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| allegrosoftwarerepo:index [2022/11/22 16:30] – [List of software] michiel | allegrosoftwarerepo:index [2023/10/31 11:39] (current) – Change name and add some more software hygate |
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| ====== Allegro Software Repository ====== | ====== Allegro Software Inventory ====== |
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| Scientific exploration of ALMA observations does not stop when an image is produced: there exists a wide range of tools that can be used to further explore your ALMA data, plot and inspect parts of your ALMA data, and compare your ALMA data to models of astrophysical objects. Many of the tools that are developed by the scientific community are publicly available. | Scientific exploration of ALMA observations does not stop when an image is produced: there exists a wide range of tools that can be used to further explore your ALMA data, plot and inspect parts of your ALMA data, and compare your ALMA data to models of astrophysical objects. Many of the tools that are developed by the scientific community are publicly available. |
| To help ALMA users navigate the many options and find tools that they may not even know exist, Allegro, with the help of the European ARC node network, has developed an interactive inventory to ALMA-related software. This inventory is not intended to be complete and will always keep growing. If you know of useful tools to be added, or find an inaccuracy, please contact us at alma@strw.leidenunv.nl. | To help ALMA users navigate the many options and find tools that they may not even know exist, Allegro, with the help of the European ARC node network, has developed an interactive inventory to ALMA-related software. This inventory is not intended to be complete and will always keep growing. If you know of useful tools to be added, or find an inaccuracy, please contact us at alma@strw.leidenunv.nl. |
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| For each piece of software we list the name of the piece of software along with a link to its website, a short description taken from the software's website, a description of experience with the software within the ARC nodes, a list of tags for the software and notes about the software. More information about the ARC Nodes experience field and the Tags field is provided below. | For each piece of software we list the name of the piece of software along with a link to its website, a short description taken from the software's website, a description of experience with the software within the European ARC nodes, a list of tags for the software and notes about the software. More information about the European ARC Nodes experience field and the Tags field is provided below. |
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| ==== ARC Nodes experience ==== | ==== European ARC Nodes experience ==== |
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| For each piece of software in the catalogue, a descriptor of the current status of how recently experience has been reported with the software by staff in the ARC Nodes is given in the "Status" column. The different statuses are as follows: | For each piece of software in the catalogue, a descriptor of the current status of how recently experience has been reported with the software by staff in the European ARC Nodes is given in the "Status" column. The different statuses are as follows: |
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| * <color #22b14c> Recent experience (<1 year ago) </color> at the ARC nodes | * <color #22b14c> Recent experience (<1 year ago) </color> at the European ARC nodes |
| * <color #00a2e8> Experience 1-2 years ago </color> at the ARC nodes | * <color #00a2e8> Experience 1-2 years ago </color> at the European ARC nodes |
| * <color #ed1c24> Experience >2 years ago </color> at the ARC nodes | * <color #ed1c24> Experience >2 years ago </color> at the European ARC nodes |
| * No or uncertain experience at the ARC nodes | * **No or limited experience at the European ARC nodes** |
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| Staff at the ARC nodes are less likely to have experience with software with a status further down this list. The experience level does not reflect the usefulness of the code, or if it will run on current platforms. Even codes that no one at the ARC nodes has experience with, may work perfectly and be just the tool you are looking for! | These colour codes are modelled after the [[https://en.wikipedia.org/wiki/Piste#Europe|ski piste rating system]]. Staff at the European ARC nodes are less likely to have experience with software with a status further down this list. The experience level does not reflect the usefulness of the code, or if it will run on current platforms. Even codes that no one at the European ARC nodes has experience with, may work perfectly and be just the tool you are looking for! |
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| ==== List of software ==== | ==== List of software ==== |
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| The full list of tags is: | The full list of tags is: |
