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pybounds

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

PyPI version

Introduction

This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.

Installing

The package can be installed from PyPi:

pip install pybounds

or from source, for development, after cloning the repo:

pip install -e .

Notebook examples

For a simple system:

For a more complex system:

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

Additional resources

To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel

To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.

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. 100% open-source, feel free to utilize the code however you like.

About

Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.

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Readme
MIT license
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GitHub - vanbreugel-lab/pybounds: Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems. · GitHub
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pybounds
Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
PyPI version
Introduction
This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.
Installing
The package can be installed from PyPi:
pip install pybounds
or from source, for development, after cloning the repo:
pip install -e .

Notebook examples
For a simple system:
For a more complex system:
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
Additional resources
To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel
To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.
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.
100% open-source, feel free to utilize the code however you like.
About
Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
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GitHub - vanbreugel-lab/pybounds: Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems. · GitHub
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pybounds
Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
PyPI version
Introduction
This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.
Installing
The package can be installed from PyPi:
pip install pybounds
or from source, for development, after cloning the repo:
pip install -e .

Notebook examples
For a simple system:
For a more complex system:
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
Additional resources
To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel
To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.
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.
100% open-source, feel free to utilize the code however you like.
About
Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
Resources
Readme
MIT license
Activity
Custom properties
Stars
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GitHub - vanbreugel-lab/pybounds: Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems. · GitHub
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pybounds
Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
PyPI version
Introduction
This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.
Installing
The package can be installed from PyPi:
pip install pybounds
or from source, for development, after cloning the repo:
pip install -e .

Notebook examples
For a simple system:
For a more complex system:
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
Additional resources
To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel
To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.
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.
100% open-source, feel free to utilize the code however you like.
About
Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
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Readme
MIT license
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GitHub - vanbreugel-lab/pybounds: Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems. · GitHub
Skip to content
BranchesTags
Repository files navigation
pybounds
Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
PyPI version
Introduction
This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.
Installing
The package can be installed from PyPi:
pip install pybounds
or from source, for development, after cloning the repo:
pip install -e .

Notebook examples
For a simple system:
For a more complex system:
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
Additional resources
To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel
To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.
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.
100% open-source, feel free to utilize the code however you like.
About
Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
Resources
Readme
MIT license
Activity
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Stars
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GitHub - vanbreugel-lab/pybounds: Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems. · GitHub
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Repository files navigation
pybounds
Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
PyPI version
Introduction
This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.
Installing
The package can be installed from PyPi:
pip install pybounds
or from source, for development, after cloning the repo:
pip install -e .

Notebook examples
For a simple system:
For a more complex system:
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
Additional resources
To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel
To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.
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.
100% open-source, feel free to utilize the code however you like.
About
Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
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2 watching
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Report repository
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GitHub - vanbreugel-lab/pybounds: Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems. · GitHub
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pybounds
Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
PyPI version
Introduction
This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.
Installing
The package can be installed from PyPi:
pip install pybounds
or from source, for development, after cloning the repo:
pip install -e .

Notebook examples
For a simple system:
For a more complex system:
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
Additional resources
To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel
To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.
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.
100% open-source, feel free to utilize the code however you like.
About
Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
Resources
Readme
MIT license
Activity
Custom properties
Stars
11 stars
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Report repository
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GitHub - vanbreugel-lab/pybounds: Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems. · GitHub
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pybounds
Python implementation of BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
PyPI version
Introduction
This repository provides python code to empirically calculate the observability level of individual states for a nonlinear (partially observable) system, and accounts for sensor noise. Below is a graphical example of how pybounds can discover active sensing motifs. Minimal working examples are described below.
Installing
The package can be installed from PyPi:
pip install pybounds
or from source, for development, after cloning the repo:
pip install -e .

Notebook examples
For a simple system:
For a more complex system:
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
Additional resources
To learn more about nonlinear observability, its relation to Fisher information, see Boyacioglu and van Breugel
To start with the basics, check out these open source course materials: Nonlinear and Data Driven Estimation.
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.
100% open-source, feel free to utilize the code however you like.
About
Empirical observability of individual state variables with BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems.
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Readme
MIT license
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