# 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

• Resample all input IFUImageModel images into IFUCubeModel data cubes

• Resampling uses CubeBuildStep to create IFUCubeModel formatted data for processing

• Resampled cubes are written out to disk if the save_intermediate_results parameter is set to True

• Create a median cube from the set of resampled IFUCubeModel cubes

• The median cube is written out to disk if the save_intermediate_results parameter is set to True

• Blot median cube to match each original 2D input image

• Resampled/blotted images are written out to disk if the save_intermediate_results parameter is set to True

• Perform statistical comparison between blotted image and original image to identify outliers

• Update 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.