gsf applies Gaussian Mixture Models in the stellar kinematic space of normalized angular momentum and binding energy on NIHAO high resolution galaxies to separate the stars into multiple components. The gsf analysis package assumes that the simulation snapshot has been pre-processed with a halo finder. It is based on pynbody (ascl:1305.002) and the scikit-learnpython package for Machine Learning; after loading, orienting, and transforming a simulation snapshot to physical units, it runs the clustering algorithm and performs the direct N-body gravity force using all the particles in the given halo.

CuspCore describes the formation of flat cores in dark matter haloes and ultra-diffuse galaxies from feedback-driven outflow episodes. The halo response is divided into an instantaneous change of potential at constant velocities followed by an energy-conserving relaxation. The core assumption of the model is that the total energy E=U+K is conserved for each shell enclosing a given dark matter mass, which is treated in the code as a least-square minimization of the difference between the final and the initial energy of each shell.