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====== Allegro Software Catalogue ======

This is a catalogue of ALMA-related software that may be useful in your work. Some of these software is currently installed on the various computing systems of the [[https://www.eso.org/sci/facilities/alma/arc.html|European ARC Network]]. If you would like any piece of software on this list to be prioritised for installation or you would like another piece of software to be installed, please contact the corresponding ARC Node. The table uses the following abbreviations for the European ARC nodes:
    * **[[https://www.alma-allegro.nl/|Allegro]]**: The Dutch ARC node (Leiden)
    * **[[https://www.asu.cas.cz/alma|CZ]]**: The Czech ARC node (Ondrejov)
    * **[[https://astro.uni-bonn.de/ARC/|DE]]**: The German ARC node (Bonn-Cologne)
    * **[[https://www.eso.org/sci/facilities/alma/arc.html|ESO]]**: European Southern Observatory (Garching)
    * **[[https://iram-institute.org/science-portal/arc-node/|IRAM]]**: The IRAM ARC node (Grenoble)
    * **[[https://arc.ira.inaf.it/|IT]]**: The Italian ARC node (Bologna)
    * **[[https://www.nordic-alma.se/|Nordic]]**: The Nordic ARC node (Onsala)
    * **[[http://almadev.jb.man.ac.uk/|UK]]**: The UK ARC Node (Manchester)

For each piece of software in the catalogue, a descriptor of the current status of how recently the software has been used or tested within the European ARC network is given in the "Expertise Level" column.
    *<color green>Tested recently</color>\\
    *<color blue>Tested (>1 year ago)</color>\\
    *<color red>Tested previously</color>\\
    *Untested

This is a searchable and sortable table of software. Click on the arrows next to column titles to sort by that column. You can also use the search box to filter the content, for example try searching ''%% Allegro%%'' to see only working software that is listed as Allegro. Software is also tagged with a number of properties that you can search by, for example try searching ''%%#visualisation%%'' to see software that can be used for visualisation.

The full list of tags is:
#analysis, #archive, #astropy, #bayesian, #C, #calibration, #CASA, #comets, #datacubes, #deconvolution, #discs, #disks, #disk, #educational, #FITS, #fortran, #galaxy, #galaxies, #GILDAS, #GPU, #gridding, #HI, #HPC, #imaging, #JAX, #kinematics, #MS, #modelling, #modeling, #moments, #noise, #observing, #pipeline, #protoplanetary, #polarisation, #polarization, #PV, #python, #pytorch, #radiative-transfer, #significance, #simulation, #source-finding, #spectroscopy, #stacking, #total-power, #visibilities, #visualisation, #visualization, #VLBI

Further information on the software packages, including documentation, tutorials and examples may be found by following the links in the "Software" column.

Note that the European ARC network cannot guarantee support for these software packages. Please credit the developers and cite their papers when publishing work that uses their software.

**We welcome feedback on this catalogue!** If you have any feedback that will help to keep this catalogue up-to-date and relevant, please contact the [[alma@strw.leidenuniv.nl|Allegro ARC node]].

