query_tap
(for Table Access Protocol) is the one
query to rule them all. It allows one to access all the information in SIMBAD with the
Astronomical Data Query Language (ADQL). ADQL is a flavor of the Structured
Query Language (SQL) adapted to astronomy. To learn more about this language,
see the ADQL documentation
or the Simbad ADQL cheat sheet.
Structure of an ADQL query¶
The method query_tap
is called with a string containing the
ADQL query.
/*ADQL queries start with selecting the columns that will be in the output. Usually,
the column name is sufficient. If there is a need to lift ambiguity, add the table
name first (table_name.column_name). This is also where the number of rows is fixed
(here 5).*/
SELECT TOP 5 basic.ra, basic.dec, main_id, nbref
/*Then comes the declaration of the tables to be included in the query. Here *basic* and
*ident*. Their common column is named *oid* in *basic* and *oidref* in *ident*.*/
FROM basic JOIN ident ON basic.oid = ident.oidref
/*The conditions come after. This query filters otypes that are not in any
cluster of star (Cl*) sub-category (..), specific redshifts, and the results should
have an NGC name in their list of names.*/
WHERE (otype != 'Cl*..') AND (rvz_redshift < 1) AND (id LIKE 'NGC%')
/*The result is then sorted so that the top 5 selected corresponds to
the objects cited by the largest number of papers.*/
ORDER BY nbref DESC
This ADQL query can be called with query_tap
:
>>> from astroquery.simbad import Simbad
>>> Simbad.query_tap("""SELECT TOP 5 basic.ra, basic.dec, main_id, nbref
... FROM basic JOIN ident ON basic.oid = ident.oidref
... WHERE (otype != 'Cl*..') AND (rvz_redshift < 1)
... AND (id LIKE 'NGC%')
... ORDER BY nbref DESC""")
<Table length=5>
ra dec main_id nbref
deg deg
float64 float64 object int32
------------------ ------------------ -------- -----
10.684708333333333 41.268750000000004 M 31 12412
13.158333333333333 -72.80027777777778 NAME SMC 10875
187.70593076725 12.391123246083334 M 87 7040
148.96845833333333 69.67970277777778 M 82 5769
23.46206906218 30.660175111980003 M 33 5737
And voilà, we get the 5 NGC objects that are the most cited in literature, are not clusters of stars, and have a redshift < 1. The following sections cover methods that help build ADQL queries. A showcase of more complex queries comes after.
Available tables¶
SIMBAD is a relational database. This means that it is a collection of tables with
links between them. You can access a graphic representation of Simbad’s tables and
their relations or print
the names and descriptions of each table with the
list_tables
method:
>>> from astroquery.simbad import Simbad
>>> Simbad.list_tables()
<Table length=30>
table_name description
object object
------------- ----------------------------------------------------------------------------
basic General data about an astronomical object
ids all names concatenated with pipe
alltypes all object types concatenated with pipe
ident Identifiers of an astronomical object
cat Catalogues name
flux Magnitude/Flux information about an astronomical object
allfluxes all flux/magnitudes U,B,V,I,J,H,K,u_,g_,r_,i_,z_
filter Description of a flux filter
has_ref Associations between astronomical objects and their bibliographic references
ref Bibliographic reference
author Author of a bibliographic reference
h_link hierarchy of membership measure
mesHerschel The Herschel observing Log
biblio Bibliography
keywords List of keywords in a paper
mesXmm XMM observing log.
mesVelocities Collection of HRV, Vlsr, cz and redshifts.
mesVar Collection of stellar variability types and periods.
mesRot Stellar Rotational Velocities.
mesPM Collection of proper motions.
mesPLX Collection of trigonometric parallaxes.
otypedef all names and definitions for the object types
mesIUE International Ultraviolet Explorer observing log.
mesISO Infrared Space Observatory (ISO) observing log.
mesFe_h Collection of metallicity, as well as Teff, logg for stars.
mesDiameter Collection of stellar diameters.
mesDistance Collection of distances (pc, kpc or Mpc) by several means.
otypes List of all object types associated with an object
mesSpT Collection of spectral types.
journals Description of all used journals in the database
To join tables, any columns sharing the same name are possible links between tables.
