  The light-curve evolution of a supernova contains information of the
exploding star. Early-time photometry of a variety of explosive transients,
including Calcium-rich transients and type IIb/Ibc and IIP supernovae shows
evidence for an early light curve peak as a result of the explosion's shock
wave passing through extended material (i.e., shock cooling emission (SCE)).
Analytic modeling of the shock cooling emission allows us to estimate
progenitor 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 a
Python-based modular package that implements four analytic models originally
developed in Piro 2015, Piro 2020 and Sapir & Waxman (2017) applied to
photometric data to obtain progenitor parameter properties via different
modeling techniques (including non-linear optimization, MCMC sampling). Our
software is easily extendable to other analytic models for SCE and different
methods of parameter estimation.
