ode-filters 0.2.1

Basic ODE filtering and smooting implementation.

ODE Filters

Probabilistic filtering and smoothing algorithms for ordinary differential equation solvers.

Overview

The package implements square-root Gaussian inference routines along with filter and smoother loops for probabilistic ODE solvers. It targets research-grade experimentation while staying close to practical applications.

Installation

1. Install uv if you have not already:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Sync the project dependencies (this installs the package in editable mode and pulls in development extras):

   uv sync --group dev
   

This creates a virtual environment under .venv/ and makes the uv run command available for executing project tools.

Note: The project targets Python 3.14 or newer.

Quickstart

• Run the full test suite:

  uv run pytest --cov=ode_filters --cov-report=term-missing
  

• Execute a specific test module (for example, the logistic consistency checks):

  uv run pytest test/test_filter_loop/test_preconditioned_logistic_consistency.py -k consistency
  

• Launch the tutorial notebook showcased in the documentation:

  uv run jupyter notebook examples.ipynb
  

Documentation

Lightweight documentation lives in docs/. Start with docs/index.md for an outline of available modules and concepts. The primary tutorial is the examples.ipynb notebook, which walks through the filtering workflow step by step.

Contributing

We welcome issues and pull requests. Please read CONTRIBUTING.md for guidance on local setup, coding standards, and pre-commit hooks. Running uv run pre-commit run --all-files before opening a pull request keeps the linters and notebook cleaner in sync with CI.