colorspace 0.5.0

A Python toolbox for manipulating and assessing colors and palettes

A Python toolbox for manipulating and assessing colors and palettes

The package provides a Python 3 version of the R colorspace package comprising:

• Color spaces: Conversions and manipulations of RGB, HSV, CIEXYZ, CIELAB, CIELUV, HCL etc.
• HCL-based palettes: Qualitative, sequential, and diverging color maps constructed using the HCL (hue-chroma-luminance) color model. These can closely approximate palettes from ColorBrewer.org, CARTO, viridis, or Crameri's scientific color maps.
• matplotlib color maps: Integration with matplotlib for use in standard plotting functions.
• Palette visualization: Plotting palettes in color swatches, spectrums of HCL and/or RGB trajectories, and illustrations of typical data visualizations.
• Interactive palette chooser: App for exploring, modifying, and picking color palettes interactively.
• Color vision deficiency: Emulation of different kinds of color blindness.
• Utilities: Various color manipulations including tools to desaturate, lighten, darken, adjust the transparency, or compute the contrast ratio.

Links

• PyPI: https://pypi.org/project/colorspace/
• GitHub: https://github.com/retostauffer/python-colorspace/
• Documentation: https://retostauffer.github.io/python-colorspace/
• Bug reports: https://github.com/retostauffer/python-colorspace/issues Bug reports and suggestions are welcome and very much appreciated!

Contributors

• Reto Stauffer (principal author & maintainer), Universität Innsbruck, Austria
• Achim Zeileis, Universität Innsbruck, Austria

Furthermore, the Python package implements the functionality from the R package that is co-written with Ross Ihaka, Paul Murrell, Kurt Hornik, Jason C. Fisher, Claus O. Wilke, Claire D. McWhite.

Development notes

Community guidelines

If you encountery any bugs or issues, or are interested in contributing to Python colorspace, please have a look at our Community guidelines.

Test and development

More detailed information on how to run automated tests, create coverage reports, test examples and render the documentation can be found on our Installation page.

GitHub Action integration (CI)

Additional notes about development and deployment are documented in GITHUBACTIONS.md.