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| allegrosoftwarerepo:index [2023/08/17 16:04] – Update UVMultiFit link and add new software (VLBI mostly) hygate | 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. |
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| This is a searchable and sortable table of software listed in the software inventory. 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. | This is a searchable and sortable table of software listed in the software inventory. 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. |
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| The full list of tags is: | The full list of tags is: |
| #analysis, #archive, #calibration, #CASA, #datacubes, #FORTRAN, #GILDAS, #GPU_computing, #imaging, #kinematics, #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 54 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 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://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 limited experience** |#archive #CASA #imaging #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 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://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> |#imaging #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 #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 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://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://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://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.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://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 #imaging #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://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://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://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 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://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://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 | |
| | [[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://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/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/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/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://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://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://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|]] | |
| | | [[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://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://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://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://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/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/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://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 limited experience** |#analysis #imaging #python #visualisation #visualization | | | | [[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://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/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/aardk/jupyter-casa|jupyter-casa]] | A Jupyter kernel for CASA. |**No or limited experience** |#CASA #imaging #python | | | | [[https://github.com/aardk/jupyter-casa|jupyter-casa]] | A Jupyter kernel for CASA. |**No or limited experience** |#CASA #imaging #python | | |
| | [[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/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/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://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://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 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://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://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://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://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/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://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://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 | | | | [[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 | | |
| | [[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://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://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://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://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://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://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://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://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://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://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://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 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://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 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. 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://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://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://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|]] | |
| </sortable> | </sortable> |
| </searchtable> | </searchtable> |
| |
