• Weighted Ensemble Python (wepy)

  #+ATTR_HTML: title="Join the chat at https://gitter.im/wepy/general" [[https://gitter.im/wepy/general?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge][file:https://badges.gitter.im/wepy/general.svg]]

[[./info/logo/wepy.svg]]

trying to make a zenodo badge but github doesn't support this

directly. Would have to add a separate build step for this.

#+begin_export html #+end_export

[[https://adicksonlab.github.io/wepy/index.html][Sphinx Documentation]]

[[https://github.com/ADicksonLab/wepy/blob/master/info/README.org][Plaintext Org-Mode Docs]]

Modular implementation and framework for running weighted ensemble (WE) simulations in pure python, where the aim is to have simple things simple and complicated things possible. The latter being the priority.

The goal of the architecture is that it should be highly modular to allow extension, but provide a "killer app" for most uses that just works, no questions asked.

Comes equipped with support for [[https://github.com/pandegroup/openmm][OpenMM]] molecular dynamics, parallelization using multiprocessing, the [[http://pubs.acs.org/doi/abs/10.1021/jp411479c][WExplore]] and [[https://pubmed.ncbi.nlm.nih.gov/31255090/][REVO]] (Resampling Ensembles by Variance Optimization) resampling algorithms, and an HDF5 file format and library for storing and querying your WE datasets that can be used from the command line.

The deeper architecture of ~wepy~ is intended to be loosely coupled, so that unforeseen use cases can be accomodated, but tightly integrated for the most common of use cases, i.e. molecular dynamics.

This allows freedom for fast development of new methods.

Full [[https://github.com/ADicksonLab/wepy/blob/master/info/introduction.org][introduction]].

** Installation

Also see: [[info/installation.org][Installation Instructions]]

We recommend running this version of wepy in a conda environment using python=3.10 or greater:

#+BEGIN_SRC bash conda create -n wepy python=3.10 conda activate wepy #+END_SRC

Next, install wepy with pip:

#+BEGIN_SRC bash pip install wepy #+END_SRC

which will also install most dependencies.

Alternatively, the latest version of wepy can be installed from the git repo source: #+BEGIN_SRC bash git clone https://github.com/ADicksonLab/wepy.git cd wepy pip install . #+END_SRC

The OpenMM package can then be installed using conda:

#+BEGIN_SRC bash conda install -c conda-forge openmm #+END_SRC

Check its installed by running the command line interface:

#+begin_src bash :tangle check_installation.bash wepy --help #+end_src

** Citations

Current [[https://zenodo.org/badge/latestdoi/101077926][Zenodo DOI]].

Cite software as:

#+begin_example Samuel D. Lotz, Nazanin Donyapour, Alex Dickson, Tom Dixon, Nicole Roussey, & Rob Hall. (2020, August 4). ADicksonLab/wepy: 1.0.0 Major version release (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3973431 #+end_example

Accompanying journal article:

• [[https://pubs.acs.org/doi/abs/10.1021/acsomega.0c03892][ACS Omega]] article