WHAM

Python package to construct free energy profiles from umbrella sampling simulation data.

Link to documentation.

Status

Code

Details

Binless formulation/MBAR (WHAM.binless) [more accurate]

• Implemented using log-likelihood maximization for superlinear convergence and self-consistent iteration (as a baseline/for debugging)
• Support for both 1D and multidimensional umbrella sampling.
• Support for reweighting 1D profiles to 2D (in a second related order parameter).

Binned formulation (WHAM.binned) [faster]

• Implemented using log-likelihood maximization for superlinear convergence and self-consistent iteration (as a baseline/for debugging)
• Support for both 1D and multidimensional^ umbrella sampling.

^-> in progress

Both log-likelihood maximization approaches can use multiple nonlinear optimization algorithms. Read the documentation to see which algorithms are available.

Installation

1. Install requirements

pip install -r requirements.txt

2. Build C extensions

python setup.py build_ext --inplace

3. Install package

pip install .

Usage

See the Jupyter notebooks in the examples/ directory.

Tests

Integration tests are in the directory tests/tests_integration and unit tests are in the directory tests/tests_unit. Navigate to a test directory and run:

pytest

References:

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