Astroquery: Exploring Metadata from the James Webb Space Telescope#
Learning Goals#
By the end of this tutorial, you will:
Understand how to use the
astroquery.mastmodule to access metadata from the James Webb Space Telescope (JWST).Run metadata queries based on coordinates, an object name, or non-positional criteria.
Use optional search parameters to further refine query results.
Table of Contents#
Introduction#
Welcome! This tutorial focuses on using the astroquery.mast module to search for metadata from the James Webb Space Telescope (JWST). Launched in December of 2021, JWST is an advanced space observatory designed for observations in the infrared light spectrum.
The Mikulski Archive for Space Telescopes (MAST) hosts publicly accessible data products from space telescopes like JWST. astroquery.mast provides access to a broad set of JWST metadata, including header keywords, proposal information, and observational parameters. The available metadata can also be found using the MAST JWST Search interface.
Please note that astroquery.mast.MastMissions and the MAST JWST Search API do not yet support data product downloads.
Imports#
This notebook uses the following packages:
astroquery.mast to query the MAST Archive
astropy.coordinates to assign coordinates of interest
from astroquery.mast import MastMissions
from astropy.coordinates import SkyCoord
Querying MAST for JWST Metadata#
Setup#
In order to make queries on JWST metadata, we will have to perform some setup. First, we will instantiate an object of the MastMissions class and assign its mission to be 'jwst'. Its service is set to the default of 'search'.
# Create MastMissions object and assign mission to 'jwst'
missions = MastMissions(mission='jwst')
print(f'Mission: {missions.mission}')
print(f'Service: {missions.service}')
Mission: jwst
Service: search
When writing queries, keyword arguments can be used to specify output characteristics (see the following section) and filter on values like instrument, exposure type, and proposal ID. The available column names for a mission are returned by the get_column_list function. Below, we will print out the name, data type, and description for the first 10 columns in JWST metadata.
# Get available columns for JWST mission
columns = missions.get_column_list()
columns[:10]
Table length=10
| name | data_type | description |
|---|---|---|
| str17 | str9 | str1594 |
| search_pos | string | Search Position (RA and Dec) |
| ArchiveFileID | integer | ArchiveFileID |
| fileSetName | string | Of the form jwpppppooovvv_ggsaa_eeeee where ppppp - Program Number, ooo - Observation Number, vvv - Visit Number, gg - Visit Group, s - Parallel Sequence id (1 prime, 2-5 parallel), aa - Activity (base 36), and eeeee - Exposure Number, all padded by zeros where necessary. Source-based data set names are expressed as jwppppp-oOOO_tTTT_instrument, where: ppppp - Program number, OOO - observation number, TTT - target number, and instrument - Instrument name |
| productLevel | string | Product levels, 1b is an uncalibrated FITS file, 2a = countrate exposure, 2b = calibrated (exposure-based), 2bs = calibrated (source-based), 2c = cosmic-ray-flagged (exposure-based), 2cs = cosmic-ray-flagged (source-based), and 3 = combined data. |
| targprop | string | Proposer prefered name for the target |
| targname | string | Standard astronomical catalog name for the target |
| targ_ra | ra | Right Ascension (J2000), from 0 to 360, in degrees |
| targ_dec | dec | Declination (J2000), from -90 to +90, in degrees |
| instrume | string | Identifies the instrument used to acquire the data. |
| exp_type | string | Exposure type is used by DMS to direct Science Data Processing code (SDP) and calibration, as well as provide metadata for archive searches. For a given template the EXP_TYPE keyword value is obtained for each exposure from the EXPOSURE_TYPE exposure-level parameter in the Observatory Status File (OSF). In a few cases, additional information is required to set the value for the EXP_TYPE keyword |
Optional Search Parameters#
Before we dive in to the actual queries, it’s important to know how we can refine our results with optional keyword arguments. The following parameters are available:
radius: For positional searches only. Only return results within a certain distance from an object or set of coordinates. Default is 3 arcminutes.limit: The maximum number of results to return. Default is 5000.offset: Skip the first n results. Useful for paging through results.select_cols: A list of columns to be returned in the response.
As we walk through different types of queries, we will see these parameters in action!
Query by Object Name#
We’ve reached our first query! We can use object names to perform metadata queries using the query_object function.
To start, let’s query for the Messier 1 object, a supernova remnant in the Taurus constellation. You may know it better as the Crab Nebula!