<searchtable>
<sortable>
^ Software ^ Tags ^ Requirements / Platform ^ Expertise Level ^ Description ^
| [[https://editeodoro.github.io/Bbarolo/|3D-Barolo]] | #analysis #datacubes #galaxy #galaxies #discs #disks #kinematics #modelling #modeling #protoplanetary #python | Python 3 | Untested | A tool for fitting 3D tilted-ring models to emission-line data cubes to derive kinematics of disc-like objects. It helps determine rotation curves and morphological parameters. Described in [[https://academic.oup.com/mnras/article/451/3/3021/1198172|Teodoro et al. (2015)]]. |
| [[https://admit.astro.umd.edu/|ADMIT]] | #analysis #archive #CASA #datacubes #moments #pipeline #python | CASA, \\ Python 3 | Untested | The ALMA Data Mining Toolkit (ADMIT) is a software package which enables quick access to archival ALMA data products and includes a set of tools for analysing data cubes. It automatically identifies spectral lines, generates moment maps, and provides a comprehensive summary of the data. Described in [[https://ui.adsabs.harvard.edu/abs/2015isms.confEWI15F/abstract|Douglas et al. 2015]]. There is also a [[https://casaguides.nrao.edu/index.php/ADMIT_Products_and_Usage_CASA_6|CASA Guide]] on how to use ADMIT in a CASA environment. |
| [[http://www.alma.inaf.it/index.php/ALMA_FITS_Keywords|ALMA FITS Keywords filler]] | #archive #CASA #FITS #python | CASA, \\ Python | <color green>Tested recently</color>\\ (Allegro) | A CASA task to populate ALMA FITS files with new keywords. The AKF command functions as a normal CASA function; typing ''inp(AKF)'' will show input options. Described in this [[http://www.alma.inaf.it/images/AKF_v1.2.pdf|Memo by Liuzzo et al.]]. |
| [[https://almasim.readthedocs.io/en/stable/|ALMASim]] | #CASA #datacubes #galaxy #galaxies #HI #HPC #imaging #python #simulation #visibilities | CASA, \\ Python 3.8 | <color green>Tested recently</color>\\ (Allegro) | A Python-based simulator for creating realistic ALMA observations of HI line galaxies. It allows users to generate mock interferometric data (visibilities) and simulated images. Described in [[https://academic.oup.com/mnras/article/518/3/3407/6825518|Delli Veneri et al. (2022)]]. |
| [[https://almascience.eso.org/proposing/observing-tool|ALMA Observing Tool]] | #observing | Java | <color green>Tested recently</color>\\ (Allegro) | The official Java application used to prepare and submit ALMA observing proposals. It allows users to define scientific goals, specify instrumental setups, and estimate observing time. An [[https://www.youtube.com/watch?v=D0Fv8DkGV-s|I-TRAIN video]] is available.|
| [[https://alminer.readthedocs.io/en/latest/|ALminer]] | #analysis #archive #python #visualisation #visualization | Python 3 | <color blue>Tested (>1 year ago)</color>\\ (Allegro) | A Python package for querying, analysing, and visualising data from the [[https://almascience.nrao.edu/aq/|ALMA Science Archive]]. It simplifies finding and downloading datasets and provides tools for quick-look analysis. An online [[https://nbviewer.org/github/emerge-erc/ALminer/blob/main/notebooks/tutorial/ALminer_tutorial.ipynb?flush_cache=True|tutorial notebook]] and an [[https://www.youtube.com/watch?v=LxNHoYcbI9Q|I-TRAIN video]] is available. |
| [[https://launchpad.net/apsynsim|APSYNSIM]] | #deconvolution #educational #python #simulation #visibilities #visualisation #visualization | Python 3 | <color green>Tested recently</color>\\ (Allegro) | An interactive Python-based tool for simulating aperture synthesis observations. It helps to visualise the uv-coverage and the resulting synthesised beam and dirty image for a given array configuration and source model. APSYNSIM is ideal for teaching aperture synthesis techniques in radio astronomy. Described in [[https://arxiv.org/abs/1706.00936|Martí-Vidal (2017)]]. |
