From aa594b35a53fd10730daf72c46ca1427e1990075 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 12 Oct 2023 13:38:00 +0100
Subject: [PATCH 01/67] initialise the model

---
 pypeit/calibrations.py        |  94 ++++++++++++++++++-
 pypeit/images/combineimage.py |   2 +-
 pypeit/images/rawimage.py     |   9 +-
 pypeit/scattlight.py          | 171 ++++++++++++++++++++++++++++++++++
 4 files changed, 272 insertions(+), 4 deletions(-)
 create mode 100644 pypeit/scattlight.py

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index 8291fe1d9d..f3cf15098e 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -20,6 +20,7 @@
 from pypeit import alignframe
 from pypeit import flatfield
 from pypeit import edgetrace
+from pypeit import scattlight
 from pypeit import slittrace
 from pypeit import wavecalib
 from pypeit import wavetilts
@@ -537,6 +538,95 @@ def get_bpm(self, frame=None):
         # Return
         return self.msbpm
 
+    def get_scattlight(self):
+        """
+        Load or generate the scattered light model.
+
+        Returns:
+            :class:`~pypeit.scattlight.ScatteredLight`: The processed calibration image including the model.
+        """
+        # Initialise to None
+        self.msscattlight = None
+
+        # Check for existing data
+        if not self._chk_objs(['msbpm', 'slits']):
+            msgs.warn('Must have the bpm and the slits defined to make a scattered light image!  '
+                      'Skipping and may crash down the line')
+            return self.msscattlight
+
+        # Check internals
+        self._chk_set(['det', 'calib_ID', 'par'])
+
+        # Prep
+        frame = {'type': 'trace', 'class': scattlight.ScatteredLight}
+        raw_scattlight_files, cal_file, calib_key, setup, calib_id, detname = \
+            self.find_calibrations(frame['type'], frame['class'])
+        # raw_lampoff_files = self.fitstbl.find_frame_files('scattlight', calib_ID=self.calib_ID)
+
+        if len(raw_scattlight_files) == 0 and cal_file is None:
+            msgs.warn(f'No raw {frame["type"]} frames found and unable to identify a relevant '
+                      'processed calibration frame.  Continuing...')
+            return self.msscattlight
+
+        # If a processed calibration frame exists and we want to reuse it, do
+        # so:
+        if cal_file.exists() and self.reuse_calibs:
+            self.msscattlight = frame['class'].from_file(cal_file)
+            return self.msscattlight
+
+        # Scattered light model does not exist or we're not reusing it.
+        # Need to build everything from scratch.  Start with the trace image.
+        msgs.info('Creating edge tracing calibration frame using files: ')
+        for f in raw_scattlight_files:
+            msgs.prindent(f'{Path(f).name}')
+
+        # Reset the BPM
+        self.get_bpm(frame=raw_scattlight_files[0])
+
+        scattlightImage = buildimage.buildimage_fromlist(self.spectrograph, self.det,
+                                                         self.par['scattlightframe'], raw_scattlight_files,
+                                                         bias=self.msbias, bpm=self.msbpm,
+                                                         dark=self.msdark, calib_dir=self.calib_dir,
+                                                         setup=setup, calib_id=calib_id)
+
+        model, modelpar, success = self.spectrograph.scattered_light(scattlightImage, self.slits, binning=binning)
+
+        if not success:
+            # Something went amiss
+            msgs.warn('Scattered light computation failed.  Continuing, but likely to fail soon...')
+            self.success = False
+            return self.msscattlight
+
+        # The model has been generated. Store the result in the DataModel
+        self.msscattlight = scattlight.ScatteredLight(scattlight_raw=scattlightImage,
+                                                      scattlight_model=model,
+                                                      scattlight_param=modelpar)
+        # TODO :: Need to add these to the line above
+        # datamodel = {'PYP_SPEC': dict(otype=str, descr='PypeIt spectrograph name'),
+        #              'pypeline': dict(otype=str, descr='PypeIt pypeline name'),
+        #              'detname': dict(otype=str, descr='Identifier for detector or mosaic'),
+        #              'nspec': dict(otype=int,
+        #                            descr='Number of pixels in the image spectral direction.'),
+        #              'nspat': dict(otype=int,
+        #                            descr='Number of pixels in the image spatial direction.'),
+        #              'binspec': dict(otype=int,
+        #                              descr='Number of pixels binned in the spectral direction.'),
+        #              'binspat': dict(otype=int,
+        #                              descr='Number of pixels binned in the spatial direction.'),
+        #              'pad': dict(otype=int,
+        #                          descr='Integer number of pixels to mask beyond the slit edges.'),
+
+        # Show the result if requested
+        if self.show:
+            self.msscattlight.show()
+
+        if self.msscattlight is not None:
+            self.msscattlight.set_paths(self.calib_dir, setup, calib_id, detname)
+            # Save the master scattered light model
+            self.msscattlight.to_file()
+
+        return self.msscattlight
+
     def get_flats(self):
         """
         Load or generate the flat-field calibration images.
@@ -1254,7 +1344,7 @@ def default_steps():
         # Order matters!  And the name must match a viable "get_{step}" method
         # in Calibrations.
         # TODO: Does the bpm need to be done after the dark?
-        return ['bias', 'dark', 'bpm', 'slits', 'arc', 'tiltimg', 'wv_calib', 'tilts', 'flats']
+        return ['bias', 'dark', 'bpm', 'slits', 'arc', 'tiltimg', 'wv_calib', 'tilts', 'scattlight', 'flats']
 