# Query for Messier 1 ('M1')
results = missions.query_object('M1')
# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]
Total number of results: 249
Table masked=True length=5
| ArchiveFileID | fileSetName | productLevel | targprop | targ_ra | targ_dec | instrume | exp_type | opticalElements | date_obs | duration | program | observtn | visit | publicReleaseDate | pi_name | proposal_type | proposal_cycle | targtype | access | cal_ver | ang_sep | s_region |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| int64 | str25 | str14 | str18 | float64 | float64 | str6 | str9 | str153 | str27 | float64 | int64 | int64 | int64 | str19 | str10 | str2 | int64 | str5 | str6 | str5 | float64 | str1417 |
| 244890661 | jw01714001003_02101_00001 | 1b, 2a, 2b, 2c | CRAB-NEBULA | 83.63604958333333 | 22.01641666666667 | MIRI | MIR_IMAGE | F560W | 2023-02-24T02:49:57.2040000 | 138.752 | 1714 | 1 | 3 | 2024-02-24T13:38:23 | Temim, Tea | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 83.620763784 22.002789691 83.654481962 22.001837212 83.655563535 22.033234147 83.621792264 22.034478016 |
| 244890685 | jw01714001003_02101_00002 | 1b, 2a, 2b, 2c | CRAB-NEBULA | 83.63604958333333 | 22.01641666666667 | MIRI | MIR_IMAGE | F560W | 2023-02-24T02:53:39.2200000 | 138.752 | 1714 | 1 | 3 | 2024-02-24T13:38:36 | Temim, Tea | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 83.615725007 22.003119338 83.649443299 22.002167938 83.650523705 22.033564908 83.616752308 22.034807696 |
| 244890676 | jw01714001003_02101_00003 | 1b, 2a, 2b, 2c | CRAB-NEBULA | 83.63604958333333 | 22.01641666666667 | MIRI | MIR_IMAGE | F560W | 2023-02-24T02:57:23.9880000 | 138.752 | 1714 | 1 | 3 | 2024-02-24T13:36:13 | Temim, Tea | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 83.616528154 22.007520938 83.650247486 22.006569363 83.651328117 22.037966327 83.617555678 22.039209290 |
| 244890655 | jw01714001003_02101_00004 | 1b, 2a, 2b, 2c | CRAB-NEBULA | 83.63604958333333 | 22.01641666666667 | MIRI | MIR_IMAGE | F560W | 2023-02-24T03:01:06.0030000 | 138.752 | 1714 | 1 | 3 | 2024-02-24T13:35:43 | Temim, Tea | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 83.620386882 22.006718593 83.654105998 22.005766255 83.655187449 22.037163195 83.621415234 22.038406922 |
| 244890605 | jw01714001003_02103_00001 | 1b, 2a, 2b, 2c | CRAB-NEBULA | 83.63604958333333 | 22.01641666666667 | MIRI | MIR_IMAGE | F1130W | 2023-02-24T03:06:50.0870000 | 105.452 | 1714 | 1 | 3 | 2024-02-24T13:36:54 | Temim, Tea | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 83.620747224 22.002797268 83.654465081 22.001844591 83.655546548 22.033241738 83.621775841 22.034485516 |
There were 250 total results, meaning that 250 JWST datasets were targeting the Crab Nebula. Now, let’s try refining our search a bit more.
Each dataset is associated with a celestial coordinate, given by
targ_ra(right ascension) andtarg_dec(declination). By default, the query returns all datasets that fall within 3 arcminutes from the object’s coordinates. Let’s set theradiusparameter to be 1 arcminute instead.Say that we’re not interested in the first 4 results. We can assign
offsetto skip a certain number of rows.By default, a subset of recommended columns are returned for each query. However, we can specify exactly which columns to return using the
select_colskeyword argument. TheArchiveFileIDcolumn is included automatically.
# Refined query for Messier 1 ('M1')
results = missions.query_object('M1',
radius=1, # Search within a 1 arcminute radius
offset=4, # Skip the first 4 results
select_cols=['fileSetName', 'targprop', 'date_obs']) # Select certain columns
# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]
Total number of results: 161
Table masked=True length=5
| ArchiveFileID | fileSetName | targprop | date_obs | s_region |
|---|---|---|---|---|
| int64 | str25 | str18 | str27 | str1417 |
| 244890605 | jw01714001003_02103_00001 | CRAB-NEBULA | 2023-02-24T03:06:50.0870000 | POLYGON ICRS 83.620747224 22.002797268 83.654465081 22.001844591 83.655546548 22.033241738 83.621775841 22.034485516 |
| 244890641 | jw01714001003_02103_00002 | CRAB-NEBULA | 2023-02-24T03:09:58.8230000 | POLYGON ICRS 83.615708382 22.003127034 83.649426355 22.002175472 83.650506615 22.033572655 83.616735782 22.034815315 |
| 244890635 | jw01714001003_02103_00003 | CRAB-NEBULA | 2023-02-24T03:13:07.5590000 | POLYGON ICRS 83.616511436 22.007528594 83.650230450 22.006576867 83.651310924 22.037974045 83.617539048 22.039216871 |
| 244890611 | jw01714001003_02103_00004 | CRAB-NEBULA | 2023-02-24T03:16:18.9830000 | POLYGON ICRS 83.620370189 22.006726264 83.654088987 22.005773770 83.655170286 22.037170923 83.621398634 22.038414517 |
| 244890629 | jw01714001003_02105_00001 | CRAB-NEBULA | 2023-02-24T03:21:10.3800000 | POLYGON ICRS 83.620742930 22.002804912 83.654460815 22.001852254 83.655542067 22.033249470 83.621771597 22.034493185 |
Exercise 1#
Now it’s your turn! Try querying for the Whirlpool Galaxy object. Search within a radius of 1 arcminute, skip the first 300 results, and select the fileSetName and opticalElements columns.
# # Query for Whirlpool Galaxy
# results = missions.query_object(...) # Write your query!
# # Display the first 5 results
# print(f'Total number of results: {len(results)}')
# results[:5]
Query by Region#
The missions object also allows us to query by a region in the sky. By passing in a set of coordinates to the query_region function, we can return datasets that fall within a certain radius value of that point. This type of search is also known as a cone search.