| [[https://home.strw.leidenuniv.nl/~michiel/artist/artist_download.html|ARTIST]] | #CASA #discs #disks #modelling #modeling #polarisation #polarization #python #radiative-transfer | CASA > 4.7, \\ Python 2.7 | Untested | The ALMA Radiative Transfer Interactive Tool Set (ARTIST) is a collection of tools for multi-dimensional radiative transfer calculations of line and continuum emissions, including their polarisation. It operates within the CASA environment. Described in [[https://arxiv.org/abs/1102.4815|Padovani & Jørgensen (2011)]]. |
| [[https://confluence.alma.cl/display/EAPR/uv+coverage+assessment+with+assess_ms+3|assess_ms]] | #analysis #CASA #MS #python #visibilities | CASA, \\ Python 3 | <color green>Tested recently</color>\\ (ESO \\ Allegro) | A CASA-based Python tool for assessing the quality of an ALMA measurement set (MS). It evaluates uv-coverage, calculates the sensitivity of the observation to the accessible range of angular scales, and derives effective parameters such as angular resolution and maximum recoverable scale. Details can be found on the [[https://almascience.eso.org/tools/eu-arc-network/i-train|I-TRAIN website]] (I-TRAIN #25). |
| [[https://www.iram.fr/IRAMFR/GILDAS/doc/html/astro-html/astro.html|ASTRO]] | #GILDAS #observing | GILDAS | <color green>Tested recently</color>\\ (IRAM \\ Allegro) | A program within the GILDAS software suite used for planning astronomical observations. It helps calculate source positions, parallactic angles, and atmospheric transmission for various observatories. |
| [[https://bettermoments.readthedocs.io/en/latest/?badge=latest|Better Moments]] | #analysis #datacubes #moments #noise #python #significance | Python 3 | <color green>Tested recently</color>\\ (Allegro) | A Python package for creating moment maps from data cubes. Several traditional and non-traditional methods are implemented, some of which are described in [[https://iopscience.iop.org/article/10.3847/2515-5172/aae265|Teague & Foreman-Mackey (2018)]] and [[https://iopscience.iop.org/article/10.3847/2515-5172/ab2125|Teague (2019)]]. |
| [[https://blobcat.sourceforge.net/|BLOBCAT]] | #analysis #FITS #python #source-finding | Python 2.7 | Untested | A source extraction tool, based on the flood fill algorithm, designed to find and catalogue extended sources in astronomical FITS images of total intensity or linear polarisation. Described in [[https://academic.oup.com/mnras/article/425/2/979/1202064|Hales et al. (2012)]]. |
| [[https://cartavis.org/|CARTA]] | #analysis #datacubes #moments #PV #python #visualisation #visualization | Python 3 | <color green>Tested recently</color>\\ (Allegro \\ IT) | The Cube Analysis and Rendering Tool for Astronomy (CARTA) is a high-performance image viewer for large data cubes, offering remote visualisation, spectral profile analysis, and the generation of moment maps and PV-diagrams. An [[https://www.youtube.com/watch?v=K71rFeAhQ5o|I-TRAIN video]] is available. |
| [[https://casadocs.readthedocs.io/en/stable/|CASA]] | #analysis #calibration #CASA #datacubes #imaging #moments #python #visibilities | CASA, \\ Python 3 | <color green>Tested recently</color>\\ (Allegro \\ IT \\ Nordic) | The Common Astronomy Software Applications (CASA) package is the primary data processing software for ALMA and VLA, used for calibration, imaging, and analysis of interferometric and single-dish data. Tutorials are available in the [[https://casaguides.nrao.edu/index.php/ALMA_Tutorials|CASA guides]]. |
| [[https://github.com/onsala-space-observatory/casairing|CASAIRING]] | #analysis #CASA #discs #disks #python | CASA, \\ Python 3 | <color green>Tested recently</color>\\ (Nordic \\ Allegro) | A CASA task which computes the radial profile of an image (cube). The results are plotted and saved in an ASCII file. It functions as a normal CASA task with ''inp(casairing)'' and ''help casairing'' commands. |
