Gaia TAP+ (astroquery.gaia
)¶
Gaia is an ambitious mission to chart a three-dimensional map of our Galaxy, the Milky Way, in the process revealing the composition, formation and evolution of the Galaxy. Gaia will provide unprecedented positional and radial velocity measurements with the accuracies needed to produce a stereoscopic and kinematic census of about one billion stars in our Galaxy and throughout the Local Group. This amounts to about 1 per cent of the Galactic stellar population.
If you use public Gaia DR1 data in your paper, please take note of our guide on how to acknowledge and cite Gaia DR1.
This package allows the access to the European Space Agency Gaia Archive (http://gea.esac.esa.int/archive/)
Gaia Archive access is based on a TAP+ REST service. TAP+ is an extension of Table Access Protocol (TAP: http://www.ivoa.net/documents/TAP/) specified by the International Virtual Observatory Alliance (IVOA: http://www.ivoa.net).
The TAP query language is Astronomical Data Query Language (ADQL: http://www.ivoa.net/documents/ADQL/2.0), which is similar to Structured Query Language (SQL), widely used to query databases.
TAP provides two operation modes: Synchronous and Asynchronous:
- Synchronous: the response to the request will be generated as soon as the request received by the server. (Do not use this method for queries that generate a big amount of results.)
- 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.
Gaia TAP+ server provides two access mode: public and authenticated:
- Public: this is the standard TAP access. A user can execute ADQL queries and upload tables to be used in a query ‘on-the-fly’ (these tables will be removed once the query is executed). The results are available to any other user and they will remain in the server for a limited space of time.
- Authenticated: some functionalities are restricted to authenticated users only.
The results are saved in a private user space and they will remain in the server
for ever (they can be removed by the user).
- ADQL queries and results are saved in a user private area.
- Cross-match operations: a catalog cross-match operation can be executed. Cross-match operations results are saved in a user private area.
- Persistence of uploaded tables: a user can upload a table in a private space. These tables can be used in queries as well as in cross-matches operations.
This python module provides an Astroquery API access. Nevertheless, only
query_object
and query_object_async
are implemented.
The Gaia Archive table used for the methods where no table is specified is
gaiadr1.gaia_source
Examples¶
1. Non authenticated access¶
1.1. Query object¶
>>> import astropy.units as u
>>> from astropy.coordinates import SkyCoord
>>> from astroquery.gaia import Gaia
>>>
>>> coord = SkyCoord(ra=280, dec=-60, unit=(u.degree, u.degree), frame='icrs')
>>> width = u.Quantity(0.1, u.deg)
>>> height = u.Quantity(0.1, u.deg)
>>> r = Gaia.query_object_async(coordinate=coord, width=width, height=height)
>>> r.pprint()
dist solution_id ... ecl_lat
... Angle[deg]
--------------------- ------------------- ... -------------------
0.0026029414438061079 1635378410781933568 ... -36.779151653783892
0.0038537557334594502 1635378410781933568 ... -36.773899692008634
0.0045451702670639632 1635378410781933568 ... -36.772645786277522
0.0056131312891700424 1635378410781933568 ... -36.781488832325074
0.0058494547209840585 1635378410781933568 ... -36.770812028764119
0.0062076788443168303 1635378410781933568 ... -36.780588167751368
0.008201843586626921 1635378410781933568 ... -36.784730288359086
0.0083377863521668077 1635378410781933568 ... -36.784848302904727
0.0084057202175603796 1635378410781933568 ... -36.784556953222634
0.0092437652172596384 1635378410781933568 ... -36.767784193150469
... ... ... ...
