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])
intentType obs_collection provenance_name ... srcDen obsid distance
---------- -------------- --------------- ... ------ ----------- --------
science SWIFT -- ... 5885.0 15000731855 0.0
science SWIFT -- ... 5885.0 15000731856 0.0
science SWIFT -- ... 5885.0 15000790494 0.0
science SWIFT -- ... 5885.0 15000731857 0.0
science SWIFT -- ... 5885.0 15000791686 0.0
science SWIFT -- ... 5885.0 15000791687 0.0
science SWIFT -- ... 5885.0 15000729841 0.0
science SWIFT -- ... 5885.0 15000754475 0.0
science SWIFT -- ... 5885.0 15000779206 0.0
science SWIFT -- ... 5885.0 15000779204 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])
intentType obs_collection provenance_name ... srcDen obsid distance
---------- -------------- --------------- ... ------ ----------- --------
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 0.0
science SPITZER_SHA SSC Pipeline ... nan 19000016510 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Note: The obstype keyword has been replaced by intentType, with valid values “calibration” and “science.”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 ... intentType obsid objID
---------------- ----------- -------------- ... ---------- ---------- ----------
image 1 HST ... science 2003520267 2023816094
image 1 HST ... science 2003520266 2023816134
image 1 HST ... science 2003520268 2025756935
...
>>> obs_table = Observations.query_criteria(filters=["*UV","Kepler"],objectname="M101")
>>> print(obs_table)
dataproduct_type calib_level obs_collection ... objID1 distance
---------------- ----------- -------------- ... ---------- ------------------
image 2 GALEX ... 1000045952 0.0
image 2 GALEX ... 1000001327 371.71837196246395
image 2 GALEX ... 1000016641 0.0
image 2 GALEX ... 1000016644 229.81061601101433
image 2 GALEX ... 1000001326 0.0
image 2 GALEX ... 1000004203 0.0
image 2 GALEX ... 1000004937 3.8329068532314046
image 2 GALEX ... 1000045953 371.71837196246395
image 2 GALEX ... 1000048357 0.0
image 2 GALEX ... 1000048943 3.8329068532314046
image 2 GALEX ... 1000055044 0.0
image 2 GALEX ... 1000055047 229.81061601101433
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"))
2364
...
>>> print(Observations.query_object_count("M8",radius=".02 deg"))
469
...
>>> 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())
['BEFS', 'EUVE', 'FUSE', 'GALEX', 'HLA', 'HLSP', 'HST', 'HUT', 'IUE', 'JWST', 'K2', 'K2FFI', 'Kepler', 'KeplerFFI', 'OPO', 'PS1', 'SPITZER_SHA', 'SWIFT', 'TESS', 'TUES', 'WUPPE']
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
--------------- ---------------- ... ----------------------------------
intentType Observation Type ... Valid values: science, calibration
obs_collection Mission ... E.g. SWIFT, PS1, HST, IUE
provenance_name Provenance Name ... E.g. TASOC, CALSTIS, PS1
instrument_name Instrument ... E.g. WFPC2/WFC, UVOT, STIS/CCD
project Project ... E.g. HST, HLA, EUVE, hlsp_legus
...
>>> 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 dataproduct_type ... size parent_obsid
----------- -------------- ---------------- ... ------- ------------
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
... ... ... ... ... ...
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 8648640 19000016510
Length = 1153 rows
...
>>> obsids = obs_table[0:2]['obsid']
>>> data_products_by_id = Observations.get_product_list(obsids)
>>> print(data_products_by_id)
obsID obs_collection dataproduct_type ... size parent_obsid
----------- -------------- ---------------- ... ------- ------------
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
19000016510 SPITZER_SHA image ... 316800 19000016510
... ... ... ... ... ...
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 57600 19000016510
19000016510 SPITZER_SHA image ... 8648640 19000016510
Length = 1153 rows
...
