ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Welcome to the ASCL

The Astrophysics Source Code Library (ASCL) is a free online registry for source codes of interest to astronomers and astrophysicists and lists codes that have been used in research that has appeared in, or been submitted to, peer-reviewed publications. The ASCL is indexed by the SAO/NASA Astrophysics Data System (ADS) and is citable by using the unique ascl ID assigned to each code. The ascl ID can be used to link to the code entry by prefacing the number with ascl.net (i.e., ascl.net/1201.001).


Most Recently Added Codes

2017 Nov 16

[submitted] clustep: initial conditions for galaxy cluster halo simulations in a format that can be read in GADGET2 or RAMSES

Generates a snapshot in the GADGET-2 format (which can be read in RAMSES using the DICE patch) containing a galaxy cluster halo in equilibrium. The halo is made of a dark matter component and a gas component, with the latter representing the ICM. Each of these components follows a Dehnen density profile (Dehnen 1993), with gamma=0 or gamma=1. If gamma=1, then the profile corresponds to a Hernquist profile (Hernquist 1990). See Ruggiero & Lima Neto (2017) for a discussion on the difference between these two options.

[submitted] galstep: initial conditions for spiral galaxy simulations in formats that can be read in GADGET2, RAMSES and GIZMO

This code uses the algorithm described in Springel, Di Matteo & Hernquist (2005) for generating the initial conditions for a disk galaxy simulation with the codes GADGET-2, RAMSES (using the DICE patch) and GIZMO (with the HDF5 file format), including a stellar disk, a gaseous disk, a dark matter halo and a stellar bulge. The first two components follow an exponential density profile, and the last two a Dehnen density profile with gamma=1 by default, corresponding to a Hernquist profile. You can check out the expressions in Ruggiero & Lima Neto (2017).

[submitted] RGW

A lightweight R-language implementation of the affine-invariant Markov Chain Monte Carlo sampling method of Goodman & Weare (2010).

2017 Nov 08

[submitted] correlcalc: Two-point correlation function from redshift surveys

correlcalc calculates two-point correlation function (2pCF) of galaxies/quasars using redshift surveys. It can be used for any assumed geometry or Cosmology model. Using BallTree algorithms to reduce the computational effort for large datasets, it is a parallelised code suitable for running on clusters as well as personal computers. It takes redshift (z), Right Ascension (RA) and Declination (DEC) data of galaxies and random catalogs as inputs in form of ascii or fits files. If random catalog is not provided, it generates one of desired size based on the input redshift distribution and mangle polygon file (in .ply format) describing the survey geometry. It also calculates different realisations of (3D) anisotropic 2pCF. Optionally it makes healpix maps of the survey providing visualization.

2017 Nov 06

[submitted] FTbg: Background removal using Fourier Transform

FTbg is a handy Python script which takes a FITS image, perform Fourier transform, and separate low- and high-spatial frequency components by a user-specified cut. Both components are then inverse Fourier transformed back to image domain. It can be used to remove large-scale background/foreground emission in many astrophysical applications. FTbg has been designed to identify and remove Galactic background emission in Herschel/Hi-GAL continuum images, but it is applicable to any other (e.g., Planck) images when background/foreground emission is a concern.

2017 Oct 31

[ascl:1710.024] pred_loggs: Predicting individual galaxy G/S probability distributions

pred_loggs models the entire PGF probability density field, enabling iterative statistical modeling of upper limits and prediction of full G/S probability distributions for individual galaxies.

[ascl:1710.023] LIMEPY: Lowered Isothermal Model Explorer in PYthon

LIMEPY solves distribution function (DF) based lowered isothermal models. It solves Poisson's equation used on input parameters and offers fast solutions for isotropic/anisotropic, single/multi-mass models, normalized DF values, density and velocity moments, projected properties, and generates discrete samples.

[ascl:1710.022] galario: Gpu Accelerated Library for Analyzing Radio Interferometer Observations

The galario library exploits the computing power of modern graphic cards (GPUs) to accelerate the comparison of model predictions to radio interferometer observations. It speeds up the computation of the synthetic visibilities given a model image (or an axisymmetric brightness profile) and their comparison to the observations.

[ascl:1710.021] OSIRIS Toolbox: OH-Suppressing InfraRed Imaging Spectrograph pipeline

OSIRIS Toolbox reduces data for the Keck OSIRIS instrument, an integral field spectrograph that works with the Keck Adaptive Optics System. It offers real-time reduction of raw frames into cubes for display and basic analysis. In this real-time mode, it takes about one minute for a preliminary data cube to appear in the “quicklook” display package. The reduction system also includes a growing set of final reduction steps including correction of telluric absorption and mosaicing of multiple cubes.

2017 Oct 26

[submitted] BayesVP: a Bayesian Voigt profile fitting package

We introduce a Bayesian approach for modeling Voigt profiles in absorption spectroscopy and its implementation in the python package, BayesVP, publicly available at https://github.com/cameronliang/BayesVP. The code fits the absorption line profiles within specified wavelength ranges and generates posterior distributions for the column density, Doppler parameter, and redshifts of the corresponding absorbers. The code uses publicly available efficient parallel sampling packages to sample posterior and thus can be run on parallel platforms. BayesVP supports simultaneous fitting for multiple absorption components in high-dimensional parameter space. We provide other useful utilities in the package, such as explicit specification of priors of model parameters, continuum model, Bayesian model comparison criteria, and posterior sampling convergence check.