# Create coordinate object
coords = SkyCoord(210.80227, 54.34895, unit=('deg'))
# Query for results within 10 arcminutes of coords
results = missions.query_region(coords, radius=10)
# Display results
print(f'Total number of results: {len(results)}')
results[:5]
Total number of results: 574
Table masked=True length=5
| ArchiveFileID | fileSetName | productLevel | targprop | targ_ra | targ_dec | instrume | exp_type | opticalElements | date_obs | duration | program | observtn | visit | publicReleaseDate | pi_name | proposal_type | proposal_cycle | targtype | access | cal_ver | ang_sep | s_region |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| int64 | str36 | str14 | str25 | float64 | float64 | str7 | str17 | str89 | str27 | float64 | int64 | int64 | int64 | str19 | str25 | str6 | int64 | str7 | str16 | str6 | float64 | str2821 |
| 240850266 | jw01995001002_02101_00001 | 1b, 2a, 2b, 2c | MESSIER-101 | 210.876465 | 54.36092777777778 | NIRCAM | NRC_IMAGE | F115W;CLEAR, F444W;CLEAR | 2023-05-30T15:53:41.4040000 | 311.366 | 1995 | 1 | 2 | 2024-05-30T23:57:44 | Freedman, Wendy L. | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 210.885405667 54.363539181 210.903686820 54.349472486 210.927631420 54.359958338 210.909710699 54.374069380 |
| 240850260 | jw01995001002_02101_00002 | 1b, 2a, 2b, 2c | MESSIER-101 | 210.876465 | 54.36092777777778 | NIRCAM | NRC_IMAGE | F115W;CLEAR, F444W;CLEAR | 2023-05-30T16:00:29.4040000 | 311.366 | 1995 | 1 | 2 | 2024-05-30T23:55:45 | Freedman, Wendy L. | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 210.843638596 54.346485701 210.861621019 54.332379602 210.885819711 54.342694913 210.868007005 54.356993477 |
| 240850156 | jw01995001002_02101_00003 | 1b, 2a, 2b, 2c | MESSIER-101 | 210.876465 | 54.36092777777778 | NIRCAM | NRC_IMAGE | F115W;CLEAR, F444W;CLEAR | 2023-05-30T16:07:06.7160000 | 311.366 | 1995 | 1 | 2 | 2024-05-30T23:55:43 | Freedman, Wendy L. | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 210.844059704 54.346499323 210.862041993 54.332393164 210.886240795 54.342708393 210.868428223 54.357007017 |
| 240849851 | jw01995001003_02101_00001 | 1b, 2a, 2b, 2c | MESSIER-101 | 210.876465 | 54.36092777777778 | NIRCAM | NRC_IMAGE | F115W;CLEAR, F444W;CLEAR | 2023-05-30T16:23:18.9390000 | 311.366 | 1995 | 1 | 3 | 2024-05-30T23:49:52 | Freedman, Wendy L. | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 210.837964482 54.374557592 210.875409895 54.345677384 210.924326179 54.366758903 210.888373410 54.395950436 |
| 240849515 | jw01995001003_02101_00002 | 1b, 2a, 2b, 2c | MESSIER-101 | 210.876465 | 54.36092777777778 | NIRCAM | NRC_IMAGE | F115W;CLEAR, F444W;CLEAR | 2023-05-30T16:29:56.1220000 | 311.366 | 1995 | 1 | 3 | 2024-05-30T23:42:15 | Freedman, Wendy L. | GO | 1 | FIXED | PUBLIC | 2.0.1 | 0.0 | POLYGON ICRS 210.814759400 54.342150595 210.832749852 54.328048799 210.856938736 54.338369897 210.839117838 54.352664198 |
395 JWST datasets fall within our cone search. In other words, their target coordinates are within 10 arcminutes of the coordinates that we defined.
Exercise 2:#
JWST has observed the star Vega, which has a right ascension of 279.23473 degrees and a declination of 38.78369 degrees. Use the query_region function to search for datasets within 15 arcminutes of Vega. Select the fileSetName, targprop, targ_ra, and targ_dec columns.
# # Vega coordinates
# vega = SkyCoord(_, _, unit=('deg')) # Fill in with Vega's coordinates
# # Query for datasets around Vega
# results = missions.query_region(...) # Write your query!
# # Display the first 5 results
# print(f'Total number of results: {len(results)}')
# results[:5]
Query by Criteria#
In some cases, we may want to run queries with non-positional parameters. To accomplish this, we use the query_criteria function.
For any of our query functions, we can filter our results by the value of columns in the dataset.
Let’s say that we only want observations from JWST’s Near Infrared Camera (NIRCam) instrument, and that we only want datasets connected to program number 1189.
