    \begin{algorithmic}
        \vspace{1mm}
        \State{// \texttt{Iterate over all the haloes in catalogue}}
        \For{ halo in catalogue }
            \vspace{3mm}
            \State{// \texttt{Compute probability of central}}
            \State{$p_\text{cen} \leftarrow \text{model.}N_\text{cen}( $ halo.mass $ )$ }
            \vspace{3mm}
            \State{// \texttt{Define a binomial random variable}}
            \State{select $\leftarrow$ random.Binomial(1, $p_\text{cen}$)}
            \If{ select }
                \State{halo $\leftarrow$ central}
            \EndIf
            \vspace{3mm}
            \State{// \texttt{Compute average number of satellites}}
            \State{$\overline{N}_\text{sat} \leftarrow \text{model.}N_\text{sat}( $ halo.mass $ )$}
            \vspace{3mm}
            \State{// \texttt{Define a Poisson random variable}}
            \State{$N_\text{sat} = $ random.Poisson( $\overline{N}_\text{sat}$ )}
            \State{halo $\leftarrow$ select randomly $N_\text{sat}$ objects among satellites}
        \EndFor
        \vspace{3mm}
    \end{algorithmic}

\begin{algorithmic}
\vspace{1mm}
\State{// \texttt{Iterate over all the haloes in catalogue}}
\For{ halo in catalogue }
\vspace{3mm}
\State{// \texttt{Compute probability of central}}
\State{$p_\text{cen} \leftarrow \text{model.}N_\text{cen}( $ halo.mass $ )$ }
\vspace{3mm}
\State{// \texttt{Define a binomial random variable}}
\State{select $\leftarrow$ random.Binomial(1, $p_\text{cen}$)}
\If{ select }
\State{halo $\leftarrow$ central}
\EndIf
\vspace{3mm}
\State{// \texttt{Compute average number of satellites}}
\State{$\overline{N}_\text{sat} \leftarrow \text{model.}N_\text{sat}( $ halo.mass $ )$}
\vspace{3mm}
\State{// \texttt{Define a Poisson random variable}}
\State{$N_\text{sat} = $ random.Poisson( $\overline{N}_\text{sat}$ )}
\State{halo $\leftarrow$ select randomly $N_\text{sat}$ objects among satellites}
\EndFor
\vspace{3mm}
\end{algorithmic}
{
    "title": "scampy -- a sub-halo clustering & abundance matching based python   interface for painting galaxies on the dark matter halo/sub-halo hierarchy",
    "id": "2002.07179",
    "abstract": "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.",
    "categories": "astro-ph.co",
    "doi": "10.1093/mnras/staa2201",
    "created": "2020-02-17",
    "updated": "2024-02-14",
    "authors": [
        "tommaso ronconi",
        "andrea lapi",
        "matteo viel",
        "alberto sartori"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2002.07179"
}
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