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 Web of Science 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 (i.e.,

Most Recently Added Codes

2019 Jun 26

[submitted] Sacc: Save All Correlations and Covariances

SACC (Save All Correlations and Covariances) is a format and reference library for general storage
of summary statistic measurements for the Dark Energy Science Collaboration (DESC) within and from the Large Synoptic Survey Telescope (LSST) project's Dark Energy Science Collaboration.

[submitted] Forecasts and interpolations using Ornstein-Uhlenbeck finite mixtures as an application for ALMA calibrator variability

Perl module 'Kalman' defines two subroutines, 'OU_FSI_gralMix' and 'backtrack_smoother_ND,' which models an inhomogeneous time series of measurements at different frequencies as noisy sampling from a finite mixture of Gaussian Ornstein-Uhlenbeck processes. This mixture aims to reproduce the variability of the fluxes and of the spectral indices of the quasars used as calibrators in the Atacama Large Millimeter/Sub-millimeter Array (ALMA), assuming sensible parameters are provided to the model (obtained, for example, from maximum likelihood estimation). 'OU_FSI_gralMix' calculates best forecast estimations based on a state space representation of the stochastic model using Kalman recursions. 'backtrack_smoother_ND' calculates the smoothed estimation (or interpolations) of the measurements and of the state space also using Kalman recursions. The code does not include optimization routines to calculate best fit parameters for the stochastic processes.

2019 Jun 18

[submitted] Transit and Radial velocity Interactive Fitting tool for Orbital analysis and N-body simulations : The Exo-Striker

The Exo-Striker is a new, powerful and fast GUI tool for exoplanet orbital analysis
and N-body simulations. It can model the RV stellar reflex motion caused by dynamically interacting
planets in multi-planetary systems. The Exo-Striker tool offers a broad range of tools for detailed analysis of transit and Doppler data. Key features are:

- Power spectrum analysis for Doppler & transit data
- Keplerian and dynamical modeling of multi-planet systems
- Joint Transit and RV modeling
- MCMC sampling
- Nested sampling
- Gaussian Processes modeling
- Long-term stability check of multi-planet systems
- Mean Motion Resonance (MMR) analysis
- Fully interactive, fast plots
- Import/export of working sessions
- Export of ready-to-use LaTeX tables with best-fit parameters, errors and statistics
- Text editor
- Integrated Bash-shell (Linux only)
- Integrated Jupyter shell & many more!

The Exo-Striker is cross-platform compatible (MAC OS, Linux, Windows) and it combines Fortran efficiency and Python flexibility. The tool relies on open-source packages like:

- RVmod engine (Trifonov in prep.)
- GLS (Zechmeister & K├╝rster 2009)
- TLS (Hippke & Heller 2019)
- emcee (Foreman-Mackey et al. 2013)
- celerite (Foreman-Mackey et al. 2017)
- batman (Kreidberg 2015)
- Swift/SyMBA (Duncan et al. 1998)
- dynesty (Speagle 2019) & others

[submitted] FREDDA - A fast, real-time engine for de-dispersing amplitudes

FREDDA is a code for detecting Fast Radio Burst (FRBs) in power data. It is optimised for use at ASKAP: namely GHz frequencies with 10s of beams, 100s of channels and millisecond integration times. The code is written in CUDA for NVIDIA Graphics Processing Units.

2019 Jun 15

[submitted] Blimpy: Breakthrough Listen I/O Methods for Python

The Blimpy--Breakthrough Listen I/O Methods for Python--package provides Python 2.7+/3.6+ utilities for viewing and interacting with the data formats used within the Breakthrough Listen program. This includes Sigproc filterbank (.fil) and HDF5 (.h5) files that contain dynamic spectra (aka 'waterfalls'), and guppi raw (.raw) files that contain voltage-level data. Python methods for data extraction, calibration, and visualization are provided. A suite of command-line utilities are also available.

2019 Jun 10

[submitted] Astroalign

Astroalign is a python module that will try to register (align) two stellar astronomical images, especially when there is no WCS information available.

It does so by finding similar 3-point asterisms (triangles) in both images and deducing the affine transformation between them.

Generic registration routines try to match feature points, using corner detection routines to make the point correspondence. These generally fail for stellar astronomical images, since stars have very little stable structure and so, in general, indistinguishable from each other. Asterism matching is more robust, and closer to the human way of matching stellar images.

Astroalign can match images of very different field of view, point-spread function, seeing and atmospheric conditions.

It may not work, or work with special care, on images of extended objects with few point-like sources or in crowded fields.

2019 Jun 08

[submitted] HaloAnalysis

HaloAnalysis reads and analyzes halo/galaxy catalogs, generated from Rockstar (ascl:1210.008) or AHF (ascl:1102.009), and merger trees generated from Consistent Trees (ascl:1210.011). Written in Python 3, it offers the following functionality: reads halo/galaxy/tree catalogs from multiple file formats; assigns baryonic particles and properties to dark-matter halos; combines and re-generates halo/galaxy/tree files in hdf5 format; analyzes properties of halos/galaxies; selects halos to generate zoom-in initial conditions. Includes a Jupyter notebook tutorial.

[submitted] GizmoAnalysis: read and analyze Gizmo simulations

GizmoAnalysis reads and analyzes N-body simulations run with the Gizmo code (ascl:1410.003). Written in Python 3, we developed it primarily to analyze FIRE simulations, though it is useable with any Gizmo snapshot files. It offers the following functionality: reads snapshot files and converts particle data to physical units; provides a flexible dictionary class to store particle data and compute derived quantities on the fly; plots images and properties of particles; generates region files for input to MUSIC (ascl:1311.011) to generate cosmological zoom-in initial conditions; computes rates of supernovae and stellar winds, including their nucleosynthetic yields, as used in FIRE simulations. Includes a Jupyter notebook tutorial.

2019 May 31

[ascl:1905.027] PyPDR: Python Photo Dissociation Regions

PyPDR calculates the chemistry, thermal balance and molecular excitation of a slab of gas under FUV irradiation in a self-consistent way. The effect of FUV irradiation on the chemistry is that molecules get photodissociated and the gas is heated up to several 1000 K, mostly by the photoelectric effect on small dust grains or UV pumping of H2 followed by collision de-excitation. The gas is cooled by molecular and atomic lines, thus indirectly the chemical composition also affects the thermal structure through the abundance of molecules and atoms. To find a self-consistent solution between heating and cooling, the code iteratively calculates the chemistry, thermal-balance and molecular/atomic excitation.

[ascl:1905.026] SEDPY: Modules for storing and operating on astronomical source spectral energy distribution

SEDPY performs a variety of tasks for astronomical spectral energy distributions. It can generate synthetic photometry through any filter, provides detailed modeling of extinction curves, and offers basic aperture photometry algorithms. SEDPY can also store and interpolate model SEDs, convolve absolute or apparent fluxes, and calculate rest-frame magnitudes.