Category Archives: codes

February additions to the ASCL

Twenty-two codes were added to the ASCL in February 2020:

Apercal: Pipeline for the Westerbork Synthesis Radio Telescope Apertif upgrade
Bayesfit: Command-line program for combining Tempo2 and MultiNest components
Cobra: Bayesian pulsar searching
CR-SISTEM: Symplectic integrator for lunar core-mantle and orbital dynamics
DASH: Deep Automated Supernova and Host classifier

DISKMODs: Accretion Disk Radial Structure Models
DMRadon: Radon Transform calculation tools
ExoCAM: Exoplanet Community Atmospheric Model
ExoRT: Two-stream radiative transfer code
ExoSim: Simulator for predicting signal and noise in transit spectroscopy observations

GizmoAnalysis: Read and analyze Gizmo simulations
GWecc: Calculator for pulsar timing array signals due to eccentric supermassive binaries
HaloAnalysis: Read and analyze halo catalogs and merger trees
libstempo: Python wrapper for Tempo2
ODUSSEAS: Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes

ORIGIN: detectiOn and extRactIon of Galaxy emIssion liNes
ProSpect: Spectral generation package
PyHammer: Python spectral typing suite
RASCAS: Resonant line transfer in AMR simulations
ScamPy: Sub-halo Clustering and Abundance Matching Python interface

SDAR: Slow-Down Algorithmic Regularization code for solving few-body problems
triceratops: Candidate exoplanet rating tool

January additions to the ASCL

Fifteen codes were added to the ASCL in January 2020:

BTS: Behind The Spectrum
CosMOPED: Compressed Planck likelihood
DebrisDiskFM: Debris Disk Forward Modeling
ExoTETHyS: Exoplanetary transits and eclipsing binaries modeler
FAKEOBS: Model visibilities generator

FragMent: Fragmentation techniques for studying filaments
gnm: The MCMC Jagger
MCMCI: Markov Chain Monte Carlo + Isochrones method for characterizing exoplanetary systems
Min-CaLM: Mineral compositional analysis on debris disk spectra
ORCS: Analysis engine for SITELLE spectral cubes

Peasoup: C++/CUDA GPU pulsar searching library
Protostellar Evolution: Stellar evolution simulator
RPPPS: Re-analyzing Pipeline for Parkes Pulsar Survey
sf3dmodels: Star-forming regions 3D modelling package
TRANSPHERE: 1-D spherical continuum radiative transfer

December additions to the ASCL

Twenty codes were added to the ASCL in December 2019:

anesthetic: Nested sampling visualization
ASKAPsoft: ASKAP science data processor software
AstroAccelerate: Accelerated software package for processing time-domain radio astronomy data
Athena++: Radiation GR magnetohydrodynamics code
casacore: Suite of C++ libraries for radio astronomy data processing

DALiuGE: Data Activated Liu Graph Engine
Enterprise: Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE
FORSTAND: Flexible ORbit Superposition Toolbox for ANalyzing Dynamical models
GAME: GAlaxy Machine learning for Emission lines
GriSPy: Fixed-radius nearest neighbors grid search in Python

GWpy: Python package for studying data from gravitational-wave detectors
HARMPI: 3D massively parallel general relativictic MHD code
HSIM: HARMONI simulation pipeline
MRExo: Non-parametric mass-radius relationship for exoplanets
Polyspectrum: Computing polyspectra using an FFT estimator

PopSyCLE: Population Synthesis for Compact object Lensing Events
PTMCMCSampler: Parallel tempering MCMC sampler package written in Python
QSOSIM: Simulated Quasar Spectrum Generator
STACKER: Stack sources in interferometric data
Tangos: Framework and web interface for database-driven analysis of numerical structure formation simulations

ASCL poster at AAS235


Abstract: Software citation is good for research transparency and reproducibility, and maybe, if you work it right, for your CV, too. You can get credit and recognition through citations for your code! This presentation highlights several powerful methods for increasing the probability that use of your research software will be cited, and cited correctly. The presentation covers how to create codemeta.json and CITATION.cff automagically from Astrophysics Source Code Library (ASCL ascl.net) entries, edit, and use these files, the value of including such files on your code site(s), and efforts underway in astronomy and other fields to improve software citation and credit.

