class jwst.outlier_detection.outlier_detection_spec.OutlierDetectionSpec(input_models, reffiles=None, **pars)[source]

Bases: OutlierDetection

Class definition for performing outlier detection on spectra.

This is the controlling routine for the outlier detection process. It loads and sets the various input data and parameters needed by the various functions and then controls the operation of this process through all the steps used for the detection.


This routine performs the following operations:

1. Extracts parameter settings from input model and merges
   them with any user-provided values
2. Resamples all input images into grouped observation mosaics.
3. Creates a median image from all grouped observation mosaics.
4. Blot median image to match each original input image.
5. Perform statistical comparison between blotted image and original
   image to identify outliers.
6. Updates input data model DQ arrays with mask of detected outliers.

Initialize class with input_models.

  • input_models (list of DataModels, str) – list of data models as ModelContainer or ASN file, one data model for each input image

  • reffiles (dict of jwst.datamodels.DataModel) – Dictionary of datamodels. Keys are reffile_types.

  • pars (dict, optional) – Optional user-specified parameters to modify how outlier_detection will operate. Valid parameters include: - resample_suffix

Attributes Summary


Methods Summary


Flag outlier pixels in DQ of input images.

Attributes Documentation

default_suffix = 's2d'

Methods Documentation


Flag outlier pixels in DQ of input images.