# Query with column criteria
results = missions.query_criteria(instrume='NIRCAM', # From Near Infrared Camera
program=1189,
select_cols=['fileSetName', 'instrume', 'exp_type', 'program', 'pi_name'])
# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]
Total number of results: 79
Table masked=True length=5
| ArchiveFileID | fileSetName | instrume | exp_type | program | pi_name | s_region |
|---|---|---|---|---|---|---|
| int64 | str25 | str6 | str13 | int64 | str17 | str931 |
| 248476425 | jw01189010001_02101_00001 | NIRCAM | NRC_IMAGE | 1189 | Roellig, Thomas L | POLYGON ICRS 277.120172080 26.849875305 277.132098971 26.835443799 277.148131087 26.845798799 277.136492608 26.860301167 |
| 248476431 | jw01189010001_02101_00002 | NIRCAM | NRC_IMAGE | 1189 | Roellig, Thomas L | POLYGON ICRS 277.120504877 26.847671217 277.144537450 26.818512371 277.176774641 26.839236061 277.153714071 26.868685780 |
| 248476413 | jw01189010001_02101_00003 | NIRCAM | NRC_IMAGE | 1189 | Roellig, Thomas L | POLYGON ICRS 277.136816889 26.856546237 277.148340587 26.841976875 277.164498570 26.852047061 277.153228186 26.866725025 |
| 248475864 | jw01189010001_02101_00004 | NIRCAM | NRC_IMAGE | 1189 | Roellig, Thomas L | POLYGON ICRS 277.119663090 26.843112943 277.143694918 26.813954244 277.175930652 26.834678131 277.152870785 26.864127708 |
| 248475903 | jw01189010001_04101_00001 | NIRCAM | NRC_IMAGE | 1189 | Roellig, Thomas L | POLYGON ICRS 277.120171897 26.849875466 277.132100171 26.835444596 277.148132310 26.845799169 277.136492187 26.860300978 |
To exclude and filter out a certain value from the results, we can prepend the value with !.
Let’s run the same query as above, but this time, we will filter out datasets coming from the NIRCam instrument.
# Filtered query, excluding NIRCam datasets
results = missions.query_criteria(program=1189,
instrume='!NIRCAM', # Exclude datasets from the NIRCam instrument
select_cols=['fileSetName', 'instrume', 'exp_type', 'program', 'pi_name'])
# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]
Total number of results: 92
Table masked=True length=5
| ArchiveFileID | fileSetName | instrume | exp_type | program | pi_name | s_region |
|---|---|---|---|---|---|---|
| int64 | str36 | str7 | str17 | int64 | str17 | str429 |
| 248474248 | jw01189001001_02101_00001 | NIRSPEC | NRS_WATA | 1189 | Roellig, Thomas L | POLYGON ICRS 57.028706514 -60.379718934 57.029997036 -60.379034506 57.028628578 -60.378418468 57.027338587 -60.379103093 |
| 248474269 | jw01189001001_02101_00002 | NIRSPEC | NRS_TACONFIRM | 1189 | Roellig, Thomas L | POLYGON ICRS 57.028868052 -60.379725570 57.030158570 -60.379041140 57.028790110 -60.378425103 57.027500121 -60.379109730 |
| 248474336 | jw01189001001_04101_00001 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029098138 -60.378811466 57.027719263 -60.379546534 57.027799291 -60.379582617 57.029178187 -60.378847556 |
| 248474158 | jw01189001001_04101_00002 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029431441 -60.378633666 57.028052583 -60.379368738 57.028132612 -60.379404822 57.029511491 -60.378669756 |
| 248474176 | jw01189001001_04101_00003 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029845143 -60.378413088 57.028466303 -60.379148165 57.028546331 -60.379184248 57.029925192 -60.378449179 |
We can also use wildcards for more advanced filtering. Let’s use the same query from above, but we will add an exposure type filter for fixed slits (FS) spectroscopy.
# Filtered query with wildcard
results = missions.query_criteria(program=1189,
instrume='!NIRCAM', # Exclude datasets from the NIRCam instrument
exp_type='*FIXEDSLIT*', # Any exposure type that contains 'FIXEDSLIT'
select_cols=['fileSetName', 'instrume', 'exp_type', 'program', 'pi_name'])
# Display the first 10 results
print(f'Total number of results: {len(results)}')
results[:10]
Total number of results: 38
Table masked=True length=10
| ArchiveFileID | fileSetName | instrume | exp_type | program | pi_name | s_region |
|---|---|---|---|---|---|---|
| int64 | str36 | str7 | str17 | int64 | str17 | str121 |
| 248474336 | jw01189001001_04101_00001 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029098138 -60.378811466 57.027719263 -60.379546534 57.027799291 -60.379582617 57.029178187 -60.378847556 |
| 248474158 | jw01189001001_04101_00002 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029431441 -60.378633666 57.028052583 -60.379368738 57.028132612 -60.379404822 57.029511491 -60.378669756 |
| 248474176 | jw01189001001_04101_00003 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029845143 -60.378413088 57.028466303 -60.379148165 57.028546331 -60.379184248 57.029925192 -60.378449179 |
| 248474380 | jw01189001001_06101_00001 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029094706 -60.378814882 57.027715806 -60.379549981 57.027795835 -60.379586065 57.029174756 -60.378850973 |
| 248473626 | jw01189001001_06101_00002 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029428241 -60.378637024 57.028049357 -60.379372127 57.028129386 -60.379408211 57.029508291 -60.378673115 |
| 248474140 | jw01189001001_06101_00003 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029841922 -60.378416519 57.028463057 -60.379151626 57.028543086 -60.379187710 57.029921972 -60.378452609 |
| 248474272 | jw01189001001_08101_00001 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029093839 -60.378817409 57.027714791 -60.379552569 57.027794819 -60.379588653 57.029173888 -60.378853500 |
| 248474371 | jw01189001001_08101_00002 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029427587 -60.378639357 57.028048551 -60.379374519 57.028128579 -60.379410603 57.029507635 -60.378675448 |
| 248474167 | jw01189001001_08101_00003 | NIRSPEC | NRS_FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029841266 -60.378418864 57.028462251 -60.379154030 57.028542278 -60.379190114 57.029921315 -60.378454954 |
| 248474035 | jw01189002001_03102_00001 | MIRI | MIR_LRS-FIXEDSLIT | 1189 | Roellig, Thomas L | POLYGON ICRS 57.029000075 -60.378579009 57.028710817 -60.378582026 57.028770560 -60.379893934 57.029060321 -60.379890607 |
To filter by multiple values for a single column, we use a string of the values delimited by commas.
