IRSA Queries (astroquery.ipac.irsa)¶
Getting started¶
This module can has methods to perform different types of queries on the
catalogs present in the IRSA general catalog service. All queries can be
performed by calling query_region(), with
different keyword arguments. There are 4 different region queries that are
supported: Cone, Box, Polygon and All-Sky. All successful
queries return the results in a Table. We now look at some
examples.
Available catalogs¶
All region queries require a catalog keyword argument, which is the name of
the catalog in the IRSA database, on which the query must be performed. To take
a look at all the available catalogs:
>>> from astroquery.ipac.irsa import Irsa
>>> Irsa.list_catalogs()
{'a1763t2': 'Abell 1763 Source Catalog',
'a1763t3': 'Abell 1763 MIPS 70 micron Catalog',
'acs_iphot_sep07': 'COSMOS ACS I-band photometry catalog September 2007',
'akari_fis': 'Akari/FIS Bright Source Catalogue',
'akari_irc': 'Akari/IRC Point Source Catalogue',
'astsight': 'IRAS Minor Planet Survey',
...
...
'xmm_cat_s05': "SWIRE XMM_LSS Region Spring '05 Spitzer Catalog"}
To access the full VOTable of the catalog information, use the full keyword argument.
>>> from astroquery.ipac.irsa import Irsa
>>> Irsa.list_catalogs(full=True)
<DALResultsTable length=934>
table_index schema_name table_name description ... irsa_access_flag irsa_nrows irsa_odbc_datasource irsa_spatial_idx_name
int32 object object object ... int32 int64 object object
----------- ----------- ---------------------------------- --------------------------------------------- ... ---------------- ---------- -------------------- ---------------------
303 spitzer spitzer.m31irac_image M31IRAC Images ... 30 4 postgres
304 spitzer mipslg MIPS Local Galaxies Catalog ... 30 240 spitzer SPT_IND_MIPSLG
305 spitzer spitzer.mips_lg_images MIPS Local Galaxies Images ... 30 606 postgres
...
Performing a cone search¶
A cone search query is performed by setting the spatial keyword to
Cone. The target name or the coordinates of the search center must also be
specified. The radius for the cone search may also be specified - if this is
missing, it defaults to a value of 10 arcsec. The radius may be specified in
any appropriate unit using a Quantity object. It may also be
entered as a string that is parsable by Angle.
>>> from astroquery.ipac.irsa import Irsa
>>> import astropy.units as u
>>> table = Irsa.query_region("m31", catalog="fp_psc", spatial="Cone",
... radius=2 * u.arcmin)
>>> print(table)
ra dec err_maj err_min ... coadd_key coadd htm20
deg deg arcsec arcsec ...
---------- ---------- ------- ------- ... --------- ----- -------------------
10.692216 41.260162 0.10 0.09 ... 1590591 33 4805203678124326400
10.700059 41.263481 0.31 0.30 ... 1590591 33 4805203678125364736
10.699131 41.263248 0.28 0.20 ... 1590591 33 4805203678125474304
... ... ... ... ... ... ... ...
10.661414 41.242363 0.21 0.20 ... 1590591 33 4805203679644192256
10.665184 41.240238 0.14 0.13 ... 1590591 33 4805203679647824896
10.663245 41.240646 0.24 0.21 ... 1590591 33 4805203679649555456
Length = 774 rows
The coordinates of the center may be specified rather than using the target
name. The coordinates can be specified using a SkyCoord
object or a string resolvable by the SkyCoord constructor.
>>> from astroquery.ipac.irsa import Irsa
>>> from astropy.coordinates import SkyCoord
>>> coord = SkyCoord(121.1743, -21.5733, unit='deg', frame='galactic')
>>> table = Irsa.query_region(coordinates=coord,
... catalog='fp_psc', radius='0d2m0s')
>>> print(table)
ra dec err_maj err_min ... coadd_key coadd htm20
deg deg arcsec arcsec ...
---------- ---------- ------- ------- ... --------- ----- -------------------
10.692216 41.260162 0.10 0.09 ... 1590591 33 4805203678124326400
10.700059 41.263481 0.31 0.30 ... 1590591 33 4805203678125364736
10.699131 41.263248 0.28 0.20 ... 1590591 33 4805203678125474304
... ... ... ... ... ... ... ...
