This module can be used to query the SIMBAD service. Presented below are examples that illustrate the different types of queries that can be formulated. If successful all the queries will return the results in a ~astropy.table.Table.
The SIMBAD database is widely used and has to limit the rate of incoming queries. If you spam the server with more that ~5-10 queries per second you will be blacklisted for up to an hour. This can happen when a query method is called within a loop. There is always a way to send the information in a bigger query rather than in a lot of smaller ones. Frequent use cases are that you can pass a vector of coordinates to ~astroquery.simbad.SimbadClass.query_region or a list of names to ~astroquery.simbad.SimbadClass.query_objects, and SIMBAD will treat this submission as a single query. If this does not fit your use case, then you'll need to either use Wildcards or a custom :ref:`query TAP <query-tap>`.
The SIMBAD module follows evolutions of the SIMBAD database. Some of these changes are documented into more details here:
.. toctree::
:maxdepth: 2
/simbad/simbad_evolution
The SIMBAD module described here provides methods that write ADQL queries. These methods are described in the next sections.
A more versatile option is to query SIMBAD directly with your own ADQL queries via Table Access Protocol (TAP) with the ~astroquery.simbad.SimbadClass.query_tap method. This is described in this section: :ref:`query TAP <query-tap>`.
This is useful if you want to query an object by a known identifier (name). For instance to query the messier object M1:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> result_table = Simbad.query_object("m1")
>>> print(result_table)
main_id ra dec ... coo_wavelength coo_bibcode matched_id
deg deg ...
------- ------- ------- ... -------------- ------------------- ----------
M 1 83.6324 22.0174 ... X 2022A&A...661A..38P M 1
Wildcards are supported. Note that this makes the query case-sensitive. This allows, for instance, to query messier objects from 1 through 9:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> result_table = Simbad.query_object("M [1-9]", wildcard=True) # doctest: +SKIP
>>> print(result_table) # doctest: +SKIP
main_id ra ... coo_bibcode matched_id
deg ...
--------- ------------------ ... ------------------- ----------
M 1 83.6287 ... 1995AuJPh..48..143S M 1
M 2 323.36258333333336 ... 2010AJ....140.1830G M 2
M 3 205.5484166666666 ... 2010AJ....140.1830G M 3
NGC 6475 268.44699999999995 ... 2021A&A...647A..19T M 7
NGC 6405 265.06899999999996 ... 2021A&A...647A..19T M 6
M 4 245.89675000000003 ... 2010AJ....140.1830G M 4
M 8 270.90416666666664 ... M 8
M 9 259.79908333333333 ... 2002MNRAS.332..441F M 9
M 5 229.63841666666673 ... 2010AJ....140.1830G M 5
The messier objects from 1 to 9 are found. Their main identifier main_id is not
necessarily the one corresponding to the wildcard expression.
The column matched_id contains the identifier that was matched.
Note that in this example, the wildcard parameter could have been replaced by a way faster query done with ~astroquery.simbad.SimbadClass.query_objects.
- Wildcards are supported in these methods:
- ~astroquery.simbad.SimbadClass.query_object
- ~astroquery.simbad.SimbadClass.query_objects
- ~astroquery.simbad.SimbadClass.query_bibcode
They allow to provide a pattern that the query will match. To see the available wildcards and their meaning:
>>> from astroquery.simbad import Simbad
>>> Simbad().list_wildcards()
*: Any string of characters (including an empty one)
?: Any character (exactly one character)
[abc]: Exactly one character taken in the list. Can also be defined by a range of characters: [A-Z]
[^0-9]: Any (one) character not in the list.These queries can be used to retrieve all of the names (identifiers) associated with an object.
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> result_table = Simbad.query_objectids("Polaris")
>>> result_table
<Table length=46>
id
object
-----------------------
HIP 11767
TIC 303256075
NAME Lodestar
PLX 299
SBC9 76
* 1 UMi
...
ADS 1477 AP
** WRH 39
WDS J02318+8916A
** STF 93A
2MASS J02314822+8915503
NAME North Star
WEB 2438
SIMBAD also records hierarchy links between objects. For example, two galaxies in a pair of galaxies are siblings, a cluster of stars is composed of stars: its children. This information can be accessed with the ~astroquery.simbad.SimbadClass.query_hierarchy method.