To illustrate this, we will use a slightly different query. We query for datasets that have a fixed slits spectroscopy exposure type and targets with moving coordinates (targtype='MOVING'). We will add another filter to match three different last names for principal investigators (PIs).
# Filtered query with multiple values
results = missions.query_criteria(exp_type='*FIXEDSLIT*', # Any exposure type that contains 'FIXEDSLIT'
targtype='MOVING', # Only return moving targets
pi_name='Stansberry, Parker, Lunine', # Last name of PI can be any of these 3 values
select_cols=['fileSetName', 'targtype', 'instrume', 'exp_type', 'program', 'pi_name'])
# Display the first 10 results
print(f'Total number of results: {len(results)}')
results[:10]
Total number of results: 15
Table masked=True length=10
| ArchiveFileID | fileSetName | instrume | exp_type | program | pi_name | targtype | s_region |
|---|---|---|---|---|---|---|---|
| int64 | str25 | str4 | str17 | int64 | str21 | str6 | str125 |
| 245400831 | jw01191018001_03102_00001 | MIRI | MIR_LRS-FIXEDSLIT | 1191 | Stansberry, John A. | MOVING | POLYGON ICRS 27.119130631 -0.633808628 27.118993907 -0.633850603 27.119380875 -0.635104608 27.119517747 -0.635062267 |
| 245401185 | jw01191018001_03102_00002 | MIRI | MIR_LRS-FIXEDSLIT | 1191 | Stansberry, John A. | MOVING | POLYGON ICRS 27.118986627 -0.633313429 27.118849904 -0.633355404 27.119236871 -0.634609408 27.119373743 -0.634567068 |
| 245424730 | jw01191-o018_t002_miri | MIRI | MIR_LRS-FIXEDSLIT | 1191 | Stansberry, John A. | MOVING | POLYGON ICRS 27.118933045 -0.633289957 27.119494797 -0.635109701 27.119358137 -0.635151883 27.118796384 -0.633332139 |
| 240569542 | jw01254001001_03102_00001 | MIRI | MIR_LRS-FIXEDSLIT | 1254 | Parker, Alex Harrison | MOVING | POLYGON ICRS 200.084989101 21.875603741 200.084839511 21.875638116 200.084501536 21.874363824 200.084651465 21.874329687 |
| 240569426 | jw01254001001_03102_00002 | MIRI | MIR_LRS-FIXEDSLIT | 1254 | Parker, Alex Harrison | MOVING | POLYGON ICRS 200.085097655 21.876183716 200.084948065 21.876218091 200.084610087 21.874943800 200.084760016 21.874909662 |
| 240581763 | jw01254-o001_t001_miri | MIRI | MIR_LRS-FIXEDSLIT | 1254 | Parker, Alex Harrison | MOVING | POLYGON ICRS 200.085077697 21.876190767 200.084587433 21.874341477 200.084437789 21.874375643 200.084928050 21.876224932 |
| 243416315 | jw01273001001_04102_00001 | MIRI | MIR_LRS-FIXEDSLIT | 1273 | Lunine, Jonathan I. | MOVING | POLYGON ICRS 218.302364746 15.229464463 218.302216747 15.229456762 218.302291894 15.228146471 218.302440133 15.228154490 |
| 243416980 | jw01273001001_04102_00002 | MIRI | MIR_LRS-FIXEDSLIT | 1273 | Lunine, Jonathan I. | MOVING | POLYGON ICRS 218.302255562 15.230070428 218.302107563 15.230062728 218.302182711 15.228752436 218.302330951 15.228760456 |
| 243416133 | jw01273009001_04102_00001 | MIRI | MIR_LRS-FIXEDSLIT | 1273 | Lunine, Jonathan I. | MOVING | POLYGON ICRS 218.299827529 15.232021871 218.299679528 15.232014170 218.299754675 15.230703879 218.299902916 15.230711898 |
| 243416070 | jw01273009001_04102_00002 | MIRI | MIR_LRS-FIXEDSLIT | 1273 | Lunine, Jonathan I. | MOVING | POLYGON ICRS 218.299717915 15.232627744 218.299569914 15.232620043 218.299645062 15.231309752 218.299793303 15.231317771 |
For columns with numeric or date values, we can filter using comparison values:
<: Return values less than or before the given number/date>: Return values greater than or after the given number/date<=: Return values less than or equal to the given number/date>=: Return values greater than or equal to the given number/date
As an example, let’s write a query to return all datasets with an observation date before February 1, 2022.
