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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<title>Default Color changes — Matplotlib 3.3.0 documentation</title>

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<div id="unreleased-message"> You are reading an old version of the documentation (v3.3.0). For the latest version see <a href="https://matplotlib.org/stable/devel/color_changes.html">https://matplotlib.org/stable/devel/color_changes.html</a></div>

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<h3><a href="../contents.html">Table of Contents</a></h3>

<ul>

<li><a class="reference internal" href="#">Default Color changes</a><ul>

<li><a class="reference internal" href="#default-heat-map-colormap">Default Heat Map Colormap</a><ul>

<li><a class="reference internal" href="#example-script">Example script</a></li>

<li><a class="reference internal" href="#proposed-colormaps">Proposed Colormaps</a></li>

</ul>

</li>

<li><a class="reference internal" href="#default-scatter-colormap">Default Scatter Colormap</a><ul>

<li><a class="reference internal" href="#id1">Example script</a></li>

<li><a class="reference internal" href="#id2">Proposed Colormaps</a></li>

</ul>

</li>

<li><a class="reference internal" href="#color-cycle-qualitative-color-map">Color Cycle / Qualitative color map</a><ul>

<li><a class="reference internal" href="#id3">Example script</a></li>

<li><a class="reference internal" href="#proposed-color-cycle">Proposed Color cycle</a></li>

</ul>

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<h1>Default Color changes<a class="headerlink" href="#default-color-changes" title="Permalink to this headline">¶</a></h1>

As discussed at length elsewhere [insert links], <code class="docutils literal notranslate">jet</code> is an

empirically bad color map and should not be the default color map.

Due to the position that changing the appearance of the plot breaks

backward compatibility, this change has been put off for far longer

than it should have been. In addition to changing the default color

map we plan to take the chance to change the default color-cycle on

plots and to adopt a different color map for filled plots (<code class="docutils literal notranslate">imshow</code>,

<code class="docutils literal notranslate">pcolor</code>, <code class="docutils literal notranslate">contourf</code>, etc) and for scatter like plots.

<h2>Default Heat Map Colormap<a class="headerlink" href="#default-heat-map-colormap" title="Permalink to this headline">¶</a></h2>

The choice of a new color map is fertile ground to bike-shedding ("No,

it should be _this_ color") so we have a proposed set criteria (via

Nathaniel Smith) to evaluate proposed color maps.

<li>it should be a sequential colormap, because diverging colormaps are

really misleading unless you know where the "center" of the data is,

and for a default colormap we generally won't.</li>

<li>it should be perceptually uniform, i.e., human subjective judgments

of how far apart nearby colors are should correspond as linearly as

possible to the difference between the numerical values they

represent, at least locally.</li>

<li>it should have a perceptually uniform luminance ramp, i.e. if you

convert to greyscale it should still be uniform. This is useful both

in practical terms (greyscale printers are still a thing!) and

because luminance is a very strong and natural cue to magnitude.</li>

<li>it should also have some kind of variation in hue, because hue

variation is a really helpful additional cue to perception, having

two cues is better than one, and there's no reason not to do it.</li>

<li>the hue variation should be chosen to produce reasonable results

even for viewers with the more common types of

colorblindness. (Which rules out things like red-to-green.)</li>

<li>For bonus points, it would be nice to choose a hue ramp that still

works if you throw away the luminance variation, because then we

could use the version with varying luminance for 2d plots, and the

version with just hue variation for 3d plots. (In 3d plots you

really want to reserve the luminance channel for lighting/shading,

because your brain is really good at extracting 3d shape from

luminance variation. If the 3d surface itself has massively varying

luminance then this screws up the ability to see shape.)</li>

<li>Not infringe any existing IP</li>

</ul>

<h3>Example script<a class="headerlink" href="#example-script" title="Permalink to this headline">¶</a></h3>

</div>

<h3>Proposed Colormaps<a class="headerlink" href="#proposed-colormaps" title="Permalink to this headline">¶</a></h3>

</div>

<h2>Default Scatter Colormap<a class="headerlink" href="#default-scatter-colormap" title="Permalink to this headline">¶</a></h2>

For heat-map like applications it can be desirable to cover as much of

the luminance scale as possible, however when color mapping markers,

having markers too close to white can be a problem. For that reason

we propose using a different (but maybe related) color map to the

heat map for marker-based. The design parameters are the same as

above, only with a more limited luminance variation.

<h3>Example script<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>

<div class="highlight-default notranslate"><div class="highlight"><pre>import numpy as np

import matplotlib.pyplot as plt

np.random.seed(1234)

fig, (ax1, ax2) = plt.subplots(1, 2)

N = 50

x = np.random.rand(N)

y = np.random.rand(N)

colors = np.random.rand(N)

area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses

ax1.scatter(x, y, s=area, c=colors, alpha=0.5)

X,Y = np.meshgrid(np.arange(0, 2*np.pi, .2),

np.arange(0, 2*np.pi, .2))

U = np.cos(X)

V = np.sin(Y)

Q = ax2.quiver(X, Y, U, V, units='width')

qd = np.random.rand(np.prod(X.shape))

Q.set_array(qd)

</pre></div>

</div>

<h3>Proposed Colormaps<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>

</div>

<h2>Color Cycle / Qualitative color map<a class="headerlink" href="#color-cycle-qualitative-color-map" title="Permalink to this headline">¶</a></h2>

When plotting lines it is frequently desirable to plot multiple lines

or artists which need to be distinguishable, but there is no inherent

ordering.

<h3>Example script<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>

<div class="highlight-default notranslate"><div class="highlight"><pre>import numpy as np

import matplotlib.pyplot as plt

x = np.linspace(0, 1, 10)

for j in range(10):

ax1.plot(x, x * j)

th = np.linspace(0, 2*np.pi, 1024)

for j in np.linspace(0, np.pi, 10):

ax2.plot(th, np.sin(th + j))

ax2.set_xlim(0, 2*np.pi)

</pre></div>

</div>

<h3>Proposed Color cycle<a class="headerlink" href="#proposed-color-cycle" title="Permalink to this headline">¶</a></h3>

</div>

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Last updated on Jul 17, 2020.

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