Weighted Ensemble Python (wepy)

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

conda create -n wepy python=3.10
conda activate wepy

Next, install `wepy` with pip:

pip install wepy

which will also install most dependencies.

Alternatively, the latest version of `wepy` can be installed from the git repo source:

git clone https://github.com/ADicksonLab/wepy.git
cd wepy
pip install .

The OpenMM package can then be installed using conda:

conda install -c conda-forge openmm

Check its installed by running the command line interface:

wepy --help

Citations

Current Zenodo DOI.

Cite software as:

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

Accompanying journal article:

ACS Omega article

Weighted Ensemble Python (wepy)

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 OpenMM molecular dynamics, parallelization using multiprocessing, the WExplore and 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 introduction.

Installation

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

conda create -n wepy python=3.10
conda activate wepy

Next, install `wepy` with pip:

pip install wepy

which will also install most dependencies.

Alternatively, the latest version of `wepy` can be installed from the git repo source:

git clone https://github.com/ADicksonLab/wepy.git
cd wepy
pip install .

The OpenMM package can then be installed using conda:

conda install -c conda-forge openmm

Check its installed by running the command line interface:

wepy --help

Citations

Current Zenodo DOI.

Cite software as:

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

Accompanying journal article:

ACS Omega article

Weighted Ensemble Python (wepy)

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 OpenMM molecular dynamics, parallelization using multiprocessing, the WExplore and 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 introduction.

Installation

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

conda create -n wepy python=3.10
conda activate wepy

Next, install `wepy` with pip:

pip install wepy

which will also install most dependencies.

Alternatively, the latest version of `wepy` can be installed from the git repo source:

git clone https://github.com/ADicksonLab/wepy.git
cd wepy
pip install .

The OpenMM package can then be installed using conda:

conda install -c conda-forge openmm

Check its installed by running the command line interface:

wepy --help

Citations

Current Zenodo DOI.

Cite software as:

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

Accompanying journal article:

ACS Omega article

Weighted Ensemble Python (wepy)

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 OpenMM molecular dynamics, parallelization using multiprocessing, the WExplore and 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 introduction.

Installation

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

conda create -n wepy python=3.10
conda activate wepy

Next, install `wepy` with pip:

pip install wepy

which will also install most dependencies.

Alternatively, the latest version of `wepy` can be installed from the git repo source:

git clone https://github.com/ADicksonLab/wepy.git
cd wepy
pip install .

The OpenMM package can then be installed using conda:

conda install -c conda-forge openmm

Check its installed by running the command line interface:

wepy --help

Citations

Current Zenodo DOI.

Cite software as:

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

Accompanying journal article:

ACS Omega article

Weighted Ensemble Python (wepy)

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 OpenMM molecular dynamics, parallelization using multiprocessing, the WExplore and 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 introduction.

Installation

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

conda create -n wepy python=3.10
conda activate wepy

Next, install `wepy` with pip:

pip install wepy

which will also install most dependencies.

Alternatively, the latest version of `wepy` can be installed from the git repo source:

git clone https://github.com/ADicksonLab/wepy.git
cd wepy
pip install .

The OpenMM package can then be installed using conda:

conda install -c conda-forge openmm

Check its installed by running the command line interface:

wepy --help

Citations

Current Zenodo DOI.

Cite software as:

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

Accompanying journal article:

ACS Omega article

Weighted Ensemble Python (wepy)

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 OpenMM molecular dynamics, parallelization using multiprocessing, the WExplore and 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 introduction.

Installation

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

conda create -n wepy python=3.10
conda activate wepy

Next, install `wepy` with pip:

pip install wepy

which will also install most dependencies.

Alternatively, the latest version of `wepy` can be installed from the git repo source:

git clone https://github.com/ADicksonLab/wepy.git
cd wepy
pip install .

The OpenMM package can then be installed using conda:

conda install -c conda-forge openmm

Check its installed by running the command line interface:

wepy --help

Citations

Current Zenodo DOI.

Cite software as:

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

Accompanying journal article:

ACS Omega article

Weighted Ensemble Python (wepy)

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 OpenMM molecular dynamics, parallelization using multiprocessing, the WExplore and 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 introduction.

Installation

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

conda create -n wepy python=3.10
conda activate wepy

Next, install `wepy` with pip:

pip install wepy

which will also install most dependencies.

Alternatively, the latest version of `wepy` can be installed from the git repo source:

git clone https://github.com/ADicksonLab/wepy.git
cd wepy
pip install .

The OpenMM package can then be installed using conda:

conda install -c conda-forge openmm

Check its installed by running the command line interface:

wepy --help

Citations

Current Zenodo DOI.

Cite software as:

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

Accompanying journal article:

ACS Omega article

Weighted Ensemble Python (wepy)

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 OpenMM molecular dynamics, parallelization using multiprocessing, the WExplore and 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 introduction.

Installation