OutlierDetectionStep

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

Bases: RomanStep

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 wrapped with a ModelLibrary. DQ arrays are modified in place.

Parameters:

input_data (ModelLibrary) – A ModelLibrary object.

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_models)

Perform outlier detection processing on input data.

Attributes Documentation

class_alias = 'outlier_detection'

spec

weight_type = option('ivm','exptime',default='ivm') # Weighting type to use to create the median image
pixfrac = float(default=1.0) # Fraction by which input pixels are shrunk before being drizzled onto the output image grid
kernel = string(default='square') # Shape of the kernel used for flux distribution onto output images
fillval = string(default='NaN') # Value assigned to output pixels that have zero weight or no flux during drizzling
maskpt = float(default=0.7) # Percentage of weight image values below which they are flagged as bad pixels
snr = string(default='5.0 4.0') # The signal-to-noise values to use for bad pixel identification
scale = string(default='1.2 0.7') # The scaling factor applied to derivative used to identify bad pixels
backg = float(default=0.0) # User-specified background value to subtract during final identification step
save_intermediate_results = boolean(default=False) # Specifies whether or not to write out intermediate products to disk
resample_data = boolean(default=True) # Specifies whether or not to resample the input images when performing outlier detection
resample_on_skycell = boolean(default=True) # if association contains skycell information use the skycell wcs for resampling
good_bits = string(default="~DO_NOT_USE+NON_SCIENCE")  # DQ bit value to be considered 'good'
in_memory = boolean(default=False) # Specifies whether or not to keep all intermediate products and datamodels in memory

Methods Documentation

process(input_models)

Perform outlier detection processing on input data.