Whenever available, membership probabilities are recorded in SIMBAD as given by the authors, though rounded to an integer. When authors do not give a value but assessments, they are translated in SIMBAD as follows:
| assessment | membership certainty |
|---|---|
| member | 100 |
| likely member | 75 |
| possible member | 50 |
| likely not member | 25 |
| non member | 0 |
For gravitational lens systems, double stars, and blends (superposition of two
non-physically linked objects), the SIMBAD team does not assign a probability
value (this will be a None).
You can find more details in the hierarchy documentation of SIMBAD's webpages.
Let's find the galaxies composing the galaxy pair Mrk 116:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> galaxies = Simbad.query_hierarchy("Mrk 116",
... hierarchy="children", criteria="otype='G..'")
>>> galaxies[["main_id", "ra", "dec", "membership_certainty"]]
<Table length=2>
main_id ra dec membership_certainty
deg deg percent
object float64 float64 int16
--------- --------------- -------------- --------------------
Mrk 116A 143.50821525019 55.24105273196 --
Mrk 116B 143.509956 55.239762 --
Alternatively, if we know one member of a group, we can find the others by asking for
siblings:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> galaxies = Simbad.query_hierarchy("Mrk 116A",
... hierarchy="siblings", criteria="otype='G..'")
>>> galaxies[["main_id", "ra", "dec", "membership_certainty"]]
<Table length=2>
main_id ra dec membership_certainty
deg deg percent
object float64 float64 int16
--------- --------------- -------------- --------------------
Mrk 116A 143.50821525019 55.24105273196 --
Mrk 116B 143.509956 55.239762 --
Note that if we had not added the criteria on the object type, we would also get some stars that are part of these galaxies in the result.
And the other way around, let's find which cluster of stars contains
2MASS J18511048-0615470:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> cluster = Simbad.query_hierarchy("2MASS J18511048-0615470",
... hierarchy="parents", detailed_hierarchy=False)
>>> cluster[["main_id", "ra", "dec"]]
<Table length=1>
main_id ra dec
deg deg
object float64 float64
--------- ------- -------
NGC 6705 282.766 -6.272
- By default, we get a more detailed report with the two extra columns:
hierarchy_bibcode: the paper in which the hierarchy is established,membership_certainty: if present in the paper, a certainty index (100 meaning 100% sure).
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> cluster = Simbad.query_hierarchy("2MASS J18511048-0615470",
... hierarchy="parents",
... detailed_hierarchy=True)
>>> cluster[["main_id", "ra", "dec", "hierarchy_bibcode", "membership_certainty"]]
<Table length=13>
main_id ra dec hierarchy_bibcode membership_certainty
deg deg percent
object float64 float64 object int16
--------- ------- ------- ------------------- --------------------
NGC 6705 282.766 -6.272 2014A&A...563A..44M 100
NGC 6705 282.766 -6.272 2015A&A...573A..55T 100
NGC 6705 282.766 -6.272 2016A&A...591A..37J 100
NGC 6705 282.766 -6.272 2018A&A...618A..93C 100
NGC 6705 282.766 -6.272 2020A&A...633A..99C 100
NGC 6705 282.766 -6.272 2020A&A...640A...1C 100
NGC 6705 282.766 -6.272 2020A&A...643A..71G 100
NGC 6705 282.766 -6.272 2020ApJ...903...55P 100
NGC 6705 282.766 -6.272 2020MNRAS.496.4701J 100
NGC 6705 282.766 -6.272 2021A&A...647A..19T 100
NGC 6705 282.766 -6.272 2021A&A...651A..84M 100
NGC 6705 282.766 -6.272 2021MNRAS.503.3279S 99
NGC 6705 282.766 -6.272 2022MNRAS.509.1664J 100
Here, we see that the SIMBAD team found 13 papers mentioning the fact that
2MASS J18511048-0615470 is a member of NGC 6705 and that the authors of these
articles gave high confidence indices for this membership (membership_certainty is
close to 100 for all bibcodes).
In some tricky cases, low membership values represent extremely important information.
