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

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<title>Choosing Colormaps in Matplotlib — Matplotlib 3.5.3 documentation</title>

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Sequential

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Sequential2

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<h1>Choosing Colormaps in Matplotlib<a class="headerlink" href="#choosing-colormaps-in-matplotlib" title="Permalink to this headline">#</a></h1>

Matplotlib has a number of built-in colormaps accessible via

<a class="reference internal" href="../../api/cm_api.html#matplotlib.cm.get_cmap" title="matplotlib.cm.get_cmap"><code class="xref py py-obj docutils literal notranslate">matplotlib.cm.get_cmap</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">Creating Colormaps in Matplotlib</a>.

<h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>

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:

<li>Whether representing form or metric data (<a class="reference internal" href="#ware" id="id1">[Ware]</a>)</li>

<li>Your knowledge of the data set (e.g., is there a critical value

from which the other values deviate?)</li>

<li>If there is an intuitive color scheme for the parameter you are plotting</li>

<li>If there is a standard in the field the audience may be expecting</li>

</ul>

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.

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,

$L^*$; red-green, $a^*$; and yellow-blue, $b^*$. The lightness

parameter $L^*$ can then be used to learn more about how the matplotlib

colormaps will be perceived by viewers.

An excellent starting resource for learning about human perception of colormaps

is from <a class="reference internal" href="#ibm" id="id2">[IBM]</a>.

</section>

<h2>Classes of colormaps<a class="headerlink" href="#classes-of-colormaps" title="Permalink to this headline">#</a></h2>

Colormaps are often split into several categories based on their function (see,

e.g., <a class="reference internal" href="#moreland" id="id3">[Moreland]</a>):

<li>Sequential: change in lightness and often saturation of color

incrementally, often using a single hue; should be used for

representing information that has ordering.</li>

<li>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.</li>

<li>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.</li>

<li>Qualitative: often are miscellaneous colors; should be used to

represent information which does not have ordering or

relationships.</li>

</ol>

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

import matplotlib as mpl

import matplotlib.pyplot as plt

from matplotlib import cm

from colorspacious import cspace_converter

</pre></div>

</div>

First, we'll show the range of each colormap. Note that some seem

to change more "quickly" than others.

<div class="highlight-default notranslate"><div class="highlight"><pre><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">cmaps</a> = {}

<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">gradient</a> = <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">np.vstack</a>((<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">gradient</a>, <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">gradient</a>))

def plot_color_gradients(category, <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">cmap_list</a>):

# Create figure and adjust figure height to number of colormaps

nrows = len(<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">cmap_list</a>)

figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22

<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">fig</a>, <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">axs</a> = <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">plt.subplots</a>(nrows=nrows + 1, figsize=(6.4, figh))

<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">fig.subplots_adjust</a>(top=1 - 0.35 / figh, bottom=0.15 / figh,

left=0.2, right=0.99)

for ax, name in zip(<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">axs</a>, <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">cmap_list</a>):

<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">ax.imshow</a>(<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">gradient</a>, aspect='auto', <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">cmap</a>=plt.get_cmap(name))

<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">ax.text</a>(-0.01, 0.5, name, va='center', ha='right', fontsize=10,

transform=<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">ax.transAxes</a>)

# Turn off *all* ticks & spines, not just the ones with colormaps.

for ax in <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">axs</a>:

<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">ax.set_axis_off</a>()

# Save colormap list for later.

<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">cmaps</a>[category] = <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">cmap_list</a>

</pre></div>

</div>

<h3>Sequential<a class="headerlink" href="#sequential" title="Permalink to this headline">#</a></h3>

For the Sequential plots, the lightness value increases monotonically through

the colormaps. This is good. Some of the $L^*$ values in the colormaps

span from 0 to 100 (binary and the other grayscale), and others start around

$L^*=20$. Those that have a smaller range of $L^*$ will accordingly

have a smaller perceptual range. Note also that the $L^*$ function varies

amongst the colormaps: some are approximately linear in $L^*$ and others

are more curved.

