Cadc (astroquery.cadc)

The Canadian Astronomy Data Centre (CADC) is a world-wide distribution centre for astronomical data obtained from telescopes. The CADC specializes in data mining, processing, distribution and transferring of very large astronomical datasets.

This package allows the access to the data at the CADC.

Basic Access

Note

astroquery.cadc is dependent on the pyvo package. Please install it prior to using the astroquery.cadc module.

The CADC hosts a number of collections and get_collections returns a list of all these collections:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> for collection, details in sorted(cadc.get_collections().items()):
...    print('{} : {}'.format(collection, details))
...
APASS : {'Description': 'The APASS collection at the CADC', 'Bands': ['Optical', 'Infrared', '']}
BLAST : {'Description': 'The BLAST collection at the CADC', 'Bands': ['', 'Millimeter']}
CFHT : {'Description': 'The CFHT collection at the CADC', 'Bands': ['Infrared|Optical', '', 'Optical', 'Infrared']}
CFHTMEGAPIPE : {'Description': 'The CFHTMEGAPIPE collection at the CADC', 'Bands': ['Infrared', 'Optical']}
CFHTTERAPIX : {'Description': 'The CFHTTERAPIX collection at the CADC', 'Bands': ['Optical', 'Infrared']}
CFHTWIRWOLF : {'Description': 'The CFHTWIRWOLF collection at the CADC', 'Bands': ['Infrared']}
CGPS : {'Description': 'The CGPS collection at the CADC', 'Bands': ['Infrared', 'Radio', 'Millimeter', '']}
CHANDRA : {'Description': 'The CHANDRA collection at the CADC', 'Bands': ['X-ray']}
DAO : {'Description': 'The DAO collection at the CADC', 'Bands': ['UV|EUV|X-ray|Gamma-ray', '', 'EUV|X-ray|Gamma-ray', 'Infrared|Optical', 'Optical|UV|EUV|X-ray|Gamma-ray', 'Infrared', 'Optical', 'X-ray|Gamma-ray', 'Infrared|Optical|UV|EUV|X-ray|Ga']}
DAOPLATES : {'Description': 'The DAOPLATES collection at the CADC', 'Bands': ['Optical', '']}
DRAO : {'Description': 'The DRAO collection at the CADC', 'Bands': ['Radio']}
FUSE : {'Description': 'The FUSE collection at the CADC', 'Bands': ['UV', '']}
GEMINI : {'Description': 'The GEMINI collection at the CADC', 'Bands': ['Gamma-ray', 'Infrared|Optical|UV|EUV|X-ray|Ga', 'Infrared', 'Optical', 'Infrared|Optical', '']}
HST : {'Description': 'The HST collection at the CADC', 'Bands': ['', 'Infrared', 'Optical', 'UV']}
HSTHLA : {'Description': 'The HSTHLA collection at the CADC', 'Bands': ['Optical', 'Infrared', 'UV']}
IRIS : {'Description': 'The IRIS collection at the CADC', 'Bands': ['Infrared']}
JCMT : {'Description': 'The JCMT collection at the CADC', 'Bands': ['', 'Millimeter']}
JCMTLS : {'Description': 'The JCMTLS collection at the CADC', 'Bands': ['Millimeter', '']}
MACHO : {'Description': 'The MACHO collection at the CADC', 'Bands': ['Optical']}
MOST : {'Description': 'The MOST collection at the CADC', 'Bands': ['Optical']}
NEOSSAT : {'Description': 'The NEOSSAT collection at the CADC', 'Bands': ['Optical']}
NGVS : {'Description': 'The NGVS collection at the CADC', 'Bands': ['Infrared|Optical', '', 'Optical']}
NOAO : {'Description': 'The NOAO collection at the CADC', 'Bands': ['Optical', 'Infrared']}
OMM : {'Description': 'The OMM collection at the CADC', 'Bands': ['Optical', 'Infrared', '']}
SDSS : {'Description': 'The SDSS collection at the CADC', 'Bands': ['Infrared', 'Optical']}
SUBARU : {'Description': 'The SUBARU collection at the CADC', 'Bands': ['Optical']}
TESS : {'Description': 'The TESS collection at the CADC', 'Bands': ['Optical']}
UKIRT : {'Description': 'The UKIRT collection at the CADC', 'Bands': ['', 'Optical', 'Infrared']}
VGPS : {'Description': 'The VGPS collection at the CADC', 'Bands': ['Radio']}
VLASS : {'Description': 'The VLASS collection at the CADC', 'Bands': ['', 'Radio']}
XMM : {'Description': 'The XMM collection at the CADC', 'Bands': ['Optical', 'UV', 'X-ray']}

