API

Public Step API

jwst.outlier_detection.outlier_detection_step Module

Detect outliers and set DQ flags accordingly.

Classes

OutlierDetectionStep([name, parent, ...])

Flag outlier bad pixels and cosmic rays in DQ array of each input image.

Complete Developer API

jwst.outlier_detection.coron Module

Submodule for performing outlier detection on coronagraphy data.

Functions

detect_outliers(input_model, ...)

Flag outliers in coronography data.

jwst.outlier_detection.ifu Module

Submodule 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 to flag outliers. Pixel are flagged as outliers based on the MINIMUM difference a pixel has with its neighbor across all the input cal files.

Notes

This routine performs the following operations:

1. Extracts parameter settings from input ModelContainer and merges them with any user-provided values.

2. Loop over cal files:

   1. Read in science data.

   2. Store computed neighbor differences for all the pixels. The neighbor pixel differences are defined by the dispersion axis. For MIRI, with the dispersion axis along the y axis, the neighbors that are used to to find the differences are to the left and right of each pixel being examined. For NIRSpec, with the dispersion along the x axis, the neighbors that are used to find the differences are above and below the pixel being examined.

3. For each input file store the minimum of the pixel neighbor differences.

4. Comparing all the differences from all the input data find the minimum neighbor difference.

5. Normalize minimum difference to local median of difference array.

6. Select outliers by flagging those normalized minimum values > threshold_percent.

7. Updates input ImageModel DQ arrays with mask of detected outliers.

Functions

detect_outliers(input_models, ...)

Flag outliers in IFU data.

jwst.outlier_detection.imaging Module

Submodule for performing outlier detection on imaging data.

Functions

detect_outliers(input_models, ...[, ...])

Flag outliers in imaging data.

jwst.outlier_detection.spec Module

Perform outlier detection on spectra.

Functions

detect_outliers(input_models, ...)

Flag outliers in slit-like spectroscopic data.

jwst.outlier_detection.tso Module

Functions

detect_outliers(input_model, ...)

Flag outliers in tso data.

jwst.outlier_detection.utils Module

Utilities for outlier detection methods.

Functions

create_cube_median(cube_model, maskpt)	Compute the median over a cube of data.
median_without_resampling(input_models, ...)	Compute a median image without resampling.
median_with_resampling(input_models, resamp, ...)	Compute a median image with resampling.
flag_crs_in_models(input_models, ...[, ...])	Flag outliers in all input models without resampling.
flag_resampled_model_crs(input_model, ...[, ...])	Flag outliers in a resampled model, updating DQ array in place.
flag_crs_in_models_with_resampling(...[, ...])	Flag outliers in all input models, with resampling, modifying DQ array in place.
flag_model_crs(image, blot, snr[, median_err])	Flag outliers in a model.