['viridis', 'plasma', 'inferno', 'magma', 'cividis'])

</pre></div>

</div>

<img src="../../_images/sphx_glr_colormaps_001.png" srcset="../../_images/sphx_glr_colormaps_001.png, ../../_images/sphx_glr_colormaps_001_2_0x.png 2.0x" alt="Perceptually Uniform Sequential colormaps" class = "sphx-glr-single-img"/><div class="highlight-default notranslate"><div class="highlight"><pre>plot_color_gradients('Sequential',

['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',

'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',

'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'])

</pre></div>

</div>

<h3>Sequential2<a class="headerlink" href="#sequential2" title="Permalink to this headline">#</a></h3>

Many of the $L^*$ values from the Sequential2 plots are monotonically

increasing, but some (autumn, cool, spring, and winter) plateau or even go both

up and down in $L^*$ space. Others (afmhot, copper, gist_heat, and hot)

have kinks in the $L^*$ 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">[mycarta-banding]</a> for

an excellent example of this).

['binary', 'gist_yarg', 'gist_gray', 'gray', 'bone',

'pink', 'spring', 'summer', 'autumn', 'winter', 'cool',

'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper'])

</pre></div>

</div>

<h3>Diverging<a class="headerlink" href="#diverging" title="Permalink to this headline">#</a></h3>

For the Diverging maps, we want to have monotonically increasing $L^*$

values up to a maximum, which should be close to $L^*=100$, followed by

monotonically decreasing $L^*$ values. We are looking for approximately

equal minimum $L^*$ 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 $L^*$ values (see grayscale section below).

['PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu',

'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'])

</pre></div>

</div>

<h3>Cyclic<a class="headerlink" href="#cyclic" title="Permalink to this headline">#</a></h3>

For Cyclic maps, we want to start and end on the same color, and meet a

symmetric center point in the middle. $L^*$ 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, $L^*$ will reverse direction, which should be smoothed in

$L^*$ space to reduce artifacts. See <a class="reference internal" href="#kovesi-colormaps" id="id5">[kovesi-colormaps]</a> for more

information on the design of cyclic maps.

The often-used HSV colormap is included in this set of colormaps, although it

is not symmetric to a center point. Additionally, the $L^*$ 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">[mycarta-jet]</a>.

<div class="highlight-default notranslate"><div class="highlight"><pre>plot_color_gradients('Cyclic', ['twilight', 'twilight_shifted', 'hsv'])

</pre></div>

</div>

<h3>Qualitative<a class="headerlink" href="#qualitative" title="Permalink to this headline">#</a></h3>

Qualitative colormaps are not aimed at being perceptual maps, but looking at the

lightness parameter can verify that for us. The $L^*$ 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.

['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2',

'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b',

'tab20c'])

</pre></div>

</div>

<h3>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Permalink to this headline">#</a></h3>

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

$L^*$. 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.

The often-used jet colormap is included in this set of colormaps. We can see

that the $L^*$ 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">[mycarta-jet]</a> and <a class="reference internal" href="#turbo" id="id8">[turbo]</a>.

['flag', 'prism', 'ocean', 'gist_earth', 'terrain',

'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap',

'cubehelix', 'brg', 'gist_rainbow', 'rainbow', 'jet',

'turbo', 'nipy_spectral', 'gist_ncar'])

<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">plt.show</a>()

</pre></div>

</div>

</section>

<h2>Lightness of Matplotlib colormaps<a class="headerlink" href="#lightness-of-matplotlib-colormaps" title="Permalink to this headline">#</a></h2>

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">[list-colormaps]</a>).

<div class="highlight-default notranslate"><div class="highlight"><pre><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">mpl.rcParams.update</a>({'font.size': 12})

# Number of colormap per subplot for particular cmap categories

<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">_DSUBS</a> = {'Perceptually Uniform Sequential': 5, 'Sequential': 6,

'Sequential (2)': 6, 'Diverging': 6, 'Cyclic': 3,

'Qualitative': 4, 'Miscellaneous': 6}

# Spacing between the colormaps of a subplot

<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">_DC</a> = {'Perceptually Uniform Sequential': 1.4, 'Sequential': 0.7,

