  We find ourselves on the brink of an exciting era in observational
astrophysics, driven by groundbreaking facilities like JWST, Euclid, Rubin,
Roman, SKA, or ELT. Simultaneously, computational astrophysics has shown
significant strides, yielding highly realistic galaxy formation simulations,
thanks to both hardware and software enhancements. Bridging the gap between
simulations and observations has become paramount for meaningful comparisons.
We introduce py-ananke, a Python pipeline designed to generate synthetic
resolved stellar surveys from cosmological simulations, adaptable to various
instruments. Building upon its predecessor, ananke by Sanderson et al. 2020
(arXiv:1806.10564), which produced Gaia DR2 mock star surveys, the py-ananke
package offers a user-friendly "plug & play" experience. The pipeline employs
cutting-edge phase-space density estimation and initial mass function sampling
to convert particle data into synthetic stars, while interpolating pre-computed
stellar isochrone tracks for photometry. Additionally, it includes modules for
estimating interstellar reddening, dust-induced extinctions, and for
quantifying errors through dedicated modeling approaches. py-ananke promises to
serve as a vital bridge between computational astrophysics and observational
astronomy, facilitating preparations and making scientific predictions for the
next generation of telescopes.