The most basic ways to access the CADC data and metadata is by region or by name. The following example queries CADC for Canada France Hawaii Telescope (CFHT) data for a given region and resolves the URLs for downloading the corresponding data.

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> result = cadc.query_region('08h45m07.5s +54d18m00s', collection='CFHT')
>>> print(result)
  observationURI  sequenceNumber metaReadGroups ...           accMetaChecksum2                lastModified2          maxLastModified2
                                                ...
----------------- -------------- -------------- ... ------------------------------------ ----------------------- -----------------------
caom:CFHT/2366432        2366432                ... md5:2a6a9b9d399b5ef84899f83206eecd0d 2019-01-08T10:03:36.057 2019-01-08T10:17:16.206
caom:CFHT/2366188        2366188                ... md5:af8096be4d9d9186b2cc39fb6bd9914c 2019-01-07T11:27:37.922 2019-01-07T18:25:49.914
caom:CFHT/2480747        2480747                ... md5:c24ace389b760c290a5bf31842fb4ea9 2020-09-09T12:47:39.890 2020-09-09T12:47:39.890
caom:CFHT/2366188        2366188                ... md5:935330f6f4bb8211eaa8d84c76fbec33 2019-02-07T12:41:55.814 2019-11-06T08:37:00.616
caom:CFHT/2376828        2376828                ... md5:7c84ae4b76485a28336f03b2b5af18b3 2019-03-04T08:19:23.766 2019-03-04T10:55:53.572
caom:CFHT/2366432        2366432                ... md5:3bddcbca4ce44a337d6ed2fd7a99507d 2019-02-07T12:24:09.625 2019-11-06T08:37:54.590
caom:CFHT/2376828        2376828                ... md5:a3e8ccba7bc69d14a07d261d8615cc47 2019-04-10T22:14:33.111 2019-11-06T08:56:14.246
caom:CFHT/2480747        2480747                ... md5:a7cccd9710cbca222dc8f8b1eedff3b5 2020-09-09T12:47:39.890 2020-09-09T12:47:39.890

>>> urls = cadc.get_data_urls(result)
>>> for url in urls:
...     print(url)
...
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2366432o.fits.fz?RUNID=njvos75ijcw0vo4r
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2366188o.fits.fz?RUNID=njvos75ijcw0vo4r
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2480747p.fits.fz?RUNID=njvos75ijcw0vo4r
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2366188p.fits.fz?RUNID=njvos75ijcw0vo4r
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828o.fits.fz?RUNID=njvos75ijcw0vo4r
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2366432p.fits.fz?RUNID=njvos75ijcw0vo4r
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828p.fits.fz?RUNID=njvos75ijcw0vo4r
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2480747o.fits.fz?RUNID=njvos75ijcw0vo4r

The next example queries all the data in the same region and filters the results on the name of the target (as an example - any other filtering possible) and resolves the URLs for both the primary and auxiliary data (in this case preview files)

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> result = cadc.query_region('08h45m07.5s +54d18m00s')
>>> print(len(result))
3032

>>> urls = cadc.get_data_urls(result[result['target_name'] == 'Nr3491_1'],
...                           include_auxiliaries=True)
>>> for url in urls:
...    print(url)
...
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828o_preview_zoom_1024.jpg?RUNID=tqlxhnxndjs1xhd3
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828o_preview_256.jpg?RUNID=tqlxhnxndjs1xhd3
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828o_preview_1024.jpg?RUNID=tqlxhnxndjs1xhd3
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828o.fits.fz?RUNID=tqlxhnxndjs1xhd3
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828p_preview_1024.jpg?RUNID=tqlxhnxndjs1xhd3
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828p_preview_256.jpg?RUNID=tqlxhnxndjs1xhd3
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828p_preview_zoom_1024.jpg?RUNID=tqlxhnxndjs1xhd3
https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2376828p.fits.fz?RUNID=tqlxhnxndjs1xhd3

CADC data can also be queried on the target name. Note that the name is not resolved. Instead it is matched against the target name in the CADC metadata.