'Sequential (2)': 1.4, 'Diverging': 1.4, 'Cyclic': 1.4,

'Qualitative': 1.4, 'Miscellaneous': 1.4}

# Indices to step through colormap

# Do plot

for <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">cmap_category</a>, <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">cmap_list</a> in <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">cmaps</a>.items():

# Do subplots so that colormaps have enough space.

# Default is 6 colormaps per subplot.

<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">dsub</a> = <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">_DSUBS</a>.get(<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">cmap_category</a>, 6)

<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">nsubplots</a> = int(<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">np.ceil</a>(len(<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">cmap_list</a>) / <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">dsub</a>))

# squeeze=False to handle similarly the case of a single subplot

<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">fig</a>, <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">axs</a> = <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">plt.subplots</a>(nrows=<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">nsubplots</a>, squeeze=False,

figsize=(7, 2.6*<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">nsubplots</a>))

for <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">j</a>, <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">cmap</a> in enumerate(<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">cmap_list</a>[<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">i</a>*<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">dsub</a>:(<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">i</a>+1)*<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">dsub</a>]):

# Get RGB values for colormap and convert the colormap in

# CAM02-UCS colorspace. lab[0, :, 0] is the lightness.

<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">rgb</a> = <a href="../../api/cm_api.html#matplotlib.cm.get_cmap" title="matplotlib.cm.get_cmap" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-function">cm.get_cmap</a>(<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">cmap</a>)(<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">x</a>)[<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">np.newaxis</a>, :, :3]

# Plot colormap L values. Do separately for each category

# so each plot can be pretty. To make scatter markers change

# color along plot:

# https://stackoverflow.com/q/8202605/

if <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">cmap_category</a> == 'Sequential':

# These colormaps all start at high lightness but we want them

# reversed to look nice in the plot, so reverse the order.

else:

<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">dc</a> = <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">_DC</a>.get(<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">cmap_category</a>, 1.4) # cmaps horizontal spacing

<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">ax.scatter</a>(<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">x</a> + <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">j</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">dc</a>, <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">y_</a>, c=<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">c_</a>, <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">cmap</a>=<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">cmap</a>, s=300, linewidths=0.0)

# Store locations for colormap labels

if <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">cmap_category</a> in ('Perceptually Uniform Sequential',

'Sequential'):

<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">locs</a>.append(<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">x</a>[-1] + <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">j</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">dc</a>)

elif <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">cmap_category</a> in ('Diverging', 'Qualitative', 'Cyclic',

'Miscellaneous', 'Sequential (2)'):

<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">locs</a>.append(<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">x</a>[int(<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">x.size</a>/2.)] + <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">j</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">dc</a>)

# Set up the axis limits:

# * the 1st subplot is used as a reference for the x-axis limits

# * lightness values goes from 0 to 100 (y-axis limits)

<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">ax.set_xlim</a>(<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">axs</a>[0, 0].get_xlim())

<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">ax.set_ylim</a>(0.0, 100.0)

# Set up labels for colormaps

<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">ax.xaxis.set_ticks_position</a>('top')

<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">ticker</a> = <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">mpl.ticker.FixedLocator</a>(<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">locs</a>)

<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">ax.xaxis.set_major_locator</a>(<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">ticker</a>)

<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">formatter</a> = <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">mpl.ticker.FixedFormatter</a>(<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">cmap_list</a>[<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">i</a>*<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">dsub</a>:(<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">i</a>+1)*<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">dsub</a>])

<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">ax.xaxis.set_major_formatter</a>(<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">formatter</a>)

<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">ax.xaxis.set_tick_params</a>(rotation=50)

<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">ax.set_ylabel</a>('Lightness $L^*$', fontsize=12)

<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">ax.set_xlabel</a>(<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">cmap_category</a> + ' colormaps', fontsize=14)

<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">fig.tight_layout</a>(h_pad=0.0, pad=1.5)

</pre></div>

</div>

</ul>

</section>

<h2>Grayscale conversion<a class="headerlink" href="#grayscale-conversion" title="Permalink to this headline">#</a></h2>

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.

