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

1.3 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')

If you do not provide any parameters at all, a prompt will ask for user name and password.

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> User: user
>>> Password: pwd (not visible)

To perform a logout

>>> from astroquery.gaia import Gaia
>>> Gaia.logout()

2.2. Listing shared tables

>>> from astroquery.gaia import Gaia
>>> tables = Gaia.load_tables(only_names=True, include_shared_tables=True)
>>> for table in (tables):
>>>   print(table.get_qualified_name())

public.dual
public.tycho2
public.igsl_source
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
...
user_schema_1.table1
user_schema_2.table1
...

2.2. Uploading table to user space

It is now possible to persist a table in the private user space. The table to be uploaded can be in a url, a file, a job or a astropy Table.

The table is stored into the user private area in the database. Each user has a database schema. The schema name is ‘user_<user_login_name>’.

For instance, if a login name is ‘joe’, the database schema is ‘user_joe’.

Your uploaded table can be referenced as ‘user_joe.table_name’

2.2.1. Uploading table from url to user space

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> # Provide a URL pointing to valid VOTable resource
>>> url = "http://tapvizier.u-strasbg.fr/TAPVizieR/tap/sync/?REQUEST=doQuery&lang=ADQL&FORMAT=votable&QUERY=select+*+from+TAP_SCHEMA.columns+where+table_name='II/336/apass9'"
>>> job = Gaia.upload_table(upload_resource=url, table_name="table_test_from_url", table_description="Some description")

Job '1539932326689O' created to upload table 'table_test_from_url'.

Now, you can query your table as follows:

>>> full_qualified_table_name = 'user_<your_login_name>.table_test_from_url'
>>> query = 'select * from ' + full_qualified_table_name
>>> job = Gaia.launch_job(query=query)
>>> results = job.get_resultsjob)
>>> print(results)

2.2.2. Uploading table from file to user space

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> job = Gaia.upload_table(upload_resource="1535553556177O-result.vot", table_name="table_test_from_file", format="VOTable")

Sending file: 1535553556177O-result.vot
Uploaded table 'table_test_from_file'.

Now, you can query your table as follows:

>>> full_qualified_table_name = 'user_<your_login_name>.table_test_from_file'
>>> query = 'select * from ' + full_qualified_table_name
>>> job = Gaia.launch_job(query=query)
>>> results = job.get_resultsjob)
>>> print(results)

2.2.3. Uploading table from job to user space

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> j1 = Gaia.launch_job_async("select top 10 * from gaiadr2.gaia_source")
>>> job = Gaia.upload_table_from_job(j1)

Created table 't1539932994481O' from job: '1539932994481O'.

Now, you can query your table as follows:

>>> full_qualified_table_name = 'user_<your_login_name>.t1539932994481O'
>>> query = 'select * from ' + full_qualified_table_name
>>> job = Gaia.launch_job(query=query)
>>> results = job.get_resultsjob)
>>> print(results)

2.2.4. Uploading table from an astropy Table

>>> from astroquery.gaia import Gaia
>>> from astropy.table import Table
>>> a=[1,2,3]
>>> b=['a','b','c']
>>> table = Table([a,b], names=['col1','col2'], meta={'meta':'first table'})
>>>
>>> # Upload
>>> Gaia.login()
>>> Gaia.upload_table(upload_resource=table, table_name='my_table')

Now, you can query your table as follows:

>>> full_qualified_table_name = 'user_<your_login_name>.my_table'
>>> query = 'select * from ' + full_qualified_table_name
>>> job = Gaia.launch_job(query=query)
>>> results = job.get_resultsjob)
>>> print(results)

2.3. Deleting table from user space

>>> from astroquery.gaia import Gaia
>>> Gaia.login_gui()
>>> job = Gaia.delete_user_table("table_test_from_file")

Table 'table_test_from_file' deleted.

2.4. Updating metadata of table in user space

Metadata of a user table can be updated by specifying the changes to be done.

>>> Gaia.update_user_table(table_name, list_of_changes)

The format defined to specify a change is the following:

[“column name to be changed”, “metadata parameter to be changed”, “new value”]

metadata parameter to be changed can be ‘utype’, ‘ucd’, ‘flags’ or ‘indexed’

values for ‘utype’ and ‘ucd’ are free text value for ‘flags’ can be ‘Ra’, ‘Dec’, ‘Mag’, ‘Flux’ and ‘PK’ value for ‘indexed’ is a boolean indicating if the column is indexed

It is possible to specify a list of those changes for them to be applied at once. This is done by putting each of the changes in a list. See example below.

>>> from astroquery.gaia import Gaia
>>> Gaia.login_gui()
>>> Gaia.update_user_table(table_name="user_<user_login_name>.my_table", list_of_changes=[["recno", "ucd", "ucd sample"], ["nobs","utype","utype sample"], ["raj2000","flags","Ra"], ["dej2000","flags","Dec"]])

Retrieving table 'user_<user_login_name>.my_table'
Parsing table 'user_<user_login_name>.my_table'...
Done.
Table 'user_<user_login_name>.my_table' updated.

2.5. Tables sharing

You can share your tables to other users. You have to create a group, populate that group with users, and share your table to that group. Then, any user belonging to that group will be able to user your shared table in a query.

2.5.1 Creating a group

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> Gaia.share_group_create(group_name="my_group", description="description")

2.5.2 Removing a group

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> Gaia.share_group_delete(group_name="my_group")

2.5.3 Adding users to a group

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> Gaia.share_group_add_user(group_name="my_group",user_id="<user_login_name")

2.5.4 Removing users from a group

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> Gaia.share_group_delete_user(group_name="my_group",user_id="<user_login_name>")

2.5.5 Sharing a table to a group

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> Gaia.share_table(group_name="my_group",table_name="user_<user_loign_name>.my_table",description="description")

2.5.6 Stop sharing a table

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>> Gaia.share_table_stop(table_name="user_<user_login_name>.my_table", group_name="my_group")

2.6. Cross match

In gaia you can execute a cross match between tables based on distance.

Usually, you will use an uploaded table or a shared table.

You must be logged in in order to perform a cross match. This is required because the cross match operation will generate a join table in the user private area. That table contains the identifiers of both tables and the distance. Later, the table can be used to obtain the actual data from both tables.

The following example uploads a table and then, the table is used in a cross match:

>>> from astroquery.gaia import Gaia
>>> Gaia.login()
>>>
>>> table = file or astropy.table
>>> Gaia.upload_table(upload_resource=table, table_name='my_sources')
>>>
>>> # the table will be uploaded into the user private space into the database
>>> # the table can be referenced as <database user schema>.<table_name>
>>>
>>> full_qualified_table_name = 'user_<your_login_name>.my_sources'
>>> xmatch_table_name = 'xmatch_table'
>>> Gaia.cross_match(full_qualified_table_name_a=full_qualified_table_name, \
>>>               full_qualified_table_name_b='gaiadr2.gaia_source', \
>>>               results_table_name=xmatch_table_name, radius=1.0)
>>>
>>> # Once you have your cross match finished, you can obtain the results:
>>> xmatch_table = 'user_<your_login_name>.' + xmatch_table_name
>>> query = 'SELECT c."dist", a.*, b.* FROM gaiadr2.data_source AS a, '+\
>>> full_qualified_table_name+' AS b, '+\
>>> xmatch_table+' AS c '+\
>>> 'WHERE (c.gaia_source_source_id = a.source_id AND c.my_sources_my_sources_oid = b.my_sources_oid)'
>>> job = Gaia.launch_job(query=query)
>>> results = job.get_resultsjob)
>>> print(results)