# Query using comparison operator
results = missions.query_criteria(date_obs='<2022-02-01', # Must be observed before February 1, 2022
select_cols=['fileSetName', 'program', 'date_obs'])
# Display results
print(f'Total number of results: {len(results)}')
results
Total number of results: 7
Table masked=True length=7
| ArchiveFileID | fileSetName | date_obs | program | s_region |
|---|---|---|---|---|
| int64 | str25 | str27 | int64 | str117 |
| 215731832 | jw01173001001_02101_00001 | 2022-01-28T14:23:42.6180000 | 1173 | POLYGON ICRS 49.866526985 43.426041553 49.866521841 43.425150113 49.865098509 43.425154454 49.865103669 43.426045898 |
| 215731918 | jw01173002001_02101_00001 | 2022-01-28T14:36:10.1110000 | 1173 | POLYGON ICRS 49.834781123 43.401405192 49.833461500 43.417145218 49.855003580 43.418000890 49.856390566 43.402332025 |
| 215731590 | jw01173003001_02101_00001 | 2022-01-28T15:00:36.6550000 | 1173 | POLYGON ICRS 49.833309699 43.387232397 49.830574836 43.418875799 49.873651838 43.420534037 49.876269543 43.389175750 |
| 215731714 | jw01173004001_02101_00001 | 2022-01-28T15:09:07.1830000 | 1173 | POLYGON ICRS 49.871773665 43.425721414 49.871768435 43.424829974 49.870345111 43.424834387 49.870350357 43.425725831 |
| 215731607 | jw01173005001_02101_00001 | 2022-01-28T15:26:34.2270000 | 1173 | POLYGON ICRS 49.871781960 43.425717246 49.871776730 43.424825806 49.870353406 43.424830219 49.870358652 43.425721663 |
| 215731710 | jw01173006001_02101_00001 | 2022-01-28T15:34:23.4690000 | 1173 | POLYGON ICRS 49.871796487 43.425726630 49.871791257 43.424835190 49.870367933 43.424839603 49.870373179 43.425731048 |
| 215731494 | jw01173007001_02101_00001 | 2022-01-28T15:43:44.3010000 | 1173 | POLYGON ICRS 49.833398209 43.387251737 49.830663382 43.418895140 49.873740398 43.420553352 49.876358069 43.389195064 |
For numeric or date data types, we can also filter with ranges. This requires the following syntax: '#..#'.
Let’s write a query that uses range syntax to return datasets that belong to a program number between 1150 and 1155. We will also select for exposure durations that are greater than or equal to 100 seconds.
# Query using range operator
results = missions.query_criteria(program='1150..1155', # Program number between 1150 and 1155
duration='>100', # Exposure duration is greater than or equal to 100 seconds
select_cols=['fileSetName', 'program', 'duration'])
# Display results
print(f'Total number of results: {len(results)}')
results
Total number of results: 77
Table masked=True length=77
| ArchiveFileID | fileSetName | duration | program | s_region |
|---|---|---|---|---|
| int64 | str25 | float64 | int64 | str121 |
| 56863496 | jw01151001001_02101_00001 | 203.999 | 1151 | POLYGON ICRS 267.823568791 74.579914311 267.758823468 74.583483609 267.745046642 74.566390683 267.809916760 74.562619353 |
| 56863482 | jw01151001001_02101_00002 | 203.999 | 1151 | POLYGON ICRS 267.832636673 74.568022765 267.766940456 74.571687792 267.753017936 74.554394205 267.818800515 74.550472738 |
| 56863512 | jw01151001001_03101_00001 | 214.735 | 1151 | POLYGON ICRS 268.091803329 74.608826495 267.943092794 74.616030022 267.912318626 74.578289816 268.058609787 74.570410560 |
| 56863582 | jw01151001001_05101_00001 | 203.999 | 1151 | POLYGON ICRS 267.823569171 74.579914010 267.758823764 74.583483198 267.745047350 74.566390249 267.809917556 74.562619029 |
| 56863557 | jw01151001001_05101_00002 | 203.999 | 1151 | POLYGON ICRS 267.832637174 74.568022531 267.766940886 74.571687467 267.753018708 74.554393860 267.818801363 74.550472484 |
| 56863823 | jw01151001001_06101_00001 | 214.735 | 1151 | POLYGON ICRS 268.091803198 74.608826549 267.943092604 74.616029989 267.912318750 74.578289765 268.058609977 74.570410594 |
| 56863493 | jw01151001001_08101_00001 | 203.999 | 1151 | POLYGON ICRS 267.818105646 74.572500244 267.753393956 74.576073744 267.739607888 74.558981714 267.804444175 74.555206172 |
| 56875469 | jw01151020001_03101_00001 | 214.735 | 1151 | POLYGON ICRS 241.403250845 62.406808505 241.320362209 62.395214146 241.341730845 62.357890793 241.424148058 62.368600633 |
| 56875337 | jw01151021001_02102_00001 | 118.104 | 1151 | POLYGON ICRS 267.913114360 74.594280874 267.847798665 74.598304471 267.832287729 74.581187911 267.897484698 74.576921755 |
| ... | ... | ... | ... | ... |
| 56968123 | jw01155005001_0210a_00002 | 150.315 | 1155 | POLYGON ICRS 268.948931490 65.883692410 268.862026455 65.888823867 268.848510660 65.853853484 268.935616643 65.847844473 |
| 56968107 | jw01155005001_0210b_00001 | 150.315 | 1155 | POLYGON ICRS 268.943367232 65.865137421 268.900206631 65.867886945 268.893348799 65.850424281 268.936614729 65.847418646 |
