MAST Queries (astroquery.mast)¶
Getting Started¶
This module can be used to query the Barbara A. Mikulski Archive for Space Telescopes (MAST). Below are examples of the types of queries that can be used, and how to access data products.
Positional Queries¶
Positional queries can be based on a sky position or a target name. The observation fields are documented here.
>>> from astroquery.mast import Observations
>>> obs_table = Observations.query_region("322.49324 12.16683")
>>> print(obs_table[:10])
dataproduct_type obs_collection instrument_name ... distance
---------------- -------------- --------------- ... --------
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
cube SWIFT UVOT ... 0.0
Radius is an optional parameter and the default is 0.2 degrees.
>>> from astroquery.mast import Observations
>>> obs_table = Observations.query_object("M8",radius=".02 deg")
>>> print(obs_table[:10])
dataproduct_type obs_collection instrument_name ... distance
---------------- -------------- --------------- ... -------------
cube K2 Kepler ... 39.4914065162
spectrum IUE LWP ... 0.0
spectrum IUE LWP ... 0.0
spectrum IUE LWP ... 0.0
spectrum IUE LWR ... 0.0
spectrum IUE LWR ... 0.0
spectrum IUE LWR ... 0.0
spectrum IUE LWR ... 0.0
spectrum IUE LWR ... 0.0
spectrum IUE LWR ... 0.0
Observation Criteria Queries¶
To search for observations based on parameters other than position or target name,
use query_criteria.
Criteria are supplied as keyword arguments, where valid criteria are “coordinates”,
“objectname”, “radius” (as in query_region and
query_object), and all observation fields listed
here.
Note: The obstype keyword has been replaced by intentType, with valid values “calibration” and “science.” If the intentType keyword is not supplied, both science and calibration observations will be returned.
Argument values are one or more acceptable values for the criterion, except for fields with a float datatype where the argument should be in the form [minVal, maxVal]. For non-float type criteria, wildcards (both * and %) may be used. However, only one wildcarded value can be processed per criterion.
RA and Dec must be given in decimal degrees, and datetimes in MJD.
>>> from astroquery.mast import Observations
>>> obs_table = Observations.query_criteria(dataproduct_type=["image"],
... proposal_pi="Osten*",
... s_dec=[43.5,45.5])
>>> print(obs_table)
dataproduct_type calib_level obs_collection ... dataURL obsid objID
---------------- ----------- -------------- ... ------- ---------- ----------
image 1 HST ... None 2003520266 2011133418
image 1 HST ... None 2003520267 2011133419
image 1 HST ... None 2003520268 2011133420
>>> obs_table = Observations.query_criteria(filters=["*UV","Kepler"],objectname="M101")
>>> print(obs_table)
dataproduct_type calib_level obs_collection ... objID1 distance
---------------- ----------- -------------- ... ---------- -------------
image 2 GALEX ... 1000055044 0.0
image 2 GALEX ... 1000004937 3.83290685323
image 2 GALEX ... 1000045953 371.718371962
image 2 GALEX ... 1000055047 229.810616011
image 2 GALEX ... 1000016644 229.810616011
image 2 GALEX ... 1000045952 0.0
image 2 GALEX ... 1000048357 0.0
image 2 GALEX ... 1000001326 0.0
image 2 GALEX ... 1000001327 371.718371962
image 2 GALEX ... 1000004203 0.0
image 2 GALEX ... 1000016641 0.0
image 2 GALEX ... 1000048943 3.83290685323
Getting Observation Counts¶
To get the number of observations and not the observations themselves, query_counts functions are available. This can be useful if trying to decide whether the available memory is sufficient for the number of observations.
>>> from astroquery.mast import Observations
>>> print(Observations.query_region_count("322.49324 12.16683"))
1804
>>> print(Observations.query_object_count("M8",radius=".02 deg"))
196
>>> print(Observations.query_criteria_count(dataproduct_type="image",
... filters=["NUV","FUV"],
... t_max=[52264.4586,54452.8914]))
59033
Metadata Queries¶
To list data missions archived by MAST and avaiable through astroquery.mast, use the list_missions function.
>>> from astroquery.mast import Observations
>>> print(Observations.list_missions())
['IUE', 'Kepler', 'K2FFI', 'EUVE', 'HLA', 'KeplerFFI','FUSE',
'K2', 'HST', 'WUPPE', 'BEFS', 'GALEX', 'TUES','HUT', 'SWIFT']
To get a table of metadata associated with observation or product lists use the
get_metadata function.
>>> from astroquery.mast import Observations
>>> meta_table = Observations.get_metadata("observations")
>>> print(meta_table[:5])
Column Name Column Label ... Examples/Valid Values
----------------- ------------ ... ---------------------------------
obs_collection Mission ... E.g. SWIFT, PS1, HST, IUE
instrument_name Instrument ... E.g. WFPC2/WFC, UVOT, STIS/CCD
project Project ... E.g. HST, HLA, EUVE, hlsp_legus
filters Filters ... F469N, NUV, FUV, LOW DISP, MIRROR
wavelength_region Waveband ... EUV, XRAY, OPTICAL
>>> meta_table = Observations.get_metadata("products")
>>> print(meta_table[:3])
Column Name Column Label ... Examples/Valid Values
-------------- ---------------- ... -------------------------------------
obs_id Observation ID ... U24Z0101T, N4QF18030
obsID Product Group ID ... Long integer, e.g. 2007590987
obs_collection Mission ... HST, HLA, SWIFT, GALEX, Kepler, K2...
Downloading Data¶
Getting Product Lists¶
Each observation returned from a MAST query can have one or more associated data products.
Given one or more observations or observation ids (“obsid”)
get_product_list will return
a Table containing the associated data products.
