pytams 0.0.5

An implementation of the trajectory-adaptive multilevel splitting (TAMS) method.

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 system trajectories ensemble.

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:

git clone [e-mail]:nlesc-eTAOC/pyTAMS.git
cd pyTAMS
python -m pip install .

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:

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.