Python implementation of the multistate Bennett acceptance ratio (MBAR) method for estimating expectations and free energy differences from equilibrium samples from multiple probability densities. See our docs.
The easiest way to install the
pymbar release is via conda:
conda install -c conda-forge pymbar
You can also install
pymbar from the Python package index using
pip install pymbar
The development version can be installed directly from github via
pip install git+https://github.com/choderalab/pymbar.git
Basic usage involves importing
pymbar and constructing an
MBAR object from the reduced potential of simulation or experimental data.
Suppose we sample a 1D harmonic oscillator from a few thermodynamic states:
>>> from pymbar import testsystems >>> [x_n, u_kn, N_k, s_n] = testsystems.HarmonicOscillatorsTestCase().sample()
We have the
nsamples sampled oscillator positions
x_n (with samples from all states concatenated), reduced potentials in the
u_kn, number of samples per state in the
N_k, and indices
s_n denoting which thermodynamic state each sample was drawn from.
To analyze this data, we first initialize the
>>> mbar = MBAR(u_kn, N_k)
Estimating dimensionless free energy differences between the sampled thermodynamic states and their associated uncertainties (standard errors) simply requires a call to
>>> results = mbar.getFreeEnergyDifferences(return_dict=True)
results is a dictionary with keys
Deltaf_ij[i,j] is the matrix of dimensionless free energy differences
f_j - f_i,
dDeltaf_ij[i,j] is the matrix of standard errors in this matrices estimate, and
Theta is a covariance matrix that can be used to propagate error into quantities derived from the free energies. By default,
False and the return will be a tuple.
Expectations and associated uncertainties can easily be estimated for observables
A(x) for all states:
>>> A_kn = x_kn # use position of harmonic oscillator as observable >>> results = mbar.computeExpectations(A_kn, return_dict=True)
results is a dictionary with keys
mu[i] is the array of the estimate for the average of the observable for in state i,
sigma[i] is the estimated standard deviation of the
mu estimates, and
Theta[i,j] is the covarinace matrix of the log weights.
See the docstring help for these individual methods for more information on exact usage; in Python or IPython, you can view the docstrings with
- Kyle A. Beauchamp [email protected]
- John D. Chodera [email protected]
- Levi N. Naden [email protected]
- Michael R. Shirts [email protected]
Please cite the original MBAR paper:
Shirts MR and Chodera JD. Statistically optimal analysis of samples from multiple equilibrium states. J. Chem. Phys. 129:124105 (2008). DOI
Some timeseries algorithms can be found in the following reference:
Chodera JD, Swope WC, Pitera JW, Seok C, and Dill KA. Use of the weighted histogram analysis method for the analysis of simulated and parallel tempering simulations. J. Chem. Theor. Comput. 3(1):26-41 (2007). DOI
The automatic equilibration detection method provided in
pymbar.timeseries.detectEquilibration() is described here:
Chodera JD. A simple method for automated equilibration detection in molecular simulations. J. Chem. Theor. Comput. 12:1799, 2016. DOI
pymbar is free software and is licensed under the MIT license.
We would especially like to thank a large number of people for helping us identify issues and ways to improve
pymbar, including Tommy Knotts, David Mobley, Himanshu Paliwal, Zhiqiang Tan, Patrick Varilly, Todd Gingrich, Aaron Keys, Anna Schneider, Adrian Roitberg, Nick Schafer, Thomas Speck, Troy van Voorhis, Gupreet Singh, Jason Wagoner, Gabriel Rocklin, Yannick Spill, Ilya Chorny, Greg Bowman, Vincent Voelz, Peter Kasson, Dave Caplan, Sam Moors, Carl Rogers, Josua Adelman, Javier Palacios, David Chandler, Andrew Jewett, Stefano Martiniani, and Antonia Mey.