BadpixSelfcalStep

class jwst.badpix_selfcal.BadpixSelfcalStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)[source]

Bases: JwstStep

Flag residual artifacts as bad pixels using a median filter and percentile cutoffs.

All input exposures in the association file (or manually-provided bkg_list) are combined into a single background model using a MIN operation. The bad pixels are then identified using a median filter and percentile cutoffs, and applied to the science data by setting the flagged pixels, errors, and variances to NaN, and the DQ flag to DO_NOT_USE + OTHER_BAD_PIXEL.

Create a Step instance.

Parameters:

namestr: The name of the Step instance. Used in logging messages and in cache filenames. If not provided, one will be generated based on the class name.
parentStep: The parent step of this step. Used to determine a fully-qualified name for this step, and to determine the mode in which to run this step.
config_filestr or pathlib.Path: The path to the config file that this step was initialized with. Use to determine relative path names of other config files.
_validate_kwdsbool: Validate given kws against specs/config.
**kwsdict: Additional parameters to set. These will be set as member variables on the new Step instance.

Attributes Summary

`class_alias`
`spec`

Methods Summary

`process`(input_data[, selfcal_list, bkg_list])	Flag residual artifacts as bad pixels in the DQ array of a JWST exposure.
`save_bkg`(bkg_list[, suffix])	Save the background exposures to file with correct indexing.
`save_model`(model, args, *kwargs)	Override save_model to suppress index 0 when save_model is True.

Attributes Documentation

class_alias = 'badpix_selfcal'

spec

flagfrac_lower = float(default=0.001, min=0.0, max=0.5)  # fraction of pixels to flag on the low-flux end
flagfrac_upper = float(default=0.001, min=0.0, max=0.5)  # fraction of pixels to flag on the high-flux end
kernel_size = integer(default=15, min=1)  # size of kernel for median filter
force_single = boolean(default=False)  # force single input exposure
save_flagged_bkg = boolean(default=False)  # save flagged background exposures to file
skip = boolean(default=True)

Methods Documentation

process(input_data, selfcal_list=None, bkg_list=None)[source]

Flag residual artifacts as bad pixels in the DQ array of a JWST exposure.

Parameters:

input_dataJWST data model or association: Input science data to be corrected, or tuple of (sci, bkg, selfcal)
selfcal_listlist of ImageModels or filenames, default None: Exposures to include as part of median background model used to find bad pixels, but that are not flagged and returned as background exposures.
bkg_listlist of ImageModels or filenames, default None: Exposures to include as part of median background model used to find bad pixels, and that are flagged and returned as background exposures.

Returns:

outputJWST data model or association: Data model with CRs flagged

Notes

If an association file is read in, all exposures in the association file, including science, background, and selfcal exposures, are included in the MIN frame from which outliers are detected. If selfcal_list and/or bkg_list are specified manually, they are appended to any selfcal or background exposures found in the input association file. If selfcal_list and bkg_list are both set to None and input is a single science exposure, the step will be skipped with a warning unless the force_single parameter is set True. In that case, the input exposure will be used as the sole background exposure, i.e., true self-calibration.

save_bkg(bkg_list, suffix='badpix_selfcal_bkg')[source]

Save the background exposures to file with correct indexing.

Parameters:

bkg_listlist of ImageModels: Background exposures to save
suffixstr: Suffix to append to the filename

save_model(model, *args, **kwargs)[source]

Override save_model to suppress index 0 when save_model is True.

Parameters:

modelJWST data model: Data model to save
*argstuple: Additional arguments to pass to Step.save_model
**kwargsdict: Additional keyword arguments to pass to Step.save_model

Returns:

list[str]: List of output paths for the saved models