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> result = cadc.query_name('M31')
>>> print(len(result))
103949

>>> result = cadc.query_name('Nr3491_1')
>>> print(result)
  observationURI  sequenceNumber metaReadGroups ...           accMetaChecksum2                lastModified2          maxLastModified2
                                                ...
----------------- -------------- -------------- ... ------------------------------------ ----------------------- -----------------------
caom:CFHT/2376828        2376828                ... md5:a3e8ccba7bc69d14a07d261d8615cc47 2019-04-10T22:14:33.111 2019-11-06T08:56:14.246
caom:CFHT/2376828        2376828                ... md5:7c84ae4b76485a28336f03b2b5af18b3 2019-03-04T08:19:23.766 2019-03-04T10:55:53.572

If only a subsection of the FITS file is needed, CADC can query an area and resolve the cutout of a result.

>>> from astropy import units as u
>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> coords = '01h45m07.5s +23d18m00s'
>>> radius = 0.01*u.deg
>>> images = cadc.get_images(coords, radius, collection='CFHT')
>>> for image in images:
...    print(image)
...
[<astropy.io.fits.hdu.image.PrimaryHDU object at 0x7f3805a06ef0>]
[<astropy.io.fits.hdu.image.PrimaryHDU object at 0x7f3805b23b38>]

Alternatively, if the query result is large and data does not need to be in memory, lazy access to the downloaded FITS file can be used.

>>> from astropy import units as u
>>> from astropy.coordinates import SkyCoord
>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> coords = SkyCoord(10, 20, unit='deg')
>>> radius = 0.01*u.deg
>>> readable_objs = cadc.get_images_async(coords, radius,
...                                       collection='CFHT')
>>> for obj in readable_objs:
...    print(obj)
...
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2234132o.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451168112
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2368279p.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451142576
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2228383o.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045452176880
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2228675o.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045452234864
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2234131o.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451147584
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2228675p.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451345584
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2228383p.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451344912
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2234131p.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451345104
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2234132p.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451343808
Downloaded object from URL https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2368279o.fits.fz&RUNID=pot39nwwtaht03wc&POS=CIRCLE+26.2812589776878+23.299999818906816+0.01 with ID 140045451344768

If the cutout URLs from a complicated query are needed, the result table can be passed into the get_image_list function, along with the cutout coordinates and radius.

>>> from astroquery.cadc import Cadc
>>> from astropy import units as u
>>> cadc = Cadc()
>>> coords = '01h45m07.5s +23d18m00s'
>>> radius = 0.1*u.deg
>>> results = cadc.query_region(coords, radius, collection='CFHT')
>>> filtered_results = results[results['time_exposure'] > 120.0]
>>> image_list = cadc.get_image_list(filtered_results, coords, radius)
>>> print(image_list)
['https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2368278o.fits.fz&RUNID=dbuswaj4zwruzi92&POS=CIRCLE+26.2812589776878+23.299999818906816+0.1',
'https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2368278p.fits.fz&RUNID=dbuswaj4zwruzi92&POS=CIRCLE+26.2812589776878+23.299999818906816+0.1',
'https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2368279p.fits.fz&RUNID=dbuswaj4zwruzi92&POS=CIRCLE+26.2812589776878+23.299999818906816+0.1',
'https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/caom2ops/sync?ID=ad%3ACFHT%2F2368279o.fits.fz&RUNID=dbuswaj4zwruzi92&POS=CIRCLE+26.2812589776878+23.299999818906816+0.1']

Note that the examples above are for accessing data anonymously. Users with access to proprietary data can use authenticated sessions to instantiate the CadcClass class or call login on it before querying or accessing the data.

CADC metadata is available through a TAP service. While the above interfaces offer a quick and simple access to the data, the TAP interface presented in the next sections allows for more complex queries.