| #analysis, #archive, #calibration, #CASA, #datacubes, #GILDAS, #GPU_computing, #modeling, #modelling, #moments, #observing, #polarisation, #polarization, #python, #simulation, #visibilities, #visualisation, #visualization, #VLBI | #analysis, #archive, #calibration, #CASA, #datacubes, #FORTRAN, #GILDAS, #GPU_computing, #imaging, #kinematics, #modeling, #modelling, #moments, #observing, #polarisation, #polarization, #python, #simulation, #virtual_reality, #visibilities, #visualisation, #visualization, #VLBI, #VR |
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| There are currently 42 pieces of software in the repository. | There are currently 59 pieces of software in the repository. |
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| <searchtable> | <searchtable> |
| <sortable> | <sortable> |
| ^ Software ^ Description ^ ARC Node experience Status ^ Tags ^ Notes ^ | ^ Software ^ Description ^ EU ARC Node experience Status ^ Tags ^ Notes ^ |
| | [[https://editeodoro.github.io/Bbarolo/|3D-Barolo]] |3D-Barolo (3D-Based Analysis of Rotating Object via Line Observations) or BBarolo is a tool for fitting 3D tilted-ring models to emission-line data-cubes. |No or uncertain experience |#analysis #datacubes #modeling #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]] |3D-Barolo (3D-Based Analysis of Rotating Object via Line Observations) or BBarolo is a tool for fitting 3D tilted-ring models to emission-line data-cubes. |**No or limited experience** |#analysis #datacubes #imaging #kinematics #modeling #modelling #python |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.3021D/abstract|]] | |
| | [[https://admit.astro.umd.edu/admit/index.html|ADMIT]] |The ALMA Data Mining Toolkit (ADMIT) is a value-added Python software package which integrates with the ALMA archive and CASA to provide scientists with quick access to traditional science data products such as moment maps, as well as with new innovative tools for exploring data cubes and their many derived products. |No or uncertain experience |#archive #CASA #python | | | | [[https://admit.astro.umd.edu/admit/index.html|ADMIT]] |The ALMA Data Mining Toolkit (ADMIT) is a value-added Python software package which integrates with the ALMA archive and CASA to provide scientists with quick access to traditional science data products such as moment maps, as well as with new innovative tools for exploring data cubes and their many derived products. |**No or limited experience** |#archive #CASA #imaging #python | | |
| | [[https://www.alma.inaf.it/index.php/ALMA_FITS_Keywords|ALMA FITS Keywords filler]] |The ALMA Keywords Filler (AKF) CASA task is build to generate and eventually ingest in the headers the new FITS keywords that we suggest could be useful for a generic ALMA archive miner. |No or uncertain experience |#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 [[https://www.alma.inaf.it/images/AKF_v1.2.pdf|]] | | | [[https://www.alma.inaf.it/index.php/ALMA_FITS_Keywords|ALMA FITS Keywords filler]] |The ALMA Keywords Filler (AKF) CASA task is build to generate and eventually ingest in the headers the new FITS keywords that we suggest could be useful for a generic ALMA archive miner. |**No or limited experience** |#archive #CASA #imaging #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 [[https://www.alma.inaf.it/images/AKF_v1.2.pdf|]] | |
| | [[https://almascience.eso.org/proposing/observing-tool|ALMA Observing Tool]] |The ALMA Observing Tool (OT) is a Java desktop application used for the preparation and submission of ALMA Phase 1 proposals and, for those which are accepted, Phase 2 materials (Scheduling Blocks). It is also used for preparing and submitting Director's Discretionary Time (DDT) proposals and Supplemental Call (ACA stand-alone) proposals. |<color #22b14c> Recent experience </color> |#observing | | | | [[https://almascience.eso.org/proposing/observing-tool|ALMA Observing Tool]] |The ALMA Observing Tool (OT) is a Java desktop application used for the preparation and submission of ALMA Phase 1 proposals and, for those which are accepted, Phase 2 materials (Scheduling Blocks). It is also used for preparing and submitting Director's Discretionary Time (DDT) proposals and Supplemental Call (ACA stand-alone) proposals. |<color #22b14c> Recent experience </color> |#imaging #observing | | |
| | [[https://alminer.readthedocs.io/en/latest/|ALminer]] |alminer is a Python-based code to effectively query, analyse, and visualize the ALMA science archive. It also allows users to directly download ALMA data products and/or raw data for further image processing. |<color #22b14c> Recent experience </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]] |alminer is a Python-based code to effectively query, analyse, and visualize the ALMA science archive. It also allows users to directly download ALMA data products and/or raw data for further image processing. |<color #22b14c> Recent experience </color> |#archive #imaging #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 available for ALminer, with recording of the session available 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://launchpad.net/apsynsim|APSYNSIM]] |Aperture Synthesis Simulator for Radio Astronomy. Based on python/matplotlib, it is fully interactive and the plots are updated almost in real time. Antennas can be dragged with the mouse. Number of antennas, observing frequency, observatory-source coordinates, visibility weighting, etc. can be changed on the fly. |No or uncertain experience |#python #simulation |An arXiv paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2017arXiv170600936M/abstract|]] | | | [[https://docs.sunpy.org/projects/ndcube/en/stable/|APLpy]] |APLpy (the Astronomical Plotting Library in Python) is a Python module aimed at producing publication-quality plots of astronomical imaging data in FITS format. |<color #22b14c> Recent experience </color> |#datacubes #imaging #python #visualisation #visualization | | |
