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Matplotlib has a number of built-in colormaps accessible via
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- `.matplotlib.cm.get_cmap`. There are also external libraries like
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- [palettable]_ and [colorcet]_ that have many extra colormaps.
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+ `.matplotlib.cm.get_cmap`. There are also external libraries that
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+ have many extra colormaps, which can be viewed in the
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+ `Third-party colormaps`_ section of the Matplotlib documentation.
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Here we briefly discuss how to choose between the many options. For
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help on creating your own colormaps, see
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:doc:`/tutorials/colors/colormap-manipulation`.
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perceives changes in the lightness parameter as changes in the data
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much better than, for example, changes in hue. Therefore, colormaps
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which have monotonically increasing lightness through the colormap
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- will be better interpreted by the viewer. A wonderful example of
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- perceptually uniform colormaps is [colorcet]_.
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+ will be better interpreted by the viewer. Wonderful examples of
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+ perceptually uniform colormaps can be found in the
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+ `Third-party colormaps`_ section as well.
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Color can be represented in 3D space in various ways. One way to represent color
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is using CIELAB. In CIELAB, color space is represented by lightness,
@@ -414,7 +416,7 @@ def plot_color_gradients(cmap_category, cmap_list):
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# References
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# ==========
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#
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- # .. [colorcet] https://colorcet.pyviz. org
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+ # .. _Third-party colormaps: https://matplotlib. org/mpl-third-party/#colormaps-and-styles
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# .. [Ware] http://ccom.unh.edu/sites/default/files/publications/Ware_1988_CGA_Color_sequences_univariate_maps.pdf
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# .. [Moreland] http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf
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# .. [list-colormaps] https://gist.github.com/endolith/2719900#id7
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# .. [bw] http://www.tannerhelland.com/3643/grayscale-image-algorithm-vb6/
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# .. [colorblindness] http://www.color-blindness.com/
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# .. [IBM] https://doi.org/10.1109/VISUAL.1995.480803
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- # .. [palettable] https://jiffyclub.github.io/palettable/
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# .. [turbo] https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
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