daft-dev/daft

Render probabilistic graphical models using matplotlib

What it solves

It provides a way to create high-quality, publication-ready probabilistic graphical models (PGMs) using Python. It replaces the manual drawing of complex graphs with a scriptable, intuitive syntax.

How it works

Daft leverages the matplotlib library to render these graphs. Users write a short Python script using a specific model-building syntax to define directed and undirected graphs, which can then be saved in various formats supported by matplotlib.

Who it’s for

Researchers and data scientists who need to create professional diagrams of probabilistic graphical models for journals or online publications.

Highlights

  • Pixel-perfect rendering for publication quality.
  • Support for both directed and undirected graphs.
  • Support for all file formats supported by matplotlib.
  • Intuitive model-building syntax.

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