Welcome to ScamPy’s documentation!

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A Python interface for Sub-halo Clustering and Abundance Matching

ScamPy is a highly-optimized and flexible library for “painting” an observed population of cosmological objects on top of the DM-halo/subhalo hierarchy obtained from DM-only simulations. The method used combines the classical Halo Occupation Distribution (HOD) with the sub-halo abundance matching (SHAM), the sinergy of the two processes is dubbed Sub-halo clustering and abundance matching (SCAM). The procedure itself is quite easy since it only requires to apply the two methods in sequence:

  1. by applying the HOD scheme, the host sub-haloes are selected;
  2. the SHAM algorithm associates to each sub-halo an observable property of choice.

Basic Usage

If you wanted to populate a DM catalogue with galaxies with given luminosity, you would do something like:

# read the sub-halo catalogue from a file
from scampy import catalogue
cat = catalogue.catalogue()
cat.read_hierarchy_from_gadget( "/path/to/input_directory/subhalo_tab_snap" )
volume = 512.**3 # for a box with side-lenght = 512 Mpc/h


# build an object of type occupation probability with given parameters
from scampy import occupation_p
ocp = occupation_p.tinker10_p( Amin    = 1.e+14,
                               siglogA = 0.5,
                               Asat    = 1.e+15,
                               alpsat  = 1. )

# populate the catalogue
galaxies = cat.populate( ocp, extract = True )

# define a Schechter-like luminosity function
import numpy as np
def schechter ( mag ) :
     alpha = -1.07
     norm = 1.6e-2
     mstar = -19.7 + 5. * np.log10( 5. )
     lum = - 0.4 * ( mag - mstar )
     return 0.4 * np.log( 10 ) * norm * 10**( - 0.07 * lum ) * np.exp( - 10**lum )

# call the sub-halo abundance matching routine:
from scampy import abundance_matching
galaxies = abundance_matching.abundance_matching( galaxies, schechter, factM = 1. / volume )

The galaxies array contains the output mock-galaxies.

Installation guide

Installation of ScamPy is dealt by the Meson Build System. Each module of the API is built by a specific meson.build script.

Pre-requisites

For building:

  • meson build system tool
  • ninja

can both be installed either via conda install or with pip install

Dependencies of the library:

While GSL has to be already installed in the system, if FFTLog is not present Meson will authomatically download it along with a patch we have developed, both will be installed in the subprojects directory of the repository.

Dependencies for building the documentation locally:

  • Doxygen
  • Sphynx (with breathe, autodoc and rtd_theme extensions)

Dependencies for enabling testing:

  • Google Test (is authomatically installed by Meson)

Install

# from root directory
meson build_dir -Dprefix=/path/to/install_directory
cd build_dir

ninja install

If no -Dprefix is specified the library will be installed in /path/to/scampy/install directory.

Tutorials

Indices and tables