Coronagraphic Data

This module serves as the interface for applying outlier_detection to coronagraphic image observations. A CubeModel serves as the basic format for all processing performed by this step. This routine performs the following operations:

  1. Extract parameter settings from input model and merge them with any user-provided values. See outlier detection arguments for the full list of parameters.

  2. Do not attempt resampling; data are assumed to be aligned and have an identical WCS. This is true automatically for a CubeModel.

  3. Create a median image over the groups (exposures, planes of cube) axis, preserving the spatial (x,y) dimensions of the cube.

    • The maskpt parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as “bad”.

  4. Perform statistical comparison between median image and original image to identify outliers.

    The core detection algorithm uses the following to generate an outlier mask

    | image\_input - image\_median | > SNR*input\_err

  5. Update DQ arrays with flags and set SCI, ERR, and variance arrays to NaN at the location of identified outliers.