The product fields are documented here.
>>> from astroquery.mast import Observations
>>> obs_table = Observations.query_object("M8",radius=".02 deg")
>>> data_products_by_obs = Observations.get_product_list(obs_table[0:2])
>>> print(data_products_by_obs)
obsID obs_collection ... productFilename size
---------- -------------- ... ---------------------------------- --------
3000007760 IUE ... lwp13058.elbll.gz 185727
3000007760 IUE ... lwp13058.elbls.gz 183350
3000007760 IUE ... lwp13058.lilo.gz 612715
3000007760 IUE ... lwp13058.melol.gz 12416
3000007760 IUE ... lwp13058.melos.gz 12064
3000007760 IUE ... lwp13058.raw.gz 410846
3000007760 IUE ... lwp13058.rilo.gz 416435
3000007760 IUE ... lwp13058.silo.gz 100682
3000007760 IUE ... lwp13058.gif 8971
3000007760 IUE ... lwp13058.mxlo.gz 18206
3000007760 IUE ... lwp13058mxlo_vo.fits 48960
3000007760 IUE ... lwp13058.gif 3967
9500243833 K2 ... k2-tpf-only-target_bw_large.png 9009
9500243833 K2 ... ktwo200071160-c91_lpd-targ.fits.gz 39930404
9500243833 K2 ... ktwo200071160-c92_lpd-targ.fits.gz 62213068
9500243833 K2 ... k2-tpf-only-target_bw_thumb.png 1301
>>> obsids = obs_table[0:2]['obsid']
>>> data_products_by_id = Observations.get_product_list(obsids)
>>> print(data_products_by_id)
obsID obs_collection ... productFilename size
---------- -------------- ... ---------------------------------- --------
3000007760 IUE ... lwp13058.elbll.gz 185727
3000007760 IUE ... lwp13058.elbls.gz 183350
3000007760 IUE ... lwp13058.lilo.gz 612715
3000007760 IUE ... lwp13058.melol.gz 12416
3000007760 IUE ... lwp13058.melos.gz 12064
3000007760 IUE ... lwp13058.raw.gz 410846
3000007760 IUE ... lwp13058.rilo.gz 416435
3000007760 IUE ... lwp13058.silo.gz 100682
3000007760 IUE ... lwp13058.gif 8971
3000007760 IUE ... lwp13058.mxlo.gz 18206
3000007760 IUE ... lwp13058mxlo_vo.fits 48960
3000007760 IUE ... lwp13058.gif 3967
9500243833 K2 ... k2-tpf-only-target_bw_large.png 9009
9500243833 K2 ... ktwo200071160-c91_lpd-targ.fits.gz 39930404
9500243833 K2 ... ktwo200071160-c92_lpd-targ.fits.gz 62213068
9500243833 K2 ... k2-tpf-only-target_bw_thumb.png 1301
>>> print((data_products_by_obs == data_products_by_id).all())
True
Downloading Data Products¶
Products can be downloaded by using download_products,
with a Table of data products, or a list (or single) obsid as the argument.
>>> from astroquery.mast import Observations
>>> obsid = '3000007760'
>>> data_products = Observations.get_product_list(obsid)
>>> manifest = Observations.download_products(data_products)
Downloading URL http://archive.stsci.edu/pub/iue/data/lwp/13000/lwp13058.mxlo.gz to ./mastDownload/IUE/lwp13058/lwp13058.mxlo.gz ... [Done]
Downloading URL http://archive.stsci.edu/pub/vospectra/iue2/lwp13058mxlo_vo.fits to ./mastDownload/IUE/lwp13058/lwp13058mxlo_vo.fits ... [Done]
>>> print(manifest)
Local Path Status Message URL
------------------------------------------------ -------- ------- ----
./mastDownload/IUE/lwp13058/lwp13058.mxlo.gz COMPLETE None None
./mastDownload/IUE/lwp13058/lwp13058mxlo_vo.fits COMPLETE None None
As an alternative to downloading the data files now, the curl_flag can be used instead to instead get a curl script that can be used to download the files at a later time.
>>> from astroquery.mast import Observations
>>> Observations.download_products('2003839997',
... productType="SCIENCE",
... curl_flag=True)
Downloading URL https://mast.stsci.edu/portal/Download/stage/anonymous/public/514cfaa9-fdc1-4799-b043-4488b811db4f/mastDownload_20170629162916.sh to ./mastDownload_20170629162916.sh ... [Done]
Filtering¶
Filter keyword arguments can be applied to download only data products that meet the given criteria. Available filters are “mrp_only” (Minimum Recommended Products), “extension” (file extension), and all products fields listed here.
The ‘AND’ operation is performed for a list of filters, and the ‘OR’ operation is performed within a filter set. The below example illustrates downloading all product files with the extension “fits” that are either “RAW” or “UNCAL.”
>>> from astroquery.mast import Observations
>>> Observations.download_products('2003839997',
... productSubGroupDescription=["RAW", "UNCAL"],
... extension="fits")
Downloading URL https://mast.stsci.edu/api/v0/download/file/HST/product/ib3p11p7q_raw.fits to ./mastDownload/HST/IB3P11P7Q/ib3p11p7q_raw.fits ... [Done]
Downloading URL https://mast.stsci.edu/api/v0/download/file/HST/product/ib3p11p8q_raw.fits to ./mastDownload/HST/IB3P11P8Q/ib3p11p8q_raw.fits ... [Done]
Downloading URL https://mast.stsci.edu/api/v0/download/file/HST/product/ib3p11phq_raw.fits to ./mastDownload/HST/IB3P11PHQ/ib3p11phq_raw.fits ... [Done]
Downloading URL https://mast.stsci.edu/api/v0/download/file/HST/product/ib3p11q9q_raw.fits to ./mastDownload/HST/IB3P11Q9Q/ib3p11q9q_raw.fits ... [Done]
Product filtering can also be applied directly to a table of products without proceeding to the download step.