Let's for example look at the star V* V787 Cep:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> parents = Simbad.query_hierarchy("V* V787 Cep",
... hierarchy="parents",
... detailed_hierarchy=True)
>>> parents[["main_id", "ra", "dec", "hierarchy_bibcode", "membership_certainty"]]
<Table length=4>
main_id ra dec hierarchy_bibcode membership_certainty
deg deg percent
object float64 float64 object int16
--------- ------------------ ------- ------------------- --------------------
NGC 188 11.797999999999998 85.244 2003AJ....126.2922P 46
NGC 188 11.797999999999998 85.244 2004PASP..116.1012S 46
NGC 188 11.797999999999998 85.244 2018A&A...616A..10G 100
NGC 188 11.797999999999998 85.244 2021MNRAS.503.3279S 1
Here, we see that the link between V* V787 Cep and the open cluster NGC 188 is
opened for debate: the only way to build an opinion is to read the four articles.
This information would be hidden if we did not print the detailed hierarchy report.
These somewhat contradictory results are an inherent part of SIMBAD, which simply translates the literature into a database.
Query in a cone with a specified radius. The center can be a string with an identifier, a string representing coordinates, or a ~astropy.coordinates.SkyCoord.
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> # get 10 objects in a radius of 0.5° around M81
>>> simbad = Simbad()
>>> simbad.ROW_LIMIT = 10
>>> result_table = simbad.query_region("m81", radius="0.5d")
>>> print(result_table) # doctest: +IGNORE_OUTPUT
main_id ra dec ... coo_err_angle coo_wavelength coo_bibcode
deg deg ... deg
---------------------------------- ------------------ ----------------- ... ------------- -------------- -------------------
[PR95] 40298 149.14159166666667 69.19170000000001 ... --
[GTK91b] 19 149.03841666666668 69.21222222222222 ... --
[GTK91b] 15 149.26095833333332 69.22230555555556 ... --
PSK 212 148.86083333333332 69.15333333333334 ... --
PSK 210 148.8595833333333 69.20111111111112 ... --
[BBC91] N06 148.84166666666664 69.14222222222223 ... --
[GKP2011] M81C J095534.66+691213.7 148.89441666666667 69.20380555555556 ... -- O 2011ApJ...743..176G
[PR95] 51153 148.89568749999998 69.1995888888889 ... -- O 2012ApJ...747...15K
PSK 300 148.96499999999997 69.16638888888889 ... --
PSK 234 148.9008333333333 69.19944444444445 ... --
When no radius is specified, the radius defaults to 2 arcmin. When the radius is
explicitly specified it can be either a string accepted by
~astropy.coordinates.Angle (ex: radius='0d6m0s') or directly a
~astropy.units.Quantity object.
If the center is defined by coordinates, then the best solution is to use a astropy.coordinates.SkyCoord object.
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> Simbad.query_region(SkyCoord(31.0087, 14.0627, unit=(u.deg, u.deg),
... frame='galactic'), radius=2 * u.arcsec)
<Table length=2>
main_id ra ... coo_wavelength coo_bibcode
deg ...
object float64 ... str1 object
--------------------- ----------------- ... -------------- -------------------
NAME Barnard's Star b 269.4520769586187 ... O 2020yCat.1350....0G
NAME Barnard's star 269.4520769586187 ... O 2020yCat.1350....0G
Note
Calling ~astroquery.simbad.SimbadClass.query_region within a loop is very inefficient. If you need to query many regions, use a multi-coordinate ~astropy.coordinates.SkyCoord and a list of radii. It looks like this:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> Simbad.query_region(SkyCoord(ra=[10, 11], dec=[10, 11],
... unit=(u.deg, u.deg), frame='fk5'),
... radius=[0.1 * u.deg, 2* u.arcmin]) # doctest: +IGNORE_OUTPUT
<Table length=6>
main_id ra ... coo_bibcode
deg ...
object float64 ... object
------------------------ ------------------ ... -------------------
SDSS J004014.26+095527.0 10.059442999999998 ... 2020ApJS..250....8L
LEDA 1387229 10.988333333333335 ... 2003A&A...412...45P
IRAS 00371+0946 9.92962860161661 ... 1988NASAR1190....1B
IRAS 00373+0947 9.981768085280164 ... 1988NASAR1190....1B
PLCKECC G118.25-52.70 9.981250000000001 ... 2011A&A...536A...7P
GALEX J004011.0+095752 10.045982309580001 ... 2020yCat.1350....0G
If the radius is the same in every cone, you can also just give this single radius without
having to create the list (ex: radius = "5arcmin").
