CASDA Queries (`astroquery.casda`)¶

The CSIRO ASKAP Science Data Archive (CASDA) provides access to science-ready data products from observations at the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. These data products include source catalogues, images, spectral line and polarisation cubes, spectra and visbilities. This package allows querying of the data products available in CASDA (https://casda.csiro.au/).

Listing Data Products¶

The metadata for data products held in CASDA may be queried using the query_region() method. The results are returned in a Table. The method takes a location and either a radius or a height and width of the region to be queried. The location should be specified in ICRS coordinates or an astropy.coordinates.SkyCoord object. For example:

>>> from astroquery.casda import Casda
>>> from astropy.coordinates import SkyCoord
>>> from astropy import units as u
>>> centre = SkyCoord.from_name('NGC 7232')
>>> result_table = Casda.query_region(centre, radius=30*u.arcmin)
>>> print(result_table['obs_publisher_did','s_ra', 's_dec', 'obs_release_date'][:5])  
obs_publisher_did       s_ra           s_dec           obs_release_date
                        deg             deg
----------------- --------------- ---------------- ------------------------
        cube-1170 333.70448386919 -45.966341151806 2019-01-30T13:00:00.000Z
       cube-60339 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z
       cube-60338 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z
       cube-60337 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z
       cube-60336 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z

In most cases only public data is required. While most ASKAP data is public, some data products may not be released for quality reasons. Some derived data produced by science teams may also be embargoed to the science team for a period of team. To filter down to just the public data you can use the filter_out_unreleased() method.

For example to filter out the 30 non-public results from the above data set:

>>> public_results = Casda.filter_out_unreleased(result_table)
>>> print(public_results['obs_publisher_did','s_ra', 's_dec', 'obs_release_date'][:5])  
obs_publisher_did       s_ra           s_dec           obs_release_date
                        deg             deg
----------------- --------------- ---------------- ------------------------
        cube-1170 333.70448386919 -45.966341151806 2019-01-30T13:00:00.000Z
       cube-60339 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z
       cube-60338 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z
       cube-60337 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z
       cube-60336 333.74878924962  -46.50601666028 2022-03-18T00:32:52.674Z

Authentication¶

User authentication is required to access data files from CASDA, including calibrated visibilities, images and image cubes. Authentication is made with OPAL credentials. To register with OPAL, go to https://opal.atnf.csiro.au/ and click on the link to ‘Register’. Enter your email address, name, affiliation and a password. The OPAL application will register you straight away.

OPAL user accounts are self-managed. Please keep your account details up to date. To change user-registration details, or to request a new OPAL password, use the link to ‘Log in or reset password’.

To use download tasks, you should create an instance of the Casda class and call the login() method with a username. The password will either be taken from the keyring, or if in an interactive environment then it will be requested. e.g.:

>>> from astroquery.casda import Casda
>>> casda = Casda()
>>> casda.login(username='email@somewhere.edu.au')

Note

Prior to Astroquery v0.4.7, authentication required creating an instance of the Casda class with a username and password. e.g.: casda = Casda(username, password)

Data Access¶

New in version 0.4.4.

In order to access data in CASDA you must first stage the data using the stage_data() method. This is because only some of the data in CASDA is held on disc at any particular time. The stage_data() method should be passed an astropy Table object containing an ‘access_url’ column. This column should contain the datalink address of the data product.

Once the data has been assembled you can then download the data using the download_files() method, or using tools such as wget. Authentication is required when staging the data, but not for the download.

An example script to download public continuum images of the NGC 7232 region taken in scheduling block 2338 is shown below:

>>> from astropy import coordinates, units as u, wcs
>>> from astroquery.casda import Casda
>>> centre = coordinates.SkyCoord.from_name('NGC 7232')
>>> casda = Casda()
>>> casda.login(username='email@somewhere.edu.au')
>>> result = Casda.query_region(centre, radius=30*u.arcmin)
>>> public_data = Casda.filter_out_unreleased(result)
>>> subset = public_data[(public_data['dataproduct_subtype']=='cont.restored.t0') & (public_data['obs_id']=='2338')]
>>> url_list = casda.stage_data(subset)
>>> filelist = casda.download_files(url_list, savedir='/tmp')

Note

Due to server side changes, downloads now require Astroquery v0.4.6 or later.

