VizieR Queries (astroquery.vizier)¶
Getting started¶
This is a python interface for querying the VizieR web service. This supports querying an object as well as querying a region around the target. For region queries, the region dimensions may be specified either for a box or as a radius. Similar to the VizieR web interface, the queries may be further constrained by specifying a choice of catalogs, keywords as well as filters on individual columns before retrieving the results.
Table Discover¶
If you want to search for a set of tables, e.g. based on author name or other keywords,
the find_catalogs() tool can be used:
>>> from astroquery.vizier import Vizier
>>> catalog_list = Vizier.find_catalogs('hot jupiter exoplanet transit')
>>> for k, v in catalog_list.items():
... print(k, ":", v.description)
J/A+A/635/A205 : Ultra-hot Jupiter WASP-121b transits (Bourrier+, 2020)
J/ApJ/788/39 : Hot Jupiter exoplanets host stars EW and abundances (Teske+, 2014)
J/AJ/157/217 : Transit times of five hot Jupiter WASP exoplanets (Bouma+, 2019)
J/A+A/635/A122 : CoRoT-30b and CoRoT-31b radial velocity curves (Border+, 2020)
From this result, you could either get any of these as a complete catalog or query them for individual objects or regions.
Get a whole catalog¶
If you know the name of the catalog you wish to retrieve, e.g. from doing a
find_catalogs() search as above, you can then grab
the complete contents of those catalogs:
>>> catalogs = Vizier.get_catalogs(catalog_list.keys())
>>> print(catalogs)
TableList with 10 tables:
'0:J/A+A/635/A205/20140119' with 7 column(s) and 50 row(s)
'1:J/A+A/635/A205/20140123' with 7 column(s) and 50 row(s)
'2:J/A+A/635/A205/20171231' with 7 column(s) and 50 row(s)
'3:J/A+A/635/A205/20180114' with 7 column(s) and 50 row(s)
'4:J/A+A/635/A205/ccf-mask' with 3 column(s) and 50 row(s)
'5:J/ApJ/788/39/stars' with 7 column(s) and 17 row(s)
'6:J/ApJ/788/39/table4' with 48 column(s) and 50 row(s)
'7:J/AJ/157/217/transits' with 8 column(s) and 50 row(s)
'8:J/A+A/635/A122/table2' with 4 column(s) and 18 row(s)
'9:J/A+A/635/A122/table3' with 4 column(s) and 17 row(s)
Similarly, the Resource objects (the values of the dictionary resulting from
find_catalogs()) can be used in the same
way:
>>> catalogs = Vizier.get_catalogs(catalog_list.values())
>>> print(catalogs)
TableList with 10 tables:
'0:J/A+A/635/A205/20140119' with 7 column(s) and 50 row(s)
'1:J/A+A/635/A205/20140123' with 7 column(s) and 50 row(s)
'2:J/A+A/635/A205/20171231' with 7 column(s) and 50 row(s)
'3:J/A+A/635/A205/20180114' with 7 column(s) and 50 row(s)
'4:J/A+A/635/A205/ccf-mask' with 3 column(s) and 50 row(s)
'5:J/ApJ/788/39/stars' with 7 column(s) and 17 row(s)
'6:J/ApJ/788/39/table4' with 48 column(s) and 50 row(s)
'7:J/AJ/157/217/transits' with 8 column(s) and 50 row(s)
'8:J/A+A/635/A122/table2' with 4 column(s) and 18 row(s)
'9:J/A+A/635/A122/table3' with 4 column(s) and 17 row(s)
>>> catalogs = Vizier.get_catalogs(catalog_list.keys())
>>> print(catalogs)
TableList with 10 tables:
'0:J/A+A/635/A205/20140119' with 7 column(s) and 50 row(s)
'1:J/A+A/635/A205/20140123' with 7 column(s) and 50 row(s)
'2:J/A+A/635/A205/20171231' with 7 column(s) and 50 row(s)
'3:J/A+A/635/A205/20180114' with 7 column(s) and 50 row(s)
'4:J/A+A/635/A205/ccf-mask' with 3 column(s) and 50 row(s)
'5:J/ApJ/788/39/stars' with 7 column(s) and 17 row(s)
'6:J/ApJ/788/39/table4' with 48 column(s) and 50 row(s)
'7:J/AJ/157/217/transits' with 8 column(s) and 50 row(s)
'8:J/A+A/635/A122/table2' with 4 column(s) and 18 row(s)
'9:J/A+A/635/A122/table3' with 4 column(s) and 17 row(s)
Note that the row limit is set to 50 by default, so if you want to get a truly complete catalog, you need to change that:
>>> Vizier.ROW_LIMIT = -1
>>> catalogs = Vizier.get_catalogs(catalog_list.keys())
>>> print(catalogs)
TableList with 10 tables:
'0:J/A+A/635/A205/20140119' with 7 column(s) and 235 row(s)
'1:J/A+A/635/A205/20140123' with 7 column(s) and 195 row(s)
'2:J/A+A/635/A205/20171231' with 7 column(s) and 248 row(s)
'3:J/A+A/635/A205/20180114' with 7 column(s) and 307 row(s)
'4:J/A+A/635/A205/ccf-mask' with 3 column(s) and 1828 row(s)