0.049586988816560117 1635378410781933568 ... -36.824132319326232
0.049717306565450765 1635378410781933568 ... -36.823845008396503
0.049777020825344041 1635378410781933568 ... -36.72857293240213
0.050385912463710505 1635378410781933568 ... -36.729880776402624
0.050826536195428054 1635378410781933568 ... -36.822968947436181
0.050859645206141363 1635378410781933568 ... -36.823021426398789
0.051040085912766479 1635378410781933568 ... -36.728589237516161
0.051211160779507325 1635378410781933568 ... -36.825120633172546
0.051958453766310551 1635378410781933568 ... -36.725819366872734
0.053207596589671176 1635378410781933568 ... -36.826600298826662
Length = 152 rows
1.2. Cone search¶
>>> import astropy.units as u
>>> from astropy.coordinates import SkyCoord
>>> from astroquery.gaia import Gaia
>>>
>>> coord = SkyCoord(ra=280, dec=-60, unit=(u.degree, u.degree), frame='icrs')
>>> radius = u.Quantity(1.0, u.deg)
>>> j = Gaia.cone_search_async(coord, radius)
>>> r = j.get_results()
>>> r.pprint()
dist solution_id ... ecl_lat
... Angle[deg]
--------------------- ------------------- ... -------------------
0.0026029414438061079 1635378410781933568 ... -36.779151653783892
0.0038537557334594502 1635378410781933568 ... -36.773899692008634
0.0045451702670639632 1635378410781933568 ... -36.772645786277522
0.0056131312891700424 1635378410781933568 ... -36.781488832325074
0.0058494547209840585 1635378410781933568 ... -36.770812028764119
0.0062076788443168303 1635378410781933568 ... -36.780588167751368
0.008201843586626921 1635378410781933568 ... -36.784730288359086
0.0083377863521668077 1635378410781933568 ... -36.784848302904727
0.0084057202175603796 1635378410781933568 ... -36.784556953222634
0.0092437652172596384 1635378410781933568 ... -36.767784193150469
... ... ... ...
0.14654733241000259 1635378410781933568 ... -36.667789989774818
0.14657617264211745 1635378410781933568 ... -36.876849099093427
0.14674748663117593 1635378410781933568 ... -36.734323499168184
0.14678063354511475 1635378410781933568 ... -36.845214606267504
0.14679704339818228 1635378410781933568 ... -36.697986781654343
0.14684048305123779 1635378410781933568 ... -36.6983554058179
0.14684061095346052 1635378410781933568 ... -36.854933118845658
0.14690380253776872 1635378410781933568 ... -36.700207569397797
0.1469069007730108 1635378410781933568 ... -36.92092859296757
0.14690740362559238 1635378410781933568 ... -36.677757522466912
Length = 2000 rows
1.3 Getting public tables¶
To load only table names (TAP+ capability)
>>> from astroquery.gaia import Gaia
>>> tables = Gaia.load_tables(only_names=True)
>>> for table in (tables):
>>> print(table.get_qualified_name())
public.dual
public.tycho2
public.igsl_source
public.hipparcos
public.hipparcos_newreduction
public.hubble_sc
public.igsl_source_catalog_ids
tap_schema.tables
tap_schema.keys
tap_schema.columns
tap_schema.schemas
tap_schema.key_columns
gaiadr1.phot_variable_time_series_gfov
gaiadr1.ppmxl_neighbourhood
gaiadr1.gsc23_neighbourhood
gaiadr1.ppmxl_best_neighbour
gaiadr1.sdss_dr9_neighbourhood
...
gaiadr1.tgas_source
gaiadr1.urat1_original_valid
gaiadr1.allwise_original_valid
To load table names (TAP compatible)
>>> from astroquery.gaia import Gaia
>>> tables = Gaia.load_tables()
>>> for table in (tables):
>>> print(table.get_qualified_name())
public.dual
public.tycho2
public.igsl_source
public.hipparcos
public.hipparcos_newreduction
public.hubble_sc
public.igsl_source_catalog_ids
tap_schema.tables
tap_schema.keys
tap_schema.columns
tap_schema.schemas
tap_schema.key_columns
gaiadr1.phot_variable_time_series_gfov
gaiadr1.ppmxl_neighbourhood
gaiadr1.gsc23_neighbourhood
gaiadr1.ppmxl_best_neighbour
gaiadr1.sdss_dr9_neighbourhood
...
gaiadr1.tgas_source
gaiadr1.urat1_original_valid
gaiadr1.allwise_original_valid
To load only a table (TAP+ capability)
>>> from astroquery.gaia import Gaia
>>> table = Gaia.load_table('gaiadr1.gaia_source')
>>> print(table)
Table name: gaiadr1.gaia_source
Description: This table has an entry for every Gaia observed source as listed in the
Main Database accumulating catalogue version from which the catalogue
release has been generated. It contains the basic source parameters,
that is only final data (no epoch data) and no spectra (neither final
nor epoch).
