References & Further Reading

References

  1. J. D. Hunter, Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 2007

    The original Matplotlib paper. Describes the architecture, backend system, and design philosophy behind the library.

  2. Matplotlib Development Team, Matplotlib Documentation, 2024

    The official documentation including tutorials, API reference, and the gallery of examples. The gallery is the single best resource for finding how to create specific plot types.

  3. N. P. Rougier, Scientific Visualization: Python + Matplotlib, Self-published (GitHub), 2021

    A comprehensive free book covering advanced Matplotlib techniques including custom projections, 3D rendering, and typography.

  4. S. van der Walt and N. Smith, A Better Default Colormap for Matplotlib, 2015

    The viridis colormap design process, covering perceptual uniformity, colorblind accessibility, and grayscale conversion.

  5. IEEE, Preparation of Papers for IEEE Transactions — Graphics Guidelines, 2024

    Official IEEE guidelines for figure formatting including size, resolution, font, and color requirements.

  6. E. R. Tufte, The Visual Display of Quantitative Information, Graphics Press, 2001

    The classic reference on data visualization principles. Introduces data-ink ratio, chart junk, and small multiples.

Further Reading

  • Advanced Matplotlib customization

    N. P. Rougier, *Scientific Visualization: Python + Matplotlib*, 2021 (free at github.com/rougier/scientific-visualization-book)

    Goes far beyond the basics with custom projections, artistic figures, and typography control.

  • Color theory for scientific visualization

    K. Moreland, *Diverging Color Maps for Scientific Visualization*, ISVC 2009

    Explains the mathematics behind perceptually uniform diverging colormaps and why simple RGB interpolation fails.

  • Accessible visualization

    B. Wong, *Points of View: Color Blindness*, Nature Methods, 2011

    Practical guidelines for making figures accessible to colorblind readers, affecting ~8% of male readers.

  • Matplotlib style sheets

    Matplotlib documentation: Customizing Matplotlib with style sheets

    Learn to create project-wide .mplstyle files for consistent formatting across all figures in a paper or thesis.

ExercisesCh 16 →