  In this note we present the starry_process code, which implements an
interpretable Gaussian process (GP) for modeling variability in stellar light
curves. As dark starspots rotate in and out of view, the total flux received
from a distant star will change over time. Unresolved flux time series
therefore encode information about the spatial structure of features on the
stellar surface. The starry_process software package allows one to easily model
the flux variability due to starspots, whether one is interested in
understanding the properties of these spots or marginalizing over the stellar
variability when it is treated as a nuisance signal. The main difference
between the GP implemented here and typical GPs used to model stellar
variability is the explicit dependence of our GP on physical properties of the
star, such as its period, inclination, and limb darkening coefficients, and on
properties of the spots, such as their radius and latitude distributions. This
code is the Python implementation of the interpretable GP algorithm developed
in Luger, Foreman-Mackey, and Hedges (2021).