Num. columns: 57
Once a table is loaded, columns can be inspected
>>> from astroquery.gaia import Gaia
>>> gaiadr1_table = Gaia.load_table('gaiadr1.gaia_source')
>>> for column in (gaiadr1_table.get_columns()):
>>> print(column.get_name())
solution_id
source_id
random_index
ref_epoch
ra
ra_error
dec
dec_error
...
ecl_lon
ecl_lat
1.4 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 retrieve 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.gaia import Gaia
>>>
>>> job = Gaia.launch_job("select top 100 \
>>> solution_id,ref_epoch,ra_dec_corr,astrometric_n_obs_al,matched_observations,duplicated_source,phot_variable_flag \
>>> from gaiadr1.gaia_source order by source_id")
>>>
>>> print(job)
Jobid: None
Phase: COMPLETED
Owner: None
Output file: sync_20170223111452.xml.gz
Results: None
>>> r = job.get_results()
>>> print(r['solution_id'])
solution_id
-------------------
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
...
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
Length = 100 rows
Query saving results in a file:
>>> from astroquery.gaia import Gaia
>>> job = Gaia.launch_job("select top 100 \
>>> solution_id,ref_epoch,ra_dec_corr,astrometric_n_obs_al,matched_observations,duplicated_source,phot_variable_flag \
>>> from gaiadr1.gaia_source order by source_id", dump_to_file=True)
>>>
>>> print(job)
Jobid: None
Phase: COMPLETED
Owner: None
Output file: sync_20170223111452.xml.gz
Results: None
>>> r = job.get_results()
>>> print(r['solution_id'])
solution_id
-------------------
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
...
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
Length = 100 rows
1.5 Synchronous query on an ‘on-the-fly’ uploaded table¶
A table can be uploaded to the server in order to be used in a query.
from astroquery.gaia import Gaia
>>> upload_resource = 'my_table.xml'
>>> j = Gaia.launch_job(query="select * from tap_upload.table_test", upload_resource=upload_resource, \
>>> upload_table_name="table_test", verbose=True)
>>> r = j.get_results()
>>> r.pprint()
source_id alpha delta
--------- ----- -----
a 1.0 2.0
b 3.0 4.0
c 5.0 6.0
1.6 Asynchronous query¶
Asynchronous queries save results at server side. These queries can be accessed at any time. For anonymous users, results are kept for three days.
The results can be saved in memory (default) or in a file.
Query without saving results in a file:
>>> from astroquery.gaia import Gaia
>>>
>>> job = Gaia.launch_job_async("select top 100 * from gaiadr1.gaia_source order by source_id")
>>>
>>> print(job)
Jobid: 1487845273526O
Phase: COMPLETED
Owner: None
Output file: async_20170223112113.vot
Results: None
>>> r = job.get_results()
>>> print(r['solution_id'])
solution_id
-------------------
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
...
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
Length = 100 rows
Query saving results in a file:
>>> from astroquery.gaia import Gaia
>>>
>>> job = Gaia.launch_job_async("select top 100 * from gaiadr1.gaia_source order by source_id", dump_to_file=True)
>>>
>>> print(job)
Jobid: 1487845273526O
Phase: COMPLETED
Owner: None
Output file: async_20170223112113.vot
Results: None
>>> r = job.get_results()
>>> print(r['solution_id'])
solution_id
-------------------
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
...
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
1635378410781933568
Length = 100 rows
1.6 Asynchronous job removal¶
To remove asynchronous
>>> from astroquery.gaia import Gaia
>>> job = Gaia.remove_jobs(["job_id_1","job_id_2",...])
2. Authenticated access¶
Authenticated users are able to access to TAP+ capabilities (shared tables, persistent jobs, etc.)
In order to authenticate a user, login
or login_gui
methods must be called. After a successful
authentication, the user will be authenticated until logout
method is called.
All previous methods (query_object
, cone_search
, load_table
, load_tables
, launch_job
) explained for
non authenticated users are applicable for authenticated ones.
The main differences are:
- Asynchronous results are kept at server side for ever (until the user decides to remove one of them).
- Users can access to shared tables.
2.1. Login/Logout¶
Graphic interface
Note: Tkinter module is required to use login_gui method.
>>> from astroquery.gaia import Gaia
>>> Gaia.login_gui()
Command line
>>> from astroquery.gaia import Gaia
>>> Gaia.login(user='userName', password='userPassword')
It is possible to use a file where the credentials are stored:
The file must containing user and password in two different lines.
>>> from astroquery.gaia import Gaia
>>> Gaia.login(credentials_file='my_credentials_file')
To perform a logout
>>> from astroquery.gaia import Gaia
>>> Gaia.logout()