bauer/WHAM
1
0
Fork 0
mirror of https://github.com/dnlbauer/WHAM.git synced 2026-09-11 06:35:30 +00:00
Code Issues Packages Projects Releases Wiki Activity

readme

Browse Source
This commit is contained in:
Daniel Bauer
2018-11-13 09:51:19 +01:00
parent fe5ff0a9af
commit 7c831e5fa5
1 changed files with 26 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
README.md
View File

@@ -4,12 +4,14 @@ 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. 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.
Features Features
--- ---
- Fast, especially for small systems - Fast, especially for small systems
- Multidimensional - Multidimensional
- Error analysis
- Unit tested - Unit tested
Usage Usage
@@ -38,17 +40,31 @@ After convergence, final bias offsets (F) and the free energy will be dumped to
The output file contains the free energy and probability for each bin. Probabilities are normalized to sum to P=1.0 and The output file contains the free energy and probability for each bin. Probabilities are normalized to sum to P=1.0 and
the smallest free energy is set to 0 (with other free energies based on that). the smallest free energy is set to 0 (with other free energies based on that).
``` ```
#coord1 coord2 Free Energy Probability #coord1 coord2 Free Energy +/- Probability +/-
-3.109590 -3.109590 10.330312 0.000095 -3.108600 -3.108600 10.331716 0.000000 0.000095 0.000000
-3.046770 -3.109590 8.907360 0.000168 -3.045800 -3.108600 8.893231 0.000000 0.000170 0.000000
-2.983950 -3.109590 7.431969 0.000303 -2.983000 -3.108600 7.372765 0.000000 0.000312 0.000000
-2.921130 -3.109590 6.170882 0.000502 -2.920200 -3.108600 6.207354 0.000000 0.000498 0.000000
-2.858310 -3.109590 4.982956 0.000809 -2.857400 -3.108600 4.915298 0.000000 0.000836 0.000000
-2.795490 -3.109590 3.584741 0.001417 -2.794600 -3.108600 3.644738 0.000000 0.001392 0.000000
-2.732670 -3.109590 3.025337 0.001773 -2.731800 -3.108600 3.021743 0.000000 0.001787 0.000000
-2.669000 -3.108600 2.827463 0.000000 0.001932 0.000000
-2.606200 -3.108600 2.647531 0.000000 0.002076 0.000000
(...) (...)
``` ```
Error analysis
---
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,
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
Autocorrelation Estimates, JCTC, 6(12), 3713-3720.
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.
If no error analysis is performed, these columns are set to 0.0.
Examples Examples
--- ---
The example folder contains input and output files for two simple test systems: The example folder contains input and output files for two simple test systems:
@@ -60,7 +76,6 @@ The example folder contains input and output files for two simple test systems:
TODO TODO
--- ---
- Multithreading (?) - Multithreading (?)
- Error analysis / bootstrapping
- Autocorrelation - Autocorrelation
- Replica exchange - Replica exchange
@@ -70,4 +85,4 @@ WHAM is licensed under the GPLv3 license. Please read the LICENSE file in this
repository for more information. repository for more information.
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 (*Grossfield, Alan, "WHAM: the weighted histogram analysis method", http://membrane.urmc.rochester.edu/content/wham*). implementation of A. Grossfield (*A. Grossfield, "WHAM: the weighted histogram analysis method", http://membrane.urmc.rochester.edu/content/wham*).