| | [[https://www.alma-allegro.nl/artist/|ARTIST]] |ARTIST (Adaptable Radiative Transfer Innovations for Submillimeter Telescopes) is a set of two CASA tools that allow you to select one of nine pre-coded astrophysical models describing young stellar objects, planet forming disks, or circumstellar shells; adapt the parameters of these models; calculate the excitation of a user-selected molecule using the LIME (LIne Modeling Engine) accelerated monte-carlo code; and calculate the (sub) millimeter line emission of this object at a specified distance and orientation. |<color #ed1c24> Experience >2 years ago </color> |#CASA #modeling #modelling #python | | | | [[https://launchpad.net/apsynsim|APSYNSIM]] |Aperture Synthesis Simulator for Radio Astronomy. Based on python/matplotlib, it is fully interactive and the plots are updated almost in real time. Antennas can be dragged with the mouse. Number of antennas, observing frequency, observatory-source coordinates, visibility weighting, etc. can be changed on the fly. |**No or limited experience** |#imaging #python #simulation |An arXiv paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2017arXiv170600936M/abstract|]] | |
| | [[https://www.iram.fr/IRAMFR/GILDAS/doc/html/astro-html/astro.html|ASTRO]] |Dedicated to ephemeris and observation preparation. |<color #22b14c> Recent experience </color> |#GILDAS #observing | | | | [[https://www.alma-allegro.nl/artist/|ARTIST]] |ARTIST (Adaptable Radiative Transfer Innovations for Submillimeter Telescopes) is a set of two CASA tools that allow you to select one of nine pre-coded astrophysical models describing young stellar objects, planet forming disks, or circumstellar shells; adapt the parameters of these models; calculate the excitation of a user-selected molecule using the LIME (LIne Modeling Engine) accelerated monte-carlo code; and calculate the (sub) millimeter line emission of this object at a specified distance and orientation. |<color #ed1c24> Experience >2 years ago </color> |#CASA #imaging #modeling #modelling #python | | |
| | [[https://astroquery.readthedocs.io/en/latest/|Astroquery]] |Astroquery is a set of tools for querying astronomical web forms and databases. |<color #22b14c> Recent experience </color> |#archive #python | | | | [[https://www.iram.fr/IRAMFR/GILDAS/doc/html/astro-html/astro.html|ASTRO]] |Dedicated to ephemeris and observation preparation. |<color #22b14c> Recent experience </color> |#GILDAS #imaging #observing | | |
| | [[https://bettermoments.readthedocs.io/en/latest/?badge=latest|Better Moments]] |bettermoments creates moment maps of spectral line data and their associated uncertainties. The command-line interface makes it as seamless as possible to make all the traditional moment maps, in addition other, oftentimes more useful, maps. In addition to the many traditional statistical moments, bettermoments contains many alternative ways collapse the cube. |No or uncertain experience |#datacubes #moments #python | | | | [[https://astroquery.readthedocs.io/en/latest/|Astroquery]] |Astroquery is a set of tools for querying astronomical web forms and databases. |<color #22b14c> Recent experience </color> |#archive #imaging #python | | |
| | [[https://cartavis.org/|CARTA]] |Cube Analysis and Rendering Tool for Astronomy, is a next generation image visualization and analysis tool designed for ALMA, VLA, and SKA pathfinders. |<color #22b14c> Recent experience </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 | | | [[https://bettermoments.readthedocs.io/en/latest/?badge=latest|Better Moments]] |bettermoments creates moment maps of spectral line data and their associated uncertainties. The command-line interface makes it as seamless as possible to make all the traditional moment maps, in addition other, oftentimes more useful, maps. In addition to the many traditional statistical moments, bettermoments contains many alternative ways collapse the cube. |<color #22b14c> Recent experience </color> |#datacubes #imaging #kinematics #moments #python | | |
| | [[https://casadocs.readthedocs.io/en/stable/|CASA]] |CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and Karl G. Jansky Very Large Array (VLA), and is often used also for other radio telescopes. |<color #22b14c> Recent experience </color> |#calibration #CASA #datacubes #moments #python #visibilities | | | | [[https://blobcat.sourceforge.net/|BLOBCAT]] |BLOBCAT is a stand-alone Python program to catalogue blobs in a 2D radio-astronomy FITS image of total intensity (Stokes I) or linear polarization (L or LRM). |**No or limited experience** |#datacubes #imaging #polarisation #polarization #python |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2012MNRAS.425..979H/abstract|]] | |
| | [[https://github.com/onsala-space-observatory/casairing|CASAIRING]] |Simple task to compute radial profiles of images (and image cubes). It generates plots and ascii files with the profile values. |<color #22b14c> Recent experience </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://cartavis.org/|CARTA]] |Cube Analysis and Rendering Tool for Astronomy, is a next generation image visualization and analysis tool designed for ALMA, VLA, and SKA pathfinders. |<color #22b14c> Recent experience </color> |#datacubes #imaging #python #visualisation #visualization |There is an I-TRAIN training available for LineStacker, with recording of the session available 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 | |
