Clean up help message style and contents (#9686)

Author: melanieclarke

jwst/assign_wcs/assign_wcs_step.py

@@ -24,27 +24,7 @@
 
 
 class AssignWcsStep(Step):
-    """
-    AssignWcsStep: Create a gWCS object and store it in ``Model.meta``.
-
-    Reference file types:
-
-    camera             Camera model (NIRSPEC)
-    collimator         Collimator Model (NIRSPEC)
-    disperser          Disperser model (NIRSPEC)
-    distortion         Spatial distortion model (FGS, MIRI, NIRCAM, NIRISS)
-    filteroffset       Filter offsets (MIRI Imager)
-    fore               Transform through the FORE optics (NIRSPEC)
-    fpa                Transform in the FPA plane (NIRSPEC)
-    ifufore            Transforms from the MSA plane to the plane of the IFU slicer (NIRSPEC)
-    ifupost            Transforms from the slicer plane to the MSA plane (NIRSPEC)
-    ifuslicer          Metrology of the IFU slicer (NIRSPEC)
-    msa                Metrology of the MSA plane (NIRSPEC)
-    ote                Transform through the Optical Telescope Element (NIRSPEC)
-    specwcs            Wavelength calibration models (MIRI, NIRCAM, NIRISS)
-    regions            Stores location of the regions on the detector (MIRI)
-    wavelengthrange    Typical wavelength ranges (MIRI, NIRCAM, NIRISS, NIRSPEC)
-    """
+    """Create a gWCS object and store it in ``Model.meta.wcs``."""
 
     class_alias = "assign_wcs"
 
@@ -82,6 +62,24 @@ def process(self, input_data):
         """
         Run the assign_wcs step.
 
+        Reference file types for this step:
+
+            - camera: Camera model (NIRSPEC)
+            - collimator: Collimator Model (NIRSPEC)
+            - disperser: Disperser model (NIRSPEC)
+            - distortion: Spatial distortion model (FGS, MIRI, NIRCAM, NIRISS)
+            - filteroffset: Filter offsets (MIRI Imager)
+            - fore: Transform through the FORE optics (NIRSPEC)
+            - fpa: Transform in the FPA plane (NIRSPEC)
+            - ifufore: Transforms from the MSA plane to the plane of the IFU slicer (NIRSPEC)
+            - ifupost: Transforms from the slicer plane to the MSA plane (NIRSPEC)
+            - ifuslicer: Metrology of the IFU slicer (NIRSPEC)
+            - msa: Metrology of the MSA plane (NIRSPEC)
+            - ote: Transform through the Optical Telescope Element (NIRSPEC)
+            - specwcs: Wavelength calibration models (MIRI, NIRCAM, NIRISS)
+            - regions: Stores location of the regions on the detector (MIRI)
+            - wavelengthrange: Typical wavelength ranges (MIRI, NIRCAM, NIRISS, NIRSPEC)
+
         Parameters
         ----------
         input_data : JwstDataModel or str

jwst/clean_flicker_noise/clean_flicker_noise_step.py

@@ -7,71 +7,7 @@
 
 
 class CleanFlickerNoiseStep(Step):
-    """
-    Perform flicker noise correction.
-
-    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.
-
-    Attributes
-    ----------
-    fit_method : str, optional
-        The noise fitting algorithm to use.  Options are 'fft' and 'median'.
-    fit_by_channel : bool, optional
-        If set, flicker noise is fit independently for each detector channel.
-        Ignored for MIRI, for subarray data, and for `fit_method` = 'fft'
-    background_method : {'median', 'model', None}
-        If 'median', the preliminary background to remove and restore
-        is a simple median of the background data.  If 'model', the
-        background data is fit with a low-resolution model via
-        `~photutils.background.Background2D`.  If None, the background
-        value is 0.0.
-    background_box_size : tuple of int or None, optional
-        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 : bool, optional
-        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 : bool, optional
-        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 spectral 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.
-    n_sigma : float, optional
-        Sigma clipping threshold to be used in detecting outliers in the image.
-    fit_histogram : bool, optional
-        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 : bool, optional
-        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 : None, str, or `~jwst.datamodels.ImageModel`
-        Optional user-supplied mask image; path to file or opened datamodel.
-    save_mask : bool, optional
-        Save the computed mask image.
-    save_background : bool, optional
-        Save the computed background image.
-    save_noise : bool, optional
-        Save the computed noise image.
-    """
+    """Perform flicker noise correction."""
 
     class_alias = "clean_flicker_noise"
 
