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Prepare 2.3.1 (#378)
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* reformat the rst readme as md

* update setup.py

* address comments
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xiki-tempula committed Jul 8, 2024
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* release numbers follow "Semantic Versioning" https://semver.org

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??/??/2024 orbeckst

08/07/2024 orbeckst, xiki-tempula

* 2.3.1

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2 changes: 1 addition & 1 deletion MANIFEST.in
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include *.rst
recursive-include src/alchemlyb *.gz *.bz2 *.zip *.rst *.txt
include README.rst
include README.md
include LICENSE
include versioneer.py
include src/alchemlyb/_version.py
54 changes: 54 additions & 0 deletions README.md
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# alchemlyb: the simple alchemistry library

[![Zenodo DOI](https://zenodo.org/badge/68669096.svg)](https://zenodo.org/badge/latestdoi/68669096) [![Documentation](https://readthedocs.org/projects/alchemlyb/badge/?version=latest)](http://alchemlyb.readthedocs.io/en/latest/) [![Build Status](https://github.com/alchemistry/alchemlyb/actions/workflows/ci.yaml/badge.svg?branch=master)](https://github.com/alchemistry/alchemlyb/actions/workflows/ci.yaml) [![Code coverage](https://codecov.io/gh/alchemistry/alchemlyb/branch/master/graph/badge.svg)](https://codecov.io/gh/alchemistry/alchemlyb) [![anaconda package](https://anaconda.org/conda-forge/alchemlyb/badges/version.svg)](https://anaconda.org/conda-forge/alchemlyb)

**alchemlyb** makes alchemical free energy calculations easier to do by leveraging the full power and flexibility of the PyData stack. It includes:

1. Parsers for extracting raw data from output files of common molecular dynamics engines such as [GROMACS](http://www.gromacs.org/), [AMBER](http://ambermd.org/), [NAMD](http://www.ks.uiuc.edu/Research/namd/) and [other simulation codes](https://alchemlyb.readthedocs.io/en/latest/parsing.html).

2. Subsamplers for obtaining uncorrelated samples from timeseries data (including extracting independent, equilibrated samples [Chodera2016](#chodera2016) as implemented in the [pymbar](http://pymbar.readthedocs.io/) package).

3. Estimators for obtaining free energies directly from this data, using best-practices approaches for multistate Bennett acceptance ratio (MBAR) [Shirts2008](#shirts2008) and BAR (from [pymbar](http://pymbar.readthedocs.io/)) and thermodynamic integration (TI).

## Installation

**Install** via `pip` from [PyPi (alchemlyb)](https://pypi.org/project/alchemlyb):

```bash
pip install alchemlyb
```

or as a `conda` package from the [conda-forge (alchemlyb)](https://anaconda.org/conda-forge/alchemlyb) channel:

```bash
conda install -c conda-forge alchemlyb
```

**Update** with `pip`:

```bash
pip install --update alchemlyb
```

or with `conda` run:

```bash
conda update -c conda-forge alchemlyb
```

to get the latest released version.

## Getting involved

Contributions of all kinds are very welcome.

If you have questions or want to discuss alchemlyb please post in the [alchemlyb Discussions](https://github.com/alchemistry/alchemlyb/discussions).

If you have bug reports or feature requests then please get in touch with us through the [Issue Tracker](https://github.com/alchemistry/alchemlyb/issues).

We also welcome code contributions: have a look at our [Developer Guide](https://github.com/alchemistry/alchemlyb/wiki/Developer-Guide). Open an issue with the proposed fix or change in the [Issue Tracker](https://github.com/alchemistry/alchemlyb/issues) and submit a pull request against the [alchemistry/alchemlyb](https://github.com/alchemistry/alchemlyb) GitHub repository.

## References

- <a name="shirts2008"></a> Shirts, M.R., and Chodera, J.D. (2008). Statistically optimal analysis of samples from multiple equilibrium states. The Journal of Chemical Physics 129, 124105.
- <a name="chodera2016"></a> Chodera, J.D. (2016). A Simple Method for Automated Equilibration Detection in Molecular Simulations. Journal of Chemical Theory and Computation 12, 1799–1805.
110 changes: 0 additions & 110 deletions README.rst

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4 changes: 2 additions & 2 deletions setup.py
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packages=find_packages("src"),
package_dir={"": "src"},
license="BSD",
long_description=open("README.rst").read(),
long_description_content_type="text/x-rst",
long_description=open("README.md").read(),
long_description_content_type="text/markdown",
python_requires=">=3.9",
tests_require=["pytest", "alchemtest"],
install_requires=[
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