| [[http://cassis.irap.omp.eu/|CASSIS]] | #analysis #modelling #modeling #simulation #spectroscopy #visualisation #visualization | Java | <color green>Tested recently</color>\\ (Allegro) | The Centre d'Analyse Scientifique de Spectres Instrumentaux et Synthétiques (CASSIS) is a tool for visualising, analysing, and modelling astronomical spectra. Allegro provides a [[https://www.alma-allegro.nl/software/cassis/|cookbook]] for its use. |
| [[https://github.com/onsala-space-observatory/checkres|CHECKRES]] | #analysis #CASA #python | CASA, \\ Python 3 | Untested | A CASA interactive task to check image residuals in Fourier space. It overplots the UV tracks of the baselines corresponding to selected antennas, making it easy to locate antennas and/or baselines responsible for dynamic-range limitations. It functions as a normal CASA task; typing ''inp(checkres)'' will show input options. |
| [[https://github.com/onsala-space-observatory/closures|closures]] | #analysis #CASA #imaging #python | CASA, \\ Python 3 | Untested | A CASA task for computing and analysing closure phases and amplitudes, which are calibration-independent quantities useful for high-fidelity imaging, robust data analysis, and as diagnostics. It functions as a normal CASA task; typing ''inp(closures)'' will show input options. |
| [[https://disksurf.readthedocs.io/en/latest/index.html|disksurf]] | #analysis #datacubes #discs #disks #kinematics #protoplanetary #python | Python 3 | Untested | A Python package to extract emission surfaces from spectral cubes of protoplanetary discs. Described in [[https://www.aanda.org/articles/aa/full_html/2018/01/aa31377-17/aa31377-17.html|Pinte et al. (2018)]]. |
| [[https://stammler.github.io/dustpy/|DustPy]] | #discs #disks #modelling #modeling #protoplanetary #python #simulation | Python 3 | Untested | A Python tool for simulating the evolution of dust in protoplanetary discs. Described in [[https://iopscience.iop.org/article/10.3847/1538-4357/ac7d58|Stammler & Birnstein (2022)]]. |
| [[https://github.com/richteague/eddy|eddy]] | #analysis #datacubes #kinematics #python | Python 3 | Untested | A Python package to derive the radial velocity profiles of protoplanetary discs from spectral cubes. See [[https://joss.theoj.org/papers/10.21105/joss.01220|Teague (2019)]]. |
| [[https://github.com/takafumi291/ESSENCE|ESSENCE]] | #analysis #noise #python #significance | python 3.7.7, \\ astropy 4.3.1, \\ spectral_cube 0.6.0, \\ numpy 1.21.5, \\ scipy 1.7.3, \\ multiprocess 0.70.13 | Untested | A Python tool for estimating the noise properties and statistical significance of features in astronomical images, particularly under correlated noise. Described in [[https://doi.org/10.1117/1.JATIS.9.1.018001|Tsukui et al. (2023)]]. |
| [[https://github.com/onsala-space-observatory/fakeobs|FAKEOBS]] | #CASA #python #simulation | CASA, \\ Python 3 | Untested | A CASA task to create model visibilities from an existing measurement set. This task substitutes the visibilities of the existing measurement with those computed from a model image. It functions as a normal CASA task; typing ''inp(fakeobs)'' will show input options. |
| [[https://discsim.github.io/frank/py_API.html|FRANK]] | #analysis #bayesian #discs #disks #modelling #modeling #protoplanetary #python #visibilities | Python 3 | Untested | A fast parallel code for fitting 1D radial profiles to interferometric data using a Bayesian framework. It is designed for analysing protoplanetary disc observations. Described in [[https://academic.oup.com/mnras/article/495/3/3209/5838058?login=true|Jennings et al. (2020)]]. |
| [[https://k-poster.kuoni-congress.info/eas-2024/poster/803490a3-04a4-46ca-9094-22a296467039|FRELLED]] | #analysis #datacubes #moments #python #visualisation #visualization | Python 3 | <color green>Tested recently</color>\\ (UK) | A Python tool for the analysis of line emission in data cubes, providing methods for moment map creation and visualisation. Described in [[https://www.sciencedirect.com/science/article/pii/S2213133724001422|Taylor 2025]]. |
