  The absorption and emission of light by exoplanet atmospheres encode details
of atmospheric composition, temperature, and dynamics. Fundamentally,
simulating these processes requires detailed knowledge of the opacity of gases
within an atmosphere. When modeling broad wavelength ranges at high resolution,
such opacity data, for even a single gas, can take up multiple gigabytes of
system random-access memory (RAM). This aspect can be a limiting factor when
considering the number of gases to include in a simulation, the sampling
strategy used for inference, or even the architecture of the system used for
calculations. Here, we present cortecs, a Python tool for compressing opacity
data. cortecs provides flexible methods for fitting the temperature, pressure,
and wavelength dependencies of opacity data and for evaluating the opacity with
accelerated, GPU-friendly methods. The package is actively developed on GitHub
(https://github.com/arjunsavel/cortecs), and it is available for download with
pip and conda.
