exocartographer solves the exo-cartography inverse problem. This flexible forward-modeling framework, written in Python, retrieves the albedo map and spin geometry of a planet based on time-resolved photometry; it uses a Markov chain Monte Carlo method to extract albedo maps and planet spin and their uncertainties. Gaussian Processes use the data to fit for the characteristic length scale of the map and enforce smooth maps.

rfast ingests tables of opacities and generates synthetic spectra of worlds and retrieves real or simulated spectral observations. It can add noise, perform inverse modeling, and plot results. The tool can be applied to simulated and real observations spanning reflected-light, thermal emission, and transit transmission. Retrieval parameters can be toggled and parameters can be retrieved in log or linear space and adopt a Gaussian or flat prior.

coronagraph provides a Python noise model for directly imaging exoplanets with a coronagraph-equipped telescope. Based on the original IDL code for this coronagraph model, coronograph_noise (ascl:2405.018), the Python version has been expanded in a few key ways. Most notably, the Telescope, Planet, and Star objects used for reflected light coronagraph noise modeling can now be used for transmission and emission spectroscopy noise modeling, making this model a general purpose exoplanet noise model for many different types of observations.