| Name | heyoka JSON |
| Version |
7.6.0
JSON |
| download |
| home_page | None |
| Summary | Python library for ODE integration via Taylor's method and LLVM |
| upload_time | 2025-09-14 07:38:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MPL-2.0 |
| keywords |
science
math
physics
ode
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
|
| coveralls test coverage |
No coveralls.
|
heyoka.py
=========
[](https://circleci.com/gh/bluescarni/heyoka.py)
[](https://github.com/bluescarni/heyoka.py/actions?query=workflow%3A%22GitHub+CI%22)
[](https://anaconda.org/conda-forge/heyoka.py)
[](https://pypi.python.org/pypi/heyoka)
<!-- PROJECT LOGO -->
<br />
<p align="center">
<a href="https://github.com/bluescarni/heyoka.py">
<img src="doc/images/white_logo.png" alt="Logo" width="280">
</a>
<p align="center">
Modern Taylor's method via just-in-time compilation
<br />
<a href="https://bluescarni.github.io/heyoka.py/index.html"><strong>Explore the docs »</strong></a>
<br />
<br />
<a href="https://github.com/bluescarni/heyoka.py/issues/new/choose">Report bug</a>
·
<a href="https://github.com/bluescarni/heyoka.py/issues/new/choose">Request feature</a>
·
<a href="https://github.com/bluescarni/heyoka.py/discussions">Discuss</a>
</p>
</p>
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.
Raw data
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