Step Arguments

The clean_flicker_noise step has the following optional arguments to control the behavior of the processing.

--fit_method (str, default=’median’)

The noise fitting algorithm to use. Options are ‘fft’ and ‘median’.

--fit_by_channel (boolean, default=False)

If set, flicker noise is fit independently for each detector channel. Ignored for MIRI, for subarray data, and for fit_method = ‘fft’.

--background_method (str, default=’median’)

If ‘median’, the preliminary background to remove and restore after fitting the noise is a simple median of the background data. If ‘model’, the background data is fit with a low-resolution model via Background2D. If None, the background value is set to 0.0.

--background_box_size (list of int, default=None)

Box size for the data grid used by Background2D when background_method = ‘model’. For best results, use a box size that evenly divides the input image shape. If None, the largest value between 1 and 32 that evenly divides the image dimension is used.

--mask_science_regions (boolean, default=False)

For NIRSpec, mask regions of the image defined by WCS bounding boxes for slits/slices, as well as any regions known to be affected by failed-open MSA shutters. For MIRI imaging, mask regions of the detector not used for science.

--apply_flat_field (boolean, default=False)

If set, images are flat-corrected prior to fitting background and noise levels. A full-frame flat field image (reference type FLAT) is required. For modes that do not provide FLAT files via CRDS, including all NIRSpec modes, a manually generated override flat is required to enable this option. Use the override_flat parameter to provide an alternate flat image as needed (see overriding reference files).

--n_sigma (float, default=2.0)

The sigma-clipping threshold to use when searching for outliers and illuminated pixels to be excluded from use in the background and noise fitting processes.

--fit_histogram (boolean, default=False)

If set, the ‘sigma’ used with n_sigma for clipping outliers is derived from a Gaussian fit to a histogram of values. Otherwise, a simple iterative sigma clipping is performed.

--single_mask (boolean, default=True)

If set, a single mask will be created, regardless of the number of input integrations. Otherwise, the mask will be a 3D cube, with one plane for each integration.

--user_mask (string, default=None)

Path to a user-supplied mask file. If supplied, the mask is used directly and the process of creating a scene mask in the step is skipped.

The mask file must contain either a ImageModel or a CubeModel, with image dimensions matching the input science data. If an ImageModel is provided, the same mask will be used for all integrations. If a CubeModel is provided, the number of slices must equal the number of integrations in the input science data.

--save_mask (boolean, default=False)

If set, the mask constructed by the step will be saved to a file with suffix ‘mask’.

--save_background (boolean, default=False)

If set, the background fit to the group diff images will be saved to a file with suffix ‘flicker_bkg’.

--save_noise (boolean, default=False)

If set, the residual noise fit and removed from the input data will be saved to a file with suffix ‘flicker_noise’.