Query CADC metadata using TAP

Cadc TAP access is based on a TAP+ REST service. TAP+ is an extension of Table Access Protocol (TAP) specified by the International Virtual Observatory Alliance (IVOA).

The TAP query language is Astronomical Data Query Language (ADQL), which is similar to Structured Query Language (SQL), widely used to query databases.

TAP provides two operation modes:

  • Synchronous: the response to the request will be generated as soon as the request received by the server. (In general, avoid using this method for queries that take a long time to run before the first rows are returned as it might lead to timeouts on the client side.)

  • Asynchronous: the server will start a job that will execute the request. The first response to the request is the required information (a link) to obtain the job status. Once the job is finished, the results can be retrieved.

The functions can be run as an authenticated user, the list_async_jobs function will error if not authenticated. For authentication you need an account with the CADC, go to http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/, choose a language, click on Login in the top right area, click on the Request an Account link, enter your information and wait for confirmation of your account creation.

There are two types of authentication:

  • Username/Password: Cadc().login(user='yourusername', password='yourpassword')

  • Certificate: Cadc().login(certificate_file='path/to/certificate/file')

For certificate authentication to get a certificate go to https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/, choose a language, login, click on your name where the login button used to be, from the drop-down menu click Obtain a Certificate and save the certificate. When adding authentication used the path to where you saved the certificate. Remember that certificates expire and you will need to get a new one.

When logging in, both forms of authentication are allowed. Authentication will be applied to each subsequent call. When a job is created with authentication any further calls will require authentication.

There is one way to logout which will cancel any kind of authentication that was used:

  • Logout: Cadc.logout()

CADC metadata is modeled using the CAOM (Common Archive Observation Model).

Examples of TAP access

1. Non authenticated access

1.1. Get tables

To get a list of table objects:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> tables = cadc.get_tables(only_names=True)
>>> for table in tables:
...     print(table)
...
caom2.Observation
caom2.Plane
caom2.Artifact
caom2.Part
caom2.Chunk
caom2.ObservationMember
caom2.ProvenanceInput
caom2.EnumField
caom2.ObsCoreEnumField
caom2.distinct_proposal_id
caom2.distinct_proposal_pi
caom2.distinct_proposal_title
caom2.HarvestSkipURI
caom2.SIAv1
ivoa.ObsCore
ivoa.ObsFile
ivoa.ObsPart
tap_schema.schemas
tap_schema.tables
tap_schema.columns
tap_schema.keys
tap_schema.key_columns

1.2. Get table

To get a single table object:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> table=cadc.get_table(table='caom2.Observation')
>>> for col in table.columns:
...     print(col.name)
...
observationURI
obsID
collection
observationID
algorithm_name
type
intent
sequenceNumber
metaRelease
metaReadGroups
proposal_id
proposal_pi
proposal_project
proposal_title
proposal_keywords
target_name
target_targetID
target_type
target_standard
target_redshift
target_moving
target_keywords
targetPosition_coordsys
targetPosition_coordinates_cval1
targetPosition_equinox
targetPosition_coordinates_cval2
telescope_name
telescope_geoLocationX
telescope_geoLocationY
telescope_geoLocationZ
telescope_keywords
requirements_flag
instrument_name
instrument_keywords
environment_seeing
environment_humidity
environment_elevation
environment_tau
environment_wavelengthTau
environment_ambientTemp
environment_photometric
members
typeCode
metaProducer
lastModified
maxLastModified
metaChecksum
accMetaChecksum

1.3 Run synchronous query

A synchronous query will not store the results at server side. These queries must be used when the amount of data to be retrieved is ‘small’.

There is a limit of 2000 rows. If you need more than that, you must use asynchronous queries.

The results can be saved in memory (default) or in a file.