Authors: A. Allen1,2, R. Nemiroff3, P. Ryan1, J. Schmidt1, P. Teuben2
1Astrophysics Source Code Library
2Astronomy Department, University of Maryland, College Park, MD
3Michigan Technological University, Houghton, MI

Download (PDF)

The ASCL at AAS 235

The ASCL is participating in the American Astronomical Society (AAS) meeting that started yesterday in Honolulu, Hawai’i. We have two events, both on Sunday, January 5:

Best ways to let others know how to cite your research software
January 5; Poster 109.12
Software citation is good for research transparency and reproducibility, and maybe, if you work it right, for your CV, too. You can get credit and recognition through citations for your code! This presentation highlights several powerful methods for increasing the probability that use of your research software will be cited, and cited correctly. The presentation covers how to create codemeta.json and CITATION.cff automagically from Astrophysics Source Code Library (ASCL ascl.net) entries, edit, and use these files, the value of including such files on your code site(s), and efforts underway in astronomy and other fields to improve software citation and credit.

The Future and Future Governance of the Astrophysics Source Code Library
January 5, 2:00 PM – 3:30 PM; HCC – Room 301B
Over the past ten years, the Astrophysics Source Code Library (ASCL, ascl.net) has grown from a small repository holding about 40 codes with hand-coded HTML pages maintained by one person to a resource with citable entries on over 2000 codes with a modern database structure that is user- and editor-friendly maintained by a small group of volunteers. With its 20th anniversary now behind it, it’s time to look at the resource and its governance and management. Does its current structure best serve the astro community? What changes would you like to see to its governance? We don’t know the answers to these and other questions! Please join us for an open discussion on the resource and what a new governance model for the ASCL might be.

November additions to the ASCL

Twenty-four codes were added to the ASCL in November 2019:

ATHOS: A Tool for HOmogenizing Stellar parameters
ATLAS: Turning Dopplergram images into frequency shift measurements
CLUSTEREASY: Lattice simulator for evolving interacting scalar fields in an expanding universe on parallel computing clusters
comb: Spectral line data reduction and analysis package

FFTLog-and-beyond: Generalized FFTLog algorithm
frbpoppy: Fast radio burst population synthesis in Python
Fruitbat: Fast radio burst redshift estimation
HeatingRate: Radioactive heating rate and macronova (kilonova) light curve

HLattice: Scalar fields and gravity simulator for the early universe
IDG: Image Domain Gridding
LATTICEEASY: Lattice simulator for evolving interacting scalar fields in an expanding universe
MARTINI: Mock spatially resolved spectral line observations of simulated galaxies

miluphcuda: Smooth particle hydrodynamics code
MORDI: Massively-Overlapped Ring-Diagram Inversion
OpenSPH: Astrophysical SPH and N-body simulations and interactive visualization tools
OrbWeaver: Galaxy/(sub)halo orbital processing tool

PLAN: A Clump-finder for Planetesimal Formation Simulations
planetplanet: General photodynamical code for exoplanet light curves
PypeIt: Python spectroscopic data reduction pipeline
TreeFrog: Construct halo merger trees and compare halo catalogs

uvplot: Interferometric visibilities plotter
VELOCIraptor-STF: Six-dimensional Friends-of-Friends phase space halo finder
WhereWolf: Galaxy/(sub)Halo ghosting tool for N-body simulations
Zeltron: Explicit 3D relativistic electromagnetic Particle-In-Cell code

October additions to the ASCL

Twenty-two codes were added to the ASCL in October 2019:

a3cosmos-gas-evolution: Galaxy cold molecular gas evolution functions
ANNz2: Estimating photometric redshift and probability density functions using machine learning methods
AOtools: Adaptive optics modeling and analysis toolkit
AOTOOLS: Reduce IR images from Adaptive Optics
ChainConsumer: Corner plots, LaTeX tables and plotting walks

Cobaya: Bayesian analysis in cosmology
DM_phase: Algorithm for correcting dispersion of radio signals
E0102-VR: Virtual Reality application to visualize the optical ejecta in SNR 1E 0102.2-7219
ECLIPS3D: Linear wave and circulation calculations
EMERGE: Empirical ModEl for the foRmation of GalaxiEs

exoplanet: Probabilistic modeling of transit or radial velocity observations of exoplanets
GetDist: Monte Carlo sample analyzer
LEO-Py: Likelihood Estimation of Observational data with Python
MarsLux: Illumination Mars maps generator
MiSTree: Construct and analyze Minimum Spanning Tree graphs

