ok_palette_lab 0.3.0

Smooth color maps using Oklch color space for Plotly and matplotlib.

OKPaletteLab

Smooth color maps for Plotly and Matplotlib.

• Color maps are designed in the Oklch color space to create smooth gradients and improve data visualization.
• The following types of color maps are provided:
  • Sequential color maps for general ranges.
  • Diverging color maps for ranges centered at zero.
  • Cyclic color maps for periodic ranges.
• Currently supports the following libraries:
  • Plotly (graphing library for Python)
  • Matplotlib

Sample Figures

Installation

You can install the package via pip:

pip install ok_palette_lab

Basic Usage

• With Plotly:

  • ok_palette_lab.plotly package provides color maps (called "color scales" in Plotly).

  • Select one and use it as follows:

    figure = plotly.graph_objects.Figure()
    figure.add_heatmap(
        # ... your data here ...
    
        # Specify a color map.
        colorscale=ok_palette_lab.plotly.autumn,
    )
    

• With matplotlib:

  • ok_palette_lab.matplotlib package has color maps.

  • Select one and use it as follows:

    figure, axes = matplotlib.pyplot.subplots()
    heatmap = axes.imshow(
        # ... your data here ...
    
        # Specify a color map.
        cmap=ok_palette_lab.matplotlib.autumn,
    )
    

Simple Examples

• Example of a heatmap using Plotly
• Example of a heatmap using matplotlib

Documentation

Documentation is available at web site. Documentation for each version can be viewed using the version switcher at the bottom left of the page.

Development

This project was created in January 2026 and is under active development. The following features are planned for future releases:

• Color maps for dark mode.
• Support for more graphing libraries in Python.
• Support for ParaView.

Repositories

• Main repository on GitLab
• Mirror on GitHub

License

This project is licensed under the MIT License. See LICENSE.txt for details.

Graphics created using the color maps in this project can be used freely without restriction.