| [[https://www.iram.fr/IRAMFR/GILDAS/|GILDAS]] | #C #calibration #fortran #GILDAS #imaging #simulation #visualisation #visualization | GILDAS, Fortran, C | <color green>Tested recently</color>\\ (IRAM \\ IT) | A comprehensive software suite for processing and analysing radio astronomical data from single-dish telescopes and interferometers, developed by IRAM. |
| [[https://mtazzari.github.io/galario/|galario]] | #GPU #modelling #modeling #python #visibilities | Python < 3.8, CUDA | <color green>Tested recently</color>\\ (Allegro) | A Python tool for comparing interferometric visibilities to model predictions, optimised for use with GPUs. Described in [[https://academic.oup.com/mnras/article/476/4/4527/4867987?login=true|Tazzari et al. (2018)]]. |
| [[https://glueviz.org/|Glue]] | #analysis #python #visualisation #visualization | Python 3 | Untested | A Python library for exploring relationships within and between related datasets. It provides linked statistical graphs for multi-dimensional data visualisation. [[https://docs.glueviz.org/en/stable/videos.html#demo-videos|Demo videos]] are available. |
| [[https://github.com/richteague/gofish|GoFish]] | #analysis #discs #disks #protoplanetary #python #spectroscopy #stacking | Python 3 | Untested | A Python package to analyse the spectra of protoplanetary discs. The signal-to-noise ratio is increased by stacking the emissions to a comon line centre, exploiting the known rotation within the disc. Described in [[https://joss.theoj.org/papers/10.21105/joss.01632|Teague (2019)]]. |
| [[https://interferopy.readthedocs.io/en/latest/|Interferopy]] | #analysis #python #visualisation #visualization | Python 3 | <color green>Tested recently</color>\\ (Allegro) | A Python library of common tasks used in radio interferometric data analysis, originally developed to study the interstellar medium in high-redshift quasar host galaxies and to create publication quality plots ([[https://doi.org/10.5281/ZENODO.5775603|Boogard et al. 2021]]). |
| [[https://github.com/Joshiwavm/jackknify/tree/main|jackknify]] | #noise #python #significance | Python 3 | <color green>Tested recently</color>\\ (Allegro) | A Python tool for estimating uncertainties in astronomical images using the jackknife resampling technique, which is robust for correlated noise. See the [[https://joshiwavm.github.io/jackknify/|documentation]]. |
| [[https://github.com/aardk/jupyter-casa|jupyter-casa]] | #CASA #python | Docker, Python 3 | Untested | A project that provides a Jupyter kernel for CASA, allowing users to run CASA tasks and commands within a Jupyter notebook environment. The kernel is distributed as a [[https://hub.docker.com/r/penngwyn/jupytercasa|Docker Image]]. |
| [[https://github.com/lime-rt/lime|LIME]] | #C #discs #disks #modelling #modeling #protoplanetary #radiative-transfer #simulation | C | <color green>Tested recently</color>\\ (Allegro) | A 3D non-LTE molecular line excitation and radiative transfer code. It is used for modelling line emission from various astrophysical objects like molecular clouds and protoplanetary discs. Described in [[https://www.aanda.org/articles/aa/full_html/2010/15/aa15333-10/aa15333-10.html|Brinch & Hogerheijde (2010)]]. |
| [[https://jbjolly.github.io/LineStacker/|LineStacker]] | #analysis #python #spectroscopy #stacking | Python 3 | <color blue>Tested (>1 year ago)</color>\\ (Allegro) | A Python tool for stacking spectra in the image domain to detect faint emission lines by averaging signals from multiple positions or spectral channels. An [[https://www.youtube.com/watch?v=1WtImPA0jcY|I-TRAIN video]] is available. |
| [[https://www.iram.fr/IRAMFR/GILDAS/doc/html/map-html/map.html|MAPPING]] | #deconvolution #GILDAS #imaging #visibilities #visualisation #visualization | GILDAS | <color green>Tested recently</color>\\ (IRAM \\ Allegro) | A program within the GILDAS suite for imaging, deconvolution, and self-calibration of interferometric data. |