| 56968099 | jw01155005001_0210b_00002 | 150.315 | 1155 | POLYGON ICRS 268.897309483 65.868076519 268.854801968 65.870734177 268.848044059 65.853472441 268.890674791 65.850613599 |
| 56967383 | jw01155007001_02102_00001 | 1191.781 | 1155 | POLYGON ICRS 268.953421618 65.883348262 268.910258263 65.885838025 268.903866273 65.868430578 268.947013758 65.865693295 |
| 56967393 | jw01155007001_02103_00001 | 1191.781 | 1155 | POLYGON ICRS 268.900582798 65.867357807 268.858088887 65.870023805 268.851308616 65.852766994 268.893929725 65.849900846 |
| 56967357 | jw01155007001_02104_00001 | 1191.781 | 1155 | POLYGON ICRS 268.907471989 65.885893297 268.865026422 65.888465047 268.858577317 65.871266296 268.901022967 65.868503644 |
| 56969123 | jw01155-o001_t018_nircam | 150.315 | 1155 | POLYGON ICRS 268.950105056 65.884072557 268.906939249 65.886554426 268.900565087 65.869142126 268.943721766 65.866414693 |
| 56968129 | jw01155-o003_t018_nircam | 246.946 | 1155 | POLYGON ICRS 268.897309446 65.868076445 268.854801924 65.870734081 268.848044068 65.853472341 268.890674808 65.850613522 |
| 56968698 | jw01155-o005_t018_nircam | 246.946 | 1155 | POLYGON ICRS 268.897309130 65.868076445 268.854801607 65.870734077 268.848043759 65.853472337 268.890674500 65.850613521 |
| 56969356 | jw01155-o007_t018_nircam | 4767.124 | 1155 | POLYGON ICRS 268.954186883 65.883302799 268.865270829 65.889082353 268.851081068 65.852428662 268.939873462 65.846657355 |
Exercise 3#
It’s time to apply all that you’ve learned! Write a non-positional query based on the following:
Fixed targets (HINT:
targtype='FIXED')Instument is Mid-Infrared Instrument (MIRI) or Fine Guidance Sensor (FGS)
Proposal type should NOT include General Observers (
GO)Exposure type includes the string
'IMAGE'Right ascension is between 70 and 75 degrees
Program number is less than 1200.
Skip the first 5 entries.
Select the following columns:
fileSetName,targtype,instrume,proposal_type,exp_type,targ_ra,program
# # A non-positional query with column criteria
# results = missions.query_criteria(...) # Write your query here!
# # Display results
# print(f'Total number of results: {len(results)}')
# results
Additional Resources#
Exercise Solutions#
Exercise 1#
# Query for Whirlpool Galaxy
results = missions.query_object('Whirlpool',
radius=1, # Search radius of 1 arcminute
offset=300, # Skip the first 300 rows
select_cols=['fileSetName', 'opticalElements'])
# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]
Total number of results: 93
Table masked=True length=5
| ArchiveFileID | fileSetName | opticalElements | s_region |
|---|---|---|---|
| int64 | str25 | str12 | str121 |
| 239439476 | jw03435006001_16101_00002 | F100LP;G140M | POLYGON ICRS 202.479792673 47.194664201 202.481599708 47.194664201 202.481599708 47.195968681 202.479792673 47.195968681 |
| 239439515 | jw03435006001_24101_00002 | F170LP;G235M | POLYGON ICRS 202.479797906 47.194662185 202.481604924 47.194662185 202.481604924 47.195966514 202.479797906 47.195966514 |
| 239439053 | jw03435006001_32101_00002 | F170LP;G235M | POLYGON ICRS 202.479798157 47.194662236 202.481605176 47.194662236 202.481605176 47.195966564 202.479798157 47.195966564 |
| 239438758 | jw03435006001_40101_00002 | F290LP;G395M | POLYGON ICRS 202.479800918 47.194661243 202.481608015 47.194661243 202.481608015 47.195965715 202.479800918 47.195965715 |
| 239438713 | jw03435006001_48101_00002 | F290LP;G395M | POLYGON ICRS 202.479801192 47.194661268 202.481608289 47.194661268 202.481608289 47.195965740 202.479801192 47.195965740 |
Exercise 2#
# Vega coordinates
vega = SkyCoord(279.23473, 38.78369, unit=('deg'))
# Query for datasets around Vega
results = missions.query_region(vega,
radius=15, # Search radius of 15 arcminutes
select_cols=['fileSetName', 'targprop', 'targ_ra', 'targ_dec'])
# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]
Total number of results: 65
Table masked=True length=5
| ArchiveFileID | fileSetName | targprop | targ_ra | targ_dec | s_region |
|---|---|---|---|---|---|
| int64 | str25 | str20 | float64 | float64 | str823 |
| 248497983 | jw01193026001_02101_00001 | VEGA | 279.2364266145171 | 38.78556769656323 | POLYGON ICRS 279.221729906 38.783407254 279.235141651 38.771659296 279.250116156 38.782158642 279.236818815 38.793910852 |
| 248508904 | jw01193026001_02101_00002 | VEGA | 279.2364266153141 | 38.7855676974483 | POLYGON ICRS 279.217902494 38.787018585 279.231315639 38.775271128 279.246290259 38.785771032 279.232991522 38.797522747 |