'5:J/ApJ/788/39/stars' with 7 column(s) and 17 row(s)
'6:J/ApJ/788/39/table4' with 48 column(s) and 106 row(s)
'7:J/AJ/157/217/transits' with 8 column(s) and 236 row(s)
'8:J/A+A/635/A122/table2' with 4 column(s) and 18 row(s)
'9:J/A+A/635/A122/table3' with 4 column(s) and 17 row(s)
>>> Vizier.ROW_LIMIT = 50
Get a catalog’s associated metadata¶
The method get_catalog_metadata retrieves information
about VizieR’s catalogs. It returns a table with the following columns:
title
authors
abstract
origin_article – the bibcode of the associated article
webpage – a link to VizieR, contains more information about the catalog
created – date of creation of the catalog in VizieR
updated – date of the last modification applied to the entry, this is often about metadata, with no appearance in the history on the webpage but sometimes it is about a data erratum, which will appear in the history tab
Note
This value can be extra useful to check if you need to download some catalog again from VizieR or if you can work safely with a result you saved previously on disk. Also note that the VizieR team actively maintains the catalogs but if a published erratum was missed we’d gladly receive a notification from you!
waveband
doi – the catalog doi when it exists
>>> from astroquery.vizier import Vizier
>>> Vizier(catalog="VII/74A").get_catalog_metadata()
<Table length=1>
title authors ... waveband doi
object object ... object object
-------------------------- -------- ... -------- ------
Atlas of Peculiar Galaxies Arp H.C. ... optical --
Query an object¶
For instance to query Sirius across all catalogs:
>>> from astroquery.vizier import Vizier
>>> vizier = Vizier()
>>> result = vizier.query_object("sirius")
>>> print(result)
TableList with 416 tables:
'0:METAobj' with 5 column(s) and 7 row(s)
'1:ReadMeObj' with 5 column(s) and 7 row(s)
'2:I/34/greenw2a' with 16 column(s) and 1 row(s)
...
All the results are returned as a TableList object. This
is a container for Table objects. It is basically an
extension to OrderedDict for storing a Table
against its name.
To access an individual table from the TableList object:
>>> interesting_table = result['IX/8/catalog']
>>> print(interesting_table)
_2XRS RAB1950 DEB1950 Xname ... Int _RA.icrs _DE.icrs
... uJy
--------- ------------ ------------ ----- ... --- ------------ ------------
06429-166 06 42 54.000 -16 39 00.00 ... -- 06 45 08.088 -16 42 11.29
06429-166 06 42 54.000 -16 39 00.00 ... -- 06 45 08.088 -16 42 11.29
To do some common processing to all the tables in the returned
TableList object, do just what you would do for a python
dictionary:
>>> for table_name in result.keys():
... table = result[table_name]
... # table is now an `astropy.table.Table` object
... # some code to apply on table
Query a region¶
To query a region either the coordinates or the object name around which to query should be specified along with the value for the radius (or height/width for a box) of the region. For instance to query a large region around the quasar 3C 273:
>>> from astroquery.vizier import Vizier
>>> from astropy.coordinates import Angle
>>> result = Vizier.query_region("3C 273", radius=Angle(0.1, "deg"), catalog='GSC')
Note that the radius may also be specified as a string in the format
expected by Angle. So the above query may also
be written as:
>>> result = Vizier.query_region("3C 273", radius="0d6m0s", catalog='GSC')
Or using angular units and quantities from astropy.units:
>>> import astropy.units as u
>>> result = Vizier.query_region("3C 273", radius=0.1*u.deg, catalog='GSC')
To see the result:
>>> print(result)
TableList with 5 tables:
'0:I/254/out' with 10 column(s) and 17 row(s)
'1:I/255/out' with 9 column(s) and 17 row(s)
'2:I/271/out' with 11 column(s) and 50 row(s)
'3:I/305/out' with 11 column(s) and 50 row(s)
'4:I/353/gsc242' with 35 column(s) and 50 row(s)
As mentioned earlier, the region may also be mentioned by specifying the height and width of a box. If only one of the height or width is mentioned, then the region is treated to be a square having sides equal to the specified dimension.
