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Precision cosmology with gravitational wave (GW) sources requires a deeperunderstanding of the interplay between GW source population and cosmologicalparameters governing the dynamics of the Universe. With the swift increase inGW detections, it is necessary to develop a tool for exploring many aspects ofcosmology and fundamental physics; this tools allows to simulate GW mocksamples for several populations and cosmological models. We have developed anew code called GWSim, which allows us to make a large sample of GW mock eventsfrom a broad range of configurations, while varying the cosmology, the mergerrate, and the GW source parameters (i.e. mass and spin distributions inparticular) for a given network of GW detectors. A large sample of simulatedmock GW events will be useful for improving our understanding of thestatistical properties of the distribution of GW sources, as long as it isdetectable for a given detector noise and an astrophysical and cosmologicalmodel. It will also be useful to compare simulated samples with the observeddistribution of the GW sources from data and infer the underlying population ofthe GW source parameters and cosmology. We restricted the cosmology tospatially flat universes, including models with varying dark energy equation ofstate. The GWSim code provides each mock event with a position in the sky and aredshift; these values can be those of random host galaxies coming from anisotropic and homogeneous simulated Universe or a user-supplied galaxy catalog.We used realistic detector configurations of the LIGO and Virgo network ofdetectors to demonstrate the performance of this code for the latestobservation runs and the upcoming observation run.

GWSim: Python package for creating mock GW samples for different astrophysical populations and cosmological models of binary black holes