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Merge branch 'master' of github.com:danijoo/WHAM

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Daniel Bauer
2018-11-18 12:04:58 +01:00
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[![Build Status](https://travis-ci.com/danijoo/WHAM.svg?branch=master)](https://travis-ci.com/danijoo/WHAM) [![crates.io](https://img.shields.io/badge/crates.io-orange.svg?longCache=true)](https://www.crates.io/crates/wham) [![Build Status](https://travis-ci.com/danijoo/WHAM.svg?branch=master)](https://travis-ci.com/danijoo/WHAM) [![crates.io](https://img.shields.io/badge/crates.io-orange.svg?longCache=true)](https://www.crates.io/crates/wham) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1488598.svg)](https://doi.org/10.5281/zenodo.1488598)
Weighted Histogram Analysis Method (WHAM) Weighted Histogram Analysis Method (WHAM)
=== ===
This is an fast implementation of the weighted histogram analysis method This is an fast implementation of the weighted histogram analysis method
written in Rust. It allows the calculation of multidimensional free energy profiles written in Rust. It allows the calculation of multidimensional free energy profiles
from umbrella sampling simulations. For more details on the method, I suggest Roux, B. from umbrella sampling simulations. For more details on the method, I suggest *Roux, B.
(1995). The calculation of the potential of mena force using computer simulations, CPC, 91(1), 275-282. (1995). The calculation of the potential of mean force using computer simulations, CPC, 91(1), 275-282.*
Features Features
--- ---
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WHAM can perform error analysis using the bayesian bootstrapping method. Every simulation window is assumed to be an WHAM can perform error analysis using the bayesian bootstrapping method. Every simulation window is assumed to be an
individual set of data point. By calculating probabilities N times with randomly assigned weights for each window, individual set of data point. By calculating probabilities N times with randomly assigned weights for each window,
one can estimate the error as standard deviation between the N bootstrapping runs. For more details see one can estimate the error as standard deviation between the N bootstrapping runs. For more details see
van der Spoel, D. et al. (2010). g_wham—A Free Weighted Histogram Analysis Implementation Including Robust Error and *Van der Spoel, D. et al. (2010). g_wham—A Free Weighted Histogram Analysis Implementation Including Robust Error and
Autocorrelation Estimates, JCTC, 6(12), 3713-3720. Autocorrelation Estimates, JCTC, 6(12), 3713-3720*.
To perform bayesian bootstrapping in WHAM, use the ```-bt <RUNS>``` flag to perform <RUNS> individual bootstrapping To perform bayesian bootstrapping in WHAM, use the ```-bt <RUNS>``` flag to perform <RUNS> individual bootstrapping
runs. The error estimates of bin probabilities and free energy will be given as separate column (+/-) in the output file. runs. The error estimates of bin probabilities and free energy will be given as separate column (+/-) in the output file.
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- Autocorrelation - Autocorrelation
- Replica exchange - Replica exchange
License License & Citing
--- ---
WHAM is licensed under the GPL-3.0 license. Please read the LICENSE file in this WHAM is licensed under the GPL-3.0 license. Please read the LICENSE file in this
repository for more information. repository for more information.
There's no publication for this WHAM implementation. However, there is a citeabe DOI. If you use this software for your work, please consider citing it: *Bauer, D, WHAM - An efficient weighted histogram analysis implementation written in Rust, Zenodo. https://doi.org/10.5281/zenodo.1488597*
Parts of this work, especially some perfomance optimizations and the I/O format, are inspired by the Parts of this work, especially some perfomance optimizations and the I/O format, are inspired by the
implementation of A. Grossfield (*A. Grossfield, "WHAM: the weighted histogram analysis method", http://membrane.urmc.rochester.edu/content/wham*). implementation of A. Grossfield (*Grossfield, A, WHAM: the weighted histogram analysis method, http://membrane.urmc.rochester.edu/content/wham*).