>>> from astroquery.vizier import Vizier
>>> import astropy.units as u
>>> import astropy.coordinates as coord
>>> result = Vizier.query_region(coord.SkyCoord(ra=299.590, dec=35.201,
... unit=(u.deg, u.deg),
... frame='icrs'),
... width="30m",
... catalog=["NOMAD", "UCAC"])
>>> print(result)
TableList with 4 tables:
'0:I/297/out' with 19 column(s) and 50 row(s)
'1:I/289/out' with 13 column(s) and 50 row(s)
'2:I/322A/out' with 24 column(s) and 50 row(s)
'3:I/340/ucac5' with 20 column(s) and 50 row(s)
One more thing to note in the above example is that the coordinates may be
specified by using the appropriate coordinate object from
astropy.coordinates. Especially for ICRS coordinates, some support
also exists for directly passing a properly formatted string as the
coordinate. Finally the catalog keyword argument may be passed in either
query_object() or
query_region() methods. This may be a string
(if only a single catalog) or a list of strings otherwise.
Last but not least, query_region() also supports
constraints on the columns of the returned tables by mean of the column_filters keyword.
>>> from astroquery.vizier import Vizier
>>> import astropy.units as u
>>> from astropy.coordinates import SkyCoord
>>> result = Vizier.query_region(SkyCoord.from_name('M81'),
... radius=10*u.arcmin,
... catalog='I/345/gaia2',
... column_filters={'Gmag': '<19'})
>>> print(result[0]['Gmag'].max())
18.9508
Specifying keywords, output columns and constraints on columns¶
To specify keywords on which to search as well as conditions on the output
columns, an instance of the VizierClass class specifying these must be first
created. All further queries may then be performed on this instance rather than
on the Vizier class.
>>> v = Vizier(columns=['_RAJ2000', '_DEJ2000','B-V', 'Vmag', 'Plx'],
... column_filters={"Vmag":">10"}, keywords=["optical", "xry"])
Note that whenever an unknown keyword is specified, a warning is emitted and that keyword is discarded from further consideration. The behavior for searching with these keywords is the same as defined for the web interface (for details see here). Now we call the different query methods on this Vizier instance:
>>> v = Vizier(columns=['_RAJ2000', '_DEJ2000','B-V', 'Vmag', 'Plx'],
... column_filters={"Vmag":">10"}, keywords=["optical", "xry"])
>>> result = v.query_object("HD 226868", catalog=["NOMAD", "UCAC"])
>>> print(result)
TableList with 4 tables:
'0:I/297/out' with 3 column(s) and 50 row(s)
'1:I/289/out' with 2 column(s) and 18 row(s)
'2:I/322A/out' with 3 column(s) and 10 row(s)
'3:I/340/ucac5' with 2 column(s) and 26 row(s)
>>> print(result['I/322A/out'])
_RAJ2000 _DEJ2000 Vmag
deg deg mag
------------- ------------- ------
299.572418900 35.194234200 15.986
299.580291200 35.176888900 13.274
299.582571200 35.185225300 14.863
299.594171800 35.179994800 14.690
299.601402100 35.198107800 14.644
299.617668600 35.186998700 14.394
299.561497700 35.201692800 15.687
299.570216500 35.225663400 14.878
299.601080600 35.233337800 13.170
299.617995000 35.205863700 13.946
When specifying the columns of the query, sorting of the returned table can be
requested by adding + (or - for reverse sorting order) in front of the column
name. In the following example, the standard ("*") columns and the calculated
distance column ("_r") of the 2MASS catalog (II/246) are queried, 20 arcsec
around HD 226868. The result is sorted in increasing distance, as requested with
the "+" in front of "_r".
>>> v = Vizier(columns=["*", "+_r"], catalog="II/246")
>>> result = v.query_region("HD 226868", radius="20s")
>>> print(result[0])
_r RAJ2000 DEJ2000 _2MASS Jmag ... Bflg Cflg Xflg Aflg
deg deg mag ...
------ ---------- ---------- ---------------- ------ ... ---- ---- ---- ----
0.134 299.590280 35.201599 19582166+3512057 6.872 ... 111 000 0 0
10.135 299.587491 35.203217 19582099+3512115 10.285 ... 111 c00 0 0
11.167 299.588599 35.198849 19582126+3511558 13.111 ... 002 00c 0 0
12.288 299.586356 35.200542 19582072+3512019 14.553 ... 111 ccc 0 0
17.691 299.586254 35.197994 19582070+3511527 16.413 ... 100 c00 0 0
Note: The special column "*" requests just the default columns of a
catalog; "**" would request all the columns.
