Hoki: Making BPASS accessible through Python
  We now know that a large number of stars are born in multiple systems.
Additionally, more than 70% of massive stars are found in close binary systems,
meaning that they will interact over the course of their lifetime. This has
strong implications for their evolution as well as the transients (e.g
supernovae) and the potential gravitational wave progenitors they produce.
Therefore, in order to understand and correctly interpret astronomical
observations of stellar populations, we must use theoretical models able to
account for the effects of binary stars. This is the case of the Binary
Population and Spectral Synthesis code (BPASS), which has been a staple of the
field for over 10 years. As is the case for most other theoretical models, the
data products of BPASS are large, varied and complex. As a result, their use
requires a level of expertise that is not immediately accessible to a wider
community that may hold key observational data. The goal of hoki is to bridge
the gap between observation and theory, by providing a set of tools to make
BPASS data easily accessible and facilitate analysis. The use of Python is
deliberate as it is a ubiquitous language within Astronomy. This allows BPASS
results to be used naturally within the pre-existing workflow of most
astronomers.
