What are Associations?¶
Associations are basically just lists of things, mostly exposures, that are somehow related. With respect to JWST and the Data Management System (DMS), associations have the following characteristics:
Relationships between multiple exposures are captured in an association.
An association is a means of identifying a set of exposures that belong together and may be dependent upon one another.
The association concept permits exposures to be calibrated, archived, retrieved, and reprocessed as a set rather than as individual objects.
For each association, DMS will generate the most combined and least combined data products.
Associations and JWST¶
The basic chunk in which science data arrives from the observatory is termed an exposure. An exposure contains the data from a single set of integrations per detector per instrument. In general, it takes many exposures to make up a single observation, and a whole program is made up of a large number of observations.
On first arrival, an exposure is termed to be at Level1b: The only transformation that has occurred is the extraction of the science data from the observatory telemetry into a FITS file. At this point, the science exposures enter the calibration pipeline.
The pipeline consists of three stages: Stage 1, Stage 2, and Stage 3 processing. Stage 2 processing is the calibration necessary to remove instrumental effects from the data. The resulting files contain flux and spatially calibrated data, called Stage 2b data. The information is still in individual exposures.
Older documentation and code may refer to the stages as levels. They are synonymous.
To be truly useful, the exposures need to be combined and, in the case of multi-object spectrometry, separated, into data that is source-oriented. This type of calibration is called Stage 3 processing. Due to the nature of the individual instruments, observing modes, and the interruptibility of the observatory itself, how to group the right exposures together is not straight-forward.
Enter the Association Generator. Given a set of exposures, called the Association Pool, and a set of rules found in an Association Registry, the generator groups the exposures into individual associations. These associations are then used as input to the Stage 3 calibration steps to perform the transformation from exposure-based data to source-based, high(er) signal-to-noise data.
In short, Stage 2 and Stage 3 associations are created running the
asn_generate task on an
using the default Level2 and Level
3 association rules to produce Stage 2 and Stage 3 associations.
Users should not need to run the generator. Instead, it is expected that one edits an already existing association that accompanies the user’s JWST data. Or, if need be, an association can be created based on the existing Stage 2 or Stage 3 examples. If, however, the user does need to run the generator, the Utilities and Association Generator documentation will be helpful.
Once an association is in-hand, one can pass it as input to a pipeline routine. For example:
% strun calwebb_image3 jw12345-o001_20210311t170002_image3_001_asn.json
Programmatically, to read in an Association, one uses the
from jwst.associations import load_asn with open('jw12345-o001_20210311t170002_image3_001_asn.json') as fp: asn = load_asn(fp)
What exactly is returned depends on what the association is. However,
for all Stage 2 and Stage 3 associations, a Python
dict is returned,
whose structure matches that of the JSON or YAML file. Continuing
from the above example, the following shows how to access the first
exposure file name of a Stage 3 associations:
exposure = asn['products']['members']['expname']
Since the JWST pipeline uses associations extensively, higher-level access is gained by opening an association as a JWST Data Model:
from jwst.datamodels import open as dm_open container_model = dm_open('jw12345-o001_20210311t170002_image3_001_asn.json')