| | [[http://cassis.irap.omp.eu/|CASSIS]] |A free interactive spectrum analyser. |<color #22b14c> Recent experience </color> |#modeling #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]]. | | | [[https://casadocs.readthedocs.io/en/stable/|CASA]] |CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and Karl G. Jansky Very Large Array (VLA), and is often used also for other radio telescopes. |<color #22b14c> Recent experience </color> |#calibration #CASA #datacubes #imaging #moments #python #visibilities |More information about the ALMA pipeline, which version was used for which cycle and known issues can be found at [[https://almascience.eso.org/processing/science-pipeline|]] . | |
| | [[https://github.com/onsala-space-observatory/checkres|CHECKRES]] |CASA interactive task for a quick check of image residuals, but in Fourier space. It overplots the UV tracks of the baselines corresponding to selected antennas, so it should be easy to locate the antennas (and/or baselines) responsible of dynamic-range limitations. |<color #22b14c> Recent experience </color> |#CASA #python |The checkres command functions as a normal CASA function, typing ''inp(checkres)'' will show input options. | | | [[https://github.com/onsala-space-observatory/casairing|CASAIRING]] |Simple task to compute radial profiles of images (and image cubes). It generates plots and ascii files with the profile values. |<color #00a2e8> Experience 1-2 years ago (>1 year ago) </color> |#CASA #imaging #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/closures|closures]] | CASA task to plot closure phases (or amplitudes) vs. time or frequency. |<color #22b14c> Recent experience </color> |#CASA #python |The closures command functions as a normal CASA function, typing ''inp(closures)'' will show input options. | | | [[http://cassis.irap.omp.eu/|CASSIS]] |A free interactive spectrum analyser. |<color #22b14c> Recent experience </color> |#imaging #modeling #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]]. | |
| | [[https://github.com/onsala-space-observatory/fakeobs|FAKEOBS]] |FAKEOBS is a CASA task to generate model visibilities from already-existing measurement sets. |<color #22b14c> Recent experience </color> |#CASA #python #simulation |The fakeobs command functions as a normal CASA function, typing ''inp(fakeobs)'' will show input options. | | | [[https://github.com/onsala-space-observatory/checkres|CHECKRES]] |CASA interactive task for a quick check of image residuals, but in Fourier space. It overplots the UV tracks of the baselines corresponding to selected antennas, so it should be easy to locate the antennas (and/or baselines) responsible of dynamic-range limitations. |<color #22b14c> Recent experience </color> |#CASA #imaging #python |The checkres command functions as a normal CASA function, typing ''inp(checkres)'' will show input options. | |
| | [[https://mtazzari.github.io/galario/|galario]] |galario is a library that exploits the computing power of modern graphic cards (GPUs) to accelerate the comparison of model predictions to radio interferometer observations. |No or uncertain experience |#GPU_computing #modeling #modelling #python #visibilities | | | | [[https://github.com/onsala-space-observatory/closures|closures]] | CASA task to plot closure phases (or amplitudes) vs. time or frequency. |<color #22b14c> Recent experience </color> |#CASA #imaging #python |The closures command functions as a normal CASA function, typing ''inp(closures)'' will show input options. | |
| | [[https://glueviz.org/|Glue]] |Glue is an open-source Python library to explore relationships within and between related datasets |No or uncertain experience |#python #visualisation #visualization | | | | [[https://github.com/richteague/disksurf|disksurf]] |disksurf is a package which contains the functions to measure the height of optically thick emission, or photosphere, using the method presented in Pinte et al. (2018) (with associated example script). |**No or limited experience** |#imaging #kinematics #python |A paper on the method can be found at [[https://ui.adsabs.harvard.edu/abs/2018A%26A...609A..47P/abstract|]] and a paper on the tool itself can be found at [[https://joss.theoj.org/papers/10.21105/joss.03827#|]] | |
| | [[https://github.com/richteague/gofish|GoFish]] | |No or uncertain experience |#python |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1632T/abstract|]] | | | [[https://stammler.github.io/dustpy/|DustPy]] |DustPy is a Python package to simulate the evolution of dust in protoplanetary disks. DustPy simulates the radial evolution of gas and dust in protoplanetary disks, including viscous evolution of the gas, advection and diffusion of the dust, as well as dust growth by solving the Smoluchowski equation. |**No or limited experience** |#imaging #modeling #modelling #python #simulation | | |
