Python Step Design: OutlierDetectionStep

This module provides the sole interface to all methods of performing outlier detection on JWST observations. The outlier_detection step supports multiple algorithms and determines the appropriate algorithm for the type of observation being processed. This step supports:

• Image modes: ‘FGS_IMAGE’, ‘MIR_IMAGE’, ‘NRC_IMAGE’, ‘NIS_IMAGE’

• Spectroscopic modes: ‘MIR_LRS-FIXEDSLIT’, ‘NRS_FIXEDSLIT’, ‘NRS_MSASPEC’

• Time-Series-Observation(TSO) Spectroscopic modes: ‘MIR_LRS-SLITLESS’, ‘NRC_TSGRISM’, ‘NIS_SOSS’, ‘NRS_BRIGHTOBJ’

• IFU Spectroscopic modes: ‘MIR_MRS’, ‘NRS_IFU’

• TSO Image modes: ‘NRC_TSIMAGE’

• Coronagraphic Image modes: ‘MIR_LYOT’, ‘MIR_4QPM’, ‘NRC_CORON’

This step uses the following logic to apply the appropriate algorithm to the input data:

• Interpret inputs (ASN table, ModelContainer or CubeModel) to identify all input observations to be processed

• Read in type of exposures in input by interpreting meta.exposure.type from inputs

• Read in parameters set by user

• Select outlier detection algorithm based on exposure type

  • Images: like those taken with NIRCam, will use OutlierDetection as described in Default Outlier Detection Algorithm

  • Coronagraphic observations: use OutlierDetection with resampling turned off as described in Default Outlier Detection Algorithm

  • Time-Series Observations(TSO): both imaging and spectroscopic modes, use OutlierDetection with resampling turned off as described in Default Outlier Detection Algorithm

  • IFU observations: use OutlierDetectionIFU as described in Outlier Detection for IFU Data

  • Long-slit spectroscopic observations: use OutlierDetectionSpec as described in Outlier Detection for Slit-like Spectroscopic Data

• Instantiate and run outlier detection class determined for the exposure type using parameter values interpreted from inputs.

• Return input models with DQ arrays updated with flags for identified outliers

jwst.outlier_detection.outlier_detection_step Module

Public common step definition for OutlierDetection processing.

Classes

OutlierDetectionStep([name, parent, ...])

Flag outlier bad pixels and cosmic rays in DQ array of each input image.

Class Inheritance Diagram