diff --git a/README.md b/README.md index c4ee5ea..c308ea0 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,13 @@ # `berny` — Molecular optimizer +[![build](https://img.shields.io/travis/azag0/pyberny/master.svg)](https://travis-ci.org/azag0/pyberny) +[![coverage](https://img.shields.io/codecov/c/github/azag0/pyberny.svg)](https://codecov.io/gh/azag0/pyberny) +![python](https://img.shields.io/pypi/pyversions/pyberny.svg) +[![pypi](https://img.shields.io/pypi/v/pyberny.svg)](https://pypi.org/project/pyberny/) +[![commits since](https://img.shields.io/github/commits-since/azag0/pyberny/latest.svg)](https://github.com/azag0/pyberny/releases) +[![last commit](https://img.shields.io/github/last-commit/azag0/pyberny.svg)](https://github.com/azag0/pyberny/commits/master) +[![license](https://img.shields.io/github/license/azag0/pyberny.svg)](https://github.com/azag0/pyberny/blob/master/LICENSE) + This Python 2/3 package can optimize molecular and crystal structures with respect to total energy, using nuclear gradient information. In each step, it takes energy and Cartesian gradients as an input, and returns a new structure estimate. @@ -8,7 +16,7 @@ The algorithm is an amalgam of several techniques, comprising redundant internal ## Installing -Install and update using [pip](https://pip.pypa.io/en/stable/quickstart/): +Install and update using [Pip](https://pip.pypa.io/en/stable/quickstart/): ``` pip install -U pyberny @@ -27,4 +35,4 @@ for geom in optimizer: ## Links -- Documentation: +- Documentation: diff --git a/docs/conf.py b/docs/conf.py index 3909393..bdec0f8 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -7,17 +7,19 @@ sys.path.insert(0, os.path.abspath('..')) + class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() + MOCK_MODULES = ['numpy', 'numpy.linalg'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) metadata = toml.load(open('../pyproject.toml'))['tool']['poetry'] -project = 'pyberny' +project = 'berny' version = metadata['version'] author = ' '.join(metadata['authors'][0].split()[:-1]) description = metadata['description']