What is Hoki?

Bridging the gap between theory and observations

Historically, theoretical models have been released to the community with the expectation that users would create their own data handling scripts as they perform the analysis. This was in line with a research culture where most people had their own code, resulting in a duplication of effort and creating the potential for a reproducibility crisis, as most personal scripts are not shared with the community.

In order to facilitate the application of BPASS to a wide range of scientific investigations, we have developped the tools necessary for observers to take full advantage of our models in a stream-lined, intuitive manner.

Hoki is a dedicated python package designed to provide a user friendly interface to the BPASS models in order to bridge the gap between theory and observations.

The varsatility of Hoki allows you to focus on the science, and worry less about the technical nitty gritty that comes with using the varied outputs of a theoretical code.

Built with data analysis in mind

Our vision for hoki is to provide tools that can be integrated in your everyday workflow and make your life extra simple! Do you want to spend ages figuring out 5 by 100 by 100 by 3 by 3 data object contained in a single ASCII file? Great news, you don’t need to!

We leverage the powerful capabilites of ubiquitous packages such as numpy, pandas and matplotlib in order to make the outputs of BPASS easily accessible and usable for scientific data analysis. This way you get to spend more time looking at data and less time looking at code.