 
 class IFUCalibrations(Calibrations):
@@ -1272,7 +1362,7 @@ def default_steps():
         """
         # Order matters!
         return ['bias', 'dark', 'bpm', 'arc', 'tiltimg', 'slits', 'wv_calib', 'tilts', 'align',
-                'flats']
+                'scattlight', 'flats']
 
 
 def check_for_calibs(par, fitstbl, raise_error=True, cut_cfg=None):
diff --git a/pypeit/images/combineimage.py b/pypeit/images/combineimage.py
index 37d36203c4..7951b07e55 100644
--- a/pypeit/images/combineimage.py
+++ b/pypeit/images/combineimage.py
@@ -194,7 +194,7 @@ def run(self, bias=None, flatimages=None, ignore_saturation=False, sigma_clip=Tr
             # Load raw image
             rawImage = rawimage.RawImage(ifile, self.spectrograph, self.det)
             # Process
-            pypeitImage = rawImage.process(self.par, bias=bias, bpm=bpm, dark=dark,
+            pypeitImage = rawImage.process(self.par, scattlight=scattlight, bias=bias, bpm=bpm, dark=dark,
                                            flatimages=flatimages, slits=slits, mosaic=mosaic)
 
             if self.nfiles == 1:
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 7c66283deb..41d9b6d7da 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -373,7 +373,7 @@ def build_rn2img(self, units='e-', digitization=False):
                                             digitization=digitization)
         return np.array(rn2)
 
-    def process(self, par, bpm=None, flatimages=None, bias=None, slits=None, dark=None,
+    def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, slits=None, dark=None,
                 mosaic=False, debug=False):
         """
         Process the data.
@@ -445,6 +445,9 @@ def process(self, par, bpm=None, flatimages=None, bias=None, slits=None, dark=No
             #. :func:`spatial_flexure_shift`: Measure any spatial shift due to
                flexure.
 
+            #. :func:`subtract_scattlight`: Generate a model of the scattered light
+               contribution and subtract it.
+
             #. :func:`flatfield`: Divide by the pixel-to-pixel, spatial and
                spectral response functions.
 
