Visualizations can be clear or obscure depending on the color scheme used to represent the data, and careful use of color can also be attractive. However, colormaps have not generally received the attention they deserve, given their significance. The colors used carry the responsibility of conveying data honestly and accurately. They should generally be perceptually uniform so that equal steps through the dataset are represented by equal perceptual jumps in the colormap. They should be intuitive to help support quick, natural understanding of the data. They should match basic properties of the data, like showing the presence of information (sequential) or anomalies in a field (diverging). Additionally, just as different variables are typically represented with different specific Greek letters when written, different variables should also be represented with different colormaps when plotted. A suite of colormaps called cmocean have been developed to meet the needs of oceanographers, and can be used by any plotter out there. The suite is freely available for many different software packages (including Python and R). You can use these colormaps to help convey your data honestly and accurately.

1. Custom Colormaps for Your Field
Kristen Thyng
Research Assistant Professor
Oceanography
Texas A&M University
_________
kristenthyng.com @thyngkm
November 17, 2016

2. Why are colormaps important?

3. Why are colormaps important?
What is being shown here?
Rogowitz, B. E., & Treinish, L. A. (2009). Why should engineers and scientists be worried
about color. See URL http://www. research. ibm. com/people/l/lloydt/color/color. HTM.

4. Why are colormaps important?
What is being shown here?
Rogowitz, B. E., & Treinish, L. A. (2009). Why should engineers and scientists be worried
about color. See URL http://www. research. ibm. com/people/l/lloydt/color/color. HTM.
Florida land and
surrounding ocean

5. • Disclaimer: there are both facts and opinions about
many aspects of colormaps
• I will cover a few of both and give guidelines
• Anyone’s particular application may have unique
considerations
What makes a good colormap?

6. Basic function of colormap
• Represent data with no preferential values
• All data should be perceived as equally
important
• Changes in value are perceived uniformly
across the colormap

7. Colorspaces
• Represent color in 3 dimensions

8. Colorspaces
• Represent color in 3 dimensions
• There are different choices for what 3
dimensions to use

9. Colorspaces
• Represent color in 3 dimensions
• There are different choices for what 3
dimensions to use
• Common colorspace is RGB
• 3 dimensions are amount of red, green,
and blue making up a color

10. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.

11. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.
Lightness

12. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.
Lightness
Red to green

13. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.
Lightness
Red to green
Yellow to blue

14. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.

15. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.

16. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.
Equal distance
in color space is
equal perceptual
jump

17. Perceptually uniform colorspace:
CAM02-UCS
Li, C., M. Ronnier Luo, C. Li, and G. Cui. 2012. The CRI-CAM02UCS colour rendering index. Color
Research & Application 37(3):160–167, http://dx.doi.org/10.1002/col.20682.
Equal-spaced
steps through
colorspace
gives
perceptually-
uniform
colormap

18. Perceptually uniform colormap doesn’t
incidentally add or remove information

19. Perceptually uniform colormap doesn’t
incidentally add or remove information

20. Perceptually uniform colormap doesn’t
incidentally add or remove information

21. Perceptually uniform colormap doesn’t
incidentally add or remove information

22. Even nice colormaps can wash out
detail if not perceptually uniform
matplotlib YlGn cmocean speed

23. Even nice colormaps can wash out
detail if not perceptually uniform
Perceptual changes
YlGn speed

24. Importance of lightness
• People interpret spatial structure of data
best when mapped by lightness
• not hue!
• Use lightness strategically to show data
Ware, C. (1988). Color sequences for univariate maps: Theory, experiments and principles. IEEE Computer
Graphics and Applications, 8(5), 41-49.

25. Importance of lightness

26. Importance of lightness
Sequential:
increments of data

27. Importance of lightness
Diverging: anomalies
around critical value

28. Sequential data: temperature
NOAA Extended Reconstructed Sea Surface Temperature (SST) V4, http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v4.html

29. Sequential data: temperature
NOAA Extended Reconstructed Sea Surface Temperature (SST) V4, http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v4.html
No critical value

30. Diverging data: sea surface height
Data compiled by Robert Leben, found at http://www.gcoos.org/products/index.php/model-resources/ssha/

31. Diverging data: sea surface height
There is a critical value (0),
set as white
Data compiled by Robert Leben, found at http://www.gcoos.org/products/index.php/model-resources/ssha/

