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:

  • 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(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
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_data (DataModel) – Input datamodel to be corrected

Returns:

output_model – The flicker noise corrected datamodel

Return type:

DataModel