>>> from astroquery.mast import Observations
>>> products = Observations.get_product_list('2003839997')
>>> print(len(products))
31
>>> products = Observations.filter_products(data_products,
... productSubGroupDescription=["RAW", "UNCAL"],
... extension="fits")
>>> print(len(products))
4
Downloading a Single File¶
You can download a single data product file using the download_file method, and passing in
a MAST dataURL. The default is to download the file the current working directory, which can be changed with
the local_path keyword argument.
>>> from astroquery.mast import Observations
>>> product = 'mast:IUE/url/pub/iue/data/lwp/13000/lwp13058.elbll.gz'
>>> result = Observations.download_file(product)
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:IUE/url/pub/iue/data/lwp/13000/lwp13058.elbll.gz to ./lwp13058.elbll.gz ... [Done]
>>> print(result)
('COMPLETE', None, None)
Cloud Data Access¶
Public datasets from the Hubble, Kepler and TESS telescopes are also available for free on Amazon Web Services in public S3 buckets.
Using AWS resources to process public data no longer requires an AWS account for all AWS regions. To enable cloud data access for the Hubble, Kepler, and TESS missions, follow the steps below:
You can enable cloud data access via the enable_cloud_dataset function, which sets AWS to become the preferred source for data access as opposed to on-premise MAST until it is disabled with disable_cloud_dataset.
To directly access a list of cloud URIs for a given dataset, use the get_cloud_uris
function (Python will prompt you to enable cloud access if you haven’t already).
When cloud access is enabled, the standard download function
download_products preferentially pulls files from AWS when they are available. When set to True, the cloud_only parameter in download_products skips all data products not available in the cloud.
Getting a list of S3 URIs:
>>> import os
>>> from astroquery.mast import Observations
>>> # Simply call the `enable_cloud_dataset` method from `Observations`. The default provider is `AWS`, but we will write it in manually for this example:
>>> Observations.enable_cloud_dataset(provider='AWS')
INFO: Using the S3 STScI public dataset [astroquery.mast.core]
>>> # Getting the cloud URIs
>>> obs_table = Observations.query_criteria(obs_collection='HST',
... filters='F606W',
... instrument_name='ACS/WFC',
... proposal_id=['12062'],
... dataRights='PUBLIC')
>>> products = Observations.get_product_list(obs_table)
>>> filtered = Observations.filter_products(products,
... productSubGroupDescription='DRZ')
>>> s3_uris = Observations.get_cloud_uris(filtered)
>>> print(s3_uris)
['s3://stpubdata/hst/public/jbev/jbeveo010/jbeveo010_drz.fits', 's3://stpubdata/hst/public/jbev/jbeveo010/jbeveo010_drz.fits', 's3://stpubdata/hst/public/jbev/jbevet010/jbevet010_drz.fits', 's3://stpubdata/hst/public/jbev/jbevet010/jbevet010_drz.fits']
>>> Observations.disable_cloud_dataset()
Downloading data products from S3:
>>> import os
>>> from astroquery.mast import Observations
>>> # Simply call the `enable_cloud_dataset` method from `Observations`. The default provider is `AWS`, but we will write it in manually for this example:
>>> Observations.enable_cloud_dataset(provider='AWS')
INFO: Using the S3 STScI public dataset [astroquery.mast.core]
>>> # Downloading from the cloud
>>> obs_table = Observations.query_criteria(obs_collection=['Kepler'],
... objectname="Kepler 12b", radius=0)
>>> products = Observations.get_product_list(obs_table[0])
>>> manifest = Observations.download_products(products[:10], cloud_only=True)
manifestDownloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/dv_files/0118/011804465/kplr011804465-01-20160209194854_dvs.pdf to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465-01-20160209194854_dvs.pdf ...
|==========================================| 1.5M/1.5M (100.00%) 0s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/dv_files/0118/011804465/kplr011804465-20160128150956_dvt.fits to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465-20160128150956_dvt.fits ...
|==========================================| 17M/ 17M (100.00%) 1s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/dv_files/0118/011804465/kplr011804465-20160209194854_dvr.pdf to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465-20160209194854_dvr.pdf ...
|==========================================| 5.8M/5.8M (100.00%) 0s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/dv_files/0118/011804465/kplr011804465_q1_q17_dr25_obs_tcert.pdf to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465_q1_q17_dr25_obs_tcert.pdf ...
|==========================================| 2.2M/2.2M (100.00%) 0s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/previews/0118/011804465/kplr011804465-2013011073258_llc_bw_large.png to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465-2013011073258_llc_bw_large.png ...
|==========================================| 24k/ 24k (100.00%) 0s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/target_pixel_files/0118/011804465/kplr011804465_tpf_lc_Q111111110111011101.tar to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465_tpf_lc_Q111111110111011101.tar ...
|==========================================| 43M/ 43M (100.00%) 4s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/lightcurves/0118/011804465/kplr011804465_lc_Q111111110111011101.tar to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465_lc_Q111111110111011101.tar ...
|==========================================| 5.9M/5.9M (100.00%) 0s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/lightcurves/0118/011804465/kplr011804465-2009131105131_llc.fits to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465-2009131105131_llc.fits ...
|==========================================| 77k/ 77k (100.00%) 0s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/lightcurves/0118/011804465/kplr011804465-2009166043257_llc.fits to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465-2009166043257_llc.fits ...
