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We present a computational framework for "painting" galaxies on top of theDark Matter Halo/Sub-Halo hierarchy obtained from N-body simulations. Themethod we use is based on the sub-halo clustering and abundance matching (SCAM)scheme which requires observations of the 1- and 2-point statistics of thetarget (observed) population we want to reproduce. This method is particularlytailored for high redshift studies and thereby relies on the observedhigh-redshift galaxy luminosity functions and correlation properties. The corefunctionalities are written in c++ and exploit Object Oriented Programming,with a wide use of polymorphism, to achieve flexibility and high computationalefficiency. In order to have an easily accessible interface, all the librariesare wrapped in python and provided with an extensive documentation. We validateour results and provide a simple and quantitative application to reionization,with an investigation of physical quantities related to the galaxy population,ionization fraction and bubble size distribution.

ScamPy -- A sub-halo clustering & abundance matching based Python interface for painting galaxies on the dark matter halo/sub-halo hierarchy