Quick Start

OSX and Linux

The recommended was to use pygauss is to download the Anaconda Scientific Python Distribution (64-bit). Once downloaded a new environment can be created in terminal and pygauss installed in one simple line:

conda create -n pg_env -c https://conda.binstar.org/cjs14 pygauss

Windows

There is currently no pygauss Conda distributable for Windows or for chemlab, which has C-extensions that need to be built using a compiler. Therefore chemlab will need to be cloned from GitHub, its extensions built, dependancies installed and finally install pygauss.

conda create -n pg_env python=2.7
conda install -n pg_env -c https://conda.binstar.org/cjs14 cclib
conda install -n pg_env -c https://conda.binstar.org/cjs14 chemview
conda install -n pg_env -c https://conda.binstar.org/cjs14 pyopengl
git clone --recursive https://github.com/chemlab/chemlab.git
cd chemlab
python setup.py build_ext --inplace
conda install -n pg_env <pil, pandas, matplotlib, scikit-learn, ...>
activate pg_env
pip install . # or add to PYTHONPATH
pip install pygauss

Troubleshooting

If you encounter difficulties it may be useful to look in working_conda_environments at conda environments known to work.

Testing

Pygauss utilises a unit test suite (nose/nose-parameterized) to ensure that computations run, and are correct. Continuous integration testing of the source code is provided by Travis CI and pass completion is an automated condition of the Conda build. These unit tests can also be run manually in the command line;

nosetests -v --with-doctest

or directly in python;

pygauss.run_nose(verbose=True)