Queries can also return all the objects from a catalogue. For instance to query the ESO catalog:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad(ROW_LIMIT=6)
>>> simbad.query_catalog('ESO')
<Table length=6>
main_id ra ... coo_bibcode catalog_id
deg ...
object float64 ... object object
--------- ------------------ ... ------------------- ----------
NGC 2573 25.40834109527 ... 2020yCat.1350....0G ESO 1-1
ESO 1-2 76.15327 ... 2020MNRAS.494.1784A ESO 1-2
ESO 1-3 80.65212083333333 ... 2006AJ....131.1163S ESO 1-3
ESO 1-4 117.37006325383999 ... 2020yCat.1350....0G ESO 1-4
ESO 1-5 133.2708583333333 ... 2006AJ....131.1163S ESO 1-5
ESO 1-6 216.83122280179 ... 2020yCat.1350....0G ESO 1-6
Note that the name in main_id is not necessarily from the queried catalog. This
information is in the catalog_id column.
To see the available catalogues, you can write a custom ADQL query
(see :ref:`query_tap <query-tap-documentation>`.) on the cat table.
For example to get the 10 biggest catalogs in SIMBAD, it looks like this:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> Simbad.query_tap('SELECT TOP 10 cat_name, description FROM cat ORDER BY "size" DESC') # doctest: +IGNORE_OUTPUT
<Table length=10>
cat_name description
object object
-------- ----------------------------------------------------------------
Gaia Gaia
2MASS 2 Micron Sky Survey, Point Sources
TIC TESS Input Catalog
SDSS Sloan Digital Sky Survey
TYC Tycho mission
OGLE Optical Gravitational Lensing Event
UCAC4 Fourth USNO CCD Astrograph Catalog
WISE Wide-field Infrared Survey Explorer Final Release Source Catalog
GSC Guide Star Catalogue
LEDA Lyon-Meudon Extragalactic DatabaseA
Where you can remove TOP 10 to get all the catalogues (there's a lot of them).
Warning
This method is case-sensitive since version 0.4.8
This retrieves information about the article corresponding to a bibcode.
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> Simbad.query_bibcode('2005A&A.430.165F')
<Table length=1>
bibcode doi journal ... volume year
object object object ... int32 int16
------------------- -------------------------- ------- ... ------ -----
2005A&A...430..165F 10.1051/0004-6361:20041272 A&A ... 430 2005
The abstract of the reference can also be added as an other column in the output by
setting the abstract parameter to True.
Wildcards can be used in these queries as well. This can be useful to retrieve all the bibcodes from a given journal in a given year:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> biblio = Simbad.query_bibcode('2013A&ARv.*', wildcard=True)
>>> biblio.sort("bibcode")
>>> biblio
<Table length=9>
bibcode doi journal ... volume year
object object object ... int32 int16
------------------- ------------------------- ------- ... ------ -----
2013A&ARv..21...59I 10.1007/s00159-013-0059-2 A&ARv ... 21 2013
2013A&ARv..21...61R 10.1007/s00159-013-0061-8 A&ARv ... 21 2013
2013A&ARv..21...62D 10.1007/s00159-013-0062-7 A&ARv ... 21 2013
2013A&ARv..21...63T 10.1007/s00159-013-0063-6 A&ARv ... 21 2013
2013A&ARv..21...64D 10.1007/s00159-013-0064-5 A&ARv ... 21 2013
2013A&ARv..21...67B 10.1007/s00159-013-0067-2 A&ARv ... 21 2013
2013A&ARv..21...68G 10.1007/s00159-013-0068-1 A&ARv ... 21 2013
2013A&ARv..21...69R 10.1007/s00159-013-0069-0 A&ARv ... 21 2013
2013A&ARv..21...70B 10.1007/s00159-013-0070-7 A&ARv ... 21 2013
or to look for articles published between 2010 and 2012 with a given keyword:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad()
>>> simbad.ROW_LIMIT = 5
>>> simbad.query_bibcode('20??A&A.*', wildcard=True,
... criteria=("\"year\" >= 2010 and \"year\" <= 2012"
... " and \"abstract\" like '%exoplanet%'"))
<Table length=5>
bibcode doi journal ... volume year
object object object ... int32 int16
------------------- --------------------------- ------- ... ------ -----
2010A&A...509A..31G 10.1051/0004-6361/200912902 A&A ... 509 2010
2010A&A...510A..21S 10.1051/0004-6361/200913675 A&A ... 510 2010
2010A&A...510A.107M 10.1051/0004-6361/200912910 A&A ... 510 2010
2010A&A...511A..36C 10.1051/0004-6361/200913629 A&A ... 511 2010
2010A&A...511L...1M 10.1051/0004-6361/201014139 A&A ... 511 2010
As you can see, some wildcards can be replaced by a criteria (ex: we could also
write: "journal" = 'A&A' in the criteria string). It is often faster to avoid
wildcards and use a criteria instead.
