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:

  1. Image modes: ‘FGS_IMAGE’, ‘MIR_IMAGE’, ‘NRC_IMAGE’, ‘NIS_IMAGE’

  2. Spectroscopic modes: ‘MIR_LRS-FIXEDSLIT’, ‘NRS_FIXEDSLIT’, ‘NRS_MSASPEC’

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

  4. IFU Spectroscopic modes: ‘MIR_MRS’, ‘NRS_IFU’

  5. TSO Image modes: ‘NRC_TSIMAGE’

  6. Coronagraphic Image modes: ‘MIR_LYOT’, ‘MIR_4QPM’, ‘NRC_CORON’

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

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

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

  3. Read in parameters set by user

  4. Select outlier detection algorithm based on exposure type

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

  6. 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.


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

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

Class Inheritance Diagram

Inheritance diagram of jwst.outlier_detection.outlier_detection_step.OutlierDetectionStep