OutlierDetection for IFU Data

This module serves as the interface for applying outlier_detection to IFU

observations, like those taken with NIRSpec and MIRI. The code implements the basic outlier detection algorithm used with HST data, as adapted to JWST IFU observations.

Specifically, this routine performs the following operations (modified from Default Outlier Detection Algorithm ):

• Extract parameter settings from input model and merge them with any user-provided values
  • the same set of parameters available to Default Outlier Detection Algorithm also applies to this code
• Resample all input IFUImageModel images into IFUCubeModel observations.
  • Resampling uses CubeBuildStep to create IFUCubeModel formatted data for processing.
  • Resampled cubes will be written out to disk if save_intermediate_results parameter has been set to True
• Creates a median image from the set of resampled IFUCubeModel observations
  • Median image will be written out to disk if save_intermediate_results parameter has been set to True
• Blot median image to match each original input exposure.
  • Resampled/blotted cubes will be written out to disk if save_intermediate_results parameter has been set to True
• Perform statistical comparison between blotted image and original image to identify outliers.
• Updates input data model DQ arrays with mask of detected outliers.

jwst.outlier_detection.outlier_detection_ifu Module

Class definition for performing outlier detection on IFU data.

Classes

OutlierDetectionIFU(input_models[, reffiles])

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

Class Inheritance Diagram