These queries can be used to retrieve all the objects that are discussed in the article specified by a bibcode:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> Simbad.query_bibobj('2006AJ....131.1163S')
<Table length=8>
main_id ra ... bibcode obj_freq
deg ...
object float64 ... object int16
----------------------- ------------------ ... ------------------- --------
NAME Lockman Hole 161.25 ... 2006AJ....131.1163S --
Cl Melotte 22 56.60099999999999 ... 2006AJ....131.1163S --
M 32 10.67427 ... 2006AJ....131.1163S --
M 31 10.684708333333333 ... 2006AJ....131.1163S --
NAME Galactic Center 266.41500889 ... 2006AJ....131.1163S --
NAME LMC 80.89416666666666 ... 2006AJ....131.1163S --
NAME SMC 13.158333333333333 ... 2006AJ....131.1163S --
2MASX J04504846-7531580 72.701925 ... 2006AJ....131.1163S --
This section describe how the default output for the SIMBAD queries can be changed.
To fetch all the rows in the result, the row limit must be set to -1. This is the default behavior. However if you're only interested in a certain number of objects, or if the result would be too large, you can change this behavior. If you want to do this only for the current python session then:
>>> from astroquery.simbad import Simbad
>>> Simbad.ROW_LIMIT = 15 # now any query except query_tap fetches at most 15 rowsIf you would like to make your choice persistent, then you can do this by modifying the setting in the Astroquery configuration file.
Note
This works with every query_*** method, except
~astroquery.simbad.SimbadClass.query_tap as the number of returned rows is fixed
in the ADQL string with the TOP instruction.
Some query methods outputs can be customized. This is the case for:
- ~astroquery.simbad.SimbadClass.query_object
- ~astroquery.simbad.SimbadClass.query_objects
- ~astroquery.simbad.SimbadClass.query_region
- ~astroquery.simbad.SimbadClass.query_catalog
- ~astroquery.simbad.SimbadClass.query_hierarchy
- ~astroquery.simbad.SimbadClass.query_bibobj
For these methods, the default columns in the output are:
- main_id
- ra
- dec
- coo_err_maj
- coo_err_min
- coo_err_angle
- coo_wavelength
- coo_bibcode
Note
The columns that will appear in the output can be printed with the ~astroquery.simbad.SimbadClass.get_votable_fields method
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad()
>>> simbad.get_votable_fields()
['basic.main_id', 'basic.ra', 'basic.dec', 'basic.coo_err_maj', 'basic.coo_err_min', 'basic.coo_err_angle', 'basic.coo_wavelength', 'basic.coo_bibcode']Here we see the lists of columns that are selected per default. They are all from
the table of basic information (basic).
