Colors

This repository helps to generate large sets of attractive and accessible color schemes based on Matthew Strom's work in How to pick the least wrong colors.

Table of Contents

• Description
• Dependencies
• Usage
• Project structure
• Background and Motivation
• License

Description

This project is, in many cases, a direct NodeJS-to-Python port of Matthew Strom's fascinating work in How to pick the least wrong colors from June 3rd, 2022. I have made minor improvements in certain areas, however, and this repository currently does not include the changes he made in 2023.

target_colors is an array of colors in hexadecimal format. If set and then used to initialize start_colors, the script will attempt to find colors that are similar to these. palette_size tells the script how many colors to return, and should match the number of colors in target_colors if set. Alternately, you may also choose starting colors at random; the script will then adjust those according to the criteria outlined below, courtesy of Matthew's README.

energy_weight, range_weight, target_weight, protanopia_weight, deuteranopia_weight, and tritanopia_weight can be any floating point number and are set in the cost function. They adjust the relative impact of each factor in the algorithm's decisionmaking:

• A higher energy weight (energy_weight) will result in colors that are more differentiable from each other.
• A higher range weight (range_weight) will result in colors that are more uniformly spread through color space.
• A higher target weight (target_weight) will result in colors that are closer to the target colors specified with target_colors.
• A higher protanopia weight (protanopia_weight) will result in colors that are more differentiable to people with protanopia.
• A higher deuteranopia weight (deuteranopia_weight) will result in colors that are more differentiable to people with deuteranopia.
• A higher tritanopia weight (tritanopia_weight) will result in colors that are more differentiable to people with tritanopia.

temperature, cooling_rate, and cutoff can also be floating point numbers and are set in the optimize function.

• temperature can be any floating point number. It is the starting point temperature of the algorithm - a higher temperature means that early iterations are more likely to be randomly-chosen than optimized.
• cooling_rate can be any floating point number. It is the decrease in temperature at each iteration. A lower cooling rate will result in more iterations.
• cutoff is the temperature at which the algorithm will stop optimizing and return results. A lower cutoff means more late-stage iterations where improvements are minimal.

Dependencies

This project requires Python 3. This project has no other dependencies, including any non-standard Python libraries.

Usage

To use this notebook, download it, open it in a Jupyter environment, and run all cells.

Project structure

./colors
|_ README.md # This file.
|
|_ colors.ipynb # Color palette generator.
|_ colors.py # Color palette generator as a script.
|
|_ makefile # Project makefile
|_ LICENSE.md # Project license.

Background and Motivation

Matthew Strom made a good case for this project in How to pick the least wrong colors, so I won't belabor the point here.

License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You can view the full text of the license in LICENSE.md. Read more about the license at the original author’s website. Generally speaking, this license allows individuals to remix this work provided they release their adaptation under the same license and cite this project as the original, and prevents anyone from turning this work or its derivatives into a commercial product.