88
99class NSCleanStep (Step ):
1010 """
11+ Perform 1/f noise correction.
12+
1113 NSCleanStep: This step performs 1/f noise correction ("cleaning")
1214 of NIRSpec images, using the "NSClean" method.
1315
1416 NOTE: This step is a deprecated alias to the ``clean_flicker_noise`` step.
17+
18+ Attributes
19+ ----------
20+ fit_method : str, optional
21+ The background fit algorithm to use. Options are 'fft' and 'median';
22+ 'fft' performs the original NSClean implementation.
23+ fit_by_channel : bool, optional
24+ If set, flicker noise is fit independently for each detector channel.
25+ Ignored for subarray data and for `fit_method` = 'fft'.
26+ background_method : {'median', 'model', None}
27+ If 'median', the preliminary background to remove and restore
28+ is a simple median of the background data. If 'model', the
29+ background data is fit with a low-resolution model via
30+ `~photutils.background.Background2D`. If None, the background
31+ value is 0.0.
32+ background_box_size : tuple of int, optional
33+ Box size for the data grid used by `Background2D` when
34+ `background_method` = 'model'. For best results, use a box size
35+ that evenly divides the input image shape.
36+ mask_spectral_regions : bool, optional
37+ Mask regions of the image defined by WCS bounding boxes for slits/slices.
38+ n_sigma : float, optional
39+ Sigma clipping threshold to be used in detecting outliers in the image.
40+ fit_histogram : bool, optional
41+ If set, the 'sigma' used with `n_sigma` for clipping outliers
42+ is derived from a Gaussian fit to a histogram of values.
43+ Otherwise, a simple iterative sigma clipping is performed.
44+ single_mask : bool, optional
45+ If set, a single mask will be created, regardless of
46+ the number of input integrations. Otherwise, the mask will
47+ be a 3D cube, with one plane for each integration.
48+ user_mask : None, str, or `~jwst.datamodels.ImageModel`
49+ Optional user-supplied mask image; path to file or opened datamodel.
50+ save_mask : bool, optional
51+ Save the computed mask image.
52+ save_background : bool, optional
53+ Save the computed background image.
54+ save_noise : bool, optional
55+ Save the computed noise image.
1556 """
1657
1758 class_alias = "nsclean"
1859
1960 spec = """
2061 fit_method = option('fft', 'median', default='fft') # Noise fitting algorithm
2162 fit_by_channel = boolean(default=False) # Fit noise separately by amplifier
22- background_method = option('median', 'model', None, default=None) # Background fitting algorithm
23- background_box_size = int_list(min=2, max=2, default=None) # Background box size for modeled background
63+ background_method = option('median', 'model', None, default=None) # Background fit
64+ background_box_size = int_list(min=2, max=2, default=None) # Background box size
2465 mask_spectral_regions = boolean(default=True) # Mask WCS-defined spectral regions
2566 n_sigma = float(default=5.0) # Clipping level for outliers
2667 fit_histogram = boolean(default=False) # Fit a value histogram to derive sigma
@@ -32,75 +73,28 @@ class NSCleanStep(Step):
3273 skip = boolean(default=True) # By default, skip the step
3374 """
3475
35- def process (self , input ):
76+ def process (self , input_data ):
3677 """
37- Fit and subtract 1/f background noise from a NIRSpec image
78+ Fit and subtract 1/f background noise from a NIRSpec image.
3879
3980 Parameters
4081 ----------
41- input : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`
82+ input_data : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`
4283 Input datamodel to be corrected.
4384
44- fit_method : str, optional
45- The background fit algorithm to use. Options are 'fft' and 'median';
46- 'fft' performs the original NSClean implementation.
47-
48- fit_by_channel : bool, optional
49- If set, flicker noise is fit independently for each detector channel.
50- Ignored for subarray data and for `fit_method` = 'fft'.
51-
52- background_method : {'median', 'model', None}
53- If 'median', the preliminary background to remove and restore
54- is a simple median of the background data. If 'model', the
55- background data is fit with a low-resolution model via
56- `~photutils.background.Background2D`. If None, the background
57- value is 0.0.
58-
59- background_box_size : tuple of int, optional
60- Box size for the data grid used by `Background2D` when
61- `background_method` = 'model'. For best results, use a box size
62- that evenly divides the input image shape.
