WHAM

Release

0.1.0

WHAM is a Python package for constructing free energy profiles from umbrella sampling simulation data.

Installation

Source code is available from https://github.com/apallath/WHAM

Obtain the sources with git

git clone https://github.com/apallath/WHAM.git

  1. Install requirements

pip install -r requirements.txt

  1. Build C extensions

python setup.py build_ext --inplace

  1. Install package [in editable state]

pip install [-e] .

Running tests

cd tests
pytest

Usage

Binless WHAM is generally a better choice for accuracy, and implements more features than binned WHAM (such as reweighting, binning 2D profiles given a related order parameter, and integrating these profiles to obtain free energy profiles in terms of a related unbiased order parameter). However, binned WHAM is faster and uses less memory than binless WHAM.

Log-likelihood maximization is a better approach than self-consistent iteration, which can suffer from slow convergence.

Choose between the two different WHAM formulations and solution approaches based on your needs.

Look at the documentation of the statistics module to understand how to use statistical checks to verify the consistency of WHAM calculations.

For examples demonstrating free energy profile calculations, see the examples directory.

References

  • Shirts, M. R., & Chodera, J. D. (2008). Statistically optimal analysis of samples from multiple equilibrium states. Journal of Chemical Physics, 129(12). [1]

  • Zhu, F., & Hummer, G. (2012). Convergence and error estimation in free energy calculations using the weighted histogram analysis method. Journal of Computational Chemistry, 33(4), 453–465. [2]

  • Tan, Z., Gallicchio, E., Lapelosa, M., & Levy, R. M. (2012). Theory of binless multi-state free energy estimation with applications to protein-ligand binding. Journal of Chemical Physics, 136(14). [3]

Contents:

Indices and tables