|==========================================| 192k/192k (100.00%) 0s
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/lightcurves/0118/011804465/kplr011804465-2009259160929_llc.fits to ./mastDownload/Kepler/kplr011804465_lc_Q111111110111011101/kplr011804465-2009259160929_llc.fits ...
|==========================================| 466k/466k (100.00%) 0s
>>> print(manifest["Status"])
Status
--------
COMPLETE
COMPLETE
COMPLETE
COMPLETE
COMPLETE
COMPLETE
COMPLETE
COMPLETE
COMPLETE
COMPLETE
>>> Observations.disable_cloud_dataset()
Catalog Queries¶
The Catalogs class provides access to a subset of the astronomical catalogs stored at MAST. The catalogs currently available through this interface are:
The Hubble Source Catalog (HSC)
The GALEX Catalog (V2 and V3)
The Gaia (DR1 and DR2) and TGAS Catalogs
The TESS Input Catalog (TIC)
The TESS Candidate Target List (CTL)
The Disk Detective Catalog
PanSTARRS (DR1, DR2)
Positional Queries¶
Positional queries can be based on a sky position or a target name. The returned fields vary by catalog, find the field documentation for specific catalogs here. If no catalog is specified, the Hubble Source Catalog will be queried.
>>> from astroquery.mast import Catalogs
>>> catalog_data = Catalogs.query_object("158.47924 -7.30962", catalog="Galex")
>>> print(catalog_data[:10])
distance_arcmin objID survey ... fuv_flux_aper_7 fuv_artifact
--------------- ------------------- ------ ... --------------- ------------
0.349380250633 6382034098673685038 AIS ... 0.047751952 0
0.76154224886 6382034098672634783 AIS ... -- 0
0.924332936617 6382034098672634656 AIS ... -- 0
1.16261573926 6382034098672634662 AIS ... -- 0
1.26708912875 6382034098672634735 AIS ... -- 0
1.4921733955 6382034098674731780 AIS ... 0.0611195639 0
1.60512357572 6382034098672634645 AIS ... -- 0
1.70541854139 6382034098672634716 AIS ... -- 0
1.74637211002 6382034098672634619 AIS ... -- 0
1.75244231529 6382034098672634846 AIS ... -- 0
Some catalogs have a maximum number of results they will return. If a query results in this maximum number of results a warning will be displayed to alert the user that they might be getting a subset of the true result set.
>>> from astroquery.mast import Catalogs
>>> catalog_data = Catalogs.query_region("322.49324 12.16683", catalog="HSC", magtype=2)
WARNING: MaxResultsWarning: Maximum catalog results returned, may not include all
sources within radius. [astroquery.mast.core]
>>> print(catalog_data[:10])
MatchID Distance MatchRA ... W3_F160W W3_F160W_Sigma W3_F160W_N
-------- ---------------- ------------- ... -------- -------------- ----------
82371983 0.00445549943203 322.493181974 ... -- -- 0
82603024 0.006890683763 322.493352058 ... -- -- 0
82374767 0.00838818765315 322.49337203 ... -- -- 0
82368728 0.0088064912074 322.493272691 ... -- -- 0
82371509 0.0104348577531 322.493354352 ... -- -- 0
82372543 0.0106808683543 322.493397455 ... -- -- 0
82371076 0.0126535758873 322.493089416 ... -- -- 0
82367288 0.0130150558411 322.493247548 ... -- -- 0
82371086 0.0135993945732 322.493248703 ... -- -- 0
82368622 0.0140289292301 322.493101406 ... -- -- 0
Radius is an optional parameter and the default is 0.2 degrees.
>>> from astroquery.mast import Catalogs
>>> catalog_data = Catalogs.query_object("M10", radius=.02, catalog="TIC")
>>> print(catalog_data[:10])
ID ra dec ... duplicate_id priority dstArcSec
--------- ------------- -------------- ... ------------ -------- -------------
189844423 254.287989 -4.099644 ... -- -- 2.21043178558
189844434 254.286301884 -4.09872352783 ... -- -- 4.69684511346
189844449 254.288157 -4.097959 ... -- -- 5.53390173242
189844403 254.286864 -4.101237 ... -- -- 7.19103845641
189844459 254.286798163 -4.0973143956 ... -- -- 7.63543964382
189844400 254.285379 -4.100856 ... -- -- 9.27452417927
189844461 254.285647884 -4.09722647575 ... -- -- 9.98427869106
189844385 254.289725042 -4.10156744653 ... -- -- 11.4468393777
189844419 254.290767 -4.099757 ... -- -- 11.9738216615
189844454 254.290349435 -4.09754191392 ... -- -- 12.2100186781
The Hubble Source Catalog, the Gaia Catalog, and the PanSTARRS Catalog have multiple versions. An optional version parameter allows you to select which version you want, the default is the highest version.
>>> catalog_data = Catalogs.query_region("158.47924 -7.30962", radius=0.1,
... catalog="Gaia", version=2)
>>> print("Number of results:",len(catalog_data))
>>> print(catalog_data[:4])
Number of results: 111
solution_id designation ... distance
------------------- ---------------------------- ... ------------------
1635721458409799680 Gaia DR2 3774902350511581696 ... 0.6327882551927051
1635721458409799680 Gaia DR2 3774901427093274112 ... 0.8438875783827048
1635721458409799680 Gaia DR2 3774902148648277248 ... 0.9198397322382648
1635721458409799680 Gaia DR2 3774902453590798208 ... 1.3578882400285217
The PanSTARRS Catalog has multiple data releases as well as multiple queryable tables. An optional data release parameter allows you to select which data release is desired, with the default being the latest version (dr2). The table to query is a required parameter.
