Thanks to visit codestin.com
Credit goes to Github.com

Skip to content

Python library for ODE integration via Taylor's method and LLVM

License

Notifications You must be signed in to change notification settings

AlexandreFoley/heyoka.py

 
 

Repository files navigation

heyoka.py

Build Status Build Status

Anaconda-Server Badge


Logo

Modern Taylor's method via just-in-time compilation
Explore the docs »

Report bug · Request feature · Discuss

The heyókȟa [...] is a kind of sacred clown in the culture of the Sioux (Lakota and Dakota people) of the Great Plains of North America. The heyoka is a contrarian, jester, and satirist, who speaks, moves and reacts in an opposite fashion to the people around them.

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. Notable features include:

  • support for double-precision, extended-precision (80-bit and 128-bit), and arbitrary-precision floating-point types,
  • the ability to maintain machine precision accuracy over tens of billions of timesteps,
  • high-precision zero-cost dense output,
  • accurate and reliable event detection,
  • batch mode integration to harness the power of modern SIMD instruction sets (including AVX/AVX2/AVX-512/Neon/VSX),
  • ensemble simulations and automatic parallelisation,
  • interoperability with SymPy.

heyoka.py is based on the heyoka C++ library.

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):

@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):

@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}
}

Documentation

The full documentation can be found here.

Authors

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

License

heyoka.py is released under the MPL-2.0 license.

About

Python library for ODE integration via Taylor's method and LLVM

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • C++ 51.7%
  • Python 42.3%
  • CMake 4.1%
  • Shell 1.9%