| | [[https://interferopy.readthedocs.io/en/latest/|Interferopy]] |A Python library of common tasks used in the observational radio/mm interferometry data analysis. The package was developed to aid in the studies of the interstellar medium in high-redshift quasar host galaxies using emission lines, as well as to create publication quality plots. |No or uncertain experience |#python #visualisation #visualization | | | | [[https://github.com/richteague/dynsity|dynsity]] |Python tools to infer dynamical densities of protoplanetary disks based on their rotational profiles, following the approach in Teague et al. (2018). This is absolutely a work in progress. The hope is that not only will one be able to extracted radial volume density profiles, but also place some constraint on the total dynamical mass of the disk and, to a lesser extent, the surface density profile. |**No or limited experience** |#imaging #kinematics #python | | |
| | [[https://github.com/aardk/jupyter-casa|jupyter-casa]] | A Jupyter kernel for CASA. |No or uncertain experience |#CASA #python | | | | [[https://github.com/richteague/eddy|eddy]] |eddy is a suite of Python tools to recover precise velocity profiles of protoplanetary disks from Doppler shifted line emission. eddy makes fitting of first moment maps and the inference of a rotation velocity from an annulus of spectra a breeze. |**No or limited experience** |#imaging #kinematics #moments #python |A paper on the tool can be found at [[https://joss.theoj.org/papers/10.21105/joss.01220|]] | |
| | [[https://github.com/lime-rt/lime|LIME]] |LIME is a 3D molecular excitation and radiation transfer code for far-infrared and (sub-)millimeter wavelength. LIME will calculate spectra of rotational transitions of atoms and molecules, given a user-supplied physical model. |<color #ed1c24> Experience >2 years ago </color> |#modeling #modelling |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2010A%26A...523A..25B/abstract|]] | | | [[https://github.com/onsala-space-observatory/fakeobs|FAKEOBS]] |FAKEOBS is a CASA task to generate model visibilities from already-existing measurement sets. |<color #22b14c> Recent experience </color> |#CASA #imaging #python #simulation |The fakeobs command functions as a normal CASA function, typing ''inp(fakeobs)'' will show input options. | |
| | [[https://jbjolly.github.io/LineStacker/|LineStacker]] |LineStacker is a new open access tool for stacking of spectral lines. LineStacker is an ensemble of both CASA tasks and native python tasks, and can stack both 3Dcubes or already extracted spectra. Additionaly a set of tools are included to help further analyse stacked spectra and stacked sample. |No or uncertain experience |#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://discsim.github.io/frank/|frank]] |Frankenstein (frank) is a library that fits the 1D radial brightness profile of an interferometric source given a set of visibilities. It uses a Gaussian process that performs the fit in <1 minute for a typical protoplanetary disc continuum dataset. |**No or limited experience** |#analysis #imaging #python #visibilities |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.3209J/abstract|]] | |
| | [[https://www.iram.fr/IRAMFR/GILDAS/doc/html/map-html/map.html|MAPPING]] |Dedicated to imaging and deconvolution of aperture synthesis data; MAPPING also includes an ALMA simulator. |<color #22b14c> Recent experience </color> |#GILDAS #visibilities #visualisation #visualization | | | | [[http://frelled.wikidot.com/start|FRELLED]] |FRELLED is the FITS Realtime Explorer of Low Latency in Every Dimension, an astronomical data viewer designed for 3D FITS files. It's primarily aimed at visualising data cubes in realtime, interactive 3D. |<color #22b14c> Recent experience </color> |#datacubes #imaging #python #virtual_reality #visualisation #visualization #VR |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2015A%26C....13...67T/abstract|]] | |
| | [[https://github.com/akdiaz/LPD|Molecular EMissiOn IdentifieR (MEMOIR)]] |MEMOIR detects the lines present in a spectrum and identifies them by comparing their frequencies against those of known-lines. |No or uncertain experience |#analysis #python | | | | [[https://mtazzari.github.io/galario/|galario]] |galario is a library that exploits the computing power of modern graphic cards (GPUs) to accelerate the comparison of model predictions to radio interferometer observations. |**No or limited experience** |#GPU_computing #imaging #modeling #modelling #python #visibilities | | |
| | [[https://photutils.readthedocs.io/en/stable/|Photutils]] |Photutils is an affiliated package of Astropy that primarily provides tools for detecting and performing photometry of astronomical sources. |No or uncertain experience |#analysis #python | | | | [[https://glueviz.org/|Glue]] |Glue is an open-source Python library to explore relationships within and between related datasets |**No or limited experience** |#imaging #python #visualisation #visualization | | |
| | [[https://github.com/marti-vidal-i/PolConvert|PolConvert]] | Advanced polarization calibration of linear feeds in VLBI observations. |No or uncertain experience |#calibration #polarisation #polarization #python #VLBI | | | | [[https://github.com/richteague/gofish|GoFish]] | |**No or limited experience** |#imaging #python |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1632T/abstract|]] | |