OCD: O’Connell Effect Detector using push-pull learning
orbitize: Orbit-fitting for directly imaged objects
PEXO: Precise EXOplanetology
PINK: Parallelized rotation and flipping INvariant Kohonen maps
PreProFit: Pressure Profile Fitter for galaxy clusters in Python

qnm: Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients calculator
TLS: Transit Least Squares

September additions to the ASCL

Fourteen codes were added to the ASCL in September 2019:

AREPO: Cosmological magnetohydrodynamical moving-mesh simulation code
Auto-multithresh: Automated masking for clean
ChempyMulti: Multi-star Bayesian inference with Chempy
CLOVER: Convolutional neural network spectra identifier and kinematics predictor
EBHLIGHT: General relativistic radiation magnetohydrodynamics with Monte Carlo transport

EPOS: Exoplanet Population Observation Simulator
fgivenx: Functional posterior plotter
HADES: Hexadecapolar Analysis for Dust Estimation in Simulations (of CMB B-mode thermal dust emission)
HISS: HI spectra stacker
MultiColorFits: Colorize and combine multiple fits images for visually aesthetic scientific plots

RascalC: Fast code for galaxy covariance matrix estimation
SecularMultiple: Hierarchical multiple system secular evolution model
TPI: Test Particle Integrator
WVTICs: SPH initial conditions using Weighted Voronoi Tesselations

August additions to the ASCL

Twenty-five codes were added to the ASCL in August 2019:

actsnclass: Active learning for supernova photometric classification
Analysator: Quantitative analysis of Vlasiator files
BEAST: Bayesian Extinction And Stellar Tool
bias_emulator: Halo bias emulator
dips: Detrending periodic signals in timeseries

DustCharge: Charge distribution for a dust grain
EBAI: Eclipsing Binaries with Artificial Intelligence
FastCSWT: Fast directional Continuous Spherical Wavelet Transform
FIRST Classifier: Automated compact and extended radio sources classifier
GBKFIT: Galaxy kinematic modeling

Gramsci: GRAph Made Statistics for Cosmological Information
JPLephem: Jet Propulsion Lab ephemerides package
MAESTROeX: Low Mach number stellar hydrodynamics code
Molsoft: Molonglo Telescope Observing Software
MosfireDRP: MOSFIRE Data Reduction Pipeline

NuRadioMC: Monte Carlo simulation package for radio neutrino detectors
oscode: Oscillatory ordinary differential equation solver
PyRADS: Python RADiation model for planetary atmosphereS
PYSAT: Python Satellite Data Analysis Toolkit
QAC: Quick Array Combinations front end to CASA

QLF: Luminosity function analysis code
SNAPDRAGONS: Stellar Numbers And Parameters Determined Routinely And Generated Observing N-body Systems
TRISTAN-MP: TRIdimensional STANford – Massively Parallel code
Vlasiator: Hybrid-Vlasov simulation code
YMW16: Electron-density model

(per apparent established practice)

I’ve set a goal of bringing the number of entries missing preferred citation information to under 1000, though that might be just beyond possible. When I started this process, there were 1284 entries without a preferred citation; I’ve examined the software sites and documentation of 150+ of these codes so far and have found explicit citation information for just over 14% of these.

In general, we include a preferred citation in an ASCL record when a code’s site or documentation explicitly states what should be cited (“cite [code] with this [ASCL entry/article/DOI/etc.]”). We don’t assume a paper listed under “References” or “Articles” is intended to be for citation, though that may be the intent of some authors listing them, as some list these papers because a code is built upon others’ work, or these papers include research that used the software.

In some cases, a particular software has no citations to the ASCL record and numerous citations (> 25, let’s say) to a code description paper even though the download site or repo does not specify how the software should be cited. Allowing this “apparent established practice” of citation to substitute for an explicit statement and listing the description paper as the preferred citation seems fair to me, and valuable to those who want to do the right thing by citing a software package but don’t find guidance for how to do so on the code’s site.

We very much prefer that authors provide explicit information on their preferred citation for their programming work, but where they don’t, and where there is an apparent established practice of citation, we will now list that citation method as the preferred citation in the ASCL entry. So far, this inferred information has been added to 15 ASCL entries.
Partial screenshot showing location of link to suggest a change or addition to an ASCL entry

Do you want to discuss different software citation methods before selecting a preferred method? Did I get your software’s preferred citation wrong or miss it entirely? If so, please let me know via email or the Suggest a change link at the bottom of your code’s ASCL entry.