Query without saving results in a file:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> results = cadc.exec_sync("SELECT top 100 observationID, intent FROM caom2.Observation")
>>> print(results)
       observationID          intent
---------------------------- -------
                C090503_0500 science
  c4d_151207_032018_opd_u_v3 science
                   ct3264072 science
                    tu558265 science
                   ct2318747 science
                   tu1826354 science
       c4d_150601_015113_ori science
                    tu212518 science
       k4i_041101_174620_zri science
                    tu072083 science
       psg_170118_012214_ori science
k4n_131022_051755_opd_KXs_v3 science
      c15s_080828_031158_ori science
  c4d_160214_072405_opi_r_v1 science
                         ...     ...
  c4d_150902_000343_opd_i_v1 science
      c09i_141005_231309_sri science
      kcfs_081028_074111_ori science
                    tu802011 science
  c4d_141122_004603_oow_u_v3 science
      c15s_071230_081528_ori science
      c15s_070924_203941_zri science
                   tu1116697 science
                   ct3429663 science
        dao_c182_2020_005631 science
        dao_c182_2020_005632 science
                C090317_0114 science
                    cp828585 science
      c09i_060720_044639_ori science
        GS-2004A-Q-27-43-006 science
Length = 100 rows

Query saving results in a file:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> job = cadc.exec_sync("SELECT TOP 10 observationID, obsID FROM caom2.Observation AS Observation",
...                      output_file='test_output_noauth.tsv', output_format='tsv')

1.5 Synchronous query with temporary uploaded table

A table can be uploaded to the server in order to be used in a query.

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> upload_resource = 'data/votable.xml'
>>> j = cadc.exec_sync("SELECT * FROM tap_upload.test_table_upload",
...                    uploads=upload_resource,
...                    output_file="test_output_table")
>>> print(j.get_results())
         uri                    contentChecksum            ...   contentType
                                                           ...
--------------------- ------------------------------------ ... ----------------
ad:IRIS/I001B1H0.fits md5:b6ead425ae84289246e4528bbdd7da9a ... application/fits
ad:IRIS/I001B2H0.fits md5:a6b082ca530bf5db5a691985d0c1a6ca ... application/fits
ad:IRIS/I001B3H0.fits md5:2ada853a8ae135e16504aeba4e47489e ... application/fits

1.6 Asynchronous query

Asynchronous queries save results at server side. These queries can be accessed at any time.

The results can be saved in memory (default) or in a file.

Query without saving results in a file:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> job = cadc.create_async("SELECT TOP 100 observationID, instrument_name, target_name FROM caom2.Observation AS Observation")
>>> job.run().wait()
>>> job.raise_if_error()
>>> print(job.fetch_result().to_table())
       observationID         instrument_name            target_name
---------------------------- ---------------- --------------------------------
                C090503_0500           CPAPIR                             SH87
  c4d_151207_032018_opd_u_v3            decam                          Field14
                   ct3264072          andicam                          2227-08
                    tu558265         mosaic_2                    xcs0058940301
                   ct2318747         ccd_spec                             test
                   tu1826354            decam                               B1
       c4d_150601_015113_ori            decam                             junk
                    tu212518          newfirm Mask for K4N09B_20091129_783db2b
       k4i_041101_174620_zri        ir_imager                        TEST bias
                    tu072083          newfirm Mask for K4N07B_20071113_776684b
       psg_170118_012214_ori          goodman                          NGC1672
k4n_131022_051755_opd_KXs_v3          newfirm Mask for K4N13B_20131020_89c812c
      c15s_080828_031158_ori         ccd_spec                              082
  c4d_160214_072405_opi_r_v1            decam        MAGLITES field: 5354-01-r
  c4d_141122_004603_oki_u_v3            decam                           Field4
 c4d_140505_000543_opw_VR_v1            decam                          AiYN1Qv
                         ...              ...                              ...
      c09i_140321_044944_ori       ccd_imager twhya filter1 = dia, filter2 = g
  c4d_150902_000343_opd_i_v1            decam                         C6p13c1A
      c09i_141005_231309_sri       ccd_imager                            sflat
      kcfs_081028_074111_ori         ccd_spec                         HD 22780
                    tu802011       mosaic_1_1                            86326
  c4d_141122_004603_oow_u_v3            decam                           Field4
      c15s_071230_081528_ori         ccd_spec                         HD 95578
      c15s_070924_203941_zri         ccd_spec                             Bias
                   tu1116697         mosaic_2                             sm43
                   ct3429663         mosaic_2                             test
        dao_c182_2020_005631 Newtonian Imager                  s2020ihc(150@0)
        dao_c182_2020_005632 Newtonian Imager                  s2020ihc(150@0)
                C090317_0114           CPAPIR                           2M1106
                    cp828585          spartan             WISEJ1741+2533 x-6y5
      c09i_060720_044639_ori       ccd_imager                    G2239n05d1243
        GS-2004A-Q-27-43-006           GMOS-S                            LMCF4
Length = 100 rows

1.7 Load job

Asynchronous jobs can be loaded. You need the jobid in order to load the job.

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> job = cadc.create_async("SELECT TOP 100 observationID, instrument_name, target_name FROM caom2.Observation AS Observation")
>>> job.run().wait()
>>> job.raise_if_error()
>>> loaded_job = cadc.load_async_job(jobid=job.job_id)
>>> print(loaded_job.fetch_result().to_table())
       observationID         instrument_name            target_name
---------------------------- ---------------- --------------------------------
                C090503_0500           CPAPIR                             SH87
  c4d_151207_032018_opd_u_v3            decam                          Field14
                   ct3264072          andicam                          2227-08
                    tu558265         mosaic_2                    xcs0058940301
                   ct2318747         ccd_spec                             test
                   tu1826354            decam                               B1
       c4d_150601_015113_ori            decam                             junk
                    tu212518          newfirm Mask for K4N09B_20091129_783db2b
       k4i_041101_174620_zri        ir_imager                        TEST bias
                    tu072083          newfirm Mask for K4N07B_20071113_776684b
       psg_170118_012214_ori          goodman                          NGC1672
k4n_131022_051755_opd_KXs_v3          newfirm Mask for K4N13B_20131020_89c812c
      c15s_080828_031158_ori         ccd_spec                              082
  c4d_160214_072405_opi_r_v1            decam        MAGLITES field: 5354-01-r
  c4d_141122_004603_oki_u_v3            decam                           Field4
 c4d_140505_000543_opw_VR_v1            decam                          AiYN1Qv
                         ...              ...                              ...
      c09i_140321_044944_ori       ccd_imager twhya filter1 = dia, filter2 = g
  c4d_150902_000343_opd_i_v1            decam                         C6p13c1A
      c09i_141005_231309_sri       ccd_imager                            sflat
      kcfs_081028_074111_ori         ccd_spec                         HD 22780
                    tu802011       mosaic_1_1                            86326
  c4d_141122_004603_oow_u_v3            decam                           Field4
      c15s_071230_081528_ori         ccd_spec                         HD 95578
      c15s_070924_203941_zri         ccd_spec                             Bias
                   tu1116697         mosaic_2                             sm43
                   ct3429663         mosaic_2                             test
        dao_c182_2020_005631 Newtonian Imager                  s2020ihc(150@0)
        dao_c182_2020_005632 Newtonian Imager                  s2020ihc(150@0)
                C090317_0114           CPAPIR                           2M1106
                    cp828585          spartan             WISEJ1741+2533 x-6y5
      c09i_060720_044639_ori       ccd_imager                    G2239n05d1243
        GS-2004A-Q-27-43-006           GMOS-S                            LMCF4
Length = 100 rows

2. Authenticated access

Some capabilities (shared tables, persistent jobs, etc.) are only available to authenticated users.

One authentication option is to instantiate the CadcClass class with a pre-existing, pyvo.auth.authsession.AuthSession or requests.Session object that contains the necessary credentials. Note that the session will be used for all the service interaction. The former session attempts to pair the credentials with the auth methods in the service capabilities while the latter sends the credentials with all requests.

The second option is to use the login method.

After a successful authentication, user credentials will be used until the logout method is called.

All previous methods (get_tables, get_table, run_query) explained for non authenticated users are applicable for authenticated ones.

2.1 Login/Logout

Login with username and password:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> cadc.login(user='userName', password='userPassword')

Login with certificate:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> cadc.login(certificate_file='/path/to/cert/file')

To perform a logout:

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> cadc.logout()
Reference/API
astroquery.cadc Package

Canadian Astronomy Data Centre (CADC).

Classes

CadcClass([url, tap_plus_handler, verbose, …])

Class for accessing CADC data.