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 inputsRead 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 AlgorithmCoronagraphic observations: use
OutlierDetection
with resampling turned off as described in Default Outlier Detection AlgorithmTime-Series Observations(TSO): both imaging and spectroscopic modes, use
OutlierDetection
with resampling turned off as described in Default Outlier Detection AlgorithmIFU observations: use
OutlierDetectionIFU
as described in Outlier Detection for IFU DataLong-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
|
Flag outlier bad pixels and cosmic rays in DQ array of each input image. |