To find the other possible joins, the list_linked_tables
method
can be useful. Here we look for possible links with the mesDiameter
table
>>> from astroquery.simbad import Simbad
>>> Simbad.list_linked_tables("mesDiameter")
<Table length=1>
from_table from_column target_table target_column
object object object object
----------- ----------- ------------ -------------
mesDiameter oidref basic oid
The output indicates that the mesDiameter
table can be linked to basic
with the following
join statement: [...] mesDiameter JOIN basic ON mesDiameter.oidref = basic.oid [...]
.
A quick view of SIMBAD’s tables. Hover the links to see the linked columns.¶
Available columns¶
list_columns
lists the columns in all or a subset of SIMBAD tables.
Calling it with no argument returns the 289 columns of SIMBAD. To restrict the output to
some tables, add their name. To get the columns of the tables ref
and biblio
:
>>> from astroquery.simbad import Simbad
>>> Simbad.list_columns("ref", "biblio")
<Table length=13>
table_name column_name datatype ... unit ucd
object object object ... object object
---------- ----------- ----------- ... ------ --------------------
biblio biblio TEXT ... meta.record;meta.bib
biblio oidref BIGINT ... meta.record;meta.id
ref abstract UNICODECHAR ... meta.record
ref bibcode CHAR ... meta.bib.bibcode
ref doi VARCHAR ... meta.code;meta.bib
ref journal VARCHAR ... meta.bib.journal
ref last_page INTEGER ... meta.bib.page
ref nbobject INTEGER ... meta.number
ref oidbib BIGINT ... meta.record;meta.bib
ref page INTEGER ... meta.bib.page
ref title UNICODECHAR ... meta.title
ref volume INTEGER ... meta.bib.volume
ref year SMALLINT ... meta.note;meta.bib
list_columns
can also be called with a keyword argument.
This returns columns from any table for witch the given keyword is either in the table name,
in the column name or in its description. This is not case-sensitive.
>>> from astroquery.simbad import Simbad
>>> Simbad.list_columns(keyword="Radial velocity")
<Table length=9>
table_name column_name ... unit ucd
object object ... object object
------------- --------------- ... ------ -------------------------------------
basic rvz_bibcode ... meta.bib.bibcode;spect.dopplerVeloc
basic rvz_err ... km.s-1 stat.error;spect.dopplerVeloc
basic rvz_err_prec ...
basic rvz_qual ... meta.code.qual;spect.dopplerVeloc
basic rvz_radvel ... km.s-1 spect.dopplerVeloc.opt
basic rvz_radvel_prec ...
basic rvz_type ...
basic rvz_wavelength ... instr.bandpass;spect.dopplerVeloc.opt
mesVelocities origin ... meta.note
Example TAP queries¶
This section lists more complex queries by looking at use cases from former astroquery issues.
Getting all bibcodes containing a certain type of measurement for a given object¶
The measurement tables – the ones with names starting with mes
– have a bibcode column
that corresponds to the paper in which the information was found.
This query joins the tables ident
to get all possible names of the object and mesrot
that is the measurement table for rotations. Their common column is oidref
.
>>> from astroquery.simbad import Simbad
>>> query = """SELECT bibcode AS "Rotation Measurements Bibcodes"
... FROM ident JOIN mesrot USING(oidref)
... WHERE id = 'Sirius';
... """
>>> Simbad.query_tap(query)
<Table length=6>
Rotation Measurements Bibcodes
object
------------------------------
2016A&A...589A..83G
2002A&A...393..897R
1995ApJS...99..135A
1970CoKwa.189....0U
1970CoAsi.239....1B
2011A&A...531A.143D
This returns six papers in which the SIMBAD team found rotation data for Sirius.
Criteria on region, measurements and object types¶
Here we search for objects that are not stars and have a redshift<0.4 in a cone search. All this information
is in the basic
column. The star..
syntax refers to any type of star.
>>> from astroquery.simbad import Simbad
>>> query = """SELECT ra, dec, main_id, rvz_redshift, otype
... FROM basic
... WHERE otype != 'star..'