@@ -468,6 +471,8 @@ def process(self, par, bpm=None, flatimages=None, bias=None, slits=None, dark=No
                 The bad-pixel mask.  This is used to *overwrite* the default
                 bad-pixel mask for this spectrograph.  The shape must match a
                 trimmed and oriented processed image.
+            scattlight (:class:`~pypeit.scattlight.ScatteredLight`, optional):
+                Scattered light model to be used to determine scattered light.
             flatimages (:class:`~pypeit.flatfield.FlatImages`, optional):
                 Flat-field images used to apply flat-field corrections.
             bias (:class:`~pypeit.images.pypeitimage.PypeItImage`, optional):
@@ -512,6 +517,8 @@ def process(self, par, bpm=None, flatimages=None, bias=None, slits=None, dark=No
             msgs.error('No bias available for bias subtraction!')
         if self.par['use_darkimage'] and dark is None:
             msgs.error('No dark available for dark subtraction!')
+        if self.par['subtract_scattlight'] and scattlight is None:
+            msgs.error('Scattered light subtraction requested, but scattered light model provided.')
         if self.par['spat_flexure_correct'] and slits is None:
             msgs.error('Spatial flexure correction requested but no slits provided.')
         if self.use_flat and flatimages is None:
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
new file mode 100644
index 0000000000..80e17fa2c9
--- /dev/null
+++ b/pypeit/scattlight.py
@@ -0,0 +1,171 @@
+"""
+Implements the objects used to hold slit edge data.
+
+.. include common links, assuming primary doc root is up one directory
+.. include:: ../include/links.rst
+
+"""
+import inspect
+
+from IPython import embed
+
+import numpy as np
+
+from pypeit import msgs
+from pypeit import datamodel
+from pypeit import calibframe
+from pypeit.display import display
+
+
+class ScatteredLight(calibframe.CalibFrame):
+    """
+    Defines a generic class for generating a model of the scattered light.
+
+    The datamodel attributes are:
+
+    .. include:: ../include/class_datamodel_scatteredlight.rst
+
+    """
+    calib_type = 'ScatteredLight'
+    """Name for type of calibration frame."""
+
+    calib_file_format = 'fits.gz'
+    """File format for the calibration frame file."""
+
+    version = '1.0.0'
+    """Scattered Light data model version."""
+
+    internals = calibframe.CalibFrame.internals
+    """
+    Attributes kept separate from the datamodel.
+    """
+
+    # Define the data model
+    datamodel = {'PYP_SPEC': dict(otype=str, descr='PypeIt spectrograph name'),
+                 'pypeline': dict(otype=str, descr='PypeIt pypeline name'),
+                 'detname': dict(otype=str, descr='Identifier for detector or mosaic'),
+                 'nspec': dict(otype=int,
+                               descr='Number of pixels in the image spectral direction.'),
+                 'nspat': dict(otype=int,
+                               descr='Number of pixels in the image spatial direction.'),
+                 'binspec': dict(otype=int,
+                                 descr='Number of pixels binned in the spectral direction.'),
+                 'binspat': dict(otype=int,
+                                 descr='Number of pixels binned in the spatial direction.'),
+                 'pad': dict(otype=int,
+                             descr='Integer number of pixels to mask beyond the slit edges.'),
+                 'scattlight_raw': dict(otype=np.ndarray, atype=np.floating,
+                                        descr='Processed, combined scattered light image'),
+                 'scattlight_model': dict(otype=np.ndarray, atype=np.floating,
+                                          descr='Model of the scattered light in scattlight_raw'),
+                 'scattlight_param': dict(otype=np.ndarray, atype=np.floating,
+                                          descr='Model parameters that define the scattered light model')}
+    """Provides the class data model."""
+
+    # TODO: Allow tweaked edges to be arguments?
+    # TODO: May want nspat to be a required argument.
+    # The INIT must contain every datamodel item or risk fail on I/O when it is a nested container
+    def __init__(self, pypeline, detname=None, nspec=None, nspat=None,
+                 PYP_SPEC=None, binspec=1, binspat=1, pad=0,
+                 scattlight_raw=None, scattlight_model=None, scattlight_param=None):
+
+        # Instantiate the DataContainer
+        args, _, _, values = inspect.getargvalues(inspect.currentframe())
+        _d = dict([(k,values[k]) for k in args[1:]])
+        # The dictionary passed to DataContainer.__init__ does not
+        # contain self.
+        # TODO: Does it matter if the calling function passes the
+        # keyword arguments in a different order? No.
+        datamodel.DataContainer.__init__(self, d=_d)
+
+    def _validate(self):
+        """
+        Validate the slit traces.
+        """
+        # Allow the object to be empty
+        if self.scattlight_model is None:
+            return
+
+        self.nspec, self.nslits = self.left_init.shape
+
+        if self.PYP_SPEC is None:
+            self.PYP_SPEC = 'unknown'
+
+    def get_model(self, image):
+        """Generate a model of the scattered light, based on an input image. This routine
+        requires that the scattered light has already been predefined.
+
+        Parameters
+        ----------
+        image : `numpy.ndarray`_
+            A 2D image that you want to determine the amount of scattered light
+
+        Returns
+        -------
+        model : `numpy.ndarray`_
+            A model of the expected scattered light in the input image. Shape is (nspec, nspat).
+        """
+        msgs.info("Generating a scattered light image")
+        # Initialise the output
+        model = None
+
+        # Generate the model
+        self.spec
+
+        # Return the model of the scattered light
+        return model
+
+    def show(self, image=None, slits=None, wcs_match=True):
+        """ Display the master scattered light frame, the model, and data-model.
+
+        Parameters
+        ----------
+        image : `numpy.ndarray`_, optional
+            A 2D image that you want to display (including a scattered light model). If None,
+            the master Scattered Light frame wil be displayed by default
+        slits : :class:`~pypeit.slittrace.SlitTraceSet`, optional
+            The current slit traces
+        wcs_match : :obj:`bool`, optional
+            Use a reference image for the WCS and match all image in other channels to it.
+        """
+        # Prepare the frames
+        _data = self.scattlight_raw if image is None else image
+        _model = self.scattlight_model if image is None else self.get_model(image)
+        _resid = _data - _model
+        modmax = np.max(_model)
+        image_list = zip([_data, _model, _resid],
+                             ['data', 'model', 'data-model'],
+                             [(0.0, modmax), (0.0, modmax), (-modmax/2,modmax/2)])
+
+        # Display frames
+        show_scattered_light(image_list, slits=slits, wcs_match=wcs_match)
+
+
+def show_scattered_light(image_list, slits=None, wcs_match=True):
+    """
+    Interface to ginga to show the quality of the Scattered Light subtraction
+
+    Parameters
+    ----------
+    image_list : zip
+        A zip of the images to show, their names, and the scales
+    slits : :class:`~pypeit.slittrace.SlitTraceSet`, optional
+        The current slit traces
+    wcs_match : :obj:`bool`, optional
+        Use a reference image for the WCS and match all image in other channels to it.
+    """
+    display.connect_to_ginga(raise_err=True, allow_new=True)
+    if slits is not None:
+        left, right, mask = slits.select_edges()
+        gpm = mask == 0
+    # Loop me
+    clear = True
+    for img, name, cut in image_list:
+        if img is None:
+            continue
+        viewer, ch = display.show_image(img, chname=name, cuts=cut, wcs_match=wcs_match, clear=clear)
+        if slits is not None:
+            display.show_slits(viewer, ch, left[:, gpm], right[:, gpm], slit_ids=slits.spat_id[gpm])
+        # Turn off clear
+        if clear:
+            clear = False

From a770600291111a28cdc6301966e37499ffb28bdd Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 12 Oct 2023 22:19:03 +0100
Subject: [PATCH 02/67] scattered light par

---
 pypeit/calibrations.py            | 36 ++++++--------
 pypeit/core/framematch.py         |  7 +--
 pypeit/images/buildimage.py       | 13 +++++
 pypeit/images/combineimage.py     |  4 +-
 pypeit/images/rawimage.py         |  6 ++-
 pypeit/par/pypeitpar.py           | 81 +++++++++++++++++++++++++++++--
 pypeit/scattlight.py              | 26 +++-------
 pypeit/spectrographs/keck_kcwi.py |  2 +-
 8 files changed, 125 insertions(+), 50 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index f3cf15098e..e3199bdb18 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -558,10 +558,10 @@ def get_scattlight(self):
         self._chk_set(['det', 'calib_ID', 'par'])
 
