Section author: Alice Jacques <alice.jacques@noirlab.edu>, Knut Olsen <knut.olsen@noirlab.edu>, Robert Nikutta <robert.nikutta@noirlab.edu>

Version: 20211119

1.4.2. Simple Image Access (SIA)

The Simple Image Access (SIA) service provides capabilities for the discovery, description, access, and retrieval of multi-dimensional image datasets, including 2-D images as well as datacubes of three or more dimensions. Use of the SIA service requires the external PyVO package, and the URL of the SIA service endpoint that you will use. Data Lab has a general SIA service containing all available images from NOIRLab Science Archive (NSA), as well as a number of survey-specific services.

The SIA service tool works to identify the set of images in an image collection that overlap the position at which an image cutout is desired. It returns a table of potential images containing information (metadata) typically found in the image headers (filter, exposure time, time of observation, etc.) as well as a URL pointing to the image cutout itself. Getting image cutouts is thus a three-step process:

  1. choose an image collection

  2. call the SIA service for that collection to retrieve information about images that overlap a position of interest

  3. retrieve the image cutouts by accessing the URLs for the selected images in the table of information

1.4.2.1. Choosing an image collection

Choosing a specific image collection is done by selecting the URL that points to that collection. Here we list the SIA service endpoints for different image collections:

Name

Description

DEF_ACCESS_URL

calibrated/all

All Instrument calibrated images

https://datalab.noirlab.edu/sia/calibrated_all

calibrated/des_y1

DES Year-1 calibrated images

https://datalab.noirlab.edu/sia/calibrated_des_y1

calibrated/des_y2

DES Year-2 calibrated images

https://datalab.noirlab.edu/sia/calibrated_des_y2

calibrated/des_y3

DES Year-3 calibrated images

https://datalab.noirlab.edu/sia/calibrated_des_y3

calibrated/des_y4

DES Year-4 calibrated images

https://datalab.noirlab.edu/sia/calibrated_des_y4

calibrated/nsa

NSA calibrated images

https://datalab.noirlab.edu/sia/calibrated_nsa

calibrated/smash_dr1

SMASH DR1 calibrated images

https://datalab.noirlab.edu/sia/calibrated_smash_dr1

calibrated/smash_dr2

SMASH DR2 calibrated images

https://datalab.noirlab.edu/sia/calibrated_smash_dr1

calibrated/splus_dr1

S-Plus DR1 calibrated images

https://datalab.noirlab.edu/sia/calibrated_splus_dr1

coadd/all

All coadded images

https://datalab.noirlab.edu/sia/coadd_all

coadd/decaps_dr1

DECaPS DR1 coadd images

https://datalab.noirlab.edu/sia/coadd_decaps_dr1

coadd/des_dr1

DES DR1 coadd images

https://datalab.noirlab.edu/sia/coadd_des_dr1

coadd/des_sva

DES SVA coadd images

https://datalab.noirlab.edu/sia/coadd_des_sva

coadd/ls_dr3

DECaLS DR3 coadd images

https://datalab.noirlab.edu/sia/coadd_ls_dr3

coadd/ls_dr4

DECaLS DR4 coadd images

https://datalab.noirlab.edu/sia/coadd_ls_dr4

coadd/ls_dr5

DECaLS DR5 coadd images

https://datalab.noirlab.edu/sia/coadd_ls_dr5

coadd/ls_dr6

DECaLS DR6 coadd images

https://datalab.noirlab.edu/sia/coadd_ls_dr6

coadd/ls_dr7

DECaLS DR7 coadd images

https://datalab.noirlab.edu/sia/coadd_ls_dr7

coadd/ls_dr8

DECaLS DR8 coadd images

https://datalab.noirlab.edu/sia/coadd_ls_dr8

coadd/nsa

NSA coadd images

https://datalab.noirlab.edu/sia/coadd_nsa

coadd/smash_dr2

SMASH DR2 coadd images

https://datalab.noirlab.edu/sia/coadd_smash_dr2

coadd/splus_dr1

S-PLUS DR1 coadd images

https://datalab.noirlab.edu/sia/coadd_splus_dr1

decaps_dr1

DECaPS DR1 images

https://datalab.noirlab.edu/sia/decaps_dr1

des_dr1

DES DR1 images

https://datalab.noirlab.edu/sia/des_dr1

des_dr2_se

DES DR2 single-epoch images

https://datalab.noirlab.edu/sia/des_dr2_se

des_dr2

DES DR2 images

https://datalab.noirlab.edu/sia/des_dr2

des_sva1

DES SVA1 images

https://datalab.noirlab.edu/sia/des_sva1

des_y1

DES Year-1 images

https://datalab.noirlab.edu/sia/des_y1

des_y2

DES Year-2 images

https://datalab.noirlab.edu/sia/des_y2

des_y3

DES Year-3 images

https://datalab.noirlab.edu/sia/des_y3

des_y4

DES Year-4 images

https://datalab.noirlab.edu/sia/des_y4

gogreen_dr1

GOGREEN DR1 images

https://datalab.noirlab.edu/sia/gogreen_dr1

ls_dr3

DECaLS DR3 images

https://datalab.noirlab.edu/sia/ls_dr3

ls_dr4

DECaLS DR4 images

https://datalab.noirlab.edu/sia/ls_dr4

ls_dr5

DECaLS DR5 images

https://datalab.noirlab.edu/sia/ls_dr5

ls_dr6

DECaLS DR6 images

https://datalab.noirlab.edu/sia/ls_dr6

ls_dr7

DECaLS DR7 images

https://datalab.noirlab.edu/sia/ls_dr7

ls_dr8

DECaLS DR8 images

https://datalab.noirlab.edu/sia/ls_dr8

ls_dr9

DECaLS DR9 images

https://datalab.noirlab.edu/sia/ls_dr9

nsa

NOAO Science Archive

https://datalab.noirlab.edu/sia/nsa

nsc_dr2

NOIRLab Source Catalog DR2

https://datalab.noirlab.edu/sia/nsc_dr2

raw/all

All Raw images

https://datalab.noirlab.edu/sia/raw_all

raw/nsa

NSA Raw images

https://datalab.noirlab.edu/sia/raw_nsa

raw/smash_dr1

SMASH DR1 raw images

https://datalab.noirlab.edu/sia/raw_smash_dr1

raw/smash_dr2

SMASH DR2 raw images

https://datalab.noirlab.edu/sia/raw_smash_dr2

sdss_dr9

SDSS DR9 sky-subtracted images

https://datalab.noirlab.edu/sia/sdss_dr9

smash_dr1

SMASH DR1 images

https://datalab.noirlab.edu/sia/smash_dr1

smash_dr2

SMASH DR2 images

https://datalab.noirlab.edu/sia/smash_dr2

splus_dr1

S-PLUS DR1 images

https://datalab.noirlab.edu/sia/splus_dr1

splus_edr

S-PLUS EDR images

https://datalab.noirlab.edu/sia/splus_edr

stripe82/calibrated

Calibrated images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_calibrated

stripe82/coadd

Calibrated images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_coadd

stripe82/decaps_dr1

DECAPS DR1 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_decaps_dr1

stripe82/des_dr1

Calibrated images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_des_dr1

stripe82/des_sva1

DES SVA images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_des_sva1

stripe82/des_y1

DES Year-1 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_des_y1

stripe82/des_y2

DES Year-2 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_des_y2

stripe82/des_y3

DES Year-3 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_des_y3

stripe82/des_y4

DES Year-4 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_des_y4

stripe82/ls_dr3

DECaLS DR3 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_ls_dr3

stripe82/ls_dr4

DECaLS DR4 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_ls_dr4

stripe82/ls_dr5

DECaLS DR5 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_ls_dr5

stripe82/ls_dr6

DECaLS DR6 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_ls_dr6

stripe82/ls_dr7

DECaLS DR7 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_ls_dr7

stripe82/ls_dr8

DECaLS DR8 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_ls_dr8

stripe82/ls_dr9

DECaLS DR9 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_ls_dr9

stripe82/nsa

NOAO Science Archive images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_nsa

stripe82/nsc_dr2

NSC DR2 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_nsc_dr2

stripe82/raw

Raw images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_raw

stripe82/sdss_dr9

SDSS DR9 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_sdss_dr9

stripe82/smash_dr1

SMASH DR1 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_smash_dr1

stripe82/smash_dr2

SMASH DR2 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_smash_dr2

stripe82/splus_dr1

S-PLUS DR1 images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_splus_dr1

stripe82/splus_edr

S-PLUS EDR images in Stripe82

https://datalab.noirlab.edu/sia/stripe82_splus_edr

1.4.2.2. Retrieving image information

We can use the search method of the SIA service object to perform a search within a specified field of view (fov) size. The search method takes the following as arguments:

(ra,dec) - a tuple containing the RA and Dec of the object in decimal degrees

fov - a tuple with the size of the cutout in each direction in degrees

For example, we will pick a location in SDSS Stripe 82 in the subset of images from the NOIRLab Science Archive (nsa) that overlap with SDSS Stripe 82:

> DEF_ACCESS_URL = "http://datalab.noirlab.edu/sia/stripe82_nsa"
> svc_stripe82 = sia.SIAService(DEF_ACCESS_URL)

We use the SDSS Stripe 82-specific SIA service above to retrieve the image information for an image cutout whose fov size is 6x6 arcmin (0.1x0.1 degrees). We want the result to be an Astropy table, so we add .to_table() at the end of the call:

> ra = 335.0
> dec = 0.0
> fov = 0.1
> imgTable = svc_stripe82.search((ra,dec), (fov/np.cos(dec*np.pi/180), fov), verbosity=2).to_table()

NOTE the search method works in RA and Dec in degrees, and does not account for the convergence of lines of RA toward the poles, so we divide by a cos(dec) term in the RA fov argument.

The result is a table of metadata for images that overlap the search position.

1.4.2.3. Retrieving image cutouts

In the result table of metadata for images obtained above, the column access_url contains the link to retrieve the image cutout. We can filter the table on any of the columns to narrow down the list to just the cutouts we want. Here we’ll select stacked images (proctype=Stack and prodtype=image) observed in the g filter (obs_bandpass that contains the letter ‘g’):

> sel = (imgTable['proctype'].astype(str)=='Stack') & (imgTable['prodtype'].astype(str)=='image') & (startswith(imgTable['obs_bandpass'].astype(str),'g'))

From the filtered table we will pick a row and retrieve the image:

> row = imgTable[sel][1]
> url = row['access_url'].decode() # get the download URL
> filename = download_file(url,cache=True,show_progress=False,timeout=120)
> hdu = fits.open(filename)[0]
> image = hdu.data
> hdr = hdu.header
> wcs = WCS(hdr) # WCS info from header to convert pixel coords to world coords

Then we’ll display the image. Note that the image might not be square for a variety of reasons, such as that the image cutout ran into a chip edge along one axis.

> fig = plt.figure(figsize=(10,10))
> ax = fig.add_subplot(projection=wcs)
> ax.imshow(image,origin='lower',cmap='bone',vmin=image.min(),vmax=image.min()+(image.max()-image.min())/170.)
SIA screenshot

1.4.2.4. Resources and References

See our How To Use SIA Service Jupyter notebook for example usage.

For more background on the SIA protocol, see the IVOA document that describes it.