OutlierDetectionStep

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

Bases: JwstStep

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 dictionary, or already opened with a ModelContainer or ModelLibrary. DQ arrays are modified in place. SCI, ERR, VAR_RNOISE, VAR_FLAT, and VAR_POISSON arrays are updated with NaN values matching the DQ flags.

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 or pathlib.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

class_alias

spec

Methods Summary

process(input_data)

Perform outlier detection processing on input data.

Attributes Documentation

class_alias = 'outlier_detection'
spec
weight_type = option('ivm','exptime',default='ivm')
pixfrac = float(default=1.0)
kernel = string(default='square') # drizzle kernel
fillval = string(default='NAN')
maskpt = float(default=0.7)
snr = string(default='5.0 4.0')
scale = string(default='1.2 0.7')
backg = float(default=0.0)
kernel_size = string(default='7 7')
threshold_percent = float(default=99.8)
rolling_window_width = integer(default=25)
ifu_second_check = boolean(default=False)
save_intermediate_results = boolean(default=False)
resample_data = boolean(default=True)
good_bits = string(default="~DO_NOT_USE")  # DQ flags to allow
search_output_file = boolean(default=False)
in_memory = boolean(default=True) # in_memory flag ignored if run within the pipeline; set at pipeline level instead

Methods Documentation

process(input_data)[source]

Perform outlier detection processing on input data.

Parameters:

input_data (asn file, ModelContainer, or ModelLibrary) – The input association. For imaging modes a ModelLibrary is expected, whereas for spectroscopic modes a ModelContainer is expected.

Returns:

result_models – The modified input data with DQ flags set for detected outliers.

Return type:

ModelContainer or ModelLibrary