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| ====== Allegro Software Catalogue ====== | ====== Allegro Software Catalogue ====== |
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| This is a catalogue of ALMA-related software that may be useful in your work. Some of these software is currently installed on our computing facilities at Allegro. 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 us at alma@strw.leidenunv.nl. | 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) |
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| ==== Software statuses at Allegro ==== | 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 |
| |
| For each piece of software in the catalogue, a descriptor of the current status of how recently the software has been reported as working at Allegro is given in the "Status" column. | 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. |
| |
| * <color #22b14c> Working recently </color> at allegro | The full list of tags is: |
| * <color #00a2e8> Working (>1 year ago) </color> at allegro | #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 |
| * <color #ed1c24> Worked previously </color> at allegro | |
| * Not working or untested at allegro | |
| |
| Software with a status further down this list is less likely to be working on the allegro machines. Please contact alma@strw.leidenunv.nl if you find that any software doesn't work and we will be happy to help you with it. | Further information on the software packages, including documentation, tutorials and examples may be found by following the links in the "Software" column. |
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| ==== Installed software ==== | 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. |
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| This is a searchable and sortable table of installed 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 ''%%Working recently%%'' to see only working software that is listed as working recently. 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. | **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]]. |
| | |
| The full list of tags is: | |
| #analysis, #archive, #calibration, #CASA, #datacubes, #GILDAS, #GPU_computing, #modelling, #moments, #observing, #polarisation, #python, #simulation, #visibilities, #visualisation, #visualization, #VLBI | |
| |
| <searchtable> | <searchtable> |
| <sortable> | <sortable> |
| ^ Software ^ Startup procedure ^ Status ^ Tags ^ Notes ^ | ^ Software ^ Tags ^ Requirements / Platform ^ Expertise Level ^ Description ^ |
| | [[https://editeodoro.github.io/Bbarolo/|3D-Barolo]] | ''import pyBBarolo'' \\ (type within a python environment) |Not working or untested |#analysis #datacubes #modelling #python |A paper on the tool can be found at https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.3021D/abstract | | | [[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]] |''source admit_start.csh'' \\ ''import admit'' (type within CASA) |Not working or untested |#archive #CASA #python | | | | [[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]] |''AKF()'' \\ (type within CASA) |<color #22b14c> Working recently </color> |#archive #CASA #python |The AKF command functions as a normal CASA function, typing ''inp(AKF)'' will show input options. A CASA memo on the tool can be accessed at [[http://www.alma.inaf.it/images/AKF_v1.2.pdf|]] | | | [[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]] | Execute ''python main.py --option value --option1 value1 value2'' in the ALMASim folder. Check the available options by running ''python main.py -h'' |<color #22b14c> Working recently </color> |#CASA #python #simulation #datacubes |A paper on the tool can be found at https://academic.oup.com/mnras/article/518/3/3407/6825518 | | | [[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]] | |<color #22b14c> Working recently </color> | #observing | | | | [[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]] | ''import alminer'' \\ (type within a python environment) |<color #00a2e8> Working (>1 year ago) </color> |#archive #python #visualisation #visualization | There is an online [[https://nbviewer.org/github/emerge-erc/ALminer/blob/main/notebooks/tutorial/ALminer_tutorial.ipynb?flush_cache=True|tutorial notebook]] that showcases alminer's various functions with examples. There is an I-TRAIN training avaliable for ALminer, with recording of the session avaliable as a [[https://www.youtube.com/watch?v=LxNHoYcbI9Q|YouTube video]]. Full details can be found on [[https://almascience.eso.org/tools/eu-arc-network/i-train|I-TRAIN website]] under the heading I-TRAIN #8: Exploring the ALMA Science Archive with ALminer | | | [[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]] | |<color #22b14c> Working recently </color>|#python #simulation |An arXiv paper on the tool can be found at https://arxiv.org/abs/1706.00936 | | | [[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]] | |Not