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pymbar

Python implementation of the multistate Bennett acceptance ratio (MBAR)

https://github.com/choderalab/pymbar

Science Score: 59.0%

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    Found 9 DOI reference(s) in README
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    Links to: ncbi.nlm.nih.gov, zenodo.org
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    6 of 22 committers (27.3%) from academic institutions
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    Low similarity (13.7%) to scientific vocabulary

Keywords

bennett-acceptance-ratio equilibrium extended-bridge-sampling free-energies mbar molecular-dynamics-simulations multistate-bennett-acceptance-ratio pymbar python research single-molecule-pulling thermodynamic-states

Keywords from Contributors

molecular-dynamics computational-chemistry molecular-simulation molecular-dynamics-simulation chemistry quantum-chemistry mdanalysis trajectory-analysis standards foyer
Last synced: 6 months ago · JSON representation

Repository

Python implementation of the multistate Bennett acceptance ratio (MBAR)

Basic Info
Statistics
  • Stars: 268
  • Watchers: 24
  • Forks: 94
  • Open Issues: 133
  • Releases: 16
Topics
bennett-acceptance-ratio equilibrium extended-bridge-sampling free-energies mbar molecular-dynamics-simulations multistate-bennett-acceptance-ratio pymbar python research single-molecule-pulling thermodynamic-states
Created almost 13 years ago · Last pushed 7 months ago
Metadata Files
Readme License

README.md

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pymbar

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.

Installation

The easiest way to install the pymbar release is via conda:

bash conda install -c conda-forge pymbar which will come with JAX to speed up the code. Or to get the non-JAX accelerated version: bash conda install -c conda-forge pymbar-core

You can also install JAX accelerated pymbar from the Python package index using pip: bash pip install pymbar[jax] or the non-jax-accelerated version with bash pip install pymbar Whether you install the JAX accelerated or non-JAX-accelerated version does not change any calls or how the code is run. The non-Jax version is smaller on disk due to smaller dependencies, but may not run as fast.

The development version can be installed directly from github via pip:

```bash

Get the compressed tarball

pip install https://github.com/choderalab/pymbar/archive/master.tar.gz

Or obtain a temporary clone of the repo with git

pip install git+https://github.com/choderalab/pymbar.git ```

Usage

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: ```python

from pymbar import testsystems xn, ukn, Nk, sn = testsystems.HarmonicOscillatorsTestCase().sample() `` We have thensamplessampled oscillator positionsxn(with samples from all states concatenated), [reduced potentials](http://www.alchemistry.org/wiki/Multistate_Bennett_Acceptance_Ratio#Reduced_potential) in the(nstates,nsamples)matrixukn, number of samples per state in thensamplesarrayNk, and indicessn` denoting which thermodynamic state each sample was drawn from.

To analyze this data, we first initialize the MBAR object:

```python

mbar = MBAR(ukn, Nk) ```

Estimating dimensionless free energy differences between the sampled thermodynamic states and their associated uncertainties (standard errors) simply requires a call to compute_free_energy_differences():

```python

results = mbar.computefreeenergy_differences() ```

Here results is a dictionary with keys Deltaf_ij, dDeltaf, and Theta. 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.

Expectations and associated uncertainties can easily be estimated for observables A(x) for all states:

```python

Akn = xkn # use position of harmonic oscillator as observable results = mbar.computeexpectations(Akn) ```

where results is a dictionary with keys mu, sigma, and Theta, where 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 covariance 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 help().

JAX needs 64-bit mode

PyMBAR needs 64-bit floats to provide reliable answers. JAX by default uses 32-bit (Single) bitsize. PyMBAR will turn on JAX's 64-bit mode, which may cause issues with some separate uses of JAX in the same code as PyMBAR, such as existing Neural Network (NN) Models for machine learning.

Authors

References

  • 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

License

pymbar is free software and is licensed under the MIT license.

Thanks

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.

Notes

Owner

  • Name: Chodera lab // Memorial Sloan Kettering Cancer Center
  • Login: choderalab
  • Kind: organization
  • Email: john.chodera@choderalab.org
  • Location: Memorial Sloan-Kettering Cancer Center, Manhattan, NY

GitHub Events

Total
  • Issues event: 11
  • Watch event: 27
  • Delete event: 1
  • Issue comment event: 41
  • Push event: 10
  • Pull request review comment event: 4
  • Pull request review event: 9
  • Pull request event: 8
  • Fork event: 4
  • Create event: 1
Last Year
  • Issues event: 11
  • Watch event: 27
  • Delete event: 1
  • Issue comment event: 41
  • Push event: 10
  • Pull request review comment event: 4
  • Pull request review event: 9
  • Pull request event: 8
  • Fork event: 4
  • Create event: 1