| [[https://cab.inta-csic.es/madcuba/|MADCUBA]] | #analysis #datacubes #spectroscopy #visualisation #visualization | IDL | Untested | A software package for the analysis of spectral line data cubes. It includes tools for line identification, fitting, and deriving physical parameters of the molecular species. Described in [[https://www.aanda.org/articles/aa/pdf/2019/11/aa36144-19.pdf|Martín et al. (2019)]]. |
| [[https://github.com/MPoL-dev/MPoL|MPoL]] | #analysis #imaging #GPU #python #pytorch #visibilities | Python 3 | Untested | A Python package for regularised maximum likelihood imaging of interferometric data. It uses modern optimisation techniques to reconstruct images from visibilities. Described in [[https://iopscience.iop.org/article/10.1088/1538-3873/acdf84|Zawadzki et al. (2023)]]. |
| [[https://github.com/akdiaz/LPD|MEMOIR]] | #analysis #python #spectroscopy | Python 3 | Untested | A tool for identifying molecular emission lines in astronomical spectra by comparing the observed frequency with the frequencies of known lines. |
| [[https://photutils.readthedocs.io/en/stable/|Photutils]] | #analysis #astropy #python #source-finding | Python 3, \\ Astropy | Untested | An Astropy-affiliated package for photometry and source detection in astronomical images. It provides tools for aperture photometry, background estimation, and source finding. |
| [[https://github.com/marti-vidal-i/PolConvert|PolConvert]] | #calibration #polarisation, #polarization #python #VLBI | Python 3 | Untested | A software package for calibrating and converting mixed-polarisation VLBI observations. Described in [[https://www.aanda.org/articles/aa/full_html/2016/03/aa26063-15/aa26063-15.html|Martí-Vida et al. (2016)]]. |
| [[https://github.com/onsala-space-observatory/polsimulate|POLSIMULATE]] | #CASA #polarisation, #polarization #python #simulation | CASA, \\ Python 3 | Untested | A CASA task for simulating polarised emission from a model source and observing it with an interferometer. It functions as a normal CASA task; typing ''inp(polsimulate)'' will show input options. |
| [[https://pvextractor.readthedocs.io/en/latest/|Position-Velocity Slice Extractor]] | #analysis #datacubes #kinematics #PV #python #visualisation #visualization | Python 3 | <color green>Tested recently</color>\\ (IT \\ Allegro) | A Python tool for creating position-velocity (PV) diagrams from spectral cubes by extracting slices along a specified path. Described in [[https://ui.adsabs.harvard.edu/abs/2015ASPC..499..363G/abstract|Ginsburg et al. (2015)]]. |
| [[https://pyspeckit.readthedocs.io/en/latest/|PySpecKit]] | #analysis #datacubes #modelling #modeling #python #spectroscopy | Python 3 | <color green>Tested recently</color>\\ (Allegro) | A Python toolkit for spectroscopic analysis. It allows for fitting and modelling of spectral lines, baseline subtraction, and measurement of line properties. Described in [[https://iopscience.iop.org/article/10.3847/1538-3881/ac695a|Ginsburg et al. (2022)]]. |
| [[https://github.com/RadioAstronomySoftwareGroup/pyuvdata|pyuvdata]] | #python #visibilities | Python 3 | <color green>Tested recently</color>\\ (Allegro) | A Python package for reading, writing, and manipulating interferometric visibility data. It supports various data formats, including CASA measurement sets and UVFITS. |
| [[https://github.com/mneeleman/qubefit|Qubefit]] | #analysis #bayesian #datacubes #discs #disks #galaxy #galaxies #kinematics #modelling #modeling #moments #python | Python 3 | Untested | A tool for fitting kinematic and morphological models to spectral cubes using a Bayesian framework. |