>>> catalog_data = Catalogs.query_region("158.47924 -7.30962", radius=0.1,
>>> catalog="Panstarrs", data_release="dr1", table="mean")
>>> print("Number of results:",len(catalog_data))
>>> print(catalog_data[:10])
Number of results: 7007
objName objAltName1 objAltName2 ... yMeanApMagNpt yFlags distance
--------------------- ----------- ----------- ... ------------- ------ --------
PSO J158.4130-07.2557 -999 -999 ... 0 0 0
PSO J158.4133-07.2564 -999 -999 ... 0 0 0
PSO J158.4136-07.2571 -999 -999 ... 0 114720 0
PSO J158.4156-07.2530 -999 -999 ... 0 0 0
PSO J158.4157-07.2511 -999 -999 ... 0 0 0
PSO J158.4159-07.2535 -999 -999 ... 0 0 0
PSO J158.4159-07.2554 -999 -999 ... 0 114720 0
PSO J158.4160-07.2534 -999 -999 ... 0 114720 0
PSO J158.4164-07.2568 -999 -999 ... 0 0 0
PSO J158.4175-07.2574 -999 -999 ... 0 16416 0
Catalog Criteria Queries¶
The TESS Input Catalog (TIC), Disk Detective Catalog, and PanSTARRS Catalog can also be queried based on non-positional criteria.
>>> from astroquery.mast import Catalogs
>>> catalog_data = Catalogs.query_criteria(catalog="Tic",Bmag=[30,50],objType="STAR")
>>> print(catalog_data)
ID version HIP TYC ... disposition duplicate_id priority objID
--------- -------- --- --- ... ----------- ------------ -------- ---------
81609218 20171221 -- -- ... -- -- -- 217917514
23868624 20171221 -- -- ... -- -- -- 296973171
406300991 20171221 -- -- ... -- -- -- 400575018
>>> from astroquery.mast import Catalogs
>>> catalog_data = Catalogs.query_criteria(catalog="Ctl",
... objectname='M101', radius=1, Tmag=[10.75,11])
>>> print(catalog_data)
ID version HIP TYC ... wdflag ctlPriority objID
--------- -------- --- ------------ ... ------ -------------------- ---------
441639577 20190415 -- 3852-00429-1 ... 0 0.00138923974233085 150848150
441662028 20190415 -- 3855-00941-1 ... 0 0.00100773800289492 151174508
233458861 20190415 -- 3852-01407-1 ... 0 0.000843468567169446 151169732
441658008 20190415 -- 3852-00116-1 ... 0 0.000337697695047815 151025336
154258521 20190415 -- 3852-01403-1 ... 0 0.000791883530388075 151060938
441658179 20190415 -- 3855-00816-1 ... 0 0.000933466312394693 151025457
441659970 20190415 -- 3852-00505-1 ... 0 0.000894696498704202 151075682
441660006 20190415 -- 3852-00341-1 ... 0 0.000600037898043061 151075713
>>> from astroquery.mast import Catalogs
>>> catalog_data = Catalogs.query_criteria(catalog="DiskDetective",
... objectname="M10",radius=2,state="complete")
>>> print(catalog_data)
designation ... ZooniverseURL
------------------- ... ----------------------------------------------------
J165628.40-054630.8 ... https://talk.diskdetective.org/#/subjects/AWI0005cka
J165748.96-054915.4 ... https://talk.diskdetective.org/#/subjects/AWI0005ckd
J165427.11-022700.4 ... https://talk.diskdetective.org/#/subjects/AWI0005ck5
J165749.79-040315.1 ... https://talk.diskdetective.org/#/subjects/AWI0005cke
J165327.01-042546.2 ... https://talk.diskdetective.org/#/subjects/AWI0005ck3
J165949.90-054300.7 ... https://talk.diskdetective.org/#/subjects/AWI0005ckk
J170314.11-035210.4 ... https://talk.diskdetective.org/#/subjects/AWI0005ckv
The PanSTARRS catalog also accepts additional parameters to allow for query refinement. These options include column selection, sorting, column criteria, page size and page number. Additional information on PanSTARRS queries may be found here.
Columns returned from the query may be submitted with the columns parameter as a list of column names.
The query may be sorted with the sort_by parameter composed of either a single column name (to sort ascending), or a list of multiple column names and/or tuples of direction and column name (ASC/DESC, column name).
To filter the query, criteria per column name are accepted. The ‘AND’ operation is performed between all column name criteria, and the ‘OR’ operation is performed within column name criteria. Per each column name parameter, criteria may consist of either a value or a list. The list may consist of a mix of values and tuples of criteria decorator (min, gte, gt, max, lte, lt, like, contains) and value.
>>> catalog_data = Catalogs.query_criteria(coordinates="5.97754 32.53617", radius=0.01,
... catalog="PANSTARRS", table="mean", data_release="dr2",
... nStackDetections=[("gte", 2)],
... columns=["objName", "objID", "nStackDetections", "distance"],
... sort_by=[("desc", "distance")], pagesize=15)
>>> print(catalog_data[:10])
objName objID nStackDetections distance
--------------------- ------------------ ---------------- ---------------------
PSO J005.9812+32.5270 147030059812483022 5 0.009651200148871086
PSO J005.9726+32.5278 147030059727583992 2 0.0093857181370567
PSO J005.9787+32.5453 147050059787164914 4 0.009179045509852305
PSO J005.9722+32.5418 147050059721440704 4 0.007171813230776031
PSO J005.9857+32.5377 147040059855825725 4 0.007058815429178634
PSO J005.9810+32.5424 147050059809651427 2 0.006835678269917365
PSO J005.9697+32.5368 147040059697224794 2 0.006654002479439699
PSO J005.9712+32.5330 147040059711340087 4 0.006212461367287632
PSO J005.9747+32.5413 147050059747400181 5 0.0056515210592035965
PSO J005.9775+32.5314 147030059774678271 3 0.004739286624336443
Hubble Source Catalog (HSC) specific queries¶
Given an HSC Match ID, return all catalog results.