32. Importance of lightness

33. Importance of lightness
Cyclic:
data wraps around

34. Cyclic data: tidal phase
OSU/TOPEX http://volkov.oce.orst.edu/tides/global.html

35. Cyclic data: tidal phase
OSU/TOPEX http://volkov.oce.orst.edu/tides/global.html
No jump in
lightness

36. • to emphasize important aspect of data
Purposefully break the uniformity
Hypoxic region in Texas, by Veronica Ruiz
http://pong.tamu.edu/people.html#veronica

37. • to emphasize important aspect of data
Purposefully break the uniformity
Hypoxic region in Texas, by Veronica Ruiz
http://pong.tamu.edu/people.html#veronica
Range for hypoxic water

38. • to emphasize important aspect of data
Purposefully break the uniformity
Hypoxic region in Texas, by Veronica Ruiz
http://pong.tamu.edu/people.html#veronica
Range for hypoxic water Bound for
supersaturated

39. Accommodate colorblind viewers
50% red-green colorblindness
https://bids.github.io/colormap/

40. Accommodate colorblind viewers
• Avoid red and green in same plot
50% red-green colorblindness
https://bids.github.io/colormap/

41. Accommodate colorblind viewers
• Avoid red and green in same plot
• If necessary, saturate red and green to be more visible
50% red-green colorblindness
https://bids.github.io/colormap/

42. Use intuitive colors
Chlorophyll from GERG Texas A&M glider
http://gcoos2.tamu.edu/gandalf_data/deployments/tamu/unit_541/plots/sci_flbbcd_chlor_units.png

43. Use intuitive colors
Global monthly sea surface temperature reconstruction from NOAA
NOAA Extended Reconstructed Sea Surface Temperature (SST) V4, http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v4.html

44. Model from Hetland, data from DiMarco and Zimmerle 2016
jet

45. viridisjet
Model from Hetland, data from DiMarco and Zimmerle 2016

46. artisanal mapsviridisjet
Model from Hetland, data from DiMarco and Zimmerle 2016

47. artisanal colormaps
Model from Hetland, data from DiMarco and Zimmerle 2016

48. How do you make a colormap
based on these principles?
• Use viscm!
• Nathaniel Smith, Stéfan van
der Walt, and others
• Enforces linearly increasing
lightness
• Enforces uniform perceptual
changes across colormap
• You choose pathway
through colorspace
https://github.com/matplotlib/viscm https://bids.github.io/colormap/

49. cmocean
beautiful colormaps for oceanography

50. cmocean
beautiful colormaps for oceanography
pip install cmocean
or
conda install -c conda-forge cmocean
matplotlib.org/cmocean

51. Python: matplotlibcmocean
Bathymetry, by Iury Sousa
http://iuryt.github.io/

52. Easy to change colormaps in matplotlib;
easy to use cmocean
import matplotlib.pyplot as plt
import numpy as np
import cmocean.cm as cmo
Z = np.random.rand(10, 10)
plt.pcolormesh(Z, cmap=cmo.matter)

53. Plotlycmocean
2D projection of 3D surface via twitter
@plotlygraphs, @mathinpython

54. R: oce packagecmocean
Transect of temperature and salinity, by Clark Richards
http://clarkrichards.org/r/oce/section/ctd/2016/04/25/making-section-plots/

55. MATLABcmocean
Sea ice concentration around Antarctica, by Chad Greene
https://www.mathworks.com/matlabcentral/fileexchange/47638-antarctic-mapping-tools

56. General Mapping Tools (GMT)cmocean
Earthquake magnitude, by Natalie Accardo
http://www.natalieaccardo.com/#welcome

57. Paraviewcmocean
Vorticity, by Philip Wolfram
https://github.com/pwolfram

58. Think about your colormaps!
• Use perceptually uniform colormaps
• Use lightness carefully with type of data
• Be purposeful in deviations away from perceptual
uniformity
• Be intuitive and clear
• Consistently use different colormaps for different fields
• Respect your data!

59. cmocean
beautiful colormaps for oceanography
Thanks!
Kristen Thyng
kristenthyng.com @thyngkm
Paper: K.M. Thyng et al (2016) True colors of
oceanography. Oceanography.
Slides: goo.gl/gLgfDW

60. • Will print correctly to grayscale!
Importance of lightness