Default OutlierDetection Algorithm

This module serves as the interface for applying outlier_detection to direct image observations, like those taken with NIRCam and MIRI. The code implements the basic outlier detection algorithm used with HST data, as adapted to JWST.

Specifically, this routine performs the following operations:

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

  • Convert input data, as needed, to make sure it is in a format that can be processed

    • A ModelContainer serves as the basic format for all processing performed by this step, as each entry will be treated as an element of a stack of images to be processed to identify bad-pixels/cosmic-rays and other artifacts.

    • If the input data is a CubeModel, convert it into a ModelContainer. This allows each plane of the cube to be treated as a separate 2D image for resampling (if done) and for combining into a median image.

  • By default, resample all input images into grouped observation mosaics; for example, combining all NIRCam multiple detector images from a single exposure or from a dithered set of exposures.

    • Resampled images will be written out to disk if the save_intermediate_results parameter is set to True

    • If resampling is turned off, a copy of the input (as a ModelContainer) will be used for subsequent processing.

  • Create a median image from all grouped observation mosaics.

    • The median image is created by combining all grouped mosaic images or non-resampled input data (as planes in a ModelContainer) pixel-by-pixel.

    • The median image is written out to disk if the save_intermediate_results parameter is set to True.

  • By default, the median image is blotted back (inverse of resampling) to match each original input image.

    • Resampled/blotted images are written out to disk if the save_intermediate_results parameter is set to True

    • If resampling is turned off, the median image is compared directly to each input image.

  • Perform statistical comparison between blotted image and original image to identify outliers.

  • Update input data model DQ arrays with mask of detected outliers.

Outlier Detection for TSO data

Time-series observations (TSO) result in input data stored as a 3D CubeModel where each plane in the cube represents a separate integration without changing the pointing. Normal imaging data would benefit from combining all integrations into a single image. TSO data’s value, however, comes from looking for variations from one integration to the next. The outlier detection algorithm, therefore, gets run with a few variations to accomodate the nature of these 3D data.

  • Input data is converted from a CubeModel (3D data array) to a ModelContainer

    • Each model in the ModelContainer is a separate plane from the input CubeModel

  • The median image is created without resampling the input data

    • All integrations are aligned already, so no resampling or shifting needs to be performed

  • A matched median gets created by combining the single median frame with the noise model for each input integration.

  • Perform statistical comparison between the matched median with each input integration.

  • Update input data model DQ arrays with the mask of detected outliers

Note

This same set of steps also gets used to perform outlier detection on coronographic data, because it too is processed as 3D (per-integration) cubes.

Outlier Detection for IFU data

Integral Field Unit (IFU) data is handled as a 2D image on input (i.e. the state of the data before creating a 3D cube). This 2D image gets converted into a properly calibrated spectral cube (3D array) and stored as an IFUCubeModel for use within outlier detection. The many differences in data format for the IFU data relative to normal direct imaging data requires special processing in order to perform outlier detection on IFU data.

  • Convert the input 2D IFUImageModel into a 3D IFUCubeModel by calling CubeBuildStep

    • A separate IFUCubeModel is generated for each wavelength channel/band by using the single option for the CubeBuildStep.

  • All IFUCubeModels get median combined to create a single median IFUCubeModel product.

  • The IFUCubeModel median product gets resampled back to match each original input IFUImageModel dataset.

    • This resampling uses CubeBlot to perform this conversion.

  • The blotted, median 2D images are compared statistically to the original 2D input images to detect outliers.

  • The DQ array of each input dataset gets updated to mark the detected outliers.

jwst.outlier_detection.outlier_detection Module

Primary code for performing outlier detection on JWST observations.

Functions

flag_cr(sci_image, blot_image, **pars)

Masks outliers in science image by updating DQ in-place

abs_deriv(array)

Take the absolute derivate of a numpy array.

Classes

OutlierDetection(input_models[, reffiles])

Main class for performing outlier detection.

Class Inheritance Diagram

Inheritance diagram of jwst.outlier_detection.outlier_detection.OutlierDetection