Skip to main content

Hamiltonian Lorenz-like models

Project description

Hamiltonian Lorenz Models

This package implements Hamiltonian Lorenz-like models, a class of low-order dynamical systems that extend the classical Lorenz-96 and Lorenz-2005 frameworks by incorporating a Hamiltonian structure. These models are designed to preserve certain physical invariants—such as energy and Casimirs—making them particularly well-suited for studying conservative dynamical systems, geophysical flows, and chaotic transport.

Features

  • Hamiltonian structure: The time evolution of the system is derived from a Hamiltonian, preserving energy exactly as in the continuous-time limit.
  • Casimir invariants: Multiple conserved quantities beyond energy, ensuring the system evolves on a constrained manifold.
  • Symplectic integrators: Optional numerical solvers designed for long-time energy and Casimir invariant preservation.
  • Lyapunov spectrum computation: Quantifies the level of chaos in the system via Lyapunov exponents.
  • Fourier-based desymmetrization: Enables symmetry reduction to study physical variables in a more interpretable form.
  • PDF and time series visualization: Built-in tools to analyze and visualize system statistics and dynamics.

Applications

  • Modeling barotropic dynamics or simplified atmospheric flows.
  • Testing chaos detection and prediction techniques.
  • Benchmarking conservative integration schemes.

Reference

For a full mathematical formulation and analysis of these models, see:

Fedele, Chandre, Horvat, and Žagar Hamiltonian Lorenz-like models, Physica D, Vol. 472, 134494 (2025). https://doi.org/10.1016/j.physd.2024.134494

@article{HamLorenz,
  title = {Hamiltonian Lorenz-like models},
  author = {Francesco Fedele and Cristel Chandre and Martin Horvat and Nedjeljka Žagar},
  journal = {Physica D: Nonlinear Phenomena},
  volume = {472},
  pages = {134494},
  year = {2025},
  doi = {https://doi.org/10.1016/j.physd.2024.134494},
}

Documentation & Examples

Examples can be found at Examples

The full documentation, including detailed function explanations, is available on the Wiki Page.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hamlorenz-0.1.0.tar.gz (9.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hamlorenz-0.1.0-py3-none-any.whl (9.7 kB view details)

Uploaded Python 3

File details

Details for the file hamlorenz-0.1.0.tar.gz.

File metadata

  • Download URL: hamlorenz-0.1.0.tar.gz
  • Upload date:
  • Size: 9.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.15

File hashes

Hashes for hamlorenz-0.1.0.tar.gz
Algorithm Hash digest
SHA256 cf8a3be3ccdebd1bbb5727287d101ebed40df075d63857cf3fbae7a241dac541
MD5 3d46ef8301591d67ee9fdba7fb840bbc
BLAKE2b-256 1e8236b398b554323fe35451462cc33a363854e39da959cabc8796b9cd3ea11a

See more details on using hashes here.

File details

Details for the file hamlorenz-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: hamlorenz-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 9.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.15

File hashes

Hashes for hamlorenz-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 298822cf1b04054c320bfa3248ddd7fd9229cdccf393d9cf5c41d5b337465ac5
MD5 253a09a70910a6623829dfae0cc46957
BLAKE2b-256 d7b753caaea1b478a86af51be3f217b4f7ac22c6c52c99956a1989ecb4b2c333

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page