Python package to construct free energy profiles from umbrella sampling simulation data.
Link to documentation.
- 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).
- 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.
- Install requirements
pip install -r requirements.txt- Build C extensions
python setup.py build_ext --inplace- Install package
pip install .See the Jupyter notebooks in the examples/ directory.
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- 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