         # Prep
-        frame = {'type': 'trace', 'class': scattlight.ScatteredLight}
+        frame = {'type': 'scattlight', 'class': scattlight.ScatteredLight}
         raw_scattlight_files, cal_file, calib_key, setup, calib_id, detname = \
             self.find_calibrations(frame['type'], frame['class'])
-        # raw_lampoff_files = self.fitstbl.find_frame_files('scattlight', calib_ID=self.calib_ID)
+        scatt_idx = self.fitstbl.find_frames(frame['type'], calib_ID=self.calib_ID, index=True)
 
         if len(raw_scattlight_files) == 0 and cal_file is None:
             msgs.warn(f'No raw {frame["type"]} frames found and unable to identify a relevant '
@@ -583,38 +583,34 @@ def get_scattlight(self):
         # Reset the BPM
         self.get_bpm(frame=raw_scattlight_files[0])
 
+        binning = self.fitstbl[scatt_idx[0]]['binning']
         scattlightImage = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                          self.par['scattlightframe'], raw_scattlight_files,
                                                          bias=self.msbias, bpm=self.msbpm,
                                                          dark=self.msdark, calib_dir=self.calib_dir,
                                                          setup=setup, calib_id=calib_id)
 
+        embed()
+        # TODO :: Need to figure out where `pad` is stored for the call below, and work on scattered_light call on the next line
         model, modelpar, success = self.spectrograph.scattered_light(scattlightImage, self.slits, binning=binning)
 
         if not success:
-            # Something went amiss
-            msgs.warn('Scattered light computation failed.  Continuing, but likely to fail soon...')
+            # Something went awry
+            msgs.warn('Scattered light modelling failed.  Continuing, but likely to fail soon...')
             self.success = False
             return self.msscattlight
 
-        # The model has been generated. Store the result in the DataModel
-        self.msscattlight = scattlight.ScatteredLight(scattlight_raw=scattlightImage,
+        # Now generate the DataModel
+        self.msscattlight = scattlight.ScatteredLight(PYP_SPEC=self.spectrograph.name,
+                                                      pypeline=self.spectrograph.pypeline,
+                                                      detname=scattlightImage.detector.name,
+                                                      nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
+                                                      binning=scattlightImage.detector.binning,
+                                                      # TODO :: Need to set this padding correctly
+                                                      pad=self.par[''],
+                                                      scattlight_raw=scattlightImage,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
-        # TODO :: Need to add these to the line above
-        # datamodel = {'PYP_SPEC': dict(otype=str, descr='PypeIt spectrograph name'),
-        #              'pypeline': dict(otype=str, descr='PypeIt pypeline name'),
-        #              'detname': dict(otype=str, descr='Identifier for detector or mosaic'),
-        #              'nspec': dict(otype=int,
-        #                            descr='Number of pixels in the image spectral direction.'),
-        #              'nspat': dict(otype=int,
-        #                            descr='Number of pixels in the image spatial direction.'),
-        #              'binspec': dict(otype=int,
-        #                              descr='Number of pixels binned in the spectral direction.'),
-        #              'binspat': dict(otype=int,
-        #                              descr='Number of pixels binned in the spatial direction.'),
-        #              'pad': dict(otype=int,
-        #                          descr='Integer number of pixels to mask beyond the slit edges.'),
 
         # Show the result if requested
         if self.show:
diff --git a/pypeit/core/framematch.py b/pypeit/core/framematch.py
index 171bc29996..795772d0b2 100644
--- a/pypeit/core/framematch.py
+++ b/pypeit/core/framematch.py
@@ -29,9 +29,10 @@ def __init__(self):
                      ('pinhole', 'Pinhole observation used for tracing slit centers'),
                    ('pixelflat', 'Flat-field exposure used for pixel-to-pixel response'),
                    ('illumflat', 'Flat-field exposure used for illumination flat'),
-                   ('lampoffflats', 'Flat-field exposure with lamps off used to remove '
-                                   'persistence from lamp on flat exposures and/or thermal emission '
-                                   'from the telescope and dome'),
+                ('lampoffflats', 'Flat-field exposure with lamps off used to remove '
+                                 'persistence from lamp on flat exposures and/or thermal emission '
+                                 'from the telescope and dome'),
+                  ('scattlight', 'Frame (usually similar to pixelflat) used to determine scattered light model'),
                      ('science', 'On-sky observation of a primary target'),
                     ('standard', 'On-sky observation of a flux calibrator'),
                        ('trace', 'High-count exposure used to trace slit positions'),
diff --git a/pypeit/images/buildimage.py b/pypeit/images/buildimage.py
index 1d58581f55..d30183cafc 100644
--- a/pypeit/images/buildimage.py
+++ b/pypeit/images/buildimage.py
@@ -77,6 +77,18 @@ class TiltImage(pypeitimage.PypeItCalibrationImage):
     calib_type = 'Tiltimg'
 
