Astroquery: Exploring Metadata from the James Webb Space Telescope

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.mast module 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
Querying MAST for JWST Metadata
- Setup
- Optional Search Parameters
- Query by Object Name
- Query by Region
- Query by Criteria
Additional Resources
Exercise Solutions

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 Uncal FITS file, 2a = Countrate exposure, 2b = calibrated exposure, 2c = cosmic-ray-flagged exposure, 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: 250

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	ang_sep
int64	str25	str14	str18	float64	float64	str6	str9	str77	str27	float64	int64	int64	int64	str19	str10	str2	int64	str5	str16	float64
85452887	jw01714001001_02101_00001	1b, 2a, 2b, 2c	CRAB-NEBULA	83.63604958333333	22.01641666666667	MIRI	MIR_IMAGE	F560W	2023-02-24T00:50:42.9710000	138.752	1714	1	1	2024-02-24T13:30:27	Temim, Tea	GO	1	FIXED	PUBLIC	1.331273005820459
85452927	jw01714001001_02101_00002	1b, 2a, 2b, 2c	CRAB-NEBULA	83.63604958333333	22.01641666666667	MIRI	MIR_IMAGE	F560W	2023-02-24T00:54:27.7390000	138.752	1714	1	1	2024-02-24T13:33:03	Temim, Tea	GO	1	FIXED	PUBLIC	1.0681813609695832
85452908	jw01714001001_02101_00003	1b, 2a, 2b, 2c	CRAB-NEBULA	83.63604958333333	22.01641666666667	MIRI	MIR_IMAGE	F560W	2023-02-24T00:58:07.0030000	138.752	1714	1	1	2024-02-24T13:32:24	Temim, Tea	GO	1	FIXED	PUBLIC	1.2193221921015729
85452963	jw01714001001_02101_00004	1b, 2a, 2b, 2c	CRAB-NEBULA	83.63604958333333	22.01641666666667	MIRI	MIR_IMAGE	F560W	2023-02-24T01:01:46.2660000	138.752	1714	1	1	2024-02-24T13:33:18	Temim, Tea	GO	1	FIXED	PUBLIC	1.3888896315547088
85452842	jw01714001001_02103_00001	1b, 2a, 2b, 2c	CRAB-NEBULA	83.63604958333333	22.01641666666667	MIRI	MIR_IMAGE	F1130W	2023-02-24T01:07:30.3500000	105.452	1714	1	1	2024-02-24T13:34:12	Temim, Tea	GO	1	FIXED	PUBLIC	1.3305044761512126

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) and targ_dec (declination). By default, the query returns all datasets that fall within 3 arcminutes from the object’s coordinates. Let’s set the radius parameter to be 1 arcminute instead.
Say that we’re not interested in the first 4 results. We can assign offset to 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_cols keyword argument. The ArchiveFileID column 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: 151

Table masked=True length=5

ArchiveFileID	fileSetName	targprop	date_obs
int64	str25	str18	str27
85452592	jw01714001003_02101_00001	CRAB-NEBULA	2023-02-24T02:49:57.2040000
85452596	jw01714001003_02101_00002	CRAB-NEBULA	2023-02-24T02:53:39.2200000
85452584	jw01714001003_02101_00003	CRAB-NEBULA	2023-02-24T02:57:23.9880000
85452177	jw01714001003_02101_00004	CRAB-NEBULA	2023-02-24T03:01:06.0030000
85452330	jw01714001003_02103_00001	CRAB-NEBULA	2023-02-24T03:06:50.0870000

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: 395

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	ang_sep
int64	str27	str14	str25	float64	float64	str7	str17	str89	str27	float64	int64	int64	int64	str19	str25	str6	int64	str7	str16	float64
104386304	jw01995001001_02101_00001	1b, 2a, 2b, 2c	MESSIER-101	210.876465	54.36092777777778	NIRCAM	NRC_IMAGE	F115W;CLEAR, F444W;CLEAR	2023-05-30T15:23:35.5330000	311.366	1995	1	1	2024-05-30T23:55:17	Freedman, Wendy L.	GO	1	FIXED	PUBLIC	0.9317195976624251
104386207	jw01995001001_02101_00002	1b, 2a, 2b, 2c	MESSIER-101	210.876465	54.36092777777778	NIRCAM	NRC_IMAGE	F115W;CLEAR, F444W;CLEAR	2023-05-30T15:30:23.5330000	311.366	1995	1	1	2024-05-30T23:56:39	Freedman, Wendy L.	GO	1	FIXED	PUBLIC	0.9247099787034783
104386069	jw01995001001_02101_00003	1b, 2a, 2b, 2c	MESSIER-101	210.876465	54.36092777777778	NIRCAM	NRC_IMAGE	F115W;CLEAR, F444W;CLEAR	2023-05-30T15:37:00.7800000	311.366	1995	1	1	2024-05-30T23:58:44	Freedman, Wendy L.	GO	1	FIXED	PUBLIC	0.9387390545558567
104385967	jw01995001001_02201_00001	1b, 2a, 2b, 2c	MESSIER-101	210.876465	54.36092777777778	NIRISS	NIS_IMAGE	CLEAR;F115W	2023-05-30T15:23:38.9100000	311.366	1995	1	1	2024-05-30T23:55:55	Freedman, Wendy L.	GO	1	FIXED	PUBLIC	7.239582698074742
104385970	jw01995001001_02201_00002	1b, 2a, 2b, 2c	MESSIER-101	210.876465	54.36092777777778	NIRISS	NIS_IMAGE	CLEAR;F115W	2023-05-30T15:30:26.3340000	311.366	1995	1	1	2024-05-30T23:55:49	Freedman, Wendy L.	GO	1	FIXED	PUBLIC	7.233995178717605

