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:

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
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_inputDataModel

Input datamodel to the step

Returns:

resultDataModel

Result of applying the reference pixel correction step