OutlierDetectionStep¶
- class romancal.outlier_detection.outlier_detection_step.OutlierDetectionStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)[source]¶
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 ModelContainer. DQ arrays are modified in place.
- Parameters:
input_data (
ModelContainer
) – AModelContainer
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 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
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='INDEF') # Value assigned to output pixels that have zero weight or no flux during drizzling nlow = integer(default=0) # The number of low values in each pixel stack to ignore when computing the median value nhigh = integer(default=0) # The number of high values in each pixel stack to ignore when computing the median value maskpt = float(default=0.7) # Percentage of weight image values below which they are flagged as bad pixels grow = integer(default=1) # The distance beyond the rejection limit for additional pixels to be rejected in an image 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 kernel_size = string(default='7 7') # Size of kernel to be used during resampling of the data 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 good_bits = string(default="0") # DQ bit value to be considered 'good' allowed_memory = float(default=None) # Fraction of memory to use for the combined image in_memory = boolean(default=False) # Specifies whether or not to keep all intermediate products and datamodels in memory
Methods Documentation