>>> print((data_products_by_obs == data_products_by_id).all())
True
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('25119363',
... productType=["SCIENCE", "PREVIEW"],
... extension="fits")
<Table length=3>
Local Path Status Message URL
str47 str8 object object
----------------------------------------------- -------- ------- ------
./mastDownload/HST/fa2f0101m/fa2f0101m_a1f.fits COMPLETE None None
./mastDownload/HST/fa2f0101m/fa2f0101m_a2f.fits COMPLETE None None
./mastDownload/HST/fa2f0101m/fa2f0101m_a3f.fits COMPLETE None None
Product filtering can also be applied directly to a table of products without proceeding to the download step.
>>> from astroquery.mast import Observations
...
>>> data_products = Observations.get_product_list('25588063')
>>> print(len(data_products))
27
>>> products = Observations.filter_products(data_products,
... productType=["SCIENCE", "PREVIEW"],
... extension="fits")
>>> print(len(products))
8
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
...
>>> single_obs = Observations.query_criteria(obs_collection="IUE",obs_id="lwp13058")
>>> data_products = Observations.get_product_list(single_obs)
...
>>> manifest = Observations.download_products(data_products, productType="SCIENCE")
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=http://archive.stsci.edu/pub/iue/data/lwp/13000/lwp13058.mxlo.gz to ./mastDownload/IUE/lwp13058/lwp13058.mxlo.gz ... [Done]
Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=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
...
>>> single_obs = Observations.query_criteria(obs_collection="IUE", obs_id="lwp13058")
>>> data_products = Observations.get_product_list(single_obs)
...
>>> table = Observations.download_products(data_products, 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]
Downloading a Single File¶
You can download a single data product file using the download_file
method, and passing in a MAST Data URI. 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
...
>>> single_obs = Observations.query_criteria(obs_collection="IUE",obs_id="lwp13058")
>>> data_products = Observations.get_product_list(single_obs)
...
>>> product = data_products[0]["dataURI"]
>>> print(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, TESS, and GALEX 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()
Mission Searches¶
Mission-Specific Search Queries¶
These queries allow for searches based on mission-specific metadata for a given data collection. Currently it provides access to a broad set of Hubble Space Telescope (HST) metadata, including header keywords, proposal information, and observational parameters. The available metadata includes all information that was previously available in the original HST web search form, and are present in the current Mission Search interface.
Note: this API interface does not yet support data product download, only metadata earch access.
An object of MastMissions class is instantiated with a default mission of ‘hst’ and default service set to ‘search’.
>>> from astroquery.mast import MastMissions
>>> missions = MastMissions()
>>> missions.mission
'hst'
>>> missions.service
'search'
The missions object can be used to search metadata using by sky position, or other criteria.
The keyword arguments can be used to specify output characteristics like selec_cols and
sort_by and conditions that filter on values like proposal id, pi last name etc.
The available column names for a mission are returned by the
get_column_list function.
>>> from astroquery.mast import MastMissions
>>> missions = MastMissions(mission='hst')
>>> columns = missions.get_column_list()
For positional searches, the columns “ang_sep”, “sci_data_set_name”, “search_key” and “search_position” will always be included, in addition to any columns specified using “select_cols”. For non-positional searches, “search_key” and “sci_data_set_name” will always be included, in addition to any columns specified using “select_cols”.
For a non positional search, select_cols would always include 'search_key' and 'sci_data_set_name'.
