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The light-curve evolution of a supernova contains information of theexploding star. Early-time photometry of a variety of explosive transients,including Calcium-rich transients and type IIb/Ibc and IIP supernovae showsevidence for an early light curve peak as a result of the explosion's shockwave passing through extended material (i.e., shock cooling emission (SCE)).Analytic modeling of the shock cooling emission allows us to estimateprogenitor properties such as the radius and mass of extended material (e.g.,the stellar envelope) as well as the shock velocity. In this work, we present aPython-based modular package that implements four analytic models originallydeveloped in Piro 2015, Piro 2020 and Sapir & Waxman (2017) applied tophotometric data to obtain progenitor parameter properties via differentmodeling techniques (including non-linear optimization, MCMC sampling). Oursoftware is easily extendable to other analytic models for SCE and differentmethods of parameter estimation.

shock_cooling_curve: A Python-Based Package for Extensive and Efficient Modeling of Shock Cooling Emission in Supernovae