  Literate programming - the bringing together of program code and natural
language narratives - has become a ubiquitous approach in the realm of data
science. This methodology is appealing as well for the domain of Density
Functional Theory (DFT) calculations, particularly for interactively developing
new methodologies and workflows. However, effective use of literate programming
is hampered by old programming paradigms and the difficulties associated with
using High Performance Computing (HPC) resources. Here we present two Python
libraries that aim to remove these hurdles. First, we describe the PyBigDFT
library, which can be used to setup materials or molecular systems and provides
high-level access to the wavelet based BigDFT code. We then present the related
remotemanager library, which is able to serialize and execute arbitrary Python
functions on remote supercomputers. We show how together these libraries enable
transparent access to HPC based DFT calculations and can serve as building
blocks for rapid prototyping and data exploration.