Query with table¶
A Table can also be used to specify the coordinates in a
region query if it contains the columns _RAJ2000 and _DEJ2000. The
following example starts by looking for AGNs in the Veron & Cety catalog with a
Vmag between 10.0 and 11.0. Based on the result of this first query, guide
stars with a Kmag brighter than 9.0 are looked for, with a separation
between 2 and 30 arcsec. The column _q in the guide table is a 1-based
index to the agn table (not the 0-based python convention).
>>> agn = Vizier(catalog="VII/258/vv10",
... columns=['*', '_RAJ2000', '_DEJ2000']).query_constraints(Vmag="10.0..11.0")[0]
>>> print(agn)
_RAJ2000 _DEJ2000 Cl nR Name ... Sp n_Vmag Vmag B-V r_z
deg deg ... mag mag
---------- ---------- --- --- ------------- ... ---- ------ ----- ----- ----
10.684583 41.269444 Q M 31 ... S2 10.57 1.08 1936
60.277917 -16.110833 Q NPM1G-16.0168 ... R 10.16 -- 988
27.238750 5.906667 A * NGC 676 ... S2 10.50 -- 1034
40.669583 -0.013056 A NGC 1068 ... S1h 10.83 0.87 58
139.759583 26.269722 A NGC 2824 ... S? 10.88 -- 2528
147.592083 72.279167 A NGC 2985 ... S1.9 10.61 0.76 1033
173.144167 53.067778 A NGC 3718 ... S3b 10.61 0.74 1033
184.960833 29.613889 A UGC 7377 ... S3 10.47 0.99 2500
185.028750 29.280833 A NGC 4278 ... S3b 10.87 0.98 1033
186.453750 33.546667 A NGC 4395 ... S1.8 10.27 0.53 1033
192.719583 41.119444 A NGC 4736 ... S 10.85 0.85 1032
208.361250 40.283056 A NGC 5353 ... S? R 10.91 -- 368
>>> guide = Vizier(catalog="II/246", column_filters={"Kmag":"<9.0"}).query_region(agn, radius="30s", inner_radius="2s")[0]
>>> guide.pprint()
_q RAJ2000 DEJ2000 _2MASS Jmag ... Rflg Bflg Cflg Xflg Aflg
deg deg mag ...
--- ---------- ---------- ---------------- ------ ... ---- ---- ---- ---- ----
1 10.686015 41.269630 00424464+4116106 9.399 ... 020 020 0c0 2 0
1 10.685657 41.269550 00424455+4116103 10.773 ... 200 200 c00 2 0
1 10.685837 41.270599 00424460+4116141 9.880 ... 020 020 0c0 2 0
1 10.683263 41.267456 00424398+4116028 12.136 ... 200 100 c00 2 0
1 10.683465 41.269676 00424403+4116108 11.507 ... 200 100 c00 2 0
3 27.238636 5.906066 01485727+0554218 8.961 ... 112 111 000 0 0
4 40.669277 -0.014225 02424062-0000512 11.795 ... 200 100 c00 2 0
4 40.668802 -0.013064 02424051-0000470 11.849 ... 200 100 c00 2 0
4 40.669219 -0.012236 02424061-0000440 12.276 ... 200 100 c00 2 0
4 40.670761 -0.012208 02424098-0000439 12.119 ... 200 100 c00 2 0
4 40.670177 -0.012830 02424084-0000461 11.381 ... 200 100 c00 2 0
11 192.721982 41.121040 12505327+4107157 10.822 ... 200 100 c00 2 0
11 192.721179 41.120201 12505308+4107127 9.306 ... 222 111 000 2 0
Troubleshooting¶
If you are repeatedly getting failed queries, or bad/out-of-date results, try clearing your cache:
>>> from astroquery.vizier import Vizier
>>> Vizier.clear_cache()
If this function is unavailable, upgrade your version of astroquery.
The clear_cache function was introduced in version 0.4.7.dev8479.
Reference/API¶
astroquery.vizier Package¶
VizieR Query Tool¶
This package is for querying the VizieR service, primarily hosted at: https://vizier.cds.unistra.fr
Note: If the access to catalogues with VizieR was helpful for your research work, the following acknowledgment would be appreciated:
This research has made use of the VizieR catalogue access tool, CDS,
Strasbourg, France. The original description of the VizieR service was
published in A&AS 143, 23
Classes¶
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Configuration parameters for |