class jwst.outlier_detection.outlier_detection_step.OutlierDetectionStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)[source]

Bases: jwst.stpipe.Step

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

Input images can be listed in an input association file or already opened with a ModelContainer. DQ arrays are modified in place.


input (asn file or ModelContainer) – Single filename association table, or a datamodels.ModelContainer.

Create a Step instance.

  • name (str, optional) – 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.

  • parent (Step instance, optional) – 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_file (str path, optional) – The path to the config file that this step was initialized with. Use to determine relative path names of other config files.

  • **kws (dict) – Additional parameters to set. These will be set as member variables on the new Step instance.

Attributes Summary


Methods Summary


Use this method to determine whether input is valid or not.


Perform outlier detection processing on input data.

Attributes Documentation

spec = '\n weight_type = option(\'exptime\',\'error\',None,default=\'exptime\')\n pixfrac = float(default=1.0)\n kernel = string(default=\'square\') # drizzle kernel\n fillval = string(default=\'INDEF\')\n nlow = integer(default=0)\n nhigh = integer(default=0)\n maskpt = float(default=0.7)\n grow = integer(default=1)\n snr = string(default=\'4.0 3.0\')\n scale = string(default=\'0.5 0.4\')\n backg = float(default=0.0)\n save_intermediate_results = boolean(default=False)\n resample_data = boolean(default=True)\n good_bits = string(default="~DO_NOT_USE") # DQ flags to allow\n scale_detection = boolean(default=False)\n search_output_file = boolean(default=False)\n '

Methods Documentation


Use this method to determine whether input is valid or not.


Perform outlier detection processing on input data.