Python Step Design: OutlierDetectionStep¶
This module provides the sole interface to all methods of performing outlier
detection on JWST observations. The OutlierDetectionStep
supports multiple
algorithms and determines the appropriate algorithm for the type of observation
being processed. This step supports:
- Image modes: ‘NRC_IMAGE’, ‘MIR_IMAGE’, ‘NIS_IMAGE’, ‘FGS_IMAGE’
- Spectroscopic modes: ‘NRC_WFSS’, ‘MIR_LRS-FIXEDSLIT’, ‘NRS_FIXEDSLIT’, ‘NRS_MSASPEC’, ‘NIS_WFSS’
- Time-Series-Observation(TSO) Spectroscopic modes: ‘NIS_SOSS’, ‘MIR_LRS-SLITLESS’, ‘NRC_TSGRISM’, ‘NRS_BRIGHTOBJ’
- IFU Spectroscopic modes: ‘NRS_IFU’, ‘MIR_MRS’
- TSO Image modes:’NRC_TSIMAGE’
- Coronagraphic Image modes: ‘NRC_CORON’, ‘MIR_LYOT’, ‘MIR_4QPM’
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 OutlierDetection Algorithm - Coronagraphic observations: use
OutlierDetection
with resampling turned off as described in Default OutlierDetection Algorithm - Time-Series Observations(TSO): both imaging and spectroscopic modes, will use
OutlierDetection
with resampling turned off as described in Default OutlierDetection Algorithm - NIRSpec and MIRI IFU observations: use
OutlierDetectionIFU
as described in OutlierDetection for IFU Data - Long-slit spectroscopic observations: use
OutlierDetectionSpec
as described in OutlierDetection for Long-Slit Spectroscopic Data
- Images: like those taken with NIRCam, will use
- 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. |