Jaxonomy documentation

Jaxonomy is a Python package for simulating hybrid dynamical systems described as block diagrams: wired blocks (integrators, gains, custom subsystems, acausal networks, and more), continuous and discrete states, and event/zero-crossing logic. The runtime is built around JAX, so you get JIT-friendly execution and automatic differentiation where the model allows it, while keeping a NumPy-style API for numerics.

The library runs entirely locally and can serialise models to Collimator-format JSON. There is no hosted cloud service — see the About page for the project's scope.

The source lives at github.com/machinavitalis/jaxonomy.

Install

pip install jaxonomy

Use a virtual environment when possible. Platform notes, optional extras ([safe], [nmpc], [all]), and development installs from a git clone are covered in the installation guide.

Where to go next

Goal	Link
First simulation walkthrough	Tutorials → Getting started
Shorter topical guides	Tutorials index
Applied notebooks (control, MPC, ML, …)	Examples
`DiagramBuilder`, `LeafSystem`, ports	Framework
Built-in blocks	Block library
`simulate`, solvers, options	Simulation
Training / optimization helpers	Optimization

Minimal pattern

Add blocks with DiagramBuilder, connect outputs to inputs, then build().
Call jaxonomy.simulate(diagram, start_time, end_time, ...) (see Simulation for SimulatorOptions and results handling).

The Getting started tutorial builds a simple mass–spring–damper-style diagram step by step.

Build this site locally

pip install -r requirements.docs.txt
mkdocs serve

Source for this page: docs/index.md.

License and attribution

This project is released under the MIT License. See the LICENSE.md file in the repository for the full text.

Provenance: This library is derived from the MIT-licensed open-source Python package pycollimator, developed by Collimator, Inc.