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:

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

Inheritance diagram of jwst.outlier_detection.outlier_detection_step.OutlierDetectionStep