CSC Workshop: Exatomic
Mr. Thomas J. Duignan and Mr. Alexander V. Marchenko
Department of Chemistry, University at Buffalo – SUNY, Buffalo (NY)
Reproducibility is a hot-button issue in the computational sciences. A significant contributor to this issue is that workflows often focus only on what is reported, while neglecting other details of a computation. Another contributor is the loss of relational information and the chain of command of data as it moves through a workflow. We propose a new Python package called exa, which leverages the Python science data stack in a unified container that encompasses high-performance computation (pandas/numpy), database-quality data control (sqlalchemy), and direct visualization (traitlets/ipywidgets) inside the Jupyter notebook. We apply this general purpose container to the field of computational chemistry in the package exatomic.
Join us as we demonstrate some of the basic functionality of exatomic in this workshop. Apart from showing you some nice visualizations, we aim to convince you of the following:
- exa and exatomic are useful
- it is easy to get started
- development, contributions, and ideas are welcome
This is your opportunity to get in on the ground floor of what could be a useful package for the python/computational science community at large.