63-
64- mask_spectral_regions : bool, optional
65- Mask regions of the image defined by WCS bounding boxes for slits/slices.
66-
67- n_sigma : float, optional
68- Sigma clipping threshold to be used in detecting outliers in the image.
69-
70- fit_histogram : bool, optional
71- If set, the 'sigma' used with `n_sigma` for clipping outliers
72- is derived from a Gaussian fit to a histogram of values.
73- Otherwise, a simple iterative sigma clipping is performed.
74-
75- single_mask : bool, optional
76- If set, a single mask will be created, regardless of
77- the number of input integrations. Otherwise, the mask will
78- be a 3D cube, with one plane for each integration.
79-
80- user_mask : None, str, or `~jwst.datamodels.ImageModel`
81- Optional user-supplied mask image; path to file or opened datamodel.
82-
83- save_mask : bool, optional
84- Save the computed mask image.
85-
86- save_background : bool, optional
87- Save the computed background image.
88-
89- save_noise : bool, optional
90- Save the computed noise image.
91-
9285 Returns
9386 -------
9487 output_model : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`
9588 The 1/f corrected datamodel.
9689 """
97- message = ("The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' "
98- "and will be removed in future builds." )
90+ message = (
91+ "The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' "
92+ "and will be removed in future builds."
93+ )
9994 self .log .warning (message )
10095
10196 # Open the input data model
102- with datamodels .open (input ) as input_model :
103-
97+ with datamodels .open (input_data ) as input_model :
10498 # clean_flicker_noise allows flat handling, but NIRSpec is
10599 # not supported via CRDS files, since it does not have a full
106100 # frame flat. Since this step is for NIRSpec only, don't allow
@@ -109,37 +103,45 @@ def process(self, input):
109103
110104 # Do the NSClean correction
111105 result = clean_flicker_noise .do_correction (
112- input_model , input_dir = self .input_dir , fit_method = self .fit_method ,
106+ input_model ,
107+ input_dir = self .input_dir ,
108+ fit_method = self .fit_method ,
113109 fit_by_channel = self .fit_by_channel ,
114110 background_method = self .background_method ,
115111 background_box_size = self .background_box_size ,
116112 mask_science_regions = self .mask_spectral_regions ,
117- flat_filename = flat_filename , n_sigma = self .n_sigma ,
118- fit_histogram = self .fit_histogram , single_mask = self .single_mask ,
119- user_mask = self .user_mask , save_mask = self .save_mask ,
120- save_background = self .save_background , save_noise = self .save_noise )
113+ flat_filename = flat_filename ,
114+ n_sigma = self .n_sigma ,
115+ fit_histogram = self .fit_histogram ,
116+ single_mask = self .single_mask ,
117+ user_mask = self .user_mask ,
118+ save_mask = self .save_mask ,
119+ save_background = self .save_background ,
120+ save_noise = self .save_noise ,
121+ )
121122 output_model , mask_model , background_model , noise_model , status = result
122123
123124 # Save the mask, if requested
124125 if self .save_mask and mask_model is not None :
125- mask_path = self .make_output_path (
126- basepath = input_model .meta .filename , suffix = 'mask' )
126+ mask_path = self .make_output_path (basepath = input_model .meta .filename , suffix = "mask" )
127127 self .log .info (f"Saving mask file { mask_path } )
128128 mask_model .save (mask_path )
129129 mask_model .close ()
130130
131131 # Save the background, if requested
132132 if self .save_background and background_model is not None :
133133 bg_path = self .make_output_path (
134- basepath = input_model .meta .filename , suffix = 'flicker_bkg' )
134+ basepath = input_model .meta .filename , suffix = "flicker_bkg"
135+ )
135136 self .log .info (f"Saving background file { bg_path } )
136137 background_model .save (bg_path )
137138 background_model .close ()
138139
139140 # Save the noise, if requested
140141 if self .save_noise and noise_model is not None :
141142 noise_path = self .make_output_path (
142- basepath = input_model .meta .filename , suffix = 'flicker_noise' )
143+ basepath = input_model .meta .filename , suffix = "flicker_noise"
144+ )
143145 self .log .info (f"Saving noise file { noise_path } )
144146 noise_model .save (noise_path )
145147 noise_model .close ()
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