pytams
An implementation of the trajectory-adaptive multilevel splitting (TAMS) method.
Science Score: 67.0%
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Repository
An implementation of the trajectory-adaptive multilevel splitting (TAMS) method.
Basic Info
- Host: GitHub
- Owner: nlesc-eTAOC
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://nlesc-etaoc.github.io/pyTAMS/
- Size: 1.24 MB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 10
- Releases: 5
Metadata Files
README.md
pyTAMS
Overview
pyTAMS is a modular implementation of the trajectory-adaptive multilevel splitting (TAMS) method introduced by Lestang et al.. This method aims at predicting rare events probabilities in dynamical systems by biasing an ensemble of system trajectories.
The main objective of pyTAMS is to provide a general framework for applying TAMS to high-dimensional systems such as the ones encountered in geophysical or engineering applications.
Installation
To install pyTAMS from GitHub repository, do:
console
git clone git@github.com:nlesc-eTAOC/pyTAMS.git
cd pyTAMS
python -m pip install .
Note that the latest version of pyTAMS is available on PyPI here
and can be installed with pip install pytams.
Finally, a few example cases are shipped with pyTAMS, but additional dependencies are required. To install the examples dependencies, run:
console
python -m pip install .[exec]
Quick start
To get started with pyTAMS, let's have a look at the classical double-well potential case. Although it is not a high-dimensional system, it provides a good overview of pyTAMS capabilities. A 3D version of the double-well is available in the examples folder. To run the case, simply do:
console
cd examples
python doubleWell3D.py -i input_dw3D.toml
This minimal example runs TAMS 10 times in order to get an estimate of the transition probability as well as the corresponding standard error. For a more in-depth explanation about this case, setting up the model and running the simulations, have a look at the tutorial here.
Documentation
pyTAMS documentation is hosted on GitHub here
Contributing
If you want to contribute to the development of pyTAMS, have a look at the contribution guidelines.
Acknowledgements
The development of pyTAMS was supported by the Netherlands eScience Center in collaboration with the Institute for Marine and Atmospheric research Utrecht IMAU.
This package was created with Cookiecutter and the NLeSC/python-template.
Owner
- Name: Tipping of the Atlantic Ocean Circulation
- Login: nlesc-eTAOC
- Kind: organization
- Repositories: 1
- Profile: https://github.com/nlesc-eTAOC
Project of Netherlands eScience Center and Utrecht University
Citation (CITATION.cff)
# YAML 1.2
---
cff-version: "1.2.0"
title: "pyTAMS"
authors:
-
family-names: Esclapez
given-names: Lucas
orcid: "https://orcid.org/0000-0002-2438-7292"
date-released: 2025-01-29
doi: 10.5281/zenodo.14762701
version: "0.0.5"
repository-code: "git@github.com:nlesc-eTAOC/pyTAMS"
keywords:
- Rare events probability
- Stochastic systems
message: "If you use this software, please cite it using these metadata."
license: Apache-2.0
GitHub Events
Total
- Create event: 42
- Issues event: 21
- Release event: 2
- Watch event: 2
- Delete event: 41
- Issue comment event: 65
- Push event: 90
- Pull request event: 75
Last Year
- Create event: 42
- Issues event: 21
- Release event: 2
- Watch event: 2
- Delete event: 41
- Issue comment event: 65
- Push event: 90
- Pull request event: 75
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 14
- Total pull requests: 41
- Average time to close issues: about 2 months
- Average time to close pull requests: about 9 hours
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 0.43
- Average comments per pull request: 0.9
- Merged pull requests: 36
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 13
- Pull requests: 41
- Average time to close issues: 18 days
- Average time to close pull requests: about 9 hours
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.38
- Average comments per pull request: 0.9
- Merged pull requests: 36
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- esclapez (23)
Pull Request Authors
- esclapez (61)
- ValerianJD (1)
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