RefPixStep

class jwst.refpix.RefPixStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)

Bases: JwstStep

Use reference pixels to correct bias drifts.

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
reference_file_types
spec

Methods Summary

process(step_input)

Execute the reference pixel correction step.

Attributes Documentation

class_alias = 'refpix'

reference_file_types: ClassVar = ['refpix', 'sirskernel']

spec

odd_even_columns = boolean(default=True) # Compute reference signal separately for even/odd columns
use_side_ref_pixels = boolean(default=True) # Use side reference pixels for reference signal for each row
side_smoothing_length = integer(default=11) # Median window smoothing height for side reference signal
side_gain = float(default=1.0) # Multiplicative factor for side reference signal before subtracting from rows
odd_even_rows = boolean(default=True) # Compute reference signal separately for even- and odd-numbered rows
ovr_corr_mitigation_ftr = float(default=3.0) # Factor to avoid overcorrection of bad reference pixels for IRS2
preserve_irs2_refpix = boolean(default=False) # Preserve reference pixels in output
irs2_mean_subtraction = boolean(default=False) # Apply a mean offset subtraction before IRS2 correction
refpix_algorithm = option("median", "sirs", default="median") # NIR full-frame side pixel algorithm
sigreject = float(default=4.0) # Number of sigmas to reject as outliers
gaussmooth = float(default=1.0) # Width of Gaussian smoothing kernel to use as a low-pass filter
halfwidth = integer(default=30) # Half-width of convolution kernel to build

Methods Documentation

process(step_input)

Execute the reference pixel correction step.

Parameters:

step_input (DataModel) – Input datamodel to the step

Returns:

result – Result of applying the reference pixel correction step

Return type:

DataModel