>>> from astroquery.mast import Catalogs
>>> catalog_data = Catalogs.query_object("M10", radius=.02, catalog="HSC")
>>> matchid = catalog_data[0]["MatchID"]
>>> print(matchid)
17554326
>>> matches = Catalogs.query_hsc_matchid(matchid)
>>> print(matches)
CatID MatchID ... cd_matrix
--------- -------- ... ------------------------------------------------------
303940283 17554326 ... -1.10059e-005 6.90694e-010 6.90694e-010 1.10059e-005
303936256 17554326 ... -1.10059e-005 6.90694e-010 6.90694e-010 1.10059e-005
303938261 17554326 ... -1.10059e-005 6.90694e-010 6.90694e-010 1.10059e-005
301986299 17554326 ... -1.10049e-005 -1.6278e-010 -1.6278e-010 1.10049e-005
301988274 17554326 ... -1.10049e-005 -1.6278e-010 -1.6278e-010 1.10049e-005
301990418 17554326 ... -1.10049e-005 -1.6278e-010 -1.6278e-010 1.10049e-005
206511399 17554326 ... -1.38889e-005 -1.36001e-009 -1.36001e-009 1.38889e-005
206507082 17554326 ... -1.38889e-005 -1.36001e-009 -1.36001e-009 1.38889e-005
HSC spectra accessed through this class as well. get_hsc_spectra does not take any arguments, and simply loads all HSC spectra.
>>> from astroquery.mast import Catalogs
>>> all_spectra = Catalogs.get_hsc_spectra()
>>> print(all_spectra[:10])
ObjID DatasetName MatchID ... PropID HSCMatch
----- -------------------------------------------- -------- ... ------ --------
20010 HAG_J072655.67+691648.9_J8HPAXAEQ_V01.SPEC1D 19657846 ... 9482 Y
20011 HAG_J072655.69+691648.9_J8HPAOZMQ_V01.SPEC1D 19657846 ... 9482 Y
20012 HAG_J072655.76+691729.7_J8HPAOZMQ_V01.SPEC1D 19659745 ... 9482 Y
20013 HAG_J072655.82+691620.0_J8HPAOZMQ_V01.SPEC1D 19659417 ... 9482 Y
20014 HAG_J072656.34+691704.7_J8HPAXAEQ_V01.SPEC1D 19660230 ... 9482 Y
20015 HAG_J072656.36+691704.7_J8HPAOZMQ_V01.SPEC1D 19660230 ... 9482 Y
20016 HAG_J072656.36+691744.9_J8HPAOZMQ_V01.SPEC1D 19658847 ... 9482 Y
20017 HAG_J072656.37+691630.2_J8HPAXAEQ_V01.SPEC1D 19660827 ... 9482 Y
20018 HAG_J072656.39+691630.2_J8HPAOZMQ_V01.SPEC1D 19660827 ... 9482 Y
20019 HAG_J072656.41+691734.9_J8HPAOZMQ_V01.SPEC1D 19656620 ... 9482 Y
Individual or ranges of spectra can be downloaded using the download_hsc_spectra function.
>>> from astroquery.mast import Catalogs
>>> all_spectra = Catalogs.get_hsc_spectra()
>>> manifest = Catalogs.download_hsc_spectra(all_spectra[100:104])
Downloading URL https://hla.stsci.edu/cgi-bin/ecfproxy?file_id=HAG_J072704.61+691530.3_J8HPAOZMQ_V01.SPEC1D.fits to ./mastDownload/HSC/HAG_J072704.61+691530.3_J8HPAOZMQ_V01.SPEC1D.fits ... [Done]
Downloading URL https://hla.stsci.edu/cgi-bin/ecfproxy?file_id=HAG_J072704.68+691535.9_J8HPAOZMQ_V01.SPEC1D.fits to ./mastDownload/HSC/HAG_J072704.68+691535.9_J8HPAOZMQ_V01.SPEC1D.fits ... [Done]
Downloading URL https://hla.stsci.edu/cgi-bin/ecfproxy?file_id=HAG_J072704.70+691530.2_J8HPAOZMQ_V01.SPEC1D.fits to ./mastDownload/HSC/HAG_J072704.70+691530.2_J8HPAOZMQ_V01.SPEC1D.fits ... [Done]
Downloading URL https://hla.stsci.edu/cgi-bin/ecfproxy?file_id=HAG_J072704.73+691808.0_J8HPAOZMQ_V01.SPEC1D.fits to ./mastDownload/HSC/HAG_J072704.73+691808.0_J8HPAOZMQ_V01.SPEC1D.fits ... [Done]
>>> print(manifest)
Local Path ... URL
-------------------------------------------------------------------- ... ----
./mastDownload/HSC/HAG_J072704.61+691530.3_J8HPAOZMQ_V01.SPEC1D.fits ... None
./mastDownload/HSC/HAG_J072704.68+691535.9_J8HPAOZMQ_V01.SPEC1D.fits ... None
./mastDownload/HSC/HAG_J072704.70+691530.2_J8HPAOZMQ_V01.SPEC1D.fits ... None
./mastDownload/HSC/HAG_J072704.73+691808.0_J8HPAOZMQ_V01.SPEC1D.fits ... None
TESSCut¶
TESSCut is MAST’s tool to provide full-frame image (FFI) cutouts from the Transiting Exoplanet Survey Satellite (TESS). The cutouts are returned in the form of target pixel files that follow the same format as TESS pipeline target pixel files. This tool can be accessed in Astroquery by using the Tesscut class.