10.661414 41.242363 0.21 0.20 ... 1590591 33 4805203679644192256
10.665184 41.240238 0.14 0.13 ... 1590591 33 4805203679647824896
10.663245 41.240646 0.24 0.21 ... 1590591 33 4805203679649555456
Length = 774 rows
Queries over a polygon¶
Polygon queries can be performed by setting spatial='Polygon'. The search
center is optional in this case. One additional parameter that must be set for
these queries is polygon. This is a list of coordinate pairs that define a
convex polygon. The coordinates may be specified as usual by using the
appropriate SkyCoord object. In addition to using a list of
SkyCoord objects, one additional convenient means of specifying
the coordinates is also available - Coordinates may also be entered as a list of
tuples, each tuple containing the ra and dec values in degrees. Each of these
options is illustrated below:
>>> from astroquery.ipac.irsa import Irsa
>>> from astropy import coordinates
>>> table = Irsa.query_region("m31", catalog="fp_psc", spatial="Polygon",
... polygon=[coordinates.SkyCoord(ra=10.1, dec=10.1, unit=(u.deg, u.deg), frame='icrs'),
... coordinates.SkyCoord(ra=10.0, dec=10.1, unit=(u.deg, u.deg), frame='icrs'),
... coordinates.SkyCoord(ra=10.0, dec=10.0, unit=(u.deg, u.deg), frame='icrs')
... ])
>>> print(table)
ra dec err_maj err_min ... coadd_key coadd htm20
deg deg arcsec arcsec ...
---------- ---------- ------- ------- ... --------- ----- -------------------
10.015839 10.038061 0.09 0.06 ... 1443005 91 4805087709670704640
10.015696 10.099228 0.10 0.07 ... 1443005 91 4805087709940635648
10.011170 10.093903 0.23 0.21 ... 1443005 91 4805087710032524288
10.031016 10.063082 0.19 0.18 ... 1443005 91 4805087710169327616
10.036776 10.060278 0.11 0.06 ... 1443005 91 4805087710175392768
10.059964 10.085445 0.23 0.20 ... 1443005 91 4805087710674674176
10.005549 10.018401 0.16 0.14 ... 1443005 91 4805087784811171840
Another way to specify the polygon is directly as a list of tuples - each tuple is an ra, dec pair expressed in degrees:
>>> from astroquery.ipac.irsa import Irsa
>>> table = Irsa.query_region("m31", catalog="fp_psc", spatial="Polygon",
... polygon = [(10.1, 10.1), (10.0, 10.1), (10.0, 10.0)])
>>> print(table)
ra dec err_maj err_min ... coadd_key coadd htm20
deg deg arcsec arcsec ...
---------- ---------- ------- ------- ... --------- ----- -------------------
10.015839 10.038061 0.09 0.06 ... 1443005 91 4805087709670704640
10.015696 10.099228 0.10 0.07 ... 1443005 91 4805087709940635648
10.011170 10.093903 0.23 0.21 ... 1443005 91 4805087710032524288
10.031016 10.063082 0.19 0.18 ... 1443005 91 4805087710169327616
10.036776 10.060278 0.11 0.06 ... 1443005 91 4805087710175392768
10.059964 10.085445 0.23 0.20 ... 1443005 91 4805087710674674176
10.005549 10.018401 0.16 0.14 ... 1443005 91 4805087784811171840
Selecting Columns¶
The IRSA service allows to query either a subset of the default columns for
a given table, or additional columns that are not present by default. This
can be done by listing all the required columns separated by a comma (,) in
a string with the columns argument.
An example where the AllWISE Source Catalog needs to be queried around the star HIP 12 with just the ra, dec and w1mpro columns would be:
>>> from astroquery.ipac.irsa import Irsa
>>> table = Irsa.query_region("HIP 12", catalog="allwise_p3as_psd", spatial="Cone", columns="ra,dec,w1mpro")
>>> print(table)
ra dec w1mpro
deg deg mag
----------- ----------- -------
0.0407905 -35.9602605 4.837
A list of available columns for each catalog can be found at https://irsa.ipac.caltech.edu/holdings/catalogs.html. The “Long Form” button at the top of the column names table must be clicked to access a full list of all available columns.