Cutouts¶

New in version 0.4.7.

As well as accessing full data products, the CASDA service can produce cutout images and cubes from larger data products. The cutout support in AstroQuery allows both spatial and spectral cutouts. To produce a spatial cutout, pass in a coordinate and either a radius or a height and a width to the cutout() method. To produce a spectral cutout, pass in either a band or a channel value. For band, the value must be a list or tuple of two astropy.units.Quantity objects specifying a low and high frequency or wavelength. For an open ended range use None as the open value. For channel, the value must be a list or tuple of two integers specifying the low and high channels (i.e. planes of a cube) inclusive. Spatial and spectral parameters can be combined to produce sub-cubes.

Once completed, the cutouts can be downloaded as described in the section above.

An example script to download a 2D cutout from the Rapid ASKAP Continuum Survey (RACS) at a specified position is shown below:

>>> from astropy import coordinates, units as u, wcs
>>> from astroquery.casda import Casda
>>> centre = coordinates.SkyCoord.from_name('2MASX J08161181-7039447')
>>> casda = Casda()
>>> casda.login(username='email@somewhere.edu.au')
>>> result = Casda.query_region(centre, radius=30*u.arcmin)
>>> public_data = Casda.filter_out_unreleased(result)
>>> subset = public_data[((public_data['obs_collection'] == 'The Rapid ASKAP Continuum Survey') & #
                  (np.char.startswith(public_data['filename'], 'RACS-DR1_')) & #
                  (np.char.endswith(public_data['filename'], 'A.fits'))
                 )]
>>> url_list = casda.cutout(subset[:1], coordinates=centre, radius=14*u.arcmin)
>>> filelist = casda.download_files(url_list, savedir='/tmp')

An example script to download a 3D cutout from the WALLABY Pre-Pilot Eridanus cube at a specified position and velocity range is shown below:

>>> from astropy import coordinates, units as u, wcs
>>> from astroquery.casda import Casda
>>> centre = coordinates.SkyCoord.from_name('NGC 1371')
>>> casda = Casda()
>>> casda.login(username='email@somewhere.edu.au')
>>> result = Casda.query_region(centre, radius=30*u.arcmin)
>>> public_data = Casda.filter_out_unreleased(result)
>>> eridanus_cube = public_data[public_data['filename'] == 'Eridanus_full_image_V3.fits']
>>> vel = np.array([1250, 1600])*u.km/u.s
>>> freq = vel.to(u.Hz, equivalencies=u.doppler_radio(1.420405751786*u.GHz))
>>> url_list = casda.cutout(eridanus_cube, coordinates=centre, radius=9*u.arcmin, band=freq)
>>> filelist = casda.download_files(url_list, savedir='/tmp')

An example script to download a 3D cutout of a spectral channel range from the WALLABY Pre-Pilot Eridanus cube is shown below:

>>> from astropy import coordinates, units as u, wcs
>>> from astroquery.casda import Casda
>>> centre = coordinates.SkyCoord.from_name('NGC 1371')
>>> casda = Casda()
>>> casda.login(username='email@somewhere.edu.au')
>>> result = Casda.query_region(centre, radius=30*u.arcmin)
>>> public_data = Casda.filter_out_unreleased(result)
>>> eridanus_cube = public_data[public_data['filename'] == 'Eridanus_full_image_V3.fits']
>>> channel = (5, 10)
>>> url_list = casda.cutout(eridanus_cube, channel=channel)
>>> filelist = casda.download_files(url_list, savedir='/tmp')

Troubleshooting¶

If you are repeatedly getting failed queries, or bad/out-of-date results, try clearing your cache:

>>> from astroquery.casda import Casda
>>> Casda.clear_cache()

If this function is unavailable, upgrade your version of astroquery. The clear_cache function was introduced in version 0.4.7.dev8479.

Reference/API¶

astroquery.casda Package¶

CSIRO ASKAP Science Data Archive (CASDA)

Classes¶

CasdaClass()

Class for accessing ASKAP data through the CSIRO ASKAP Science Data Archive (CASDA).

Navigation

CASDA Queries (astroquery.casda)¶