@@ -98,6 +34,18 @@ def process(self, input_data):
         """
         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

jwst/combine_1d/combine_1d_step.py

@@ -11,29 +11,7 @@
 
 
 class Combine1dStep(Step):
-    """
-    Combine 1D spectra.
-
-    Attributes
-    ----------
-    exptime_key : str
-        A case-insensitive string that identifies the metadata element
-        (or FITS keyword) for the weight to apply to the input data.  The default
-        is "exposure_time".  If the string is "effinttm" or starts with
-        "integration", the integration time (FITS keyword EFFINTTM) is used
-        as the weight.  If the string is "effexptm" or starts with "exposure",
-        the exposure time (FITS keyword EFFEXPTM) is used as the weight.  If
-        the string is "unit_weight" or "unit weight", the same weight (1) will
-        be used for all input spectra.  If the string is anything else, a warning
-        will be logged and unit weight will be used.
-
-    sigma_clip : float or None
-        Optional factor for sigma clipping outliers when combining spectra. If
-        a floating point value is provided for ``sigma_clip``, this value will be
-        used to set an outlier threshold for any pixels in the input spectra that
-        deviate from the median and median absolute deviation of the inputs.
-        Defaults to None (such that no clipping is performed).
-    """
+    """Combine 1D spectra."""
 
     class_alias = "combine_1d"
 

jwst/cube_build/cube_build_step.py

@@ -14,23 +14,7 @@
 
 
 class CubeBuildStep(Step):
-    """
-    Create a 3-D spectral cube from IFU data.
-
-    This is the controlling routine for building IFU Spectral Cubes.
-    It loads and sets the various input data and parameters needed by
-    the cube_build_step.
-
-    This routine does the following operations:
-
-       1. Extracts the input parameters from the cubepars reference file and
-       merges them with any user-provided values.
-       2. Creates the output WCS from the input images and defines the mapping
-       between all the input arrays and the output array.
-       3. Passes the input data to the function to map all their input data
-       to the output array.
-       4. Updates the output data model with correct meta data.
-    """
+    """Create a 3-D spectral cube from IFU data."""
 
     class_alias = "cube_build"
 
@@ -71,6 +55,20 @@ def process(self, input_data):
         """
         Build an IFUCube from overlapping IFUImage data.
 
+        This is the controlling routine for building IFU Spectral Cubes.
+        It loads and sets the various input data and parameters needed by
+        the cube_build_step.
+
+        This routine does the following operations:
+
+           1. Extracts the input parameters from the cubepars reference file and
+              merges them with any user-provided values.
+           2. Creates the output WCS from the input images and defines the mapping
+              between all the input arrays and the output array.
+           3. Passes the input data to the function to map all their input data
+              to the output array.
+           4. Updates the output data model with correct meta data.
+
         Parameters
         ----------
         input_data : list of DataModel or str

