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']