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.

中文翻译：

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ScamPy - 基于子晕聚类和丰度匹配的 Python 界面，用于在暗物质晕/子晕层次上绘制星系

我们提出了一个计算框架，用于在从 N 体模拟获得的暗物质晕/子晕层次结构顶部“绘制”星系。我们使用的方法基于子晕聚类和丰度匹配 (SCAM) 方案，该方案需要观察我们想要重现的目标（观察到的）种群的 1 点和 2 点统计数据。这种方法特别适用于高红移研究，因此依赖于观察到的高红移星系光度函数和相关特性。核心功能采用c++编写，利用面向对象编程，广泛使用多态，实现灵活性和高计算效率。为了有一个易于访问的界面，所有的库都用 python 包装，并提供了大量的文档。