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 818
  • Total Committers: 22
  • Avg Commits per committer: 37.182
  • Development Distribution Score (DDS): 0.648
Past Year
  • Commits: 9
  • Committers: 3
  • Avg Commits per committer: 3.0
  • Development Distribution Score (DDS): 0.444
Top Committers
Name Email Commits
Michael Shirts m****s@g****m 288
kyleabeauchamp k****p@g****m 184
Levi Naden l****n@v****u 126
John Chodera (MSKCC) c****j@m****g 85
Jaime RGP j****e@g****m 61
Michael Shirts m****s@v****u 35
Stefano Martiniani s****i@g****m 8
ChayaSt c****n@c****g 5
Mike Henry 1****y 5
Matthew W. Thompson m****n@p****m 4
Jennifer A Clark j****3@g****m 3
Josh Fass j****s@r****m 2
Richard Gowers r****s@g****m 2
Robert McGibbon r****o@g****m 2
Tucker Burgin t****n@u****u 1
Bradley Dice b****e@b****m 1
David Dotson d****l@g****m 1
Forrest York b****a 1
Iván Pulido 2****s 1
Marc Schuh s****c@s****t 1
James Barnett j****4@t****u 1
Chris c****4@u****u 1
Committer Domains (Top 20 + Academic)
u.boisestate.edu: 1 tulane.edu: 1 students.boku.ac.at: 1 bradleydice.com: 1 umich.edu: 1 relaytx.com: 1 choderalab.org: 1 virginia.edu: 1 mskcc.org: 1 vt.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 86
  • Total pull requests: 89
  • Average time to close issues: 10 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 42
  • Total pull request authors: 19
  • Average comments per issue: 3.03
  • Average comments per pull request: 4.84
  • Merged pull requests: 61
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 13
  • Pull requests: 16
  • Average time to close issues: about 13 hours
  • Average time to close pull requests: 7 days
  • Issue authors: 10
  • Pull request authors: 6
  • Average comments per issue: 0.54
  • Average comments per pull request: 4.25
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • mrshirts (25)
  • xiki-tempula (10)
  • jaimergp (4)
  • Lnaden (4)
  • jchodera (2)
  • mattwthompson (2)
  • jaclark5 (2)
  • shawnhsueh (2)
  • hannahbaumann (2)
  • francoviscarra (1)
  • maxentile (1)
  • badisa (1)
  • mikemhenry (1)
  • udlich (1)
  • svandenhaute (1)
Pull Request Authors
  • mrshirts (25)
  • mikemhenry (12)
  • Lnaden (10)
  • jaimergp (8)
  • jaclark5 (7)
  • badisa (5)
  • mattwthompson (4)
  • zhang-ivy (3)
  • schuhmc (2)
  • nielskm (2)
  • IAlibay (2)
  • Nithishwer (2)
  • shawnhsueh (1)
  • jchodera (1)
  • rosadche (1)
Top Labels
Issue Labels
pymbar 4 (7) enhancement (5) bug (4) question (3) documentation (2) pymbar-3-lts (1) tests (1) pymbar 5 (1)
Pull Request Labels
pymbar 4 (5) pymbar-3-lts (2) bug (1) high priority (1)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 15,577 last-month
  • Total docker downloads: 101
  • Total dependent packages: 21
    (may contain duplicates)
  • Total dependent repositories: 45
    (may contain duplicates)
  • Total versions: 23
  • Total maintainers: 2
pypi.org: pymbar

Python implementation of the multistate Bennett acceptance ratio (MBAR) method

  • Versions: 14
  • Dependent Packages: 6
  • Dependent Repositories: 28
  • Downloads: 15,577 Last month
  • Docker Downloads: 101
Rankings
Dependent packages count: 1.4%
Dependent repos count: 2.7%
Docker downloads count: 3.3%
Average: 3.7%
Stargazers count: 4.8%
Forks count: 4.9%
Downloads: 5.1%
Maintainers (2)
lnaden jchodera
Last synced: 6 months ago
conda-forge.org: pymbar
  • Versions: 9
  • Dependent Packages: 15
  • Dependent Repositories: 17
Rankings
Dependent packages count: 4.2%
Dependent repos count: 8.6%
Average: 15.2%
Forks count: 20.5%
Stargazers count: 27.5%
Last synced: 6 months ago

Dependencies

docs/rtd_requirements.txt pypi
  • numpy *
  • numpydoc *
  • pytest *
  • scipy *
  • sphinxcontrib-bibtex *
.github/workflows/CI.yaml actions
  • actions/checkout v3 composite
  • actions/checkout v1 composite
  • codecov/codecov-action v1 composite
  • mamba-org/provision-with-micromamba main composite
setup.py pypi
  • jax *
  • jaxlib *
  • numexpr *
  • numpy >=1.12
  • scipy *