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).
|
Class for accessing CADC data. |