 
+class ScatteredLightImage(pypeitimage.PypeItCalibrationImage):
+    """
+    Simple DataContainer for the Scattered Light Image
+    """
+    # version is inherited from PypeItImage
+
+    # I/O
+    output_to_disk = ('SCATTLIGHT_IMAGE', 'SCATTLIGHT_FULLMASK', 'SCATTLIGHT_DETECTOR')
+    hdu_prefix = 'SCATTLIGHT_'
+    calib_type = 'ScattLight'
+
+
 class TraceImage(pypeitimage.PypeItCalibrationImage):
     """
     Simple DataContainer for the Trace Image
@@ -133,6 +145,7 @@ def construct_file_name(cls, calib_key, calib_dir=None, basename=None):
     arc=ArcImage,
     tilt=TiltImage,
     trace=TraceImage,
+    scattlight=ScatteredLightImage,
     align=AlignImage)
 """
 The list of classes that :func:`buildimage_fromlist` should use to decorate the
diff --git a/pypeit/images/combineimage.py b/pypeit/images/combineimage.py
index 7951b07e55..8432e77585 100644
--- a/pypeit/images/combineimage.py
+++ b/pypeit/images/combineimage.py
@@ -63,8 +63,8 @@ def __init__(self, spectrograph, det, par, files):
             msgs.error('CombineImage requires a list of files to instantiate')
 
     def run(self, bias=None, flatimages=None, ignore_saturation=False, sigma_clip=True,
-            bpm=None, sigrej=None, maxiters=5, slits=None, dark=None, combine_method='mean',
-            mosaic=False):
+            bpm=None, sigrej=None, maxiters=5, slits=None, dark=None, scattlight=None,
+            combine_method='mean', mosaic=False):
         r"""
         Process and combine all images.
 
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 41d9b6d7da..eb1f3dc737 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -643,7 +643,7 @@ def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, sl
 
         #   - Subtract scattered light... this needs to be done before flatfielding.
         if self.par['subtract_scattlight']:
-            self.subtract_scattlight()
+            self.subtract_scattlight(scattlight)
 
         # Flat-field the data.  This propagates the flat-fielding corrections to
         # the variance.  The returned bpm is propagated to the PypeItImage
@@ -1118,7 +1118,7 @@ def subtract_continuum(self, force=False):
         #cont = ndimage.median_filter(self.image, size=(1,101,3), mode='reflect')
         self.steps[step] = True
 
-    def subtract_scattlight(self):
+    def subtract_scattlight(self, scattlight):
         """
         Analyze and subtract the scattered light from the image.
 
@@ -1135,6 +1135,8 @@ def subtract_scattlight(self):
         # Loop over the images
         for ii in range(self.nimg):
             binning = self.detector[0].binning
+            # TODO :: Need to pass in scattlight or scattlight.scattlight_param
+            embed()
             scatt_img = self.spectrograph.scattered_light(self.image[ii, ...], binning)
             self.image[ii, ...] -= scatt_img
         self.steps[step] = True
diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index 2fa8156edc..9da6dbc53f 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -979,7 +979,66 @@ def from_dict(cls, cfg):
 
         badkeys = np.array([pk not in parkeys for pk in k])
         if np.any(badkeys):
-            raise ValueError('{0} not recognized key(s) for WaveTiltsPar.'.format(k[badkeys]))
+            raise ValueError('{0} not recognized key(s) for AlignPar.'.format(k[badkeys]))
+
+        kwargs = {}
+        for pk in parkeys:
+            kwargs[pk] = cfg[pk] if pk in k else None
+        return cls(**kwargs)
+
+    def validate(self):
+        """
+        Check the parameters are valid for the provided method.
+        """
+        pass
+
+
+class ScatteredLightPar(ParSet):
+    """
+    The parameter set used to hold arguments for modelling the scattered light.
+
+    For a table with the current keywords, defaults, and descriptions,
+    see :ref:`parameters`.
+    """
+
+    def __init__(self, pad=None):
+
+        # Grab the parameter names and values from the function
+        # arguments
+        args, _, _, values = inspect.getargvalues(inspect.currentframe())
+        pars = OrderedDict([(k, values[k]) for k in args[1:]])  # "1:" to skip 'self'
+
+        # Initialize the other used specifications for this parameter
+        # set
+        defaults = OrderedDict.fromkeys(pars.keys())
+        options = OrderedDict.fromkeys(pars.keys())
+        dtypes = OrderedDict.fromkeys(pars.keys())
+        descr = OrderedDict.fromkeys(pars.keys())
+
+        # Fill out parameter specifications.  Only the values that are
+        # *not* None (i.e., the ones that are defined) need to be set
+
+        defaults['pad'] = 5
+        dtypes['pad'] = int
+        descr['pad'] = 'Number of unbinned pixels to extend the slit edges by when masking the slits.'
+
+        # Instantiate the parameter set
+        super(ScatteredLightPar, self).__init__(list(pars.keys()),
+                                                values=list(pars.values()),
+                                                defaults=list(defaults.values()),
+                                                options=list(options.values()),
+                                                dtypes=list(dtypes.values()),
+                                                descr=list(descr.values()))
+        self.validate()
+
+    @classmethod
+    def from_dict(cls, cfg):
+        k = np.array([*cfg.keys()])
+        parkeys = ['pad']
+
+        badkeys = np.array([pk not in parkeys for pk in k])
+        if np.any(badkeys):
+            raise ValueError('{0} not recognized key(s) for ScatteredLightPar.'.format(k[badkeys]))
 
         kwargs = {}
         for pk in parkeys:
@@ -4070,8 +4129,8 @@ class CalibrationsPar(ParSet):
     def __init__(self, calib_dir=None, bpm_usebias=None, biasframe=None, darkframe=None,
                  arcframe=None, tiltframe=None, pixelflatframe=None, pinholeframe=None,
                  alignframe=None, alignment=None, traceframe=None, illumflatframe=None,
-                 lampoffflatsframe=None, skyframe=None, standardframe=None, flatfield=None,
-                 wavelengths=None, slitedges=None, tilts=None, raise_chk_error=None):
+                 lampoffflatsframe=None, scattlightframe=None, skyframe=None, standardframe=None,
+                 flatfield=None, wavelengths=None, slitedges=None, tilts=None, raise_chk_error=None):
 
