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How to choose a good colormap

How to choose a good colormap

Damon McDougall sharing information about how to choose a good colormap.

Kristen Thyng

July 10, 2014
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  1. How to choose a good colour map
    Damon McDougall
    Institute for Computational and Engineering Sciences, UT Austin, USA
    10th July 2014
    1/19

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  2. Introduction
    Most of the content is taken from this excellent article:
    http://www.research.ibm.com/people/l/lloydt/color/color.HTM
    2/19

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  3. Introduction
    • Data is a huge aspect of science
    • By and large we (scientists) treat data well. . .
    • . . . and we visualise it poorly. Why?
    • Colour maps
    • Data is of some field f : Ω ⊂ R2 → [0, 1]
    • A colour is assigned to the output of f (a scalar). Seems reasonable.
    • Colour map is a function g : [0, 1] → Ω ⊂ R3
    • Mismatch in dimensions: R3 versus R
    • The point? Colour maps can be misleading.
    3/19

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  4. Hating on the jet colour map
    What is this?
    4/19

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  5. Hating on the jet colour map
    Did anybody see Florida?
    • Left: Linear interpolation in RGB space between red and blue.
    • Right: Changes in data are perceived as proportional changes in
    colour (subjective)
    • Right: Domain specific knowledge used to reveal important features
    5/19

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  6. Hating on the jet colour map
    6/19

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  7. Hating on the jet colour map
    7/19

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  8. Hating on the jet colour map
    8/19

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  9. Hating on the jet colour map
    9/19

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  10. Hating on the jet colour map
    10/19

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  11. What have we learned?
    • Jet is not a great colourmap (or is it?)
    • Two types of information one can glean from a colourmap1
    • ‘Value’ or ‘metric’ information
    • ‘Form’ or ‘structure’ information
    • Jet is not bad for value information (but not everywhere)
    • Jet is awful for form information
    • Jet is not alone—but it is very commonly used
    • How to pick a good colour map? It depends!
    1C. Ware, Color sequences for univariate maps: theory, experiments, and principles,
    IEEE Computer Graphics and Appliations, 1998.
    11/19

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  12. What is good for form/structure information?
    • Colour has 3 dimensions: hue, saturation, and luminance
    • Saturation-varying colourmaps are good for low-frequency data
    • Luminance-varying colourmaps are good for high-frequency data
    • The human brain is very bad at interpolating hue2
    • Perceptually-based colourmaps
    • Equal steps in data are perceived as equal steps in the colour space
    2Conclusion from psychophysical experiments by S. S. Stevens (formerly at
    Harvard)
    12/19

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  13. Perceptually-based colourmaps
    13/19

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  14. Perceptually-based colourmaps
    14/19

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  15. Perceptually-based colourmaps
    15/19

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  16. Perceptually-based colourmaps
    16/19

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  17. Perceptually-based colourmaps
    17/19

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  18. Perceptually-based colourmaps
    18/19

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  19. Thank you.
    Pssst. Jet is the default colourmap in matplotlib. Anybody want to fix
    it? Submit a PR!
    Link to slides:
    https://github.com/dmcdougall/scipy14-colormaps
    19/19

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