| [[https://home.strw.leidenuniv.nl/~moldata/radex.html|RADEX]] | #modelling #modeling #radiative-transfer #simulation | Fortran, Online | <color green>Tested recently</color>\\ (Allegro) | A non-LTE radiative transfer code for predicting molecular line intensities in homogeneous interstellar clouds. It can be used via a [[https://sronpersonalpages.nl/~vdtak/radex/index.shtml|terminal]] or an [[https://var.sron.nl/radex/radex.php|online application]]. Described in [[https://www.aanda.org/component/article?access=bibcode&bibcode=2007A%2526A...468..627VFUL|van der Tak et al. (2007)]]. |
| [[https://github.com/radio-astro-tools/radio-beam|Radio Beam]] | #astropy #python | Python 3, \\ Astropy | Untested | An Astropy-affiliated Python package for handling synthesised beams from radio interferometers. It provides tools for beam arithmetic and convolution. Described in [[https://science.nrao.edu/facilities/alma/science_sustainability/Spectral_Cube_and_Radio_Beam_Ginsburg.pdf|Koch et al. (2022)]]. |
| [[https://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/index.php|RADMC-3D]] | #modelling #modeling #python #radiative-transfer #simulation | Fortran, \\ Python 3 | <color green>Tested recently</color>\\ (Allegro) | A radiative transfer code for simulating continuum and line emission in arbitrary 1D, 2D, and 3D geometries ([[https://ui.adsabs.harvard.edu/abs/2012ascl.soft02015D/abstract|Dullemond et al. (2012)]]). |
| [[https://home.strw.leidenuniv.nl/~michiel/ratran/ | RATRAN]] | #modelling #modeling #python #radiative-transfer #simulation | Fortran, \\ Python 2.7 | Untested | A radiative transfer code for modelling molecular line emission. Described in [[https://arxiv.org/abs/astro-ph/0008169|Hogerheijde & van der Tak (2000)]]. |
| [[https://github.com/henrikju/resolve|RESOLVE]] | #analysis #bayesian #GPU #imaging #JAX| Python 3 | <color green>Tested recently</color>\\ (ESO \\ Allegro) | A Bayesian imaging software for interferometry based on information field theory. RESOLVE is best suited for imaging extended emissions. Described in [[https://www.aanda.org/articles/aa/full_html/2015/09/aa23465-14/aa23465-14.html|Junklewitz et al. (2015)]], [[https://www.aanda.org/articles/aa/full_html/2016/02/aa23094-13/aa23094-13.html|Junklewitz et al. (2016)]], and [[https://arxiv.org/abs/1605.04317|Greiner et al. (2017)]]. |
| [[https://ascl.net/1905.015|rPICARD]] | #calibration #CASA #pipeline #VLBI | CASA | Untested | A CASA-based calibration pipeline for VLBI data. Described in [[https://www.aanda.org/articles/aa/full_html/2019/06/aa35181-19/aa35181-19.html|Janssen et al. (2019)]]. |
| [[https://github.com/onsala-space-observatory/SD2vis|SD2vis]] | #CASA #python #simulation #total-power #visibilities | CASA, \\ Python 3 | Untested | A CASA task to generate synthetic visibilities from a single-dish (total-power) image. It functions as a normal CASA task; typing ''inp(SD2vis)'' will show input options. |
| [[https://casadocs.readthedocs.io/en/stable/notebooks/simulation.html|sim-alma]] | #CASA #python #simulation #visibilities | CASA, \\ Python 3 | | A set of tasks within CASA used for simulating ALMA observations. It allows users to create measurement sets from model images, specifying array configurations and observing parameters. It is a core component for observation planning and data simulation. |
| [[https://github.com/mcordiner/sublime-d1dc|SUBLIME-D1DC]] | #C #comets #modelling #modeling #python #radiative-transfer #simulation | C, \\ Python 3 | Untested | A 1D radiative transfer code for modelling outflowing cometary gases. It also has a [[https://github.com/mcordiner/sublimed1dFit|python interface]]. Described in [[https://iopscience.iop.org/article/10.3847/1538-4357/ac5893|Cordiner et al. (2022)]]. Also, see [[https://www.aanda.org/articles/aa/full_html/2010/15/aa15333-10/aa15333-10.html|Brinch & Hogerheijde (2010)]]. |
| [[https://github.com/CurtinIDS/SoFiA-2 | SOFIA]] | #analysis #datacubes #moments #pipeline #source-finding | Python 3 | Untested | A pipeline for source finding and analysis in datacubes. It can detect and parameterise sources in data cubes and create moment maps. See the [[https://gitlab.com/SoFiA-Admin/SoFiA-2/-/wikis/documents/SoFiA-2_User_Manual.pdf|documentation]] and [[https://academic.oup.com/mnras/article/448/2/1922/1058751/SoFiA-a-flexible-source-finder-for-3D-spectral|Paolo et al. (2015)]]. |
