Metadata-Version: 2.4
Name: pyrecest
Version: 1.1.0
Summary: Framework for recursive Bayesian estimation in Python.
License-File: LICENSE
Author: Florian Pfaff
Author-email: pfaff@ias.uni-stuttgart.de
Requires-Python: >=3.11,<3.15
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Provides-Extra: healpy-support
Provides-Extra: jax-support
Provides-Extra: pytorch-support
Requires-Dist: beartype
Requires-Dist: matplotlib
Requires-Dist: mpmath
Requires-Dist: numpy
Requires-Dist: pyshtools
Requires-Dist: scipy (>=1.17.1,<2.0.0)
Requires-Dist: shapely
Description-Content-Type: text/markdown

# PyRecEst

*Recursive Bayesian Estimation for Python*

PyRecEst is a Python library tailored for recursive Bayesian estimation, compatible with numpy, pytorch, and jax backends.

Features of PyRecEst include:

* Distribution and Densities: Provides tools for handling distributions and densities across Euclidean spaces and manifolds.
* Filters and Trackers: Offers a suite of recursive Bayesian estimators (filters or trackers) for both Euclidean spaces and manifolds. This includes capabilities for:
  * Multi-Target Tracking (MTT)
  * Extended Object Tracking (EOT)
* Evaluation Framework: Contains an evaluation framework to facilitate comparison between different filters.
* Sampling Methods: Includes methods for sampling of the distributions and generating grids.

## Usage

Please refer to the test cases for usage examples.

## Citation

If you use **PyRecEst** in your research, please cite:

<table>
  <tr>
    <th>BibTeX</th>
    <th>BibLaTeX</th>
  </tr>
  <tr>
    <td>
      <pre><code class="language-bibtex">@misc{pfaff_pyrecest_2023,
  author       = {Florian Pfaff},
  title        = {PyRecEst: Recursive Bayesian Estimation for Python},
  year         = {2023},
  howpublished = {\url{https://github.com/FlorianPfaff/PyRecEst}},
  note         = {MIT License}
}</code></pre>
    </td>
    <td>
      <pre><code class="language-biblatex">@software{pfaff_pyrecest_2023_software,
  author    = {Florian Pfaff},
  title     = {PyRecEst: Recursive Bayesian Estimation for Python},
  year      = {2023},
  url       = {https://github.com/FlorianPfaff/PyRecEst},
  license   = {MIT},
  keywords  = {Bayesian filtering; manifolds; tracking; Python; NumPy; PyTorch; JAX}
}</code></pre>
    </td>
  </tr>
</table>

## Credits

- Florian Pfaff (<pfaff@ias.uni-stuttgart.de>)

PyRecEst borrows its structure from libDirectional and follows its code closely for many classes. libDirectional, a project to which I contributed extensively, is [available on GitHub](https://github.com/libDirectional). The backend implementations are based on those of [geomstats](https://github.com/geomstats/geomstats).

## License
`PyRecEst` is licensed under the MIT License.