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
int64	str25	str6	str13	int64	str17
103431933	jw01189010001_02101_00001	NIRCAM	NRC_IMAGE	1189	Roellig, Thomas L
103431875	jw01189010001_02101_00002	NIRCAM	NRC_IMAGE	1189	Roellig, Thomas L
103431770	jw01189010001_02101_00003	NIRCAM	NRC_IMAGE	1189	Roellig, Thomas L
103431722	jw01189010001_02101_00004	NIRCAM	NRC_IMAGE	1189	Roellig, Thomas L
103431669	jw01189010001_04101_00001	NIRCAM	NRC_IMAGE	1189	Roellig, Thomas L

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: 96

Table masked=True length=5

ArchiveFileID	fileSetName	instrume	exp_type	program	pi_name
int64	str27	str7	str17	int64	str17
103431025	jw01189001001_02101_00001	NIRSPEC	NRS_WATA	1189	Roellig, Thomas L
103430996	jw01189001001_02101_00002	NIRSPEC	NRS_TACONFIRM	1189	Roellig, Thomas L
103431005	jw01189001001_04101_00001	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430949	jw01189001001_04101_00002	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430874	jw01189001001_04101_00003	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L

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: 42

Table masked=True length=10

ArchiveFileID	fileSetName	instrume	exp_type	program	pi_name
int64	str27	str7	str17	int64	str17
103431005	jw01189001001_04101_00001	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430949	jw01189001001_04101_00002	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430874	jw01189001001_04101_00003	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430827	jw01189001001_06101_00001	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430762	jw01189001001_06101_00002	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430721	jw01189001001_06101_00003	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430672	jw01189001001_08101_00001	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430621	jw01189001001_08101_00002	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103430556	jw01189001001_08101_00003	NIRSPEC	NRS_FIXEDSLIT	1189	Roellig, Thomas L
103432165	jw01189002001_03102_00001	MIRI	MIR_LRS-FIXEDSLIT	1189	Roellig, Thomas L

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: 17

Table masked=True length=10

ArchiveFileID	fileSetName	instrume	exp_type	program	pi_name	targtype
int64	str25	str4	str17	int64	str21	str6
90652789	jw01191018001_03102_00001	MIRI	MIR_LRS-FIXEDSLIT	1191	Stansberry, John A.	MOVING
90652807	jw01191018001_03102_00002	MIRI	MIR_LRS-FIXEDSLIT	1191	Stansberry, John A.	MOVING
90656062	jw01191-o018_t002_miri	MIRI	MIR_LRS-FIXEDSLIT	1191	Stansberry, John A.	MOVING
90092649	jw01254001001_03102_00001	MIRI	MIR_LRS-FIXEDSLIT	1254	Parker, Alex Harrison	MOVING
90092583	jw01254001001_03102_00002	MIRI	MIR_LRS-FIXEDSLIT	1254	Parker, Alex Harrison	MOVING
90093194	jw01254-o001_t001_miri	MIRI	MIR_LRS-FIXEDSLIT	1254	Parker, Alex Harrison	MOVING
100129970	jw01273001001_04102_00001	MIRI	MIR_LRS-FIXEDSLIT	1273	Lunine, Jonathan I.	MOVING
100131628	jw01273001001_04102_00002	MIRI	MIR_LRS-FIXEDSLIT	1273	Lunine, Jonathan I.	MOVING
100131677	jw01273009001_04102_00001	MIRI	MIR_LRS-FIXEDSLIT	1273	Lunine, Jonathan I.	MOVING
100131817	jw01273009001_04102_00002	MIRI	MIR_LRS-FIXEDSLIT	1273	Lunine, Jonathan I.	MOVING

