https://github.com/darioizzo/heyoka.py

https://github.com/darioizzo/heyoka.py

Repository

https://github.com/darioizzo/heyoka.py/blob/main/

heyoka.py
=========

[![Build Status](https://img.shields.io/circleci/project/github/bluescarni/heyoka.py/main.svg?style=for-the-badge)](https://circleci.com/gh/bluescarni/heyoka.py)
[![Build Status](https://img.shields.io/github/actions/workflow/status/bluescarni/heyoka.py/gh_actions_ci.yml?branch=main&style=for-the-badge)](https://github.com/bluescarni/heyoka.py/actions?query=workflow%3A%22GitHub+CI%22)

[![Anaconda-Server Badge](https://img.shields.io/conda/vn/conda-forge/heyoka.py.svg?style=for-the-badge)](https://anaconda.org/conda-forge/heyoka.py)
[![PyPI](https://img.shields.io/pypi/v/heyoka.svg?style=for-the-badge)](https://pypi.python.org/pypi/heyoka)





  
    
  
  

    Modern Taylor's method via just-in-time compilation
    

    Explore the docs 
    

    

    Report bug
    
    Request feature
    
    Discuss
  


heyoka.py is a Python library for the integration of ordinary differential equations
(ODEs) via Taylor's method, based on automatic differentiation techniques and aggressive just-in-time
compilation via [LLVM](https://llvm.org/). Notable features include:

* support for single-precision, double-precision, extended-precision (80-bit and 128-bit),
  and arbitrary-precision floating-point types,
* high-precision zero-cost dense output,
* accurate and reliable event detection,
* builtin support for analytical mechanics - bring your own Lagrangians/Hamiltonians
  and let heyoka.py formulate and solve the equations of motion,
* builtin support for operational Earth-orbiting spacecraft analysis, including frame
  transformations, high-fidelity geopotential models, Earth Orientation Parameters (EOP),
  atmospheric models, space weather effects, ephemeris-based third-body perturbations,
* builtin support for high-order variational equations - compute not only the solution,
  but also its partial derivatives,
* builtin support for machine learning applications via neural network models,
* the ability to maintain machine precision accuracy over
  tens of billions of timesteps,
* batch mode integration to harness the power of modern
  [SIMD](https://en.wikipedia.org/wiki/SIMD) instruction sets
  (including AVX/AVX2/AVX-512/Neon/VSX),
* ensemble simulations and automatic parallelisation,
* interoperability with [SymPy](https://www.sympy.org/en/index.html).

heyoka.py is based on the [heyoka C++ library](https://github.com/bluescarni/heyoka).

If you are using heyoka.py as part of your research, teaching, or other activities, we would be grateful if you could star
the repository and/or cite our work. For citation purposes, you can use the following BibTex entry, which refers
to the heyoka.py paper ([arXiv preprint](https://arxiv.org/abs/2105.00800)):

```bibtex
@article{10.1093/mnras/stab1032,
    author = {Biscani, Francesco and Izzo, Dario},
    title = "{Revisiting high-order Taylor methods for astrodynamics and celestial mechanics}",
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {504},
    number = {2},
    pages = {2614-2628},
    year = {2021},
    month = {04},
    issn = {0035-8711},
    doi = {10.1093/mnras/stab1032},
    url = {https://doi.org/10.1093/mnras/stab1032},
    eprint = {https://academic.oup.com/mnras/article-pdf/504/2/2614/37750349/stab1032.pdf}
}
```

heyoka.py's novel event detection system is described in the following paper ([arXiv preprint](https://arxiv.org/abs/2204.09948)):

```bibtex
@article{10.1093/mnras/stac1092,
    author = {Biscani, Francesco and Izzo, Dario},
    title = "{Reliable event detection for Taylor methods in astrodynamics}",
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {513},
    number = {4},
    pages = {4833-4844},
    year = {2022},
    month = {04},
    issn = {0035-8711},
    doi = {10.1093/mnras/stac1092},
    url = {https://doi.org/10.1093/mnras/stac1092},
    eprint = {https://academic.oup.com/mnras/article-pdf/513/4/4833/43796551/stac1092.pdf}
}
```

Installation
------------

Via pip:

```console
$ pip install heyoka
```

Via conda + [conda-forge](https://conda-forge.org/):

```console
$ conda install heyoka.py
```

Documentation
-------------

The full documentation can be found [here](https://bluescarni.github.io/heyoka.py/).

Authors
-------

* Francesco Biscani (European Space Agency)
* Dario Izzo (European Space Agency)

License
-------

heyoka.py is released under the [MPL-2.0](https://www.mozilla.org/en-US/MPL/2.0/FAQ/)
license.

Owner

GitHub Events