  Stellar atmosphere modelling predicts the luminosity and temperature of a
star, together with parameters such as the effective gravity and the
metallicity, by reproducing the observed spectral energy distribution. Most
observational data comes from photometric surveys, using a variety of
passbands. We herein present the Python Stellar Spectral Energy Distribution
(PySSED) routine, designed to combine photometry from disparate catalogues, fit
the luminosity and temperature of stars, and determine departures from stellar
atmosphere models such as infrared or ultraviolet excess. We detail the
routine's operation, and present use cases on both individual stars, stellar
populations, and wider regions of the sky. PySSED benefits from fully automated
processing, allowing fitting of arbitrarily large datasets at the rate of a few
seconds per star.
