OutlierDetectionStep

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

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:

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)

Perform outlier detection processing on input data.

Attributes Documentation

class_alias = 'outlier_detection'

spec = '\n        weight_type = option(\'ivm\',\'exptime\',default=\'ivm\')\n        pixfrac = float(min=0.0, max=1.0, default=1.0)  # Pixel shrinkage factor\n        kernel = option(\'square\',\'point\',\'turbo\',default=\'square\')  # Flux distribution kernel\n        fillval = string(default=\'NAN\')\n        maskpt = float(default=0.7)\n        snr = string(default=\'5.0 4.0\')\n        scale = string(default=\'1.2 0.7\')\n        backg = float(default=0.0)\n        kernel_size = string(default=\'7 7\')\n        threshold_percent = float(default=99.8)\n        rolling_window_width = integer(default=25)\n        ifu_second_check = boolean(default=False)\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        search_output_file = boolean(default=False)\n        in_memory = boolean(default=True) # in_memory flag ignored if run within the pipeline; set at pipeline level instead\n    '

Methods Documentation

process(input_data)

Perform outlier detection processing on input data.

Parameters:

input_dataasn file, ModelContainer, or ModelLibrary

The input association. For imaging modes a ModelLibrary is expected, whereas for spectroscopic modes a ModelContainer is expected.

Returns:

result_modelsModelContainer or ModelLibrary

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