CleanFlickerNoiseStep

class jwst.clean_flicker_noise.CleanFlickerNoiseStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)[source]

Bases: JwstStep

Perform flicker noise correction.

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(input_data)

Fit and subtract 1/f background noise from a ramp data set.

Attributes Documentation

class_alias = 'clean_flicker_noise'

reference_file_types: ClassVar = ['flat']

spec

autoparam = boolean(default=False) # Automatically select some fit and background parameters for the input data.
fit_method = option('fft', 'median', default='median')  # Noise fitting algorithm.
fit_by_channel = boolean(default=False)  # Fit noise separately by amplifier (NIR only).
background_method = option('median', 'model', None, default='median') # Background fit.
background_box_size = int_list(min=2, max=2, default=None)  # Background box size.
mask_science_regions = boolean(default=False)  # Mask known science regions.
apply_flat_field = boolean(default=False)  # Apply a flat correction before fitting.
n_sigma = float(default=2.0)  # Clipping level for non-background signal.
fit_histogram = boolean(default=False)  # Fit a value histogram to derive sigma.
single_mask = boolean(default=True)  # Make a single mask for all integrations.
user_mask = string(default=None)  # Path to user-supplied mask
save_mask = boolean(default=False)  # Save the created mask
save_background = boolean(default=False)  # Save the fit background
save_noise = boolean(default=False)  # Save the fit noise
skip = boolean(default=True)  # By default, skip the step.

Methods Documentation

process(input_data)[source]

Fit and subtract 1/f background noise from a ramp data set.

Input data is expected to be a ramp file (RampModel), in between jump and ramp fitting steps, or a rate file (ImageModel or CubeModel).

Correction algorithms implemented are:

“fft”: Background noise is fit in frequency space.
Implementation is based on the NSClean algorithm, developed by Bernard Rauscher.

“median”: Background noise is characterized by a median along the detector slow axis. Implementation is based on the “image1overf” algorithm, developed by Chris Willott.

Parameters:

input_datastr or RampModel or ImageModel or CubeModel: Filename or input datamodel to be corrected.

Returns:

output_modelRampModel or ImageModel or CubeModel: The flicker noise corrected datamodel, matching the type of the input.