JWST Datamodels

The jwst package also contains the interface for JWST Datamodels. Datamodels are the reccomended way of reading and writing JWST data files (.fits) and reference files (.fits and .asdf). JWST data are encoded in FITS files, and reference files consist of a mix of FITS and ASDF - datamodels were designed to abstract away these intricacies and provide a simple interface to the data. They represent the data in FITS extensions and meta data in FITS headers in a Python object with a tree-like structure. The following section gives a brief overview of Datamodels as they pertain to the pipeline - see .. comment out until stdatamodels is released .. ref data-models data-models for more detailed documentation on Datamodels.

Datamodels and the JWST pipeline

When running the pipeline in python, the inputs and outputs of running a pipeline or a step are JWST Datamodels.

The input to a pipeline/step can be a Datamodel, created from an input file on disk. E.g:

# running a single pipeline step, input is datamodel object
from jwst.linearity import LinearityStep
import stdatamodels.jwst.datamodels as dm
input_model = dm.open('jw00001001001_01101_00001_mirimage_uncal.fits')
result = LinearityStep.call(input_model)

If a string path to a file on disk is passed in, a DataModel object will be created internally when the pipeline/step is run.

By default, when running in Python, the corrected data will be returned in-memory as a DataModel instead of being written as an output file. See controlling output file behavior` for instrucions on how to write the returned DataModel to an output file.