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
int64	str25	str27	int64
91445440	jw01173001001_02101_00001	2022-01-28T14:23:42.6180000	1173
91445554	jw01173002001_02101_00001	2022-01-28T14:36:52.9270000	1173
91445294	jw01173003001_02101_00001	2022-01-28T15:00:36.6550000	1173
91445352	jw01173004001_02101_00001	2022-01-28T15:09:07.1830000	1173
91445310	jw01173005001_02101_00001	2022-01-28T15:26:27.6020000	1173
91445363	jw01173006001_02101_00001	2022-01-28T15:34:25.7730000	1173
91445306	jw01173007001_02101_00001	2022-01-28T15:43:44.3010000	1173

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
int64	str25	float64	int64
56863803	jw01151001001_02101_00001	203.999	1151
56863873	jw01151001001_02101_00002	203.999	1151
56863512	jw01151001001_03101_00001	214.735	1151
56863800	jw01151001001_05101_00001	203.999	1151
56863856	jw01151001001_05101_00002	203.999	1151
56863823	jw01151001001_06101_00001	214.735	1151
56863853	jw01151001001_08101_00001	203.999	1151
56875469	jw01151020001_03101_00001	214.735	1151
56876309	jw01151021001_02102_00001	118.104	1151
...	...	...	...
56968427	jw01155005001_0210a_00002	150.315	1155
56968592	jw01155005001_0210b_00001	150.315	1155
56968483	jw01155005001_0210b_00002	150.315	1155
56967461	jw01155007001_02102_00001	1191.781	1155
56967484	jw01155007001_02103_00001	1191.781	1155
56967455	jw01155007001_02104_00001	1191.781	1155
56969427	jw01155-o001_t018_nircam	246.946	1155
56969260	jw01155-o003_t018_nircam	150.315	1155
56969340	jw01155-o005_t018_nircam	246.946	1155
56969363	jw01155-o007_t018_nircam	4767.124	1155

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: 60

Table masked=True length=5

ArchiveFileID	fileSetName	opticalElements
int64	str25	str77
101357435	jw03435008001_0210l_00003	CH12;SHORT, CH34;SHORT, F770W
101357394	jw03435008001_0210l_00004	CH12;SHORT, CH34;SHORT, F770W
101357354	jw03435008001_0210n_00001	CH12;SHORT, CH34;SHORT, F770W
101357300	jw03435008001_0210n_00002	CH12;SHORT, CH34;SHORT, F770W
101357254	jw03435008001_0210n_00003	CH12;SHORT, CH34;SHORT, F770W

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: 51

Table masked=True length=5

ArchiveFileID	fileSetName	targprop	targ_ra	targ_dec
int64	str25	str20	float64	float64
98240946	jw01193026001_02101_00001	VEGA	279.2364266145171	38.78556769656323
98240935	jw01193026001_02101_00002	VEGA	279.2364266153141	38.7855676974483
98240928	jw01193026001_02101_00003	VEGA	279.2364266161172	38.78556769833999
98240908	jw01193026001_02101_00004	VEGA	279.2364266169163	38.78556769922733
98241079	jw01193027001_02101_00001	VEGA-COPY-MIRI-CORON	279.2364266191411	38.78556770169765

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
int64	str25	float64	str4	str9	int64	str3	str5
103428041	jw01189005001_02101_00006	74.72636096419268	MIRI	MIR_IMAGE	1189	GTO	FIXED
103428045	jw01189005001_02101_00007	74.72636096470079	MIRI	MIR_IMAGE	1189	GTO	FIXED
103427981	jw01189005001_02101_00008	74.72636096498769	MIRI	MIR_IMAGE	1189	GTO	FIXED
103432656	jw01189015001_02101_00001	74.72636283573928	MIRI	MIR_MRS	1189	GTO	FIXED
103432616	jw01189015001_02101_00002	74.72636283830163	MIRI	MIR_MRS	1189	GTO	FIXED
103433605	jw01189015001_02103_00001	74.72636284146016	MIRI	MIR_MRS	1189	GTO	FIXED
103432532	jw01189015001_02103_00002	74.72636284400124	MIRI	MIR_MRS	1189	GTO	FIXED
103432477	jw01189015001_02105_00001	74.72636284716255	MIRI	MIR_MRS	1189	GTO	FIXED
103432925	jw01189015001_02105_00002	74.72636284973676	MIRI	MIR_MRS	1189	GTO	FIXED
103431227	jw01189-o005_t002_miri	74.72636096235495	MIRI	MIR_IMAGE	1189	GTO	FIXED
103438341	jw01189-o015_t002_miri	74.72636283572731	MIRI	MIR_IMAGE	1189	GTO	FIXED

Citations#

If you use astroquery for published research, please cite the authors. Follow these links for more information about citing astroquery:

Citing astroquery

About this Notebook#

Author(s): Sam Bianco
Keyword(s): Tutorial, JWST, Astroquery, MastMissions
First published: June 2024
Last updated: June 2024

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