working or untested |#CASA #modelling #python |An arXiv paper on the tool can be found at https://arxiv.org/abs/1102.4815| | | [[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|asses_ms]] |''casa --nologger -c run_assess_ms_public.py''|<color #22b14c> Working recently </color>|#CASA #analysis #python #visibilities | Two arXiv papers on this tool can be found at https://arxiv.org/abs/2406.13199 and https://arxiv.org/abs/2012.08993 | | | [[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]] | |<color #22b14c> Working recently </color>|#GILDAS #observing | | | | [[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]] |''import bettermoments'' \\ (type within a python environment) |<color #22b14c> Working recently </color> |#datacubes #moments #python |Papers on this tool can be found at https://iopscience.iop.org/article/10.3847/2515-5172/aae265 and https://iopscience.iop.org/article/10.3847/2515-5172/ab2125 | | | [[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]] | |Not working or untested |#python #sourcefinder | A paper on this tool can be found at https://academic.oup.com/mnras/article/425/2/979/1202064| | | [[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]] | Terminal command: ''carta'' |<color #22b14c> Working recently </color> |#datacubes #python #visualisation #visualization |There is an I-TRAIN training avaliable for LineStacker, with recording of the session avaliable as a [[https://www.youtube.com/watch?v=K71rFeAhQ5o|YouTube video]]. Full details can be found on the [[https://almascience.eso.org/tools/eu-arc-network/i-train|I-TRAIN website]] under the heading I-TRAIN #12: CARTA tutorial. A paper on this tool can be found at https://www.sciencedirect.com/science/article/pii/S2213133720300433.| | | [[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]] |Terminal command: ''casa''|<color #22b14c> Working recently </color> |#calibration #CASA #datacubes #moments #python #visibilities |Step-by-step tutorials on how to use CASA on ALMA data can be found in the [[https://casaguides.nrao.edu/index.php/ALMA_Tutorials|CASA guides]]. The official CASA documentation can be found at https://casadocs.readthedocs.io/en/stable/| | | [[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]] |''casairing())'' \\ (type within CASA) |<color #22b14c> Working recently </color> |#CASA #python |The casairing command functions as a normal CASA function, typing ''inp(casairing)'' will show input options and ''help casairing'' will some some example of how to use the function. | | | [[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]] | |<color #22b14c> Working recently </color> |#modelling #simulation | Allegro has developed a simple cookbook that describes how to use CASSIS with a special emphasis on ALMA observations. It can be accessed from [[https://www.alma-allegro.nl/software/cassis/|this link]]. | | | [[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]] |''checkres())'' \\ (type within CASA) |Not working or untested |#CASA #python |The checkres command functions as a normal CASA function, typing ''inp(checkres)'' will show input options. | | | [[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]] |''closures())'' \\ (type within CASA) |Not working or untested |#CASA #python |The closures command functions as a normal CASA function, typing ''inp(closures)'' 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]] | ''from disksurf import observation'' \\ (type within a python environment) |Not working or untested | #python #analysis |A paper on this tool can be found at https://joss.theoj.org/papers/10.21105/joss.03827| | | [[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]] | ''import dustpy'' \\ (type within a python environment) |Not working or untested | #python #simulation |A paper on this tool can be found at https://iopscience.iop.org/article/10.3847/1538-4357/ac7d58| | | [[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/onsala-space-observatory/fakeobs|FAKEOBS]] |''fakeobs())'' \\ (type within CASA) |Not working or untested |#CASA #python #simulation |The fakeobs command functions as a normal CASA function, typing ''inp(fakeobs)'' will show input options. | | | [[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://mtazzari.github.io/galario/|galario]] | ''import galario'' \\ (type within a python environment) |<color #22b14c> Working recently </color>|#GPU_computing #modelling #python #visibilities |A paper on this tool can be found at https://academic.oup.com/mnras/article/476/4/4527/4867987. Galario works best on GPUs.| | | [[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://glueviz.org/|Glue]] | Terminal command: ''glue'' |Not working or untested |#python #visualisation #visualization |In addition to the [[https://docs.glueviz.org/en/stable/|documentation]], there are a number of [[https://docs.glueviz.org/en/stable/videos.html#demo-videos|demo video]] on this tool.