| [[https://spectral-cube.readthedocs.io/en/latest/|Spectral Cube]] | #analysis #astropy #datacubes #moments #python | Python 3, \\ Astropy | <color green>Tested recently</color>\\ (Allegro) | An Astropy-affiliated Python package for reading, writing, and analysing spectral cubes. It provides tools for cube manipulation, moment map creation, and spectral operations. |
| [[https://github.com/centowen/stacker|STACKER]] | #analysis #python #stacking #visibilities | Python 3 | Untested | A Python tool for stacking interferometric visibility data. |
| [[https://hera.ph1.uni-koeln.de/~sanchez/statcont|STATCONT]] | #analysis #python #spectroscopy | Python 3 | Untested | A tool for statistical determination of the continuum level in spectral line data, particularly useful for complex spectra with many overlapping lines. An [[https://www.youtube.com/watch?v=0XhQN-BH7Yw|I-TRAIN video]] is available. Described in [[https://www.aanda.org/articles/aa/full_html/2018/01/aa30425-17/aa30425-17.html|Sánchez-Monge et al. (2018)]] |
| [[https://perimeterinstitute.github.io/Themis/html/index.html|THEMIS]] | #analysis #bayesian #imaging #VLBI | C++ | <color green>Tested recently</color>\\ (Allegro) | A Bayesian imaging framework for radio interferometry, particularly for sparse (e.g. VLBI) data. Described in [[https://iopscience.iop.org/article/10.3847/1538-4357/ab91a4|Broderick et al. (2020)]]. |
| [[http://mural.uv.es/imarvi/docums/uvmultifit/|uvmultifit]] | #analysis #CASA #modelling #modeling #python #visibilities | CASA, \\ Python 2.7 | <color green>Tested recently</color>\\ (Allegro) | A library for fitting models directly to visibility data. UVMULTIFIT operates within CASA. An [[https://www.youtube.com/watch?v=MlARlvc_ggM|I-TRAIN video]] is available. Described in [[https://www.aanda.org/articles/aa/full_html/2014/03/aa22633-13/aa22633-13.html|Martí-Vidal et al. 2014]]. |
| [[https://uvplot.readthedocs.io/en/latest/index.html|UVPLOT]] | #CASA #python #visibilities #visualisation #visualization | CASA, \\ Python 3 | <color green>Tested recently</color>\\ (Allegro) | A Python tool for plotting and inspecting visibility data. UVPLOT can be imported within CASA. |
| [[https://wsclean.readthedocs.io/en/latest/index.html|WSClean]] | #deconvolution #GPU #gridding #imaging | C++ | <color green>Tested recently</color>\\ (Allegro) | A fast, wide-field interferometric imager. It is highly optimised for performance and can handle large datasets and wide fields of view. Described in [[https://ui.adsabs.harvard.edu/link_gateway/2014MNRAS.444..606O/PUB_HTML|Offringa et al. (2014)]]. |
| [[https://www.alma-allegro.nl/wvr-and-phase-metrics/wvr-scaling/|WVR Scaling Module]] | #astropy #calibration #CASA #python | CASA < 5.0, Python 2.7, Astropy 1.3.3 | Untested | A CASA-based tool for applying water vapor radiometer (WVR) corrections to ALMA data to improve phase calibration. Described in [[https://www.aanda.org/articles/aa/abs/2017/09/aa31197-17/aa31197-17.html|Maud et al. 2017]]. |
| [[https://xclass-pip.astro.uni-koeln.de|XCLASS]] | #analysis #datacubes #modelling #modeling #python #radiative-transfer #simulation #spectroscopy | CASA, \\ Python 3 | <color green>Tested recently</color>\\ (DE \\ Allegro) | The eXtended CASA Line Analysis Software Suite (XCLASS) a collection of functions for modelling interfeormetric and single-dish data. This includes myXCLASS which is a 1D radiative-transfer modelling code for isothermal objects. An [[https://www.youtube.com/watch?v=e8zVWTmJM7c|I-TRAIN video]] is available. Described in [[https://www.aanda.org/articles/aa/full_html/2017/02/aa27203-15/aa27203-15.html|Möller et al. (2015)]]. |
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