This can be permanently changed in astroquery's configuration files. To do this within a session or for a single query, use ~astroquery.simbad.SimbadClass.add_votable_fields:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad()
>>> simbad.add_votable_fields("otype") # here we add a single column about the main object type
Some options add a single column and others add a bunch of columns that are relevant for a theme (ex: fluxes, proper motions...). The list of possible options is printed with:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> Simbad.list_votable_fields()[["name", "description"]]
<Table length=...>
name description
object object
----------- ---------------------------------------------------------
mesDiameter Collection of stellar diameters.
mesPM Collection of proper motions.
mesISO Infrared Space Observatory (ISO) observing log.
mesSpT Collection of spectral types.
allfluxes all flux/magnitudes U,B,V,I,J,H,K,u_,g_,r_,i_,z_
ident Identifiers of an astronomical object
flux Magnitude/Flux information about an astronomical object
mesOtype Other object types associated with an object with origins
mesPLX Collection of trigonometric parallaxes.
... ...
K Magnitude K
u Magnitude SDSS u
g Magnitude SDSS g
r Magnitude SDSS r
i Magnitude SDSS i
z Magnitude SDSS z
G Magnitude Gaia G
F150W JWST NIRCam F150W
F200W JWST NIRCam F200W
F444W JWST NIRCan F444W
You can also access a single field description with ~astroquery.simbad.SimbadClass.get_field_description
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> Simbad.get_field_description("rvz_type")
'Radial velocity / redshift type'
And the columns in the output can be reset to their default value with ~astroquery.simbad.SimbadClass.reset_votable_fields.
Note
A detailed description on the ways to add fluxes is available in the :ref:`optical filters` section.
Some field names start with mes. These denote what SIMBAD calls a
"measurement table". These tables store the history on past measurements of a physical
parameter for each object.
Let's look at the star HD 200207 with the parallax measurements table mesplx:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad()
>>> simbad.add_votable_fields("mesplx")
>>> hd_200207 = simbad.query_object("HD 200207")
>>> hd_200207[["main_id", "mesplx.plx", "mesplx.plx_err", "mesplx.bibcode"]]
<Table length=5>
main_id mesplx.plx mesplx.plx_err mesplx.bibcode
mas mas
object float32 float32 object
--------- ---------- -------------- -------------------
HD 200207 3.4084 0.0195 2020yCat.1350....0G
HD 200207 3.4552 0.0426 2018yCat.1345....0G
HD 200207 3.35 0.76 1997A&A...323L..49P
HD 200207 3.72 0.62 2007A&A...474..653V
HD 200207 3.25 0.22 2016A&A...595A...2G
This field adds one line per parallax measurement: five articles have measured it for this star.
If you are only interested in the most precise measure recorded by the SIMBAD team, some measurements fields have an equivalent in the basic fields. These fields only give one line per object with the most precise currently known value:
| measurement field | basic field |
|---|---|
| mesplx | parallax |
| mespm | propermotions |
| messpt | sp |
| mesvelocities | velocity |
Here, mesplx has an equivalent in the basic fields so we could have done:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad()
>>> simbad.add_votable_fields("parallax")
>>> hd_200207 = simbad.query_object("HD 200207")
>>> hd_200207[["main_id", "plx_value", "plx_err", "plx_bibcode"]]
<Table length=1>
main_id plx_value plx_err plx_bibcode
mas mas
object float64 float32 object
--------- --------- ------- -------------------
HD 200207 3.4084 0.0195 2020yCat.1350....0G
And we only have one line per object with the value selected by SIMBAD's team.
Thus, choosing to add a measurement field or a basic field depends on your goal.
Warning
Before astroquery v0.4.8, criteria could only be used with the method query_criteria.
This method is now deprecated and is replaced by the criteria argument in every
other methods. See :ref:`SIMBAD evolutions <simbad-evolutions>`.
Most query methods take a criteria argument. They are listed here:
- ~astroquery.simbad.SimbadClass.query_object
- ~astroquery.simbad.SimbadClass.query_objects
- ~astroquery.simbad.SimbadClass.query_region
- ~astroquery.simbad.SimbadClass.query_catalog
- ~astroquery.simbad.SimbadClass.query_hierarchy
- ~astroquery.simbad.SimbadClass.query_bibobj
- ~astroquery.simbad.SimbadClass.query_bibcode
- ~astroquery.simbad.SimbadClass.query_objectids
The criteria argument expect a string written in the syntax of the WHERE clause of
an ADQL query. Some examples can be found in the
Simbad ADQL cheat sheet.