Note: TESScut limits each user to no more than 10 simultaneous calls to the service.
After the user has reached this limit TESScut will return a
503 Service Temporarily Unavailable Error.
If you use TESSCut for your work, please cite Brasseur et al. 2019 https://ui.adsabs.harvard.edu/abs/2019ascl.soft05007B/abstract
Cutouts¶
The get_cutouts function takes a coordinate or object name
(such as “M104” or “TIC 32449963”) and cutout size (in pixels or an angular quantity) and
returns the cutout target pixel file(s) as a list of HDUList objects.
If the given coordinate/object location appears in more than one TESS sector a target pixel file will be produced for each sector. If the cutout area overlaps more than one camera or ccd a target pixel file will be produced for each one.
>>> from astroquery.mast import Tesscut
>>> from astropy.coordinates import SkyCoord
>>> cutout_coord = SkyCoord(107.18696, -70.50919, unit="deg")
>>> hdulist = Tesscut.get_cutouts(coordinates=cutout_coord, size=5)
>>> hdulist[0].info()
Filename: <class '_io.BytesIO'>
No. Name Ver Type Cards Dimensions Format
0 PRIMARY 1 PrimaryHDU 55 ()
1 PIXELS 1 BinTableHDU 279 1282R x 12C [D, E, J, 25J, 25E, 25E, 25E, 25E, J, E, E, 38A]
2 APERTURE 1 ImageHDU 79 (5, 5) int32
>>> from astroquery.mast import Tesscut
>>> hdulist = Tesscut.get_cutouts(objectname="TIC 32449963", size=5)
>>> hdulist[0].info()
Filename: <class '_io.BytesIO'>
No. Name Ver Type Cards Dimensions Format
0 PRIMARY 1 PrimaryHDU 56 ()
1 PIXELS 1 BinTableHDU 280 1211R x 12C [D, E, J, 25J, 25E, 25E, 25E, 25E, J, E, E, 38A]
2 APERTURE 1 ImageHDU 80 (5, 5) int32
The download_cutouts function takes a coordinate or object name
(such as “M104” or “TIC 32449963”) and cutout size (in pixels or an angular quantity) and
downloads the cutout target pixel file(s).
If a given coordinate appears in more than one TESS sector a target pixel file will be produced for each sector. If the cutout area overlaps more than one camera or ccd a target pixel file will be produced for each one.
>>> from astroquery.mast import Tesscut
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> cutout_coord = SkyCoord(107.18696, -70.50919, unit="deg")
>>> manifest = Tesscut.download_cutouts(coordinates=cutout_coord, size=[5, 7]*u.arcmin)
Downloading URL https://mast.stsci.edu/tesscut/api/v0.1/astrocut?ra=107.18696&dec=-70.50919&y=0.08333333333333333&x=0.11666666666666667&units=d§or=1 to ./tesscut_20181102104719.zip ... [Done]
Inflating...
>>> print(manifest)
local_file
------------------------------------------------------
./tess-s0001-4-3_107.18696_-70.50919_14x21_astrocut.fits
Sector information¶
To access sector information at a particular location there is get_sectors.
>>> from astroquery.mast import Tesscut
>>> from astropy.coordinates import SkyCoord
>>> coord = SkyCoord(324.24368, -27.01029,unit="deg")
>>> sector_table = Tesscut.get_sectors(coordinates=coord)
>>> print(sector_table)
sectorName sector camera ccd
-------------- ------ ------ ---
tess-s0001-1-3 1 1 3
>>> from astroquery.mast import Tesscut
>>> sector_table = Tesscut.get_sectors(objectname="TIC 32449963")
>>> print(sector_table)
sectorName sector camera ccd
-------------- ------ ------ ---
tess-s0010-1-4 10 1 4
Zcut¶
Zcut for MAST allows users to request cutouts from various Hubble deep field surveys. The cutouts can be returned as either fits or image files (jpg and png are supported). This tool can be accessed in Astroquery by using the Zcut class. The list of supported deep field surveys can be found here: https://mast.stsci.edu/zcut/
Cutouts¶
The get_cutouts function takes a coordinate and cutout size (in pixels or
an angular quantity) and returns the cutout FITS file(s) as a list of ~astropy.io.fits.HDUList objects.
If the given coordinate appears in more than one Zcut survey, a FITS file will be produced for each survey.
>>> from astroquery.mast import Zcut
>>> from astropy.coordinates import SkyCoord
>>> cutout_coord = SkyCoord(189.49206, 62.20615, unit="deg")
>>> hdulist = Zcut.get_cutouts(coordinates=cutout_coord, size=5)
>>> hdulist[0].info()
Filename: <class '_io.BytesIO'>
No. Name Ver Type Cards Dimensions Format
0 PRIMARY 1 PrimaryHDU 11 ()
1 CUTOUT 1 ImageHDU 177 (5, 5) float32
2 CUTOUT 1 ImageHDU 177 (5, 5) float32
3 CUTOUT 1 ImageHDU 177 (5, 5) float32
The download_cutouts function takes a coordinate and cutout size (in pixels or
an angular quantity) and downloads the cutout fits file(s) as either fits files or image (png/jpg)
files.
If a given coordinate appears in more than one Zcut survey, a cutout will be produced for each survey.
