OutlierDetectionStep¶
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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 listed in an input association file or already opened with a ModelContainer. DQ arrays are modified in place.
Parameters: input (asn file or ModelContainer) – Single filename association table, or a datamodels.ModelContainer. Create a
Step
instance.Parameters: - 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.
- **kws (dict) – Additional parameters to set. These will be set as member variables on the new Step instance.
Attributes Summary
spec
Methods Summary
check_input
()Use this method to determine whether input is valid or not. process
(input)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 = integer(default=4)\n scale_detection = boolean(default=False)\n search_output_file = boolean(default=False)\n "¶
Methods Documentation