jwst/extract_1d/extract_1d_step.py

@@ -12,142 +12,7 @@
 
 
 class Extract1dStep(Step):
-    """
-    Extract a 1D spectrum from 2D data.
-
-    Attributes
-    ----------
-    subtract_background : bool or None
-        A flag which indicates whether the background should be subtracted.
-        If None, the value in the extract_1d reference file will be used.
-        If not None, this parameter overrides the value in the
-        extract_1d reference file.
-    apply_apcorr : bool
-        Switch to select whether to apply an APERTURE correction during
-        the Extract1dStep. Default is True.
-    extraction_type : str or None
-        If 'box', a standard extraction is performed, summing over an
-        aperture box. If 'optimal', a PSF-based extraction is performed.
-        If None, optimal extraction is attempted whenever use_source_posn is
-        True. Currently, optimal extraction is only available for MIRI LRS
-        Fixed Slit data.
-    use_source_posn : bool or None
-        If True, the source and background extraction regions specified in
-        the extract1d reference file will be shifted to account for the computed
-        position of the source in the data.  If None (the default), this parameter
-        is set to True for point sources in NIRSpec and MIRI LRS fixed slit modes.
-    position_offset : float
-        Number of pixels to offset the source and background extraction regions
-        in the cross-dispersion direction.  This is intended to allow a manual
-        tweak to the aperture defined via reference file; the default value is 0.0.
-    model_nod_pair : bool
-        If True, and the extraction type is 'optimal', then a negative trace
-        from nod subtraction is modeled alongside the positive source during
-        extraction.  Even if set to True, this will be attempted only if the
-        input data has been background subtracted and the dither pattern
-        indicates that only 2 nods were used.
-    optimize_psf_location : bool
-        If True, and the extraction type is 'optimal', then the placement of
-        the PSF model for the source location (and negative nod, if present)
-        will be iteratively optimized. This parameter is recommended if
-        negative nods are modeled.
-    smoothing_length : int or None
-        If not None, the background regions (if any) will be smoothed
-        with a boxcar function of this width along the dispersion
-        direction.  This should be an odd integer.
-    bkg_fit : str
-        A string indicating the type of fitting to be applied to
-        background values in each column (or row, if the dispersion is
-        vertical). Allowed values are `poly`, `mean`, and `median`.
-        Default is `None`.
-    bkg_order : int or None
-        If not None, a polynomial with order `bkg_order` will be fit to
-        each column (or row, if the dispersion direction is vertical)
-        of the background region or regions.  For a given column (row),
-        one polynomial will be fit to all background regions.  The
-        polynomial will be evaluated at each pixel of the source
-        extraction region(s) along the column (row), and the fitted value
-        will be subtracted from the data value at that pixel.
-        If both `smoothing_length` and `bkg_order` are not None, the
-        boxcar smoothing will be done first.
-    log_increment : int
-        if `log_increment` is greater than 0 (the default is 50) and the
-        input data are multi-integration (which can be CubeModel or
-        SlitModel), a message will be written to the log with log level
-        INFO every `log_increment` integrations.  This is intended to
-        provide progress information when invoking the step interactively.
-    save_profile : bool
-        If True, the spatial profile containing the extraction aperture
-        is saved to disk.  Ignored for IFU and NIRISS SOSS extractions.
-    save_scene_model : bool
-        If True, a model of the 2D flux as defined by the extraction aperture
-        is saved to disk.  Ignored for IFU and NIRISS SOSS extractions.
-    save_residual_image : bool
-        If True, the residual image (from the input minus the scene model)
-        is saved to disk.  Ignored for IFU and NIRISS SOSS extractions.
-    center_xy : int or None
-        A list of 2 pixel coordinate values at which to place the center
-        of the IFU extraction aperture, overriding any centering done by the step.
-        Two values, in x,y order, are used for extraction from IFU cubes.
-        Default is None.
-    ifu_autocen : bool
-        Switch to turn on auto-centering for point source spectral extraction
-        in IFU mode.  Default is False.
-    bkg_sigma_clip : float
-        Background sigma clipping value to use on background to remove outliers
-        and maximize the quality of the 1d spectrum. Used for IFU mode only.
-    ifu_rfcorr : bool
-        Switch to select whether or not to apply a 1d residual fringe correction
-        for MIRI MRS IFU spectra.  Default is True.
-    ifu_set_srctype : str
-        For MIRI MRS IFU data override srctype and set it to either POINT or EXTENDED.
-    ifu_rscale : float
-        For MRS IFU data a value for changing the extraction radius. The value provided is
-        the number of PSF FWHMs to use for the extraction radius. Values accepted are between
-        0.5 to 3.0. The default extraction size is set to 2 * FWHM. Values below 2 will result
-        in a smaller radius, a value of 2 results in no change to the radius and a value above
-        2 results in a larger extraction radius.
-    ifu_covar_scale : float
-        Scaling factor by which to multiply the ERR values in extracted spectra to account
-        for covariance between adjacent spaxels in the IFU data cube.
-    soss_atoca : bool, default=False
-        Switch to toggle extraction of SOSS data with the ATOCA algorithm.
-        WARNING: ATOCA results not fully validated, and require the photom step
-        be turned off. Default is False, meaning SOSS data use box extraction.
-    soss_threshold : float
-        Threshold value above which a pixel will be included when modeling the SOSS
-        trace in ATOCA. Default is 0.01.
-    soss_n_os : int
-        Oversampling factor of the underlying wavelength grid when modeling the SOSS
-        trace in ATOCA. Default is 2.
-    soss_wave_grid_in : str or SossWaveGrid or None
-        Filename or SossWaveGrid containing the wavelength grid used by ATOCA
-        to model each pixel valid pixel of the detector. If not given, the grid is determined
-        based on an estimate of the flux (soss_estimate), the relative tolerance (soss_rtol)
-        required on each pixel model and the maximum grid size (soss_max_grid_size).
-    soss_wave_grid_out : str or None
-        Filename to hold the wavelength grid calculated by ATOCA.
-    soss_estimate : str or SpecModel or None
-        Filename or SpecModel of the estimate of the target flux. The estimate must
-        be a SpecModel with wavelength and flux values.
-    soss_rtol : float
-        The relative tolerance needed on a pixel model. It is used to determine the sampling
-        of the soss_wave_grid when not directly given.
-    soss_max_grid_size: int
-        Maximum grid size allowed. It is used when soss_wave_grid is not provided
-        to make sure the computation time or the memory used stays reasonable.
-    soss_tikfac : float
-        The regularization factor used for extraction in ATOCA. If left to default
-        value of None, ATOCA will find an optimized value.
-    soss_width : float
-        Aperture width used to extract the SOSS spectrum from the decontaminated
-        trace in ATOCA. Default is 40.
-    soss_bad_pix : str
-        Method used to handle bad pixels, accepts either "model" or "masking". Default
-        method is "model".
-    soss_modelname : str
-        Filename for optional model output of ATOCA traces and pixel weights.
-    """
+    """Extract a 1D spectrum from 2D data."""
 
     class_alias = "extract_1d"
 

jwst/extract_2d/extract_2d_step.py

@@ -8,7 +8,7 @@
 
 
 class Extract2dStep(Step):
-    """Class that provides method to perform a 2D extraction of spectra."""
+    """Extract 2D spectral cutouts."""
 
     class_alias = "extract_2d"
 

jwst/firstframe/firstframe_step.py

@@ -10,8 +10,8 @@ class FirstFrameStep(Step):
     """
     Set data quality flags for the first group in MIRI ramps.
 
-    A MIRI specific task.  If the number of groups is > than 3,
-    the DO_NOT_USE group data quality flag is added to first group.
+    If the number of groups is > than 3, the DO_NOT_USE group data
+    quality flag is added to first group.
     """
 
     class_alias = "firstframe"