Direct TAP query to the IRSA server¶
The query_tap method allows for a rich variety of queries. ADQL queries
provided via the query parameter is sent directly to the IRSA TAP server, and the result is
returned as a TAPResults object. Its to_table or to_qtable method convert the result to a
Table or QTable object.
>>> from astroquery.ipac.irsa import Irsa
>>> query = ("SELECT TOP 10 ra,dec,j_m,j_msigcom,h_m,h_msigcom,k_m,k_msigcom,ph_qual,cc_flg "
... "FROM fp_psc WHERE CONTAINS(POINT('ICRS',ra, dec), CIRCLE('ICRS',202.48417,47.23056,0.4))=1")
>>> results = Irsa.query_tap(query=query).to_qtable()
>>> results
<QTable length=10>
ra dec j_m j_msigcom ... k_m k_msigcom ph_qual cc_flg
deg deg mag mag ... mag mag
float64 float64 float32 float32 ... float32 float32 object object
---------- ---------- ------- --------- ... ------- --------- ------- ------
202.900750 46.961285 16.168 0.096 ... 15.180 0.158 ABC 000
202.951614 47.024986 15.773 0.072 ... 15.541 0.234 ABD 000
202.922589 47.024452 14.628 0.032 ... 14.036 0.059 AAA 000
202.911833 47.011093 13.948 0.025 ... 13.318 0.036 AAA 000
202.925932 47.004223 16.461 0.131 ... 17.007 ——— BCU 000
202.515450 46.929302 15.967 0.088 ... 15.077 0.140 AAB 000
202.532240 46.931587 16.575 0.145 ... 15.888 ——— BDU 000
202.607930 46.932255 16.658 0.147 ... 15.430 0.193 BUC 000
202.823902 47.011593 16.555 0.143 ... 16.136 ——— BBU 000
202.809023 46.964558 15.874 0.081 ... 15.322 0.188 AAC 000
Simple image access queries¶
query_sia provides a way to access IRSA’s Simple
Image Access VO service. In the following example we are looking for Spitzer
Enhanced Imaging products in the centre of the COSMOS field as an Table.
>>> from astroquery.ipac.irsa import Irsa
>>> from astropy.coordinates import SkyCoord
>>> from astropy import units as u
>>>
>>> coord = SkyCoord('150.01d 2.2d', frame='icrs')
>>> spitzer_images = Irsa.query_sia(pos=(coord, 1 * u.arcmin), collection='spitzer_seip').to_table()
To list available collections for SIA queries, the
list_collections method is provided, and
will return a Table:
>>> from astroquery.ipac.irsa import Irsa
>>> Irsa.list_collections()
<Table length=110>
collection
object
---------------------
akari_allskymaps
blast
bolocam_gps
bolocam_lh
bolocam_planck_sz
...
wise_allsky
wise_allwise
wise_prelim
wise_prelim_2bandcryo
wise_unwise
wise_z0mgs
Now open a cutout image for one of the science images. You could either use
the the IRSA on-premise data or the cloud version of it using the
access_url or cloud_access columns. For more info about fits
cutouts, please visit Obtaining subsets from cloud-hosted FITS files.
>>> from astropy.io import fits
>>> from astropy.nddata import Cutout2D
>>> from astropy.wcs import WCS
>>> science_image = spitzer_images[spitzer_images['dataproduct_subtype'] == 'science'][0]
>>> with fits.open(science_image['access_url'], use_fsspec=True) as hdul:
... cutout = Cutout2D(hdul[0].section, position=coord, size=2 * u.arcmin, wcs=WCS(hdul[0].header))
Now plot the cutout.
>>> import matplotlib.pyplot as plt
>>> plt.imshow(cutout.data, cmap='grey')
>>> plt.show()
(Source code, png, hires.png, pdf)
Other Configurations¶
By default the maximum number of rows that is fetched is set to 500. However, this option may be changed by changing the astroquery configuration file. To change the setting only for the ongoing python session, you could also do:
>>> from astroquery.ipac.irsa import Irsa
>>> Irsa.ROW_LIMIT = 1000 # 1000 is the new value for row limit here.
Reference/API¶
astroquery.ipac.irsa Package¶
IRSA Query Tool¶
This module contains various methods for querying the IRSA Services.
Classes¶
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Configuration parameters for |