| | [[https://github.com/onsala-space-observatory/polsimulate|POLSIMULATE]] | CASA task for a basic simulator of ALMA/J-VLA full-polarization observations. |<color #22b14c> Recent experience </color> |#CASA #polarisation #polarization #python #simulation |The polsimulate command functions as a normal CASA function, typing ''inp(polsimulate)'' will show input options. | | | [[https://interferopy.readthedocs.io/en/latest/|Interferopy]] |A Python library of common tasks used in the observational radio/mm interferometry data analysis. The package was developed to aid in the studies of the interstellar medium in high-redshift quasar host galaxies using emission lines, as well as to create publication quality plots. |<color #22b14c> Recent experience </color> |#analysis #imaging #python #visualisation #visualization | | |
| | [[https://pvextractor.readthedocs.io/en/latest/|Position-Velocity Slice Extractor]] |The concept of the pvextractor package is simple - given a path defined in sky coordinates, and a spectral cube, extract a slice of the cube along that path, and along the spectral axis, producing a position-velocity or position-frequency slice. |No or uncertain experience |#datacubes #python #visualisation #visualization | | | | [[https://github.com/haavee/jiveplot|jplotter / jiveplot]] |Python based visualization tool for AIPS++/CASA MeasurementSet data |**No or limited experience** |#imaging #python #visualisation #visualization |A PDF cookbook is available at [[https://github.com/haavee/jiveplot/blob/master/jplotter-cookbook-draft-v2.pdf|]] | |
| | [[https://github.com/RadioAstronomySoftwareGroup/pyuvdata|pyuvdata]] |pyuvdata defines a pythonic interface to interferometric data sets. Currently pyuvdata supports reading and writing of miriad, uvfits, CASA measurement sets and uvh5 files and reading of FHD (Fast Holographic Deconvolution) visibility save files, SMA Mir files and MWA correlator FITS files. |No or uncertain experience |#python #visibilities | | | | [[https://github.com/aardk/jupyter-casa|jupyter-casa]] | A Jupyter kernel for CASA. |**No or limited experience** |#CASA #imaging #python | | |
| | [[https://personal.sron.nl/~vdtak/radex/index.shtml|RADEX]] |Radex is a computer program to calculate the strengths of atomic and molecular lines from interstellar clouds which are assumed to be homogeneous. |No or uncertain experience |#modeling #modelling #simulation |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2007A%26A...468..627V/abstract|]] | | | [[https://github.com/richteague/keplerian_mask|keplerian_mask]] |A script to build a Keplerian mask based to be used for CLEANing or moment map analysis. This will grab the image properties (axes, beam properties and so on) from the provide CASA image. |**No or limited experience** |#CASA #imaging #kinematics #moments #python | | |
| | [[https://github.com/radio-astro-tools/radio-beam|Radio Beam]] | A simple toolkit for reading and manipulating beams from astrophysical radio spectral data cubes. |No or uncertain experience |#python | | | | [[https://github.com/lime-rt/lime|LIME]] |LIME is a 3D molecular excitation and radiation transfer code for far-infrared and (sub-)millimeter wavelength. LIME will calculate spectra of rotational transitions of atoms and molecules, given a user-supplied physical model. |<color #ed1c24> Experience >2 years ago </color> |#imaging #modeling #modelling |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2010A%26A...523A..25B/abstract|]] | |
| | [[https://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/index.php|RADMC3D]] |RADMC-3D is a code package for diagnostic radiative transfer calculations in astronomy and astrophysics. It calculates, for a given geometrical distribution of gas and/or dust, what its images and/or spectra look like when viewed from a certain angle, allowing modelers to compare their models with observed data. |No or uncertain experience |#python #simulation | | | | [[https://jbjolly.github.io/LineStacker/|LineStacker]] |LineStacker is a new open access tool for stacking of spectral lines. LineStacker is an ensemble of both CASA tasks and native python tasks, and can stack both 3Dcubes or already extracted spectra. Additionaly a set of tools are included to help further analyse stacked spectra and stacked sample. |**No or limited experience** |#analysis #imaging #python |There is an I-TRAIN training available for LineStacker, with a recording of the session available 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/onsala-space-observatory/SD2vis|SD2vis]] | CASA task to generate synthetic visibilities based on a Total-Power (Single Dish) image. |<color #22b14c> Recent experience </color> |#CASA #python #simulation #visibilities |The SD2vis command functions as a normal CASA function, typing ''inp(SD2vis)'' will show input options. | | | [[https://www.iram.fr/IRAMFR/GILDAS/doc/html/map-html/map.html|MAPPING]] |Dedicated to imaging and deconvolution of aperture synthesis data; MAPPING also includes an ALMA simulator. |<color #22b14c> Recent experience </color> |#GILDAS #imaging #visibilities #visualisation #visualization | | |
| | [[https://spectral-cube.readthedocs.io/en/latest/|Spectral Cube]] |The spectral-cube package provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. |<color #22b14c> Recent experience </color> |#datacubes #moments #python | | | | [[https://github.com/akdiaz/LPD|Molecular EMissiOn IdentifieR (MEMOIR)]] |MEMOIR detects the lines present in a spectrum and identifies them by comparing their frequencies against those of known-lines. |**No or limited experience** |#analysis #imaging #python | | |
