NASA services can be queried from Python in multiple ways.
- Generic Virtual Observatory (VO) queries.
- Call sequence is consistent, including for non-NASA resources.
- Some reference notebooks available at https://heasarc.gsfc.nasa.gov/vo/summary/python.html
- Astroquery interfaces
- Call sequences not quite as consistent, but follow similar patterns.
- See https://astroquery.readthedocs.io/en/latest/
- Ad hoc archive-specific interfaces
# Surpress known innocuous warnings
import warnings
warnings.filterwarnings("ignore", module="astropy.io.votable.*")
from navo_utils.registry import Registry
# Simple example: Find Cone Search services related to SWIFT.
services=Registry.query(service_type='cone', keyword='swift')| Argument | Description | Examples |
|---|---|---|
| service_type (required) | Type of service | 'cone', 'image', 'spectra', 'table' |
| keyword | Results will contain the keyword in ivoid, title, or description | 'galex', 'swift' |
| waveband | Resulting services have data in the specified waveband(s) | 'gamma-ray', 'X-ray', 'EUV', 'UV' , 'optical' , 'infrared' , 'millimeter' , 'radio', 'UV,optical' |
| publisher | Name (or substring of) publishing organization | 'CDS', 'HEASARC', 'Space Telescope', 'IPAC' |
See http://docs.astropy.org/en/stable/table/ for more on working with Astropy Tables.
print(services.info) # Show the number of rows and the column names.
print(services) # Default display of Astropy Table.
print(services[:3]['short_name', 'reference_url']) # Show first 3 short names and reference URLs.
swiftuvot_services = services[services['short_name'] == 'SwiftUVOT'] # Filter the results by column value.
# or in a Jupyter notebook
services.show_in_notebook() # Display an interactive table in the notebook.
Registry.display_results(services) # Format result to make long descriptions more readable.Example: Find a cone search service for the USNO-B catalog and search it around M51 with a .5 degree radius. (More inspection could be done on the service list instead of blindly choosing the first service.) The coords argument can be anything accepted by: https://astroquery.readthedocs.io/en/latest/api/astroquery.utils.parse_coordinates.html#astroquery.utils.parse_coordinates
More information on creating Astropy SkyCoord objects: http://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html
from navo_utils.cone import Cone
services=Registry.query(service_type='cone', keyword='usno-b')
results = Cone.query(service=services[0], coords='m51',radius=.5)Example: Find an image search service for GALEX, and search it around coordinates 13:37:00.950,-29:51:55.51 (M83) with a radius of .2 degrees. Download the first file in the results.
from navo_utils.image import Image, ImageColumn
services=Registry.query(service_type='image', keyword='galex', publisher='Space Telescope')
image_results_list = Image.query(service=services[0], coords='13:37:00.950,-29:51:55.51', radius=0.2)
image_results = image_results_list[0] # List would be longer if list of services was input.
from astropy.utils.data import download_file
local_path = download_file(image_results[0][ImageColumn.ACCESS_URL])
# Or download to memory instead for display purposes.
fits_images = image_results[image_results['contentType'] == 'image/fits']
imagedata = Image.get_fits(fits_images[0])Example: Find a spectral service for x-ray data. Query it around Delta Ori, and download the first data product. Note that the results table can be inspected for potentially useful columns.
from navo_utils.spectra import Spectra, SpectraColumn
from astropy.coordinates import SkyCoord
delori = SkyCoord.from_name("delta ori")
services = Registry.query(service_type='spectr', waveband='x-ray')
spectra_results_list = Spectra.query(service=services[0], coords=delori, radius=0.1)
spectra_results = spectra_results_list[0] # List would be longer if list of services was input.
from astropy.utils.data import download_file
local_path = download_file(spectra_results[0][SpectraColumn.ACCESS_URL])Example: Find the HEASARC Table Access Protocol (TAP) service, get some information about the available tables.
from navo_utils.tap import Tap
tap_services = Registry.query(service_type='table',source='heasarc')
tap_url = str(tap_services[0]['access_url'])
Tap.list_tables(tap_url) # list table names and descriptions
Tap.list_tables(tap_url, contains='chan') # list table names and descriptions where name contains 'chan'
table_list = Tap.get_tables(tap_url) # Same as list_tables, but returns list of table objects
Tap.list_columns(tap_url,tablename="public.zcat") # list columns for table 'public.zcat'Example: Perform a cone search on the ZCAT catalog.
query = """
select name, ra, dec, bmag, redshift from public.zcat as cat
where contains(point('ICRS',cat.ra,cat.dec),circle('ICRS',202.5,47.0,1.0))=1
and cat.bmag < 14
"""
results=Tap.query(tap_url,query)Many archives have Astroquery modules for data access, including:
- HEASARC Queries (astroquery.heasarc)
- HITRAN Queries (astroquery.hitran)
- IRSA Image Server program interface (IBE) Queries (astroquery.ibe)
- IRSA Queries (astroquery.irsa)
- IRSA Dust Extinction Service Queries (astroquery.irsa_dust)
- JPL Spectroscopy Queries (astroquery.jplspec)
- MAST Queries (astroquery.mast)
- NASA ADS Queries (astroquery.nasa_ads)
- NED Queries (astroquery.ned)
For more, see https://astroquery.readthedocs.io/en/latest/
Example: Get an Astropy Table containing the objects from paper 2018ApJ...858...62K. For more on the API, see https://astroquery.readthedocs.io/en/latest/ned/ned.html
from astroquery.ned import Ned
objects_in_paper = Ned.query_refcode('2018ApJ...858...62K')