>>> from astroquery.mast import MastMissions
>>> from astropy.coordinates import SkyCoord
>>> missions = MastMissions(mission='hst')
>>> regionCoords = SkyCoord(210.80227, 54.34895, unit=('deg', 'deg'))
>>> results = missions.query_region(regionCoords, radius=3, sci_pep_id=12556,
... select_cols=["sci_stop_time", "sci_targname", "sci_start_time", "sci_status"],
... sort_by=['sci_targname'])
>>> results[:5]
<Table masked=True length=5>
sci_status sci_targname sci_data_set_name ang_sep sci_pep_id search_pos sci_pi_last_name search_key
str6 str16 str9 str20 int64 str18 str6 str27
---------- ---------------- ----------------- -------------------- ---------- ------------------ ---------------- ---------------------------
PUBLIC NUCLEUS+HODGE602 OBQU010H0 0.017460048037303017 12556 210.80227 54.34895 GORDON 210.80227 54.34895OBQU010H0
PUBLIC NUCLEUS+HODGE602 OBQU01050 0.017460048037303017 12556 210.80227 54.34895 GORDON 210.80227 54.34895OBQU01050
PUBLIC NUCLEUS+HODGE602 OBQU01030 0.022143836477276503 12556 210.80227 54.34895 GORDON 210.80227 54.34895OBQU01030
PUBLIC NUCLEUS+HODGE602 OBQU010F0 0.022143836477276503 12556 210.80227 54.34895 GORDON 210.80227 54.34895OBQU010F0
PUBLIC NUCLEUS+HODGE602 OBQU010J0 0.04381046755938432 12556 210.80227 54.34895 GORDON 210.80227 54.34895OBQU010J0
for paging through the results, offset and limit can be used to specify the starting record and the number of returned records. the default values for offset and limit is 0 and 5000 respectively.
>>> from astroquery.mast import MastMissions
>>> from astropy.coordinates import SkyCoord
>>> missions = MastMissions()
>>> results = missions.query_criteria(sci_start_time=">=2021-01-01 00:00:00",
... select_cols=["sci_stop_time", "sci_targname", "sci_start_time", "sci_status", "sci_pep_id"],
... sort_by=['sci_pep_id'], limit=1000, offset=1000)
... # MaxResultsWarning('Maximum results returned, may not include all sources within radius.')
>>> len(results)
1000
Metadata queries can also be performed using object names with the ~astroquery.mast.MastMissionsClass.query_object function.
>>> results = missions.query_object('M101', radius=3, select_cols=["sci_stop_time", "sci_targname", "sci_start_time", "sci_status"],
... sort_by=['sci_targname'])
>>> results[:5]
<Table masked=True length=5>
ang_sep search_pos sci_status search_key sci_stop_time sci_targname sci_start_time sci_data_set_name
str20 str18 str6 str27 str26 str16 str26 str9
------------------ ------------------ ---------- --------------------------- -------------------------- ------------ -------------------------- -----------------
2.751140575012458 210.80227 54.34895 PUBLIC 210.80227 54.34895LDJI01010 2019-02-19T05:52:40.020000 +164.6+9.9 2019-02-19T00:49:58.010000 LDJI01010
0.8000626246647815 210.80227 54.34895 PUBLIC 210.80227 54.34895J8OB02011 2003-08-27T08:27:34.513000 ANY 2003-08-27T07:44:47.417000 J8OB02011
1.1261718338567348 210.80227 54.34895 PUBLIC 210.80227 54.34895J8D711J1Q 2003-01-17T00:50:22.250000 ANY 2003-01-17T00:42:06.993000 J8D711J1Q
1.1454431087675097 210.80227 54.34895 PUBLIC 210.80227 54.34895JD6V01012 2017-06-15T18:33:25.983000 ANY 2017-06-15T18:10:12.037000 JD6V01012
1.1457795862361977 210.80227 54.34895 PUBLIC 210.80227 54.34895JD6V01013 2017-06-15T20:08:44.063000 ANY 2017-06-15T19:45:30.023000 JD6V01013
Metadata queries can also be performed using non-positional parameters with the
query_criteria function.
