Slit-like Spectroscopic Data

This module serves as the interface for applying outlier_detection to slit-like spectroscopic observations. The algorithm is very similar to the imaging algorithm, and much of the same code is used. Please refer to those docs for more information. A Stage 3 association, which is loaded into a ModelContainer object, serves as the input and output to this step, and the ModelContainer is converted into a ModelLibrary object to allow sharing code with the imaging mode.

This routine performs identical operations to the imaging mode, with the following exceptions:

  1. Error thresholding is handled differently: the error arrays are resampled and median-combined along with the data arrays, and the median error image is used to identify outliers instead of the input error images for each exposure. This median error image is included alongside the median datamodel (in the err extension) if save_intermediate_results is True.

  2. Resampling is handled by a different class, ResampleSpec instead of ResampleImage.

  3. The resampled images are written out to disk with suffix “outlier_s2d” instead of “outlier_i2d” if the save_intermediate_results parameter is set to True.

  4. The in_memory parameter has no effect, and all operations are performed in memory.

jwst.outlier_detection.spec Module

Perform outlier detection on spectra.

Functions

detect_outliers(input_models, ...)

Flag outliers in slit-like spectroscopic data.