What it does
Colorgorical is a research tool for creating categorical colour palettes for information visualisation. It lets users choose the number of colours, balance distinguishability against visual preference, constrain hue and lightness, and extend a starting palette.
A categorical palette assigns separate colours to groups such as regions or product types. Colorgorical exposes competing priorities: colours that are easy to tell apart may differ from those that make a preferred combination. Its controls let the user set that balance instead of accepting a single fixed recipe.
Choose a category count, provide any starting colours and restrict the hue or lightness range as needed. The research project keeps these controls separate to explore their effect on palette construction.
For local use, the official repository describes compiling its C component and then starting a Python web server. That setup targets Python 2.7 with older NumPy, Tornado and setuptools versions. Local use therefore assumes familiarity with compiling code and running a Python server.
Strengths
- Existing palette colours can serve as a starting point.
- Hue, lightness and generation priorities can be constrained separately.
Watch out for
- The local setup targets a Python 2.7-era environment and requires C compilation.
Best for
- Visualisation researchers and developers choosing colours for discrete data groups.
Sources
- Colorgorical (Oct 28, 2026)
- GitHub - connorgr/colorgorical: Colorgorical: a tool for creating categorical information visualization color palettes. · GitHub (Oct 28, 2026)
- New publication: Colorgorical: Creating discriminable and preferable color palettes for information visualization - Schloss Visual Reasoning Lab (Oct 28, 2026)