>>> results = missions.query_criteria(sci_data_set_name="Z06G0101T", sci_pep_id="1455",
... select_cols=["sci_stop_time", "sci_targname", "sci_start_time", "sci_status"],
... sort_by=['sci_targname'])
>>> results[:5]
<Table masked=True length=5>
search_key sci_stop_time sci_data_set_name sci_start_time sci_targname sci_status
str9 str26 str9 str26 str19 str6
---------- -------------------------- ----------------- -------------------------- ------------ ----------
Z06G0101T 1990-05-13T11:02:34.567000 Z06G0101T 1990-05-13T10:38:09.193000 -- PUBLIC
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
The PanSTARRS Catalog (DR1 and DR2)
The All-Sky PLATO Input Catalog (DR1)
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
------------------ ------------------- ------ ... --------------- ------------
0.3493802506329695 6382034098673685038 AIS ... 0.047751952 0
0.7615422488595471 6382034098672634783 AIS ... -- 0
0.9243329366166956 6382034098672634656 AIS ... -- 0
1.162615739258038 6382034098672634662 AIS ... -- 0
1.2670891287503308 6382034098672634735 AIS ... -- 0
1.492173395497916 6382034098674731780 AIS ... 0.0611195639 0
1.6051235757244107 6382034098672634645 AIS ... -- 0
1.705418541388336 6382034098672634716 AIS ... -- 0
1.7463721100195875 6382034098672634619 AIS ... -- 0
1.7524423152919317 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)
InputWarning: Coordinate string is being interpreted as an ICRS coordinate provided in degrees.
MaxResultsWarning: Maximum catalog results returned, may not include all sources within radius.
>>> print(catalog_data[:10])
MatchID Distance MatchRA ... W3_F160W_MAD W3_F160W_N
--------- -------------------- ------------------ ... ------------ ----------
50180585 0.003984902849540913 322.4931746094701 ... nan 0
8150896 0.006357935819940561 322.49334740450234 ... nan 0
100906349 0.00808206428937523 322.4932839715549 ... nan 0
105434103 0.011947078376104195 322.49324000530777 ... nan 0
103116183 0.01274757103013683 322.4934207202404 ... nan 0
45593349 0.013026569623011767 322.4933878707698 ... nan 0
103700905 0.01306760650244682 322.4932769229944 ... nan 0
102470085 0.014611879195009472 322.49311034430366 ... nan 0
93722307 0.01476438046135455 322.49348351134466 ... nan 0
24781941 0.015234351867433582 322.49300148743345 ... nan 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 ... wdflag dstArcSec
---------- ---------------- ----------------- ... ------ ------------------
510188144 254.287449269816 -4.09954224264168 ... -1 0.7650443624931581
510188143 254.28717785824 -4.09908635292493 ... -1 1.3400566638148848
189844423 254.287799703996 -4.0994998249247 ... 0 1.3644407138867785
1305764031 254.287147439535 -4.09866105132406 ... -1 2.656905409847388
1305763882 254.286696117371 -4.09925522448626 ... -1 2.7561196688252894
510188145 254.287431890823 -4.10017293344746 ... -1 3.036238557555728
1305763844 254.286675148545 -4.09971617257086 ... 0 3.1424781549696217
1305764030 254.287249718516 -4.09841883152995 ... -1 3.365991083435227
1305764097 254.287599269103 -4.09837925361712 ... -1 3.4590276863989
1305764215 254.28820865799 -4.09859677020253 ... -1 3.7675526728257034
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))
Number of results: 111
>>> print(catalog_data[:4])
solution_id designation ... distance
------------------- ---------------------------- ... ------------------
1635721458409799680 Gaia DR2 3774902350511581696 ... 0.6326770410972467
1635721458409799680 Gaia DR2 3774901427093274112 ... 0.8440033390947586
1635721458409799680 Gaia DR2 3774902148648277248 ... 0.9199206487344911