| 248508862 | jw01193026001_02101_00003 | VEGA | 279.2364266161172 | 38.78556769833999 | POLYGON ICRS 279.222650603 38.789514812 279.236063428 38.777766807 279.251039278 38.788266099 279.237740866 38.800018357 |
| 248508856 | jw01193026001_02101_00004 | VEGA | 279.2364266169163 | 38.78556769922733 | POLYGON ICRS 279.225070348 38.786370476 279.238482174 38.774622188 279.253457727 38.785121165 279.240160310 38.796873703 |
| 248497965 | jw01193027001_02101_00001 | VEGA-COPY-MIRI-CORON | 279.2364266191411 | 38.78556770169765 | POLYGON ICRS 279.224084557 38.786582274 279.232078751 38.779668261 279.241455366 38.786108038 279.233482375 38.793052680 |
Exercise 3#
# A non-positional query with column criteria
results = missions.query_criteria(targtype='FIXED', # Fixed target
instrume='MIRI, FGS', # Select MIRI and FGS observations
proposal_type='!GO', # Not from a general observer proposal
exp_type='*IMAGE*', # Contains the string "IMAGE"
targ_ra='70..75', # Between 70 and 75
program='<1200', # Less than 1200
offset=5, # Skip the first 5 results
select_cols=['fileSetName', 'targtype', 'instrume', 'proposal_type',
'exp_type', 'targ_ra', 'program'])
# Display results
print(f'Total number of results: {len(results)}')
results
Total number of results: 11
Table masked=True length=11
| ArchiveFileID | fileSetName | targ_ra | instrume | exp_type | program | proposal_type | targtype | s_region |
|---|---|---|---|---|---|---|---|---|
| int64 | str25 | float64 | str4 | str9 | int64 | str3 | str5 | str429 |
| 248471913 | jw01189005001_02101_00006 | 74.72636096419268 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.725719694 64.586578222 74.715281227 64.580172133 74.730349733 64.575679989 74.740807167 64.582080055 |
| 248472330 | jw01189005001_02101_00007 | 74.72636096470079 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.724549789 64.587997075 74.714111044 64.581590906 74.729180522 64.577098878 74.739638235 64.583499024 |
| 248472309 | jw01189005001_02101_00008 | 74.72636096498769 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.724549252 64.587997065 74.714110498 64.581590899 74.729179970 64.577098867 74.739637691 64.583499011 |
| 248472541 | jw01189015001_02101_00001 | 74.72636283572731 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.684302128 64.625731395 74.649537688 64.598230580 74.713855035 64.583216686 74.749315203 64.610609057 |
| 248472550 | jw01189015001_02101_00002 | 74.72636283828966 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.685737146 64.625665776 74.650971143 64.598165344 74.715287436 64.583150742 74.750749157 64.610542722 |
| 248472183 | jw01189015001_02103_00001 | 74.72636284145068 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.684301080 64.625731312 74.649536360 64.598230563 74.713853552 64.583216548 74.749314000 64.610608852 |
| 248472123 | jw01189015001_02103_00002 | 74.72636284400116 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.685736953 64.625665846 74.650970886 64.598165430 74.715287143 64.583150801 74.750748928 64.610542765 |
| 248473441 | jw01189015001_02105_00001 | 74.72636284716218 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.684301124 64.625731452 74.649536403 64.598230704 74.713853594 64.583216689 74.749314042 64.610608992 |
| 248473411 | jw01189015001_02105_00002 | 74.7263628497248 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.685737105 64.625666012 74.650971069 64.598165589 74.715287342 64.583150973 74.750749096 64.610542944 |
| 248559760 | jw01189-o005_t002_miri | 74.72636096235495 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.738145569 64.583944141 74.739445075 64.584739203 74.724356226 64.589237446 74.720784971 64.587046313 74.719770193 64.587348697 74.719767534 64.587347065 74.709343677 64.580950286 74.715827577 64.579004333 74.724398345 64.576449390 74.724401121 64.576451090 74.725503844 64.577124884 74.730349617 64.575679956 74.739761970 64.581440555 74.740805466 64.582080562 74.738462096 64.582779411 74.739638235 64.583499024 |
| 248572260 | jw01189-o015_t002_miri | 74.72636283572731 | MIRI | MIR_IMAGE | 1189 | GTO | FIXED | POLYGON ICRS 74.750058197 64.610009612 74.750742303 64.610544319 74.685737105 64.625666012 74.685475157 64.625459046 74.684303621 64.625731152 74.649556531 64.598247176 74.666365542 64.594305094 74.713851740 64.583215754 74.714123155 64.583422930 74.715287143 64.583150801 |
Citations#
If you use astroquery for published research, please cite the
authors. Follow these links for more information about citing astroquery:
About this Notebook#
Author(s): Sam Bianco
Keyword(s): Tutorial, JWST, Astroquery, MastMissions
First published: June 2024
Last updated: June 2024