| | | [[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://github.com/richteague/gofish|GoFish]] |''import gofish'' \\ (type within a python environment) |Not working or untested |#python | A paper on this tool can be found at https://joss.theoj.org/papers/10.21105/joss.01632 | | | [[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://interferopy.readthedocs.io/en/latest/|Interferopy]] | ''import interferopy'' \\ (type within a python environment) |<color #22b14c> Working recently </color>|#python #visualisation #visualization | | | | [[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://joshiwavm.github.io/jackknify/|jackknify]] |''import jackknify'' \\ (type within a python environment) |<color #22b14c> Working recently </color>|#python #modelling #noise | | | | [[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://github.com/aardk/jupyter-casa|jupyter-casa]] | |Not working or untested |#CASA #python |The jupyter kernel is distributed as a [[https://hub.docker.com/r/penngwyn/jupytercasa|Docker Image]]. | | | [[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://github.com/lime-rt/lime|LIME]] |Terminal command: ''lime [options...] <model file>'' |Not working or untested |#modelling |A paper on this tool can be found at https://www.aanda.org/articles/aa/full_html/2010/15/aa15333-10/aa15333-10.html| | | [[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://jbjolly.github.io/LineStacker/|LineStacker]] |''import LineStacker'' \\ (type within a python environment)|<color #00a2e8> Working (>1 year ago) </color> |#analysis #python |There is an I-TRAIN training avaliable for LineStacker, with a recording of the session avaliable as a [[https://www.youtube.com/watch?v=1WtImPA0jcY|YouTube video]]. Full details can be found on the [[https://almascience.eso.org/tools/eu-arc-network/i-train|I-TRAIN website]] under the heading I-TRAIN #9: Stacking spectra in the image domain with LineStacker. | | | [[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://www.iram.fr/IRAMFR/GILDAS/doc/html/map-html/map.html|MAPPING]] | |<color #22b14c> Working recently </color>|#GILDAS #visibilities #visualisation #visualization | | | | [[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/MPoL-dev/MPoL|MPoL]] |''import mpol'' \\ (type within a python environment) |Not working or untested |#analysis #python # |A paper on this tool can be found at https://iopscience.iop.org/article/10.1088/1538-3873/acdf84 | | | [[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/akdiaz/LPD|Molecular EMissiOn IdentifieR (MEMOIR)]] |Terminal command: ''memoir'' |Not working or untested |#analysis #python | | | | [[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://photutils.readthedocs.io/en/stable/|Photutils]] |''import photutils'' \\ (type within a python environment) |Not working or untested |#analysis #python | | | | [[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://github.com/marti-vidal-i/PolConvert|PolConvert]] | |Not working or untested |#calibration #polarisation #python #VLBI |A paper on this tool can be found at https://www.aanda.org/articles/aa/full_html/2016/03/aa26063-15/aa26063-15.html| | | [[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://github.com/onsala-space-observatory/polsimulate|POLSIMULATE]] |''polsimulate()'' \\ (type within CASA) |Not working or untested |#CASA #polarisation #python #simulation |The polsimulate command functions as a normal CASA function, typing ''inp(polsimulate)'' will show input options. | | | [[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://pvextractor.readthedocs.io/en/latest/|Position-Velocity Slice Extractor]] |''import pvextractor'' \\ (type within a python environment) |<color #22b14c> Working recently </color> |#datacubes #python #visualisation #visualization |Described in https://ui.adsabs.harvard.edu/abs/2015ASPC..499..363G/abstract | | | [[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://pyspeckit.readthedocs.io/en/latest/|PySpecKit]] | ''import pyspeckit | | [[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)]]. | |
| '' \\ (type within a python environment) |<color #22b14c> Working recently </color> |#datacubes #python #analysis |A paper on this tool can be found at https://iopscience.iop.org/article/10.3847/1538-3881/ac695a | | | [[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://github.com/RadioAstronomySoftwareGroup/pyuvdata|pyuvdata]] | ''import pyuvdata'' \\ (type within a python environment) |<color #22b14c> Working recently </color>|#python #visibilities | Papers on this tool can be found at https://joss.theoj.org/papers/10.21105/joss.07482 and https://joss.theoj.org/papers/10.21105/joss.00140 | | | [[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://home.strw.leidenuniv.nl/~moldata/radex.html|RADEX]] |Terminal command: ''radex'' |Not working or untested |#modelling #simulation |This tool can be used via the [[https://sronpersonalpages.nl/~vdtak/radex/index.shtml|terminal]] or through the [[https://var.sron.nl/radex/radex.php|online