OutlierDetectionIFU

class jwst.outlier_detection.outlier_detection_ifu.OutlierDetectionIFU(input_models, reffiles=None, **pars)

Bases: OutlierDetection

Sub-class defined for performing outlier detection on IFU data.

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.

Notes

This routine performs the following operations:

1. Extracts parameter settings from input ModelContainer and merges
   them with any user-provided values
2. Resamples all input images into IFUCubeModel observations.
3. Creates a median image from all IFUCubeModels.
4. Blot median image using CubeBlot to match
    each original input ImageModel.
5. Perform statistical comparison between blotted image and original
   image to identify outliers.
6. Updates input ImageModel DQ arrays with mask of detected outliers.

Initialize class for IFU data processing.

Parameters

• input_models (ModelContainer, str) – list of data models as ModelContainer or ASN file, one data model for each input 2-D ImageModel

• drizzled_models (list of objects) – ModelContainer containing drizzled grouped input images

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

Attributes Summary

default_suffix

Methods Summary

blot_median(median_image)	IFU-specific version of blot_median.
create_median(resampled_models)	IFU-specific version of create_median.
do_detection()	Flag outlier pixels in DQ of input images.

Attributes Documentation

default_suffix = 's3d'

Methods Documentation

blot_median(median_image)

IFU-specific version of blot_median.

create_median(resampled_models)

IFU-specific version of create_median.

do_detection()

Flag outlier pixels in DQ of input images.