To help writing criteria, a good tip is to inspect the columns that the query would return by querying a blank table (of zero rows). This allows to inspect the columns the method would return:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad()
>>> simbad.ROW_LIMIT = 0 # get no lines, just the table structure
>>> # add the table about proper motion measurements, and the object type column
>>> simbad.add_votable_fields("mesPM", "otype")
>>> peek = simbad.query_object("BD+30 2512") # a query on an object
>>> peek.info
<Table length=0>
name dtype unit description
------------------- ------- -------- ----------------------------------------------------------------------
main_id object Main identifier for an object
ra float64 deg Right ascension
dec float64 deg Declination
coo_err_maj float32 mas Coordinate error major axis
coo_err_min float32 mas Coordinate error minor axis
coo_err_angle int16 deg Coordinate error angle
coo_wavelength str1 Wavelength class for the origin of the coordinates (R,I,V,U,X,G)
coo_bibcode object Coordinate reference
otype object Object type
mespm.bibcode object measurement bibcode
mespm.coosystem object coordinates system designation
mespm.mespos int16 Position of a measurement in a list of measurements
mespm.pmde float32 mas / yr Proper motion DEC.
mespm.pmde_err float32 mas / yr sigma{pm-de}
mespm.pmde_err_prec int16 Precision (# of decimal positions) associated with the column pmde_err
mespm.pmde_prec int16 Precision (# of decimal positions) associated with the column pmde
mespm.pmra float32 mas / yr Proper motion R.A.
mespm.pmra_err float32 mas / yr sigma{pm-ra}
mespm.pmra_err_prec int16 Precision (# of decimal positions) associated with the column pmra_err
mespm.pmra_prec int16 Precision (# of decimal positions) associated with the column pmra
matched_id object Identifier
Now that we know which columns will be returned by the query, we can edit the number of returned rows and add a criteria.
For example, to get only proper motion measurements more recent than 2000, we can add a
constraint on the first character of the mespm.bibcode column
(the first 4 digits of a bibcode are the year of publication of the article):
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> criteria = "mespm.bibcode LIKE '2%'" # starts with 2, anything after
>>> simbad = Simbad()
>>> simbad.add_votable_fields("mesPM", "otype")
>>> pm_measurements = simbad.query_object("BD+30 2512", criteria=criteria)
>>> pm_measurements[["main_id", "mespm.pmra", "mespm.pmde", "mespm.bibcode"]]
<Table length=7>
main_id mespm.pmra mespm.pmde mespm.bibcode
mas / yr mas / yr
object float32 float32 object
----------- ---------- ---------- -------------------
BD+30 2512 -631.6 -289.5 2016ApJ...817..112S
BD+30 2512 -631.662 -308.469 2020yCat.1350....0G
BD+30 2512 -631.6 -289.5 2016ApJS..224...36K
BD+30 2512 -631.625 -308.495 2018yCat.1345....0G
BD+30 2512 -631.36 -306.88 2007A&A...474..653V
BD+30 2512 -631.0 -307.0 2005AJ....129.1483L
BD+30 2512 -630.0 -306.0 2002ApJS..141..187B
It can be useful to execute longer queries in asynchronous mode by setting the
async_job argument to True. This may take longer to start, depending on the
current number of other people using the asynchronous SIMBAD queue, but it is more
robust against transient errors. Asynchronous queries will take the timeout property
in account:
.. doctest-remote-data::
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad(timeout=2000) # in seconds
>>> simbad.query_tap("select otype, description from otypedef where otype = 'N*'",
... async_job=True)
<Table length=1>
otype description
object object
------ ------------
N* Neutron Star
If you are repeatedly getting failed queries, or bad/out-of-date results, try clearing your cache:
>>> from astroquery.simbad import Simbad
>>> Simbad.clear_cache()If this function is unavailable, upgrade your version of astroquery.
The clear_cache function was introduced in version 0.4.7.dev8479.
To declare who you are to the SIMBAD's servers, add an extra user agent to your SIMBAD instance:
>>> from astroquery.simbad import Simbad
>>> simbad = Simbad(extra_user_agents="you")If SIMBAD was useful for your research, you can read its acknowledgement page.
.. automodapi:: astroquery.simbad
:no-inheritance-diagram: