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

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(f'{collection} : {details}')
APASS : {'Description': 'The APASS collection at the CADC', 'Bands': ['Optical', 'Infrared|Optical', '']}
BLAST : {'Description': 'The BLAST collection at the CADC', 'Bands': ['', 'Millimeter']}
BRITE-Constellation : {'Description': 'The BRITE-Constellation collection at the CADC', 'Bands': ['Optical']}
CFHT : {'Description': 'The CFHT collection at the CADC', 'Bands': ['Infrared|Optical', 'Optical|UV|EUV|X-ray|Gamma-ray', 'Infrared|Optical|UV', '', 'Optical', 'Infrared']}
CFHTMEGAPIPE : {'Description': 'The CFHTMEGAPIPE collection at the CADC', 'Bands': ['', 'Infrared|Optical', 'Optical']}
CFHTTERAPIX : {'Description': 'The CFHTTERAPIX collection at the CADC', 'Bands': ['Infrared|Optical', 'Optical', 'Infrared']}
CFHTWIRWOLF : {'Description': 'The CFHTWIRWOLF collection at the CADC', 'Bands': ['Infrared']}
CGPS : {'Description': 'The CGPS collection at the CADC', 'Bands': ['Infrared', 'Radio', 'Millimeter', '', 'Millimeter|Infrared']}
SUBARU : {'Description': 'The SUBARU collection at the CADC', 'Bands': ['Optical']}
SUBARUCADC : {'Description': 'The SUBARUCADC collection at the CADC', 'Bands': ['Optical', 'Infrared|Optical']}
TESS : {'Description': 'The TESS collection at the CADC', 'Bands': ['Optical']}
UKIRT : {'Description': 'The UKIRT collection at the CADC', 'Bands': ['Infrared|Optical', '', 'Optical', 'Infrared']}
VGPS : {'Description': 'The VGPS collection at the CADC', 'Bands': ['Radio']}
VLASS : {'Description': 'The VLASS collection at the CADC', 'Bands': ['', 'Radio']}
WALLABY : {'Description': 'The WALLABY 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 ...     maxLastModified2
----------------- -------------- ... -----------------------
caom:CFHT/2366432        2366432 ... 2020-09-14T04:24:28.932
caom:CFHT/2366188        2366188 ... 2020-09-14T06:58:23.094
caom:CFHT/2366432        2366432 ... 2020-09-14T04:24:28.932
caom:CFHT/2480747        2480747 ... 2020-09-09T12:47:39.890
caom:CFHT/2366188        2366188 ... 2020-09-14T06:58:23.094
caom:CFHT/2480747        2480747 ... 2021-02-26T14:40:21.695
caom:CFHT/2583703        2583703 ... 2021-02-18T01:32:51.542
caom:CFHT/2583527        2583527 ... 2021-09-01T20:37:05.647
caom:CFHT/2583527        2583527 ... 2021-09-01T20:37:05.647
caom:CFHT/2583703        2583703 ... 2021-02-26T10:37:42.355
caom:CFHT/2376828        2376828 ... 2021-09-01T23:48:18.790
caom:CFHT/2376828        2376828 ... 2021-09-01T23:48:18.790
>>> urls = cadc.get_data_urls(result)  
>>> for url in urls:
...     print(url)   

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')
>>> urls = cadc.get_data_urls(result[result['target_name'] == 'Nr3491_1'],
...                           include_auxiliaries=True)  
>>> for url in urls:
...    print(url)  

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_m31 = cadc.query_name('M31')
>>> result = cadc.query_name('Nr3491_1')
>>> print(result)  
  observationURI  sequenceNumber ...     maxLastModified2
----------------- -------------- ... -----------------------
caom:CFHT/2376828        2376828 ... 2021-09-01T23:48:18.790
caom:CFHT/2376828        2376828 ... 2021-09-01T23:48:18.790

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')  
>>> images  
[< object at 0x7f3805a06ef0>]
[< 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')  
>>> readable_objs  
Downloaded object from URL with ID 140045451168112
Downloaded object from URL with ID 140045451142576
Downloaded object from URL with ID 140045452176880
Downloaded object from URL with ID 140045452234864
Downloaded object from URL with ID 140045451147584
Downloaded object from URL with ID 140045451345584
Downloaded object from URL with ID 140045451344912
Downloaded object from URL with ID 140045451345104
Downloaded object from URL with ID 140045451343808
Downloaded object from URL 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'
>>> results = cadc.query_region(coords, radius=0.1*u.deg, collection='CFHT')
>>> filtered_results = results[results['time_exposure'] > 120.0]
>>> image_list = cadc.get_image_list(filtered_results, coords, radius)  
>>> print(image_list)   

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, 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, 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)

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(

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
---------------------------------- -----------
    VLASS2.2.T18t28.J204443+293000     science
        c4d_141029_044031_oki_g_v1     science
    VLASS2.2.T18t28.J203534+293000     science
                               ...         ...
                      C170323_0155 calibration
                      C180513_0208     science
                     2019101223440     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",
...                      output_file='test_output_noauth.xml')

1.5 Synchronous query with temporary uploaded table

A temporary table can be uploaded to the server from a local file and used in a query. The uploads argument in exec_sync is a map where the key is the name of the table and the value is the name of the VOTable temporary file with the content. In the query, the temporary table is referred to as tap_upload.table_name. For example, if uploads = {'temp_table': 'table_file_name'}, then the simplest query to return the content of that table would be SELECT * FROM tap_upload.temp_table. Multiple temporary tables to be used at once can be specified as such.

More details about temporary table upload can be found in the IVOA TAP specification.

>>> from astroquery.cadc import Cadc
>>> cadc = Cadc()
>>> # save a few observations on a local file
>>> results = cadc.exec_sync("SELECT TOP 3 observationID FROM caom2.Observation",
...                          output_file='my_observations.xml')
>>> print(results)  
>>> # now use them to join with the remote table
>>> results = cadc.exec_sync("SELECT o.observationID, intent FROM caom2.Observation o "
...                          "JOIN tap_upload.test_upload tu ON o.observationID=tu.observationID",
...                          uploads={'test_upload': 'my_observations.xml'})  
>>> print(results)  
          observationID             intent
---------------------------------- -------
            c13a_060826_044314_ori science
tess2021167190903-s0039-1-3-0210-s science
                         tu1657207 science

The feature allows a user to save the results of a query to use them later or correlate them with data in other TAP services.

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.raise_if_error()
>>> print(job.fetch_result().to_table())  
                 observationID                     intent
----------------------------------------------- -----------
                                      j8eh03boq     science
                                      j8f635020     science
                                      jbfkb1peq     science
                                      j8ff06s2q     science
                                      icdx40oxq     science
                                      j8fd13rgq     science
                                      j8ff03020     science
                        GN-2014B-SV-101-761-010     science
                                      j8ff07020     science
                                      jbfh14020     science
                                            ...         ...
           hst_10476_50_acs_wfc_f850lp_j9fo50ul     science
                   GS-CAL20181018-10-026-G-BIAS calibration
                          GN-2020B-Q-120-40-050 calibration
                   GS-CAL20181018-10-021-G-BIAS calibration
                   GS-CAL20181018-10-036-G-BIAS calibration
                   GS-CAL20181018-10-061-G-BIAS calibration
                                      icdx13u2q     science
                    GS-CAL20181117-2-046-G-BIAS calibration
tess2019357164649-s0020-0000000159539617-0165-s     science
                    GS-CAL20181117-2-061-G-BIAS calibration
                   GS-CAL20181018-10-086-G-BIAS calibration
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.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) 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()
astroquery.cadc Package

Canadian Astronomy Data Centre (CADC).


CadcClass(*[, url, auth_session])

Class for accessing CADC data.