... AND CONTAINS(POINT('ICRS', basic.ra, basic.dec), CIRCLE('ICRS', 331.92, +12.44 , 0.25)) = 1
... AND rvz_redshift <= 0.4"""
>>> Simbad.query_tap(query)
<Table length=11>
ra dec main_id rvz_redshift otype
deg deg
float64 float64 object float64 object
--------------- ------------------ ------------------------ ------------ ------
331.86493815752 12.61105991847 SDSS J220727.58+123639.8 0.11816 EmG
331.80665742545 12.5032406228 SDSS J220713.60+123011.7 0.1477 EmG
332.022027 12.29211 SDSS J220805.28+121731.5 0.12186 G
331.984091 12.573282 SDSS J220756.18+123423.8 0.13824 G
331.87489584192 12.5830568196 SDSS J220729.97+123458.8 0.03129 G
331.77233978222 12.314639195540002 2MASX J22070538+1218523 0.079 G
331.796426 12.426641 SDSS J220711.14+122535.9 0.07886 G
331.68420630414 12.3609942055 2MASX J22064423+1221397 0.1219 G
331.951995 12.431051 SDSS J220748.47+122551.7 0.16484 G
332.171805 12.430204 SDSS J220841.23+122548.7 0.14762 G
332.084711 12.486509 SDSS J220820.33+122911.4 0.12246 G
This returns a few galaxies ‘G’ and emission-line galaxies ‘EmG’.
Get the members of a galaxy cluster¶
All membership information is in the h_link
table. We first need to retrieve the oidref
corresponding to the parent cluster SDSSCGB 350. This is done is the sub-query between parenthesis.
Then, the basic
table is joined with h_link
and the sub-query result.
>>> from astroquery.simbad import Simbad
>>> query = """SELECT main_id AS "child id",
... otype, ra, dec, membership,
... cluster_table.id AS "parent cluster"
... FROM (SELECT oidref, id FROM ident WHERE id = 'SDSSCGB 350') AS cluster_table,
... basic JOIN h_link ON basic.oid = h_link.child
... WHERE h_link.parent = cluster_table.oidref;
... """
>>> Simbad.query_tap(query)
<Table length=7>
child id otype ra ... membership parent cluster
deg ... percent
object object float64 ... int16 object
------------------------ ------ ------------------ ... ---------- --------------
SDSSCGB 350.4 G 243.18303333333336 ... 75 SDSSCGB 350
SDSS J161245.36+281652.4 G 243.18900464937997 ... 75 SDSSCGB 350
SDSSCGB 350.1 G 243.18618110644002 ... 75 SDSSCGB 350
LEDA 1831614 G 243.189153 ... 75 SDSSCGB 350
LEDA 1832284 G 243.187819 ... 100 SDSSCGB 350
SDSSCGB 350.1 G 243.18618110644002 ... 100 SDSSCGB 350
LEDA 1831614 G 243.189153 ... 100 SDSSCGB 350
Query a long list of object¶
To query a list of objects (or coordinates, of bibliographic references), we can use the
ADQL criteria IN
like so:
>>> from astroquery.simbad import Simbad
>>> Simbad.query_tap("SELECT main_id, otype FROM basic WHERE main_id IN ('M1', 'M2', 'M3')")
<Table length=3>
main_id otype
object object
------- ------
M 1 SNR
M 2 GlC
M 3 GlC
And that would work perfectly… until we reach the character limit for the ADQL query. This
is one of the example use case where the upload table capability is very useful. You can create/use
an Table
containing the desired list and use it in a JOIN
to replace an IN
:
>>> from astroquery.simbad import Simbad
>>> from astropy.table import Table
>>> list_of_objects = Table([["M1", "M2", "M3"]], names=["Messier_objects"])
>>> query = """SELECT main_id, otype FROM basic
... JOIN TAP_UPLOAD.messiers
... ON basic.main_id = TAP_UPLOAD.messiers.Messier_objects
... """
>>> Simbad.query_tap(query, messiers=list_of_objects)
<Table length=3>
main_id otype
object object
------- ------
M 1 SNR
M 2 GlC
M 3 GlC
Note
The uploaded tables are limited to 200000 lines. You might need to break your query into smaller chunks if you have longer tables.