OutlierDetectionIFU¶
-
class
jwst.outlier_detection.outlier_detection_ifu.
OutlierDetectionIFU
(input_models, reffiles=None, **pars)[source]¶ Bases:
jwst.outlier_detection.outlier_detection.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
jwst.datamodels.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
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default_suffix
= 's3d'¶
Methods Documentation