pytams 1.0.0

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

pyTAMS

Overview

Rare events algorithms are powerful techniques allowing to sample rare occurrences of a computational model at a much lower cost than brute force Monte-Carlo. However, running such algorithms on models featuring more than a handull of dimensions become cumbersome as both compute and memory requirements increase. pyTAMS is a modular implementation of the trajectory-adaptive multilevel splitting (TAMS) rare event method introduced by Lestang et al., aiming at alleviating the difficulty of performing rare event algorithms for 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 .

Note that the latest version of pyTAMS is available on PyPI here and can be installed with pip install pytams, but built-in examples are not readily available using the PyPI version.

To run the example cases shipped with pyTAMS, additional dependencies are required. To install the examples dependencies, run:

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 2D version of the double-well is available in the examples folder. To run the case, simply do:

cd examples/DoubleWell2D
python tams_dw2dim.py

This minimal example runs TAMS 10 times in order to get an estimate of the transition probability as well as the corresponding relative 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.