 
         # Grab the parameter names and values from the function
@@ -4122,6 +4181,14 @@ def __init__(self, calib_dir=None, bpm_usebias=None, biasframe=None, darkframe=N
         dtypes['darkframe'] = [ ParSet, dict ]
         descr['darkframe'] = 'The frames and combination rules for the dark-current correction'
 
+        defaults['scattlightframe'] = FrameGroupPar(frametype='scattlight',
+                                                    process=ProcessImagesPar(satpix='nothing',
+                                                                             use_pixelflat=False,
+                                                                             use_illumflat=False,
+                                                                             use_specillum=False))
+        dtypes['scattlightframe'] = [ ParSet, dict ]
+        descr['scattlightframe'] = 'The frames and combination rules for the scattered light frames'
+
         # JFH Turning off masking of saturated pixels which causes headaches becauase it was being done unintelligently
         defaults['pixelflatframe'] = FrameGroupPar(frametype='pixelflat',
                                                    process=ProcessImagesPar(satpix='nothing',
@@ -4201,6 +4268,10 @@ def __init__(self, calib_dir=None, bpm_usebias=None, biasframe=None, darkframe=N
         dtypes['alignment'] = [ ParSet, dict ]
         descr['alignment'] = 'Define the procedure for the alignment of traces'
 
+        defaults['scattlight'] = ScatteredLightPar()
+        dtypes['scattlight'] = [ ParSet, dict ]
+        descr['scattlight'] = 'Define the procedure for modelling the scattered light'
+
         defaults['flatfield'] = FlatFieldPar()
         dtypes['flatfield'] = [ ParSet, dict ]
         descr['flatfield'] = 'Parameters used to set the flat-field procedure'
@@ -4234,7 +4305,7 @@ def from_dict(cls, cfg):
         parkeys = [ 'calib_dir', 'bpm_usebias', 'raise_chk_error']
 
         allkeys = parkeys + ['biasframe', 'darkframe', 'arcframe', 'tiltframe', 'pixelflatframe',
-                             'illumflatframe', 'lampoffflatsframe',
+                             'illumflatframe', 'lampoffflatsframe', 'scattlightframe',
                              'pinholeframe', 'alignframe', 'alignment', 'traceframe', 'standardframe', 'skyframe',
                              'flatfield', 'wavelengths', 'slitedges', 'tilts']
         badkeys = np.array([pk not in allkeys for pk in k])
@@ -4262,6 +4333,8 @@ def from_dict(cls, cfg):
         kwargs[pk] = FrameGroupPar.from_dict('lampoffflats', cfg[pk]) if pk in k else None
         pk = 'pinholeframe'
         kwargs[pk] = FrameGroupPar.from_dict('pinhole', cfg[pk]) if pk in k else None
+        pk = 'scattlightframe'
+        kwargs[pk] = FrameGroupPar.from_dict('scattlight', cfg[pk]) if pk in k else None
         pk = 'alignframe'
         kwargs[pk] = FrameGroupPar.from_dict('align', cfg[pk]) if pk in k else None
         pk = 'alignment'
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 80e17fa2c9..4b80aebe4f 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -44,29 +44,19 @@ class ScatteredLight(calibframe.CalibFrame):
     datamodel = {'PYP_SPEC': dict(otype=str, descr='PypeIt spectrograph name'),
                  'pypeline': dict(otype=str, descr='PypeIt pypeline name'),
                  'detname': dict(otype=str, descr='Identifier for detector or mosaic'),
-                 'nspec': dict(otype=int,
-                               descr='Number of pixels in the image spectral direction.'),
-                 'nspat': dict(otype=int,
-                               descr='Number of pixels in the image spatial direction.'),
-                 'binspec': dict(otype=int,
-                                 descr='Number of pixels binned in the spectral direction.'),
-                 'binspat': dict(otype=int,
-                                 descr='Number of pixels binned in the spatial direction.'),
-                 'pad': dict(otype=int,
-                             descr='Integer number of pixels to mask beyond the slit edges.'),
-                 'scattlight_raw': dict(otype=np.ndarray, atype=np.floating,
-                                        descr='Processed, combined scattered light image'),
-                 'scattlight_model': dict(otype=np.ndarray, atype=np.floating,
-                                          descr='Model of the scattered light in scattlight_raw'),
-                 'scattlight_param': dict(otype=np.ndarray, atype=np.floating,
-                                          descr='Model parameters that define the scattered light model')}
+                 'nspec': dict(otype=int, descr='Number of pixels in the image spectral direction.'),
+                 'nspat': dict(otype=int, descr='Number of pixels in the image spatial direction.'),
+                 'binning': dict(otype=str, descr='Binning in PypeIt orientation (not the original)'),
+                 'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges.'),
+                 'scattlight_raw': dict(otype=np.ndarray, atype=np.floating, descr='Processed, combined scattered light image'),
+                 'scattlight_model': dict(otype=np.ndarray, atype=np.floating, descr='Model of the scattered light in scattlight_raw'),
+                 'scattlight_param': dict(otype=np.ndarray, atype=np.floating, descr='Model parameters that define the scattered light model')}
     """Provides the class data model."""
 