>>> from astroquery.mast import Zcut
>>> from astropy.coordinates import SkyCoord
>>> cutout_coord = SkyCoord(189.49206, 62.20615, unit="deg")
>>> manifest = Zcut.download_cutouts(coordinates=cutout_coord, size=[200, 300], units="px")
Downloading URL https://mast.stsci.edu/zcut/api/v0.1/astrocut?ra=189.49206&dec=62.20615&y=200&x=300&units=px&format=fits to ./zcut_20201202132247.zip ... [Done]
>>> print(manifest)
Local Path
-------------------------------------------------------------------------
./candels_gn_30mas_189.492060_62.206150_300.0pix-x-200.0pix_astrocut.fits
>>> from astroquery.mast import Zcut
>>> from astropy.coordinates import SkyCoord
>>> cutout_coord = SkyCoord(189.49206, 62.20615, unit="deg")
>>> manifest = Zcut.download_cutouts(coordinates=cutout_coord, size=[200, 300], units="px", form="jpg")
Downloading URL https://mast.stsci.edu/zcut/api/v0.1/astrocut?ra=189.49206&dec=62.20615&y=200&x=300&units=px&format=jpg to ./zcut_20201202132453.zip ... [Done]
>>> print(manifest)
Local Path
---------------------------------------------------------------------------------------------------------
./hlsp_candels_hst_acs_gn-tot-30mas_f606w_v1.0_drz_189.492060_62.206150_300.0pix-x-200.0pix_astrocut.jpg
./hlsp_candels_hst_acs_gn-tot-30mas_f814w_v1.0_drz_189.492060_62.206150_300.0pix-x-200.0pix_astrocut.jpg
./hlsp_candels_hst_acs_gn-tot-30mas_f850lp_v1.0_drz_189.492060_62.206150_300.0pix-x-200.0pix_astrocut.jpg
Survey information¶
To list the available deep field surveys at a particular location there is get_surveys.
>>> from astroquery.mast import Zcut
>>> from astropy.coordinates import SkyCoord
>>> coord = SkyCoord(189.49206, 62.20615, unit="deg")
>>> survey_list = Zcut.get_surveys(coordinates=coord)
>>> print(survey_list)
['candels_gn_60mas', 'candels_gn_30mas', 'goods_north']
Accessing Proprietary Data¶
To access data that is not publicly available users may log into their
MyST Account.
This can be done by using the login function,
or by initializing a class instance with credentials.
If a token is not supplied, the user will be prompted to enter one.
To view tokens accessible through your account, visit https://auth.mast.stsci.edu
>>> from astroquery.mast import Observations
>>> Observations.login(token="12348r9w0sa2392ff94as841")
INFO: MAST API token accepted, welcome User Name [astroquery.mast.core]
>>> sessioninfo = Observations.session_info()
eppn: user_name@stsci.edu
ezid: uname
...
>>> from astroquery.mast import Observations
>>> my_session = Observations(token="12348r9w0sa2392ff94as841")
INFO: MAST API token accepted, welcome User Name [astroquery.mast.core]
>>> sessioninfo = Observations.session_info()
eppn: user_name@stsci.edu
ezid: uname
...
* For security tokens should not be typed into a terminal or Jupyter notebook
but instead input using a more secure method such as getpass.
MAST tokens expire after 10 days of inactivity, at which point the user must generate a new token. If
the key is used within that time, the token’s expiration pushed back to 10 days. A token’s max
age is 60 days, afterward the user must generate a token.
The store_token argument can be used to store the token securely in the user’s keyring.
This token can be overwritten using the reenter_token argument.
To logout before a session expires, the logout method may be used.
Direct Mast Queries¶
The Mast class provides more direct access to the MAST interface. It requires more knowledge of the inner workings of the MAST API, and should be rarely needed. However in the case of new functionality not yet implemented in astroquery, this class does allow access. See the MAST api documentation for more information.
The basic MAST query function returns query results as an Table.
>>> from astroquery.mast import Mast
>>> service = 'Mast.Caom.Cone'
>>> params = {'ra':184.3,
... 'dec':54.5,
... 'radius':0.2}
>>> observations = Mast.service_request(service, params)
>>> print(observations)
dataproduct_type obs_collection instrument_name ... distance _selected_
---------------- -------------- --------------- ... ------------- ----------
image GALEX GALEX ... 0.0 False
image GALEX GALEX ... 0.0 False
image GALEX GALEX ... 0.0 False
image GALEX GALEX ... 0.0 False
image GALEX GALEX ... 0.0 False
image GALEX GALEX ... 302.405835798 False
image GALEX GALEX ... 302.405835798 False
If the output is not the MAST json result type it cannot be properly parsed into a Table.
In this case, the async method should be used to get the raw http response, which can then be manually parsed.
>>> from astroquery.mast import Mast
>>> service = 'Mast.Name.Lookup'
>>> params ={'input':"M8",
... 'format':'json'}
>>> response = Mast.service_request_async(service,params)
>>> result = response[0].json()
>>> print(result)
{'resolvedCoordinate': [{'cacheDate': 'Apr 12, 2017 9:28:24 PM',
'cached': True,
'canonicalName': 'MESSIER 008',
'decl': -24.38017,
'objectType': 'Neb',
'ra': 270.92194,
'resolver': 'NED',
'resolverTime': 113,
'searchRadius': -1.0,
'searchString': 'm8'}],
'status': ''}
Additional Resources¶
Accessing MAST Holdings with Astroquery, slides from an introductory MAST Astroquery talk.
The Space Telescope Science Institute Notebooks Repository includes many examples that use Astroquery.
Reference/API¶
astroquery.mast Package¶
MAST Query Tool¶
Module to query the Barbara A. Mikulski Archive for Space Telescopes (MAST).
Classes¶
|
MAST Observations query class. |
MAST catalog query class. |
|
|
MAST query class. |
MAST TESS FFI cutout query class. |
|
MAST ZCUT cutout query class. |
|
|
Configuration parameters for |