1635721458409799680 Gaia DR2 3774902453590798208 ... 1.3578181104319944
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))
Number of results: 7007
>>> print(catalog_data[:10])
ObjName objAltName1 ... yFlags distance
-------------------------- ----------- ... ------ --------------------
PSO J103359.653-071622.382 -999 ... 16416 0.04140441098310487
PSO J103359.605-071622.873 -999 ... 0 0.04121935961328582
PSO J103359.691-071640.232 -999 ... 0 0.03718729257758985
PSO J103400.268-071639.192 -999 ... 0 0.03870112803784765
PSO J103400.073-071637.358 -999 ... 0 0.03867536827891155
PSO J103359.789-071632.606 -999 ... 0 0.03921557769883566
PSO J103359.192-071654.790 -999 ... 0 0.03266232705300051
PSO J103359.959-071655.155 -999 ... 0 0.034361022297827955
PSO J103359.847-071655.610 -999 ... 0 0.033986082329893995
PSO J103400.586-071656.646 -999 ... 0 0.035645179491121386
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 ... e_Dec_orig raddflag wdflag objID
--------- -------- --- --- ... ------------------ -------- ------ ----------
125413929 20190415 -- -- ... 0.293682765259495 1 0 579825059
261459129 20190415 -- -- ... 0.200397148604244 1 0 1701625107
64575709 20190415 -- -- ... 0.21969663115091 1 0 595775997
94322581 20190415 -- -- ... 0.205286802302475 1 0 606092549
125414201 20190415 -- -- ... 0.22398993783274 1 0 579825329
463721073 20190415 -- -- ... 0.489828592248652 -1 1 710312391
81609218 20190415 -- -- ... 0.146788572369267 1 0 630541794
282024596 20190415 -- -- ... 0.548806522539047 1 0 573765450
23868624 20190415 -- -- ... 355.949 -- 0 916384285
282391528 20190415 -- -- ... 0.47766300834538 0 0 574723760
123585000 20190415 -- -- ... 0.618316068787371 0 0 574511442
260216294 20190415 -- -- ... 0.187170498094167 1 0 683390717
406300991 20190415 -- -- ... 0.0518318978617112 0 0 1411465651
>>> 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 ... raddflag wdflag objID
--------- -------- --- ------------ ... -------- ------ ---------
441639577 20190415 -- 3852-00429-1 ... 1 0 150070672
441658179 20190415 -- 3855-00816-1 ... 1 0 150246482
441658008 20190415 -- 3852-00116-1 ... 1 0 150246361
154258521 20190415 -- 3852-01403-1 ... 1 0 150281963
441659970 20190415 -- 3852-00505-1 ... 1 0 150296707
441660006 20190415 -- 3852-00341-1 ... 1 0 150296738
233458861 20190415 -- 3852-01407-1 ... 1 0 150390757
441662028 20190415 -- 3855-00941-1 ... 1 0 150395533
>>> 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)
63980492
>>> matches = Catalogs.query_hsc_matchid(matchid)
>>> print(matches)
CatID MatchID ... cd_matrix
--------- -------- ... ------------------------------------------------------
257195287 63980492 ... -1.38889e-005 -5.26157e-010 -5.26157e-010 1.38889e-005
257440119 63980492 ... -1.38889e-005 -5.26157e-010 -5.26157e-010 1.38889e-005
428373428 63980492 ... -1.10056e-005 5.65193e-010 5.65193e-010 1.10056e-005
428373427 63980492 ... -1.10056e-005 5.65193e-010 5.65193e-010 1.10056e-005
428373429 63980492 ... -1.10056e-005 5.65193e-010 5.65193e-010 1.10056e-005
410574499 63980492 ... -1.10056e-005 1.56577e-009 1.56577e-009 1.10056e-005
410574498 63980492 ... -1.10056e-005 1.56577e-009 1.56577e-009 1.10056e-005
410574497 63980492 ... -1.10056e-005 1.56577e-009 1.56577e-009 1.10056e-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, object name
(i.e. “M104” or “TIC 32449963”), or moving target (i.e. “Eleonora”) 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.
Requesting a cutout by coordinate or objectname accesses the MAST TESScut API and returns a target pixel file, with format described here.