Conversion to grayscale is done in many different ways <a class="reference internal" href="#bw" id="id10">[bw]</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 $L^*$ 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 $L^*$ values, it will print in a reasonable

manner to grayscale.

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.

# Indices to step through colormap.

def plot_color_gradients(<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">cmap_category</a>, <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">cmap_list</a>):

<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">fig</a>, <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">axs</a> = <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">plt.subplots</a>(nrows=len(<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">cmap_list</a>), ncols=2)

<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">fig.subplots_adjust</a>(top=0.95, bottom=0.01, left=0.2, right=0.99,

wspace=0.05)

<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">fig.suptitle</a>(<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">cmap_category</a> + ' colormaps', fontsize=14, y=1.0, <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">x</a>=0.6)

# Get RGB values for colormap.

<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">rgb</a> = <a href="../../api/cm_api.html#matplotlib.cm.get_cmap" title="matplotlib.cm.get_cmap" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-function">cm.get_cmap</a>(plt.get_cmap(name))(<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">x</a>)[<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">np.newaxis</a>, :, :3]

# Get colormap in CAM02-UCS colorspace. We want the lightness.

L = <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">lab</a>[0, :, 0]

L = <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">np.float32</a>(<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">np.vstack</a>((L, L, L)))

ax[0].imshow(<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">gradient</a>, aspect='auto', <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">cmap</a>=plt.get_cmap(name))

ax[1].imshow(L, aspect='auto', <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">cmap</a>='binary_r', vmin=0., vmax=100.)

pos = list(ax[0].get_position().bounds)

x_text = pos[0] - 0.01

y_text = pos[1] + pos[3]/2.

<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">fig.text</a>(x_text, y_text, name, va='center', ha='right', fontsize=10)

# Turn off *all* ticks & spines, not just the ones with colormaps.

for ax in <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">axs.flat</a>:

plot_color_gradients(<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">cmap_category</a>, <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">cmap_list</a>)

</pre></div>

</div>

</ul>

</section>

<h2>Color vision deficiencies<a class="headerlink" href="#color-vision-deficiencies" title="Permalink to this headline">#</a></h2>

There is a lot of information available about color blindness (e.g.,

<a class="reference internal" href="#colorblindness" id="id11">[colorblindness]</a>). Additionally, there are tools available to convert images

to how they look for different types of color vision deficiencies.

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.

</section>

<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>

<dd><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>

</dd>

<dt class="label" id="moreland"><a class="fn-backref" href="#id3">Moreland</a></dt>

<dd><a class="reference external" href="http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf">http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf</a>

</dd>

<dt class="label" id="list-colormaps"><a class="fn-backref" href="#id9">list-colormaps</a></dt>

<dd><a class="reference external" href="https://gist.github.com/endolith/2719900#id7">https://gist.github.com/endolith/2719900#id7</a>

</dd>

<dt class="label" id="mycarta-banding"><a class="fn-backref" href="#id4">mycarta-banding</a></dt>

<dd><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>

</dd>

<dt class="label" id="mycarta-jet">mycarta-jet(<a href="#id6">1</a>,<a href="#id7">2</a>)</dt>

<dd><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>

</dd>

<dt class="label" id="kovesi-colormaps"><a class="fn-backref" href="#id5">kovesi-colormaps</a></dt>

<dd><a class="reference external" href="https://arxiv.org/abs/1509.03700">https://arxiv.org/abs/1509.03700</a>

</dd>

<dd><a class="reference external" href="https://tannerhelland.com/3643/grayscale-image-algorithm-vb6/">https://tannerhelland.com/3643/grayscale-image-algorithm-vb6/</a>

</dd>

<dt class="label" id="colorblindness"><a class="fn-backref" href="#id11">colorblindness</a></dt>

<dd><a class="reference external" href="http://www.color-blindness.com/">http://www.color-blindness.com/</a>

</dd>

<dd><a class="reference external" href="https://doi.org/10.1109/VISUAL.1995.480803">https://doi.org/10.1109/VISUAL.1995.480803</a>

</dd>

<dt class="label" id="turbo"><a class="fn-backref" href="#id8">turbo</a></dt>

<dd><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>

</dd>

</dl>

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