Outlier Detection 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 intoIFUCubeModel
data cubes
Resampling uses
CubeBuildStep
to createIFUCubeModel
formatted data for processingResampled cubes are written out to disk if the
save_intermediate_results
parameter is set toTrue
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 toTrue
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 toTrue
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¶
|
Sub-class defined for performing outlier detection on IFU data. |