     # TODO: Allow tweaked edges to be arguments?
     # TODO: May want nspat to be a required argument.
     # The INIT must contain every datamodel item or risk fail on I/O when it is a nested container
-    def __init__(self, pypeline, detname=None, nspec=None, nspat=None,
-                 PYP_SPEC=None, binspec=1, binspat=1, pad=0,
+    def __init__(self, pypeline=None, detname=None, nspec=None, nspat=None, PYP_SPEC=None, binning=None, pad=0,
                  scattlight_raw=None, scattlight_model=None, scattlight_param=None):
 
         # Instantiate the DataContainer
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index f7163cae78..2336db64cb 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -350,7 +350,7 @@ def check_frame_type(self, ftype, fitstbl, exprng=None):
                     & self.lamps(fitstbl, 'off') & (fitstbl['hatch'] == 'Open')
         if ftype == 'bias':
             return good_exp & (fitstbl['idname'] == 'BIAS')
-        if ftype == 'pixelflat':
+        if ftype in ['pixelflat', 'scattlight']:
             # Use internal lamp
             return good_exp & (fitstbl['idname'] == 'FLATLAMP') & (fitstbl['calpos'] == 'Mirror') \
                     & self.lamps(fitstbl, 'cont_noarc')

From 9167b7451aaede93d9c249667f1f28f4eab52817 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 09:00:57 +0100
Subject: [PATCH 03/67] scattered light par

---
 pypeit/par/pypeitpar.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index 9da6dbc53f..ed7169eb4c 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -4130,7 +4130,7 @@ def __init__(self, calib_dir=None, bpm_usebias=None, biasframe=None, darkframe=N
                  arcframe=None, tiltframe=None, pixelflatframe=None, pinholeframe=None,
                  alignframe=None, alignment=None, traceframe=None, illumflatframe=None,
                  lampoffflatsframe=None, scattlightframe=None, skyframe=None, standardframe=None,
-                 flatfield=None, wavelengths=None, slitedges=None, tilts=None, raise_chk_error=None):
+                 scattlight=None, flatfield=None, wavelengths=None, slitedges=None, tilts=None, raise_chk_error=None):
 
 
         # Grab the parameter names and values from the function
@@ -4307,7 +4307,7 @@ def from_dict(cls, cfg):
         allkeys = parkeys + ['biasframe', 'darkframe', 'arcframe', 'tiltframe', 'pixelflatframe',
                              'illumflatframe', 'lampoffflatsframe', 'scattlightframe',
                              'pinholeframe', 'alignframe', 'alignment', 'traceframe', 'standardframe', 'skyframe',
-                             'flatfield', 'wavelengths', 'slitedges', 'tilts']
+                             'scattlight', 'flatfield', 'wavelengths', 'slitedges', 'tilts']
         badkeys = np.array([pk not in allkeys for pk in k])
         if np.any(badkeys):
             raise ValueError('{0} not recognized key(s) for CalibrationsPar.'.format(k[badkeys]))

From 01e7a351381294d042503b14585b4849a5cf69ea Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 14:47:46 +0100
Subject: [PATCH 04/67] implemented scattered light

---
 pypeit/calibrations.py            |  14 ++-
 pypeit/scattlight.py              |  11 +-
 pypeit/spectrographs/keck_kcwi.py | 170 ++++++++++++++++++++++++------
 3 files changed, 151 insertions(+), 44 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index e3199bdb18..095eb4e68a 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -28,6 +28,7 @@
 from pypeit.images import buildimage
 from pypeit.metadata import PypeItMetaData
 from pypeit.core import framematch
+from pypeit.core import parse
 from pypeit.par import pypeitpar
 from pypeit.spectrographs.spectrograph import Spectrograph
 from pypeit import io
@@ -590,9 +591,11 @@ def get_scattlight(self):
                                                          dark=self.msdark, calib_dir=self.calib_dir,
                                                          setup=setup, calib_id=calib_id)
 
-        embed()
-        # TODO :: Need to figure out where `pad` is stored for the call below, and work on scattered_light call on the next line
-        model, modelpar, success = self.spectrograph.scattered_light(scattlightImage, self.slits, binning=binning)
+        spatbin = parse.parse_binning(binning)[1]
+        pad = self.par['scattlight']['pad'] // spatbin
+        slitid_img_init = self.slits.slit_img(pad=pad, initial=True, flexure=None)
+
+        model, modelpar, success = self.spectrograph.scattered_light(scattlightImage.image, slitid_img_init, self.fitstbl[scatt_idx[0]])
 
         if not success:
             # Something went awry
@@ -606,12 +609,13 @@ def get_scattlight(self):
                                                       detname=scattlightImage.detector.name,
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
-                                                      # TODO :: Need to set this padding correctly
-                                                      pad=self.par[''],
+                                                      pad=self.par['scattlight']['pad'],
                                                       scattlight_raw=scattlightImage,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
 