| | [[https://github.com/centowen/stacker|STACKER]] |STACKER is a library for stacking sources in interferometric data, i.e., averaging emission from different sources. The library allows stacking to be done directly on visibility data as well as in the image domain. |No or uncertain experience |#analysis #python #visibilities | | | | [[https://docs.sunpy.org/projects/ndcube/en/stable/|ndcube]] |ndcube is a SunPy Project affiliated package designed for handling N-dimensional data cubes described by WCS (World Coordinate System) transformations. |**No or limited experience** |#datacubes #imaging #python | | |
| | [[https://hera.ph1.uni-koeln.de/~sanchez/statcont|STATCONT]] |STATCONT is a python-based tool designed to determine the continuum emission level in line-rich spectral data. The tool inspects the intensity distribution of a given spectrum and automatically determines the continuum level by using different statistical approaches. |No or uncertain experience |#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=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://photutils.readthedocs.io/en/stable/|Photutils]] |Photutils is an affiliated package of Astropy that primarily provides tools for detecting and performing photometry of astronomical sources. |<color #ed1c24> Experience >2 years ago </color> |#analysis #imaging #python | | |
| | [[http://mural.uv.es/imarvi/docums/uvmultifit/|uvmultifit]] |A CASA-based flexible visibility-fitting engine developed at the Nordic node of the ALMA Regional Center. |<color #22b14c> Recent experience </color> |#CASA #python #visibilities |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/marti-vidal-i/PolConvert|PolConvert]] | Advanced polarization calibration of linear feeds in VLBI observations. |**No or limited experience** |#calibration #imaging #polarisation #polarization #python #VLBI | | |
| | [[https://uvplot.readthedocs.io/en/latest/index.html|UVPLOT]] |A simple Python package to make nice plots of deprojected interferometric visibilities, often called uvplots. |No or uncertain experience |#python #visibilities |Some functionality is only available if imported within CASA | | | [[https://github.com/onsala-space-observatory/polsimulate|POLSIMULATE]] | CASA task for a basic simulator of ALMA/J-VLA full-polarization observations. |<color #22b14c> Recent experience </color> |#CASA #imaging #polarisation #polarization #python #simulation |The polsimulate command functions as a normal CASA function, typing ''inp(polsimulate)'' will show input options. | |
| | [[https://www.alma-allegro.nl/wvr-and-phase-metrics/wvr-scaling/|WVR Scaling Module]] |Software package to optimise the application of the WVR solutions for ALMA Observations. |No or uncertain experience |#calibration #CASA #python |Requires astropy version 1.3.3 and CASA < 5.0 (CASA 4.7.2 is the latest working version). A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2017A%26A...605A.121M/abstract|]] | | | [[https://pvextractor.readthedocs.io/en/latest/|Position-Velocity Slice Extractor]] |The concept of the pvextractor package is simple - given a path defined in sky coordinates, and a spectral cube, extract a slice of the cube along that path, and along the spectral axis, producing a position-velocity or position-frequency slice. |<color #00a2e8> Experience 1-2 years ago (>1 year ago) </color> |#datacubes #imaging #kinematics #python #visualisation #visualization | | |
| | [[https://xclass.astro.uni-koeln.de/|XCLASS]] |A toolbox for the Common Astronomy Software Applications package (CASA) containing a couple of new functions for modelling interferometric and single dish data. |No or uncertain experience |#CASA #modeling #modelling #python #visibilities | | | | [[https://pyspeckit.readthedocs.io/en/latest/|Pyspeckit]] |An extensible spectroscopic analysis toolkit for astronomy. |**No or limited experience** |#imaging |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2022AJ....163..291G/abstract|]] | |
| | | [[https://github.com/RadioAstronomySoftwareGroup/pyuvdata|pyuvdata]] |pyuvdata defines a pythonic interface to interferometric data sets. Currently pyuvdata supports reading and writing of miriad, uvfits, CASA measurement sets and uvh5 files and reading of FHD (Fast Holographic Deconvolution) visibility save files, SMA Mir files and MWA correlator FITS files. |**No or limited experience** |#imaging #python #visibilities | | |
| | | [[https://personal.sron.nl/~vdtak/radex/index.shtml|RADEX]] |Radex is a computer program to calculate the strengths of atomic and molecular lines from interstellar clouds which are assumed to be homogeneous. |<color #ed1c24> Experience >2 years ago </color> |#imaging #modeling #modelling #simulation |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2007A%26A...468..627V/abstract|]] | |
| | | [[https://github.com/radio-astro-tools/radio-beam|Radio Beam]] | A simple toolkit for reading and manipulating beams from astrophysical radio spectral data cubes. |<color #ed1c24> Experience >2 years ago </color> |#imaging #python | | |
| | | [[https://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/index.php|RADMC3D]] |RADMC-3D is a code package for diagnostic radiative transfer calculations in astronomy and astrophysics. It calculates, for a given geometrical distribution of gas and/or dust, what its images and/or spectra look like when viewed from a certain angle, allowing modelers to compare their models with observed data. |**No or limited experience** |#imaging #python #simulation | | |
