixedFormatter" class="sphx-glr-backref-module-matplotlib-ticker sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">formatter</span></a><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.axis.Axis.set_tick_params.html#matplotlib.axis.Axis.set_tick_params" title="matplotlib.axis.Axis.set_tick_params" class="sphx-glr-backref-module-matplotlib-axis sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_tick_params</span></a><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">'Lightness $L^*$'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a> <span class="o">+</span> <span class="s1">' colormaps'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<ul class="sphx-glr-horizontal">
<li><img src="../../_images/sphx_glr_colormaps_008.png" srcset="../../_images/sphx_glr_colormaps_008.png, ../../_images/sphx_glr_colormaps_008_2_00x.png 2.00x" alt="colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_009.png" srcset="../../_images/sphx_glr_colormaps_009.png, ../../_images/sphx_glr_colormaps_009_2_00x.png 2.00x" alt="colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_010.png" srcset="../../_images/sphx_glr_colormaps_010.png, ../../_images/sphx_glr_colormaps_010_2_00x.png 2.00x" alt="colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_011.png" srcset="../../_images/sphx_glr_colormaps_011.png, ../../_images/sphx_glr_colormaps_011_2_00x.png 2.00x" alt="colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_012.png" srcset="../../_images/sphx_glr_colormaps_012.png, ../../_images/sphx_glr_colormaps_012_2_00x.png 2.00x" alt="colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_013.png" srcset="../../_images/sphx_glr_colormaps_013.png, ../../_images/sphx_glr_colormaps_013_2_00x.png 2.00x" alt="colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_014.png" srcset="../../_images/sphx_glr_colormaps_014.png, ../../_images/sphx_glr_colormaps_014_2_00x.png 2.00x" alt="colormaps" class = "sphx-glr-multi-img"/></li>
</ul>
</section>
<section id="grayscale-conversion">
<h2>Grayscale conversion<a class="headerlink" href="#grayscale-conversion" title="Link to this heading">#</a></h2>
<p>It is important to pay attention to conversion to grayscale for color
plots, since they may be printed on black and white printers. If not
carefully considered, your readers may end up with indecipherable
plots because the grayscale changes unpredictably through the
colormap.</p>
<p>Conversion to grayscale is done in many different ways <a class="reference internal" href="#bw" id="id10"><span>[bw]</span></a>. Some of the
better ones use a linear combination of the rgb values of a pixel, but
weighted according to how we perceive color intensity. A nonlinear method of
conversion to grayscale is to use the <span class="math notranslate nohighlight">\(L^*\)</span> values of the pixels. In
general, similar principles apply for this question as they do for presenting
one's information perceptually; that is, if a colormap is chosen that is
monotonically increasing in <span class="math notranslate nohighlight">\(L^*\)</span> values, it will print in a reasonable
manner to grayscale.</p>
<p>With this in mind, we see that the Sequential colormaps have reasonable
representations in grayscale. Some of the Sequential2 colormaps have decent
enough grayscale representations, though some (autumn, spring, summer,
winter) have very little grayscale change. If a colormap like this was used
in a plot and then the plot was printed to grayscale, a lot of the
information may map to the same gray values. The Diverging colormaps mostly
vary from darker gray on the outer edges to white in the middle. Some
(PuOr and seismic) have noticeably darker gray on one side than the other
and therefore are not very symmetric. coolwarm has little range of gray scale
and would print to a more uniform plot, losing a lot of detail. Note that
overlaid, labeled contours could help differentiate between one side of the
colormap vs. the other since color cannot be used once a plot is printed to
grayscale. Many of the Qualitative and Miscellaneous colormaps, such as
Accent, hsv, jet and turbo, change from darker to lighter and back to darker
grey throughout the colormap. This would make it impossible for a viewer to
interpret the information in a plot once it is printed in grayscale.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict.update" title="builtins.dict.update" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-method"><span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span></a><span class="p">({</span><span class="s1">'font.size'</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
<span class="c1"># Indices to step through colormap.</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">256</span><span class="p">)</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.vstack.html#numpy.vstack" title="numpy.vstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">vstack</span></a><span class="p">((</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a><span class="p">))</span>