application]]. A paper on this tool can be found at https://www.aanda.org/component/article?access=bibcode&bibcode=&bibcode=2007A%2526A...468..627VFUL | | | [[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/radio-astro-tools/radio-beam|Radio Beam]] | ''import radio_beam'' \\ (type within a python environment)|Not working or untested |#python #astropy |Described in https://science.nrao.edu/facilities/alma/science_sustainability/Spectral_Cube_and_Radio_Beam_Ginsburg.pdf | | | [[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://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/index.php|RADMC3D]] |Terminal command: ''radmc3d ''|Not working or untested |#python #simulation | | | | [[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://home.strw.leidenuniv.nl/~michiel/ratran/ | RATRAN]] | |Not working or untested |#python #simulation |An arXiv paper on this tool can be found at https://arxiv.org/abs/astro-ph/0008169| | | [[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/henrikju/resolve|RESOLVE]] | |<color #22b14c> Working recently </color>|#bayesian #analysis #imaging |Papers on this tool can be found at https://www.aanda.org/articles/aa/full_html/2016/02/aa23094-13/aa23094-13.html and https://arxiv.org/abs/1605.04317| | | [[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://ascl.net/1905.015|rPICARD]] |Terminal command: ''picard'' |Not working or untested |#calibration #CASA |A paper on this tool can be found at https://www.aanda.org/articles/aa/full_html/2019/06/aa35181-19/aa35181-19.html| | | [[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://github.com/onsala-space-observatory/polsimulate|SD2vis]] |''SD2vis()'' \\ (type within CASA) |Not working or untested |#CASA #python #simulation #visibilities |The SD2vis command functions as a normal CASA function, typing ''inp(SD2vis)'' will show input options. | | | [[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/CurtinIDS/SoFiA-2 | SOFIA]] | |Not working or untested |#sourcefinder #momentmaps #datacubes #analysis | | | | [[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://spectral-cube.readthedocs.io/en/latest/|Spectral Cube]] |''import spectral_cube'' \\ (type within a python environment) |<color #22b14c> Working recently </color> |#datacubes #moments #python | | | | [[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://github.com/centowen/stacker|STACKER]] | |Not working or untested |#analysis #python #visibilities | | | | [[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://hera.ph1.uni-koeln.de/~sanchez/statcont|STATCONT]] |''import statcont'' \\ (type within CASA) |Not working or untested |#python |Installed on our machines, but untested. There is an I-TRAIN training avaliable for LineStacker, with a recording of the session avaliable as a [[https://www.youtube.com/watch?v=0XhQN-BH7Yw|YouTube video]]. Full details can be found on the [[https://almascience.eso.org/tools/eu-arc-network/i-train|I-TRAIN website]] under the heading I-TRAIN #11: Statistical continuum determination with STATCONT | | | [[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://perimeterinstitute.github.io/Themis/html/index.html|THEMIS]] | |<color #22b14c> Working recently </color> | #basesian #analysis #imaging | | | | [[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)]]. | |
| | [[http://mural.uv.es/imarvi/docums/uvmultifit/|uvmultifit]] |AKF command within casa |<color #22b14c> Working recently </color>|#CASA #python #visibilities |Currently working only in CASA 5 on our machines and does not work in casapy-580 on our machines. There is an I-TRAIN training avaliable for uvmultifit, with recording of the session avaliable as a [[https://www.youtube.com/watch?v=MlARlvc_ggM|YouTube video]]. Full details can be found on [[https://almascience.eso.org/tools/eu-arc-network/i-train|I-TRAIN website]] under the heading I-TRAIN #3: UVMultiFit | | | [[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://uvplot.readthedocs.io/en/latest/index.html|UVPLOT]] | ''import uvplot'' \\ (type within a python environment) |<color #22b14c> Working recently </color>|#python #visibilities | Some functionality is only available if imported within CASA | | | [[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://wsclean.readthedocs.io/en/latest/index.html|WSClean]] | |<color #22b14c> Working recently </color> | #imaging | | | | [[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://www.alma-allegro.nl/wvr-and-phase-metrics/wvr-scaling/|WVR Scaling Module]] | |Not working or untested |#calibration #CASA #python |Requires astropy version 1.3.3 and CASA < 5.0 (CASA 4.7.2 is the latest working version). | | | [[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://xclass.astro.uni-koeln.de/|XCLASS]] | |<color #22b14c> Working recently </color> |#CASA #modelling #python #visibilities | | | | [[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)]]. | |
| </sortable> | </sortable> |
| </searchtable> | </searchtable> |
| |
