liger_iris_pipeline Documentation

The Liger-IRIS Data Reduction System is based on the suite of packages from Space Telescope Science Institute used to process data from JWST and NGRST.

With stpipe and stdatamodels we can configure each step of a pipeline through one or more text based .INI style files, then we provide one input FITS file or a set of multiple inputs defined in JSON (named Associations). Custom analysis steps and pipelines for IRIS are defined as classes in the current repository liger_iris_pipeline

Then execute the pipeline from the command line using the tmtrun executable or using directly the Python library.

The pipeline also dynamically interfaces to the CRDS the Calibration References Data System, to retrieve the best calibration datasets given the metadata in the headers of the input FITS files. The CRDS client can also load data from a local cache, so for now we do not have a actual CRDS server and we only rely on a local cache.

The CRDS is not under our control, the Thirty Meter Telescope will deliver a database system to replace the CRDS and we can adapt our code to that in the future.

Getting Started

• Getting Started
  • Python Environment
  • CRDS
  • Compilation of Readout Code
  • Development Install
  • Run the unit tests

Example run

• Example pipeline execution
  • Setup CRDS
  • Get input simulations
  • Preprocess the flat frame
  • Configure the image processing pipeline
  • Define the input data
  • Execute the pipeline from the command line
  • Plot the reduced frame

Design

• IRIS Data Reduction System design
• Purpose
• Software infrastructure
• Processing levels and data product stages
• File format
• Example run
• Access calibration files via the Calibration Reference Data System (CRDS)
• Metadata

Calibration and CRDS

• Calibration
  • Calibration files
  • Access calibration files via the Calibration Reference Data System (CRDS)

Algorithms

• Available algorithms
• Algorithms
• Imager Algorithms
• IFS Algorithms
• Advanced Algorithms
• Calibration algorithms

Subarrays

• Support for subarrays

Reference/API

liger_iris_pipeline Package

Functions

monkeypatch_jwst_datamodels()

Classes

AssignWcsStep([name, parent, config_file, ...])	AssignWcsStep: Create a gWCS object and store it in Model.meta.
BackgroundStep([name, parent, config_file, ...])	BackgroundStep: Subtract background exposures from target exposures.
DQInitStep([name, parent, config_file, ...])	Initialize the Data Quality extension from the mask reference file.
DarkCurrentStep([name, parent, config_file, ...])	DarkCurrentStep: Performs dark current correction by subtracting dark current reference data from the input science data model.
FlatFieldStep([name, parent, config_file, ...])	Flat-field a science image using a flatfield reference image.
MergeSubarraysStep([name, parent, ...])	ParseSubarrayMapStep: Parse a subarray map extension, if available, and create header metadata and data quality flag accordingly.
NormalizeStep([name, parent, config_file, ...])	NormalizeStep: Normalize a frame by dividing by its own mean, median or mode
ParseSubarrayMapStep([name, parent, ...])	ParseSubarrayMapStep: Parse a subarray map extension, if available, and create header metadata and data quality flag accordingly
ProcessFlatfieldL2(*args, **kwargs)	ProcessFlatfieldL2: Remove dark and normalize exposure to create a flat field to be later added to the CRDS.
ProcessImagerL2Pipeline(*args, **kwargs)	ProcessImagerL2Pipeline: Processes JWST imaging-mode slope data from Level-2a to Level-2b.
ROPPipeline(*args, **kwargs)	Detector1Pipeline for IRIS
SubtractImagesStep([name, parent, ...])	SubtractImagesStep: Subtract two exposures from one another to accomplish background subtraction.
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Class Inheritance Diagram