<span class="k">def</span> <span class="nf">plot_color_gradients</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">):</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure.subplots_adjust" title="matplotlib.figure.Figure.subplots_adjust" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span>
<span class="n">wspace</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure.suptitle" title="matplotlib.figure.Figure.suptitle" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a> <span class="o">+</span> <span class="s1">' colormaps'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="k">for</span> <a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">):</span>
<span class="c1"># Get RGB values for colormap.</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rgb</span></a> <span class="o">=</span> <a href="../../api/cm_api.html#matplotlib.cm.ColormapRegistry" title="matplotlib.cm.ColormapRegistry" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mpl</span><span class="o">.</span><span class="n">colormaps</span></a><span class="p">[</span><span class="n">name</span><span class="p">](</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">)[</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.newaxis" title="numpy.newaxis" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span></a><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<span class="c1"># Get colormap in CAM02-UCS colorspace. We want the lightness.</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lab</span></a> <span class="o">=</span> <span class="n">cspace_converter</span><span class="p">(</span><span class="s2">"sRGB1"</span><span class="p">,</span> <span class="s2">"CAM02-UCS"</span><span class="p">)(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rgb</span></a><span class="p">)</span>
<span class="n">L</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lab</span></a><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">L</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float32" title="numpy.float32" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">np</span><span class="o">.</span><span class="n">float32</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.vstack.html#numpy.vstack" title="numpy.vstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">vstack</span></a><span class="p">((</span><span class="n">L</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">L</span><span class="p">)))</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gradient</span></a><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap</span></a><span class="o">=</span><a href="../../api/cm_api.html#matplotlib.cm.ColormapRegistry" title="matplotlib.cm.ColormapRegistry" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mpl</span><span class="o">.</span><span class="n">colormaps</span></a><span class="p">[</span><span class="n">name</span><span class="p">])</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">L</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap</span></a><span class="o">=</span><span class="s1">'binary_r'</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mf">100.</span><span class="p">)</span>
<span class="n">pos</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span><span class="o">.</span><span class="n">bounds</span><span class="p">)</span>
<span class="n">x_text</span> <span class="o">=</span> <span class="n">pos</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.01</span>
<span class="n">y_text</span> <span class="o">=</span> <span class="n">pos</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">pos</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">/</span><span class="mf">2.</span>
<a href="../../api/figure_api.html#matplotlib.figure.Figure.text" title="matplotlib.figure.Figure.text" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">text</span></a><span class="p">(</span><span class="n">x_text</span><span class="p">,</span> <span class="n">y_text</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'right'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="c1"># Turn off *all* ticks & spines, not just the ones with colormaps.</span>
<span class="k">for</span> <a href="../../api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.flatiter.html#numpy.flatiter" title="numpy.flatiter" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axs</span><span class="o">.</span><span class="n">flat</span></a><span class="p">:</span>
<a href="../../api/_as_gen/matplotlib.axes.Axes.set_axis_off.html#matplotlib.axes.Axes.set_axis_off" title="matplotlib.axes.Axes.set_axis_off" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span></a><span class="p">()</span>
<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmaps</span></a><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">plot_color_gradients</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_category</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cmap_list</span></a><span class="p">)</span>
</pre></div>
</div>
<ul class="sphx-glr-horizontal">
