Metadata-Version: 2.4
Name: pybounds
Version: 0.0.14
Summary: Bounding Observability for Uncertain Nonlinear Dynamics Systems (BOUNDS)
Home-page: https://pypi.org/project/pybounds/
Author: Ben Cellini, Burak Boyacioglu, Floris van Breugel
Author-email: bcellini00@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python
Dynamic: summary

# pybounds

Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.

<p align="center">
    <a href="https://pypi.org/project/pybounds/">
        <img src="https://badge.fury.io/py/pybounds.svg" alt="PyPI version" height="18"></a>
</p>

## Introduction

This repository provides a minimal working example demonstrating how to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise.

## Installing

The package can be installed by cloning the repo and running python setup.py install from inside the home pybounds directory.

Alternatively using pip
```bash
pip install pybounds
```

## Notebook examples
For a simple system
*  Monocular camera with optic fow measurements: [mono_camera_example.ipynb](examples%2Fmono_camera_example.ipynb)

For a more complex system
*  Fly-wind: [fly_wind_example.ipynb](examples%2Ffly_wind_example.ipynb)

## Citation

If you use the code or methods from this package, please cite the following paper:

Cellini, B., Boyacioglu, B., Lopez, A., & van Breugel, F. (2025). Discovering and exploiting active sensing motifs for estimation (arXiv:2511.08766). arXiv. https://arxiv.org/abs/2511.08766

## Related packages
This repository is the evolution of the EISO repo (https://github.com/BenCellini/EISO), and is intended as a companion to the repository directly associated with the paper above.

## License

This project utilizes the [MIT LICENSE](LICENSE.txt).
100% open-source, feel free to utilize the code however you like. 
