colorcet

Pixi manages development workflows through tasks (run via pixi run <task-name>) and environments (sets of packages).

Common Development Tasks

• List all tasks: pixi task list
• Activate the default environment: pixi shell (similar to conda activate)
• Sync Git tags: If working from a fork, sync tags to ensure versioning works correctly:

  pixi run sync-git-tags

• Launch Jupyter Lab: Use the default environment to edit documentation or debug notebooks:

  pixi run lab

Running tasks in specific environments

You can target specific environments using the -e flag. For example, to run a task in the test-core environment:

pixi run -e test-core <task-name>

To contribute to colorcet, use pixi to manage environments and tasks. The setup process creates a default environment, installs colorcet in editable mode, and installs pre-commit for linting.

Prerequisites

• Git: Installed on your system.
• GitHub Account: Required for forking and Pull Requests.
• Pixi: Install via the official guide.

Installation Steps

1. Fork and Clone the repository:

   git clone https://github.com/<Your Username Here>/colorcet

2. Initialize the environment:

   pixi run setup-dev

NOTE

The first run creates a pixi.lock file and a .pixi directory. Avoid placing the source directory in a cloud-synced folder (like Dropbox or OneDrive) as the .pixi directory can become very large.

Build Documentation

To build the documentation website locally:

pixi run docs-build

Build Distribution Packages

To build packages for distribution:

• Build PyPI package: pixi run build-pip
• Build Conda package: pixi run build-conda

Colorcet supports Python 3.10 and greater on Linux, Windows, and Mac. You can install it using conda or pip.

# Using conda
conda install colorcet

# Using pip
python -m pip install colorcet

To test the latest features before they are officially released, you can install the development version from the pyviz channel using conda.

conda install -c pyviz/label/dev colorcet

Maintain code quality using the following built-in tasks:

Linting and Formatting

colorcet uses pre-commit to lint and format code. It is installed automatically via setup-dev.

• Run linting on all files: pixi run lint
• Manual pre-commit commands (if installed globally):
  • pre-commit install
  • pre-commit run --all-files

Type Checking

Static type checking is performed using mypy:

• Run type checker: pixi run test-type

Testing

• Unit Tests: Run small tests (located in colorcet/tests/) using pytest:

  pixi run test-unit

  Note: You can run these in specific Python version environments (e.g., test-39, test-312) using pixi run -e test-312 test-unit.
• Example Tests: Executes Jupyter Notebooks in the doc/ folder using nbval to ensure they run without error:

  pixi run test-example

To use the pixi development environment within VS Code:

1. Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P).
2. Run Python: Select Interpreter.
3. Choose {'default': Pixi} from the list.

This ensures VS Code uses the correct dependencies and Python version managed by Pixi.

Colorcet is a collection of perceptually accurate 256-color colormaps designed for use with Python plotting libraries such as Bokeh, Matplotlib, HoloViews, and Datashader.

It provides two primary types of colormaps:

1. Continuous Colormaps: These are perceptually uniform, meaning each new color is equally perceptually distinct from its neighbors. They are ideal for representing numerical scales and scientific datasets where avoiding false boundaries and preserving detail is critical. Examples include fire (a perceptually uniform version of Matplotlib's hot) and rainbow4 (a version of jet).
2. Categorical Colormaps: These are designed for discrete categories rather than numerical scales. They provide well-separated, distinct colors sampled from a perceptual space, allowing for a large number of categories to be visually distinguishable.

Each colormap in colorcet follows a specific naming pattern that describes its properties:

{category}_{huesequence}_{lightnessrange}_c{meanchroma}[_s{colorshift}[_r]]

This structure allows you to identify the category, hue sequence, lightness range, mean chroma, and optional color shifts or refinements directly from the name.

When choosing a colormap in Colorcet, consider the nature of your data:

• Use Continuous Colormaps when your data represents a continuous numerical scale (e.g., temperature, density, or elevation). These maps ensure that changes in data values correspond to equal changes in perceived color, preventing the 'washing out' of detail or the creation of artificial visual boundaries.
• Use Categorical Colormaps when your data represents discrete, non-ordered groups (e.g., different species, country names, or types of objects). These maps focus on maximizing the perceptual distance between individual colors so that each category is easily recognizable.