<li><img src="../../_images/sphx_glr_colormaps_015.png" srcset="../../_images/sphx_glr_colormaps_015.png, ../../_images/sphx_glr_colormaps_015_2_00x.png 2.00x" alt="Perceptually Uniform Sequential colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_016.png" srcset="../../_images/sphx_glr_colormaps_016.png, ../../_images/sphx_glr_colormaps_016_2_00x.png 2.00x" alt="Sequential colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_017.png" srcset="../../_images/sphx_glr_colormaps_017.png, ../../_images/sphx_glr_colormaps_017_2_00x.png 2.00x" alt="Sequential (2) colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_018.png" srcset="../../_images/sphx_glr_colormaps_018.png, ../../_images/sphx_glr_colormaps_018_2_00x.png 2.00x" alt="Diverging colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_019.png" srcset="../../_images/sphx_glr_colormaps_019.png, ../../_images/sphx_glr_colormaps_019_2_00x.png 2.00x" alt="Cyclic colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_020.png" srcset="../../_images/sphx_glr_colormaps_020.png, ../../_images/sphx_glr_colormaps_020_2_00x.png 2.00x" alt="Qualitative colormaps" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_colormaps_021.png" srcset="../../_images/sphx_glr_colormaps_021.png, ../../_images/sphx_glr_colormaps_021_2_00x.png 2.00x" alt="Miscellaneous colormaps" class = "sphx-glr-multi-img"/></li>
</ul>
</section>
<section id="color-vision-deficiencies">
<h2>Color vision deficiencies<a class="headerlink" href="#color-vision-deficiencies" title="Link to this heading">#</a></h2>
<p>There is a lot of information available about color blindness (<em>e.g.</em>,
<a class="reference internal" href="#colorblindness" id="id11"><span>[colorblindness]</span></a>). Additionally, there are tools available to convert images
to how they look for different types of color vision deficiencies.</p>
<p>The most common form of color vision deficiency involves differentiating
between red and green. Thus, avoiding colormaps with both red and green will
avoid many problems in general.</p>
</section>
<section id="references">
<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
<div role="list" class="citation-list">
<div class="citation" id="ware" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Ware</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="http://ccom.unh.edu/sites/default/files/publications/Ware_1988_CGA_Color_sequences_univariate_maps.pdf">http://ccom.unh.edu/sites/default/files/publications/Ware_1988_CGA_Color_sequences_univariate_maps.pdf</a></p>
</div>
<div class="citation" id="moreland" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">Moreland</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf">http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf</a></p>
</div>
<div class="citation" id="list-colormaps" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id9">list-colormaps</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://gist.github.com/endolith/2719900#id7">https://gist.github.com/endolith/2719900#id7</a></p>
</div>
<div class="citation" id="mycarta-banding" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">mycarta-banding</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://mycarta.wordpress.com/2012/10/14/the-rainbow-is-deadlong-live-the-rainbow-part-4-cie-lab-heated-body/">https://mycarta.wordpress.com/2012/10/14/the-rainbow-is-deadlong-live-the-rainbow-part-4-cie-lab-heated-body/</a></p>
</div>
<div class="citation" id="mycarta-jet" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>mycarta-jet<span class="fn-bracket">]</span></span>
<span class="backrefs">(<a role="doc-backlink" href="#id6">1</a>,<a role="doc-backlink" href="#id7">2</a>)</span>
<p><a class="reference external" href="https://mycarta.wordpress.com/2012/10/06/the-rainbow-is-deadlong-live-the-rainbow-part-3/">https://mycarta.wordpress.com/2012/10/06/the-rainbow-is-deadlong-live-the-rainbow-part-3/</a></p>
</div>
<div class="citation" id="kovesi-colormaps" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">kovesi-colormaps</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://arxiv.org/abs/1509.03700">https://arxiv.org/abs/1509.03700</a></p>
</div>
<div class="citation" id="bw" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id10">bw</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://tannerhelland.com/3643/grayscale-image-algorithm-vb6/">https://tannerhelland.com/3643/grayscale-image-algorithm-vb6/</a></p>
</div>
<div class="citation" id="colorblindness" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id11">colorblindness</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="http://www.color-blindness.com/">http://www.color-blindness.com/</a></p>
</div>
<div class="citation" id="ibm" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">IBM</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://doi.org/10.1109/VISUAL.1995.480803">https://doi.org/10.1109/VISUAL.1995.480803</a></p>
</div>
<div class="citation" id="turbo" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id8">turbo</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html">https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html</a></p>
</div>
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Lightness of Matplotlib colormaps
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