>>> 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 56 ()
1 PIXELS 1 BinTableHDU 280 1196R x 12C [D, E, J, 25J, 25E, 25E, 25E, 25E, J, E, E, 38A]
2 APERTURE 1 ImageHDU 81 (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 3477R x 12C [D, E, J, 25J, 25E, 25E, 25E, 25E, J, E, E, 38A]
2 APERTURE 1 ImageHDU 81 (5, 5) int32
Requesting a cutout by moving_target accesses the
MAST Moving Target TESScut API
and returns a target pixel file, with format described
here.
The moving_target is an optional bool argument where True signifies that the accompanying objectname
input is the object name or ID understood by the
JPL Horizon ephemerades interface.
The default value for moving_target is set to False. Therefore, a non-moving target can be input
simply with either the objectname or coordinates.
>>> from astroquery.mast import Tesscut
...
>>> hdulist = Tesscut.get_cutouts(objectname="Eleonora", moving_target=True, size=5, sector=6)
>>> hdulist[0].info()
Filename: <class '_io.BytesIO'>
No. Name Ver Type Cards Dimensions Format
0 PRIMARY 1 PrimaryHDU 54 ()
1 PIXELS 1 BinTableHDU 150 355R x 16C [D, E, J, 25J, 25E, 25E, 25E, 25E, J, E, E, 38A, D, D, D, D]
2 APERTURE 1 ImageHDU 97 (2136, 2078) int32
The download_cutouts function takes a coordinate, cutout size
(in pixels or an angular quantity), or object name (i.e. “M104” or “TIC 32449963”) and moving target
(True or False). It uses these parameters to download the cutout target pixel file(s).
If a given coordinate/object/moving target 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, sector=9)
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=9 to ./tesscut_20210716150026.zip ... [Done]
Inflating...
...
>>> print(manifest)
Local Path
----------------------------------------------------------
./tess-s0009-4-1_107.186960_-70.509190_21x15_astrocut.fits
Sector information¶
To access sector information for a particular coordinate, object, or moving target 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-s0028-1-4 28 1 4
>>> 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
>>> from astroquery.mast import Tesscut
...
>>> sector_table = Tesscut.get_sectors(objectname="Ceres", moving_target=True)
>>> print(sector_table)
sectorName sector camera ccd
-------------- ------ ------ ---
tess-s0029-1-4 29 1 4
tess-s0043-3-3 43 3 3
tess-s0044-2-4 44 2 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_20210125155545.zip ... [Done]
Inflating...
...
>>> 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)
intentType obs_collection provenance_name ... obsid distance
---------- -------------- --------------- ... ----------- ------------------
science TESS SPOC ... 17001016097 0.0
science TESS SPOC ... 17000855562 0.0
science TESS SPOC ... 17000815577 203.70471189751947
science TESS SPOC ... 17000981417 325.4085155315165
science TESS SPOC ... 17000821493 325.4085155315165
science PS1 3PI ... 16000864847 0.0
science PS1 3PI ... 16000864848 0.0
science PS1 3PI ... 16000864849 0.0
science PS1 3PI ... 16000864850 0.0
science PS1 3PI ... 16000864851 0.0
... ... ... ... ... ...
science HLSP QLP ... 18013987996 637.806560287869
science HLSP QLP ... 18007518640 637.806560287869
science HLSP TESS-SPOC ... 18013510950 637.806560287869
science HLSP TESS-SPOC ... 18007364076 637.806560287869
science GALEX MIS ... 1000007123 0.0
science GALEX AIS ... 1000016562 0.0
science GALEX AIS ... 1000016562 0.0
science GALEX AIS ... 1000016563 0.0
science GALEX AIS ... 1000016563 0.0
science GALEX AIS ... 1000016556 302.4058357983673
science GALEX AIS ... 1000016556 302.4058357983673
Length = 77 rows
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. |
|
|
MastMissions search class. |
|
MAST query class. |
MAST TESS FFI cutout query class. |
|
MAST ZCUT cutout query class. |
|
|
Configuration parameters for |