ScamPy -- A sub-halo clustering & abundance matching based Python
  interface for painting galaxies on the dark matter halo/sub-halo hierarchy
  We present a computational framework for "painting" galaxies on top of the
Dark Matter Halo/Sub-Halo hierarchy obtained from N-body simulations. The
method 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 the
target (observed) population we want to reproduce. This method is particularly
tailored for high redshift studies and thereby relies on the observed
high-redshift galaxy luminosity functions and correlation properties. The core
functionalities are written in c++ and exploit Object Oriented Programming,
with a wide use of polymorphism, to achieve flexibility and high computational
efficiency. In order to have an easily accessible interface, all the libraries
are wrapped in python and provided with an extensive documentation. We validate
our 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.