| | | [[https://personal.sron.nl/~vdtak/ratran/frames.html|RATRAN]] |A numerical method and computer code to calculate the radiative transfer and excitation of molecular lines. Our approach is based on the Monte Carlo method, and incorporates elements from Accelerated Lambda Iteration. It combines the flexibility of the former with the speed and accuracy of the latter. |<color #ed1c24> Experience >2 years ago </color> |#analysis #FORTRAN #imaging #modeling #modelling #simulation |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2000A%26A...362..697H/abstract|]] . | |
| | | [[https://github.com/henrikju/resolve|RESOLVE]] |RESOLVE is a Bayesian inference package for image reconstruction in radio interferometry. |**No or limited experience** |#CASA #imaging #python |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2016A%26A...586A..76J/abstract|]] | |
| | | [[https://bitbucket.org/M_Janssen/picard/src/master/|rPICARD]] |the Radboud PIpeline for the Calibration of high Angular Resolution Data (rPICARD) is an open-source VLBI calibration and imaging pipeline built on top of the CASA framework. The pipeline is capable of reducing data from different VLBI arrays. |**No or limited experience** |#calibration #CASA #imaging #visibilities #VLBI |A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2019A%26A...626A..75J/abstract|]] | |
| | | [[https://github.com/onsala-space-observatory/SD2vis|SD2vis]] | CASA task to generate synthetic visibilities based on a Total-Power (Single Dish) image. |<color #22b14c> Recent experience </color> |#CASA #imaging #python #simulation #visibilities |The SD2vis command functions as a normal CASA function, typing ''inp(SD2vis)'' will show input options. | |
| | | [[https://spectral-cube.readthedocs.io/en/latest/|Spectral Cube]] |The spectral-cube package provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. |<color #22b14c> Recent experience </color> |#datacubes #imaging #moments #python | | |
| | | [[https://github.com/centowen/stacker|STACKER]] |STACKER is a library for stacking sources in interferometric data, i.e., averaging emission from different sources. The library allows stacking to be done directly on visibility data as well as in the image domain. |**No or limited experience** |#analysis #imaging #python #visibilities | | |
| | | [[https://hera.ph1.uni-koeln.de/~sanchez/statcont|STATCONT]] |STATCONT is a python-based tool designed to determine the continuum emission level in line-rich spectral data. The tool inspects the intensity distribution of a given spectrum and automatically determines the continuum level by using different statistical approaches. |<color #22b14c> Recent experience </color> |#imaging #python |There is an I-TRAIN training available for LineStacker, with a recording of the session available 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/onsala-space-observatory/UVMultiFit|uvmultifit]] |A CASA-based flexible visibility-fitting engine developed at the Nordic node of the ALMA Regional Center. |<color #22b14c> Recent experience </color> |#CASA #imaging #python #visibilities |There is an I-TRAIN training available for uvmultifit, with recording of the session available 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. A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2014A%26A...563A.136M/abstract|]] and documentation is also available at [[https://mural.uv.es/imarvi/docums/uvmultifit/|]] | |
| | | [[https://uvplot.readthedocs.io/en/latest/index.html|UVPLOT]] |A simple Python package to make nice plots of deprojected interferometric visibilities, often called uvplots. |**No or limited experience** |#imaging #python #visibilities |Some functionality is only available if imported within CASA | |
| | | [[https://github.com/jradcliffe5/VLBI_pipeline/wiki|VLBI_pipeline]] |A generic VLBI pipeline for use on clusters (with job managers SLURM & PBS) but can also be used on your home machine. |**No or limited experience** |#calibration #CASA #imaging #visibilities #VLBI |The code and installation instructions are available from [[https://github.com/jradcliffe5/VLBI_pipeline|]] and a Zenodo record is present at [[https://zenodo.org/record/4776805|]] | |
| | | [[https://wsclean.readthedocs.io/en/latest/|WSClean]] |WSClean (w-stacking clean) is a fast generic widefield imager. It implements several gridding algorithms and offers fully-automated multi-scale multi-frequency deconvolution. |**No or limited experience** |#GPU_computing #imaging #visibilities |The three papers detailing this tool can be found at this link [[https://wsclean.readthedocs.io/en/latest/citing_wsclean.html|]] | |
| | | [[https://www.alma-allegro.nl/wvr-and-phase-metrics/wvr-scaling/|WVR Scaling Module]] |Software package to optimise the application of the WVR solutions for ALMA Observations. |**No or limited experience** |#calibration #CASA #imaging #python |Requires astropy version 1.3.3 and CASA < 5.0 (CASA 4.7.2 is the latest working version). A paper on the tool can be found at [[https://ui.adsabs.harvard.edu/abs/2017A%26A...605A.121M/abstract|]] | |
| | | [[https://xclass.astro.uni-koeln.de/|XCLASS]] |A toolbox for the Common Astronomy Software Applications package (CASA) containing a couple of new functions for modelling interferometric and single dish data. |<color #22b14c> Recent experience </color> |#CASA #imaging #modeling #modelling #python #visibilities | | |
| </sortable> | </sortable> |
| </searchtable> | </searchtable> |
| |