+        # TODO :: Should we go back and recalculate the slit edges once the scattered light is known?
+
         # Show the result if requested
         if self.show:
             self.msscattlight.show()
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 4b80aebe4f..a852d7867e 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -96,14 +96,11 @@ def get_model(self, image):
             A model of the expected scattered light in the input image. Shape is (nspec, nspat).
         """
         msgs.info("Generating a scattered light image")
-        # Initialise the output
-        model = None
-
-        # Generate the model
-        self.spec
-
+        if self.scattlight_param is None:
+            msgs.warn("No scattered light parameters are available")
+            return np.zeros_like(image)
         # Return the model of the scattered light
-        return model
+        return self.spec.scattered_light_model(self.scattlight_param, image)
 
     def show(self, image=None, slits=None, wcs_match=True):
         """ Display the master scattered light frame, the model, and data-model.
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 2336db64cb..c9ab441f66 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -12,8 +12,9 @@
 from astropy import wcs, units
 from astropy.io import fits
 from astropy.time import Time
-from astropy.coordinates import SkyCoord, EarthLocation
-from scipy.optimize import curve_fit
+from astropy.coordinates import EarthLocation
+from scipy.optimize import curve_fit, least_squares
+from scipy import signal, interpolate
 
 from pypeit import msgs
 from pypeit import telescopes
@@ -1031,46 +1032,97 @@ def get_rawimage(self, raw_file, det):
         # Return
         return detpar, raw_img, hdu, exptime, rawdatasec_img, oscansec_img
 
-    def scattered_light(self, frame, binning):
-        """
-        Calculate a model of the scattered light of the input frame.
+    def scattered_light(self, frame, slitmask, tbl, nspecpad=300):
+        """ Calculate a model of the scattered light of the input frame.
+
+        For KCWI, the main contributor to the scattered light is referred to as the "narcissistic ghost"
+        by Morrissey et al. (2018), ApJ, 864, 93. This scattered light is thought to be a reflection off the
+        detector that travels back through the optical system. Some fraction gets sent back out to space, while
+        the remained comes back through the optical system and a fuzzy version of this is re-imaged onto the
+        detector. The current KCWI scattered light model is designed to account for these effects.
 
         Parameters
         ----------
         frame : `numpy.ndarray`_
             Raw 2D data frame to be used to compute the scattered light.
-        binning : str, `numpy.ndarray`_, tuple
-            Binning of the frame (e.g. '2x1' refers to a binning of 2 in the spectral
-            direction, and a binning of 1 in the spatial direction). For the supported
-            formats, refer to :func:`~pypeit.core.parse.parse_binning`.
+        TODO :: complete docstring
 
         Returns
         -------
         scatt_img : `numpy.ndarray`_
             A 2D image of the scattered light determined from the input frame
         """
-        # Determine the spatial binning. Currently, we hard-code the regions on the KCWI detector
-        # to be used for the determination of the scattered light. This is crude, and a better model
-        # is currently under development (RJC)
-        spatbin = parse.parse_binning(binning)[1]
-        ym = 4096 // (2 * spatbin)
-        y0 = ym - 180 // spatbin
-        y1 = ym + 180 // spatbin
-        # Obtain a robust (median) "spectrum" of the scattered light at the left, right, and middle of the detector
-        scattlightl = np.nanmedian(frame[:, :20//spatbin], axis=1)[:, None]
-        scattlightr = np.nanmedian(frame[:, -40//spatbin:], axis=1)[:, None]
-        scattlight = np.nanmedian(frame[:, y0:y1], axis=1)[:, None]
-        # The following algorithm is a polynomial fit to:
-        # (1) The left half of the detector (using the left and middle scattered light spectrum)
-        # (2) The right half of the detector (using the right and middle scattered light spectrum)
-        # We then ensure that the model is continuous and smooth at the middle of the detector.
-        medl = np.median(scattlightl / scattlight)
-        medr = np.median(scattlightr / scattlight)
-        spatimg = np.meshgrid(np.arange(frame.shape[1]), np.arange(frame.shape[0]))[0]
-        scatt_img = np.outer(scattlight, np.ones(frame.shape[1]))
-        scatt_img[spatimg < ym] *= 1 + (medl - 1) * (spatimg[spatimg < ym] / ym - 1) ** 2
-        scatt_img[spatimg >= ym] *= 1 + (medr - 1) * (spatimg[spatimg >= ym] / (4095//spatbin - ym) - ym / (4095//spatbin - ym)) ** 2
-        return scatt_img
+        def resid(param, wpix, img):
+            """ Residual function used to optimize the model parameters
+
+            Parameters
+            ----------
+            param : `numpy.ndarray`_
+                1D array of model parameters to use for the fitting function.
+            wpix : tuple
+                A tuple containing the x,y coordinates of the pixels in img
+                to be used for computing the residual.
+            img : `numpy.ndarray`_
+                Data image to be used to compute the residual. Shape is (nspec, nspat)
+
+            Returns
+            -------
+            resid : `numpy.ndarray`_
+                A 1D vector of the residuals
+            """
+            return img[wpix] - scattered_light_model(param, img)[wpix]
+
+        # Grab the binning for convenience
+        specbin, spatbin = parse.parse_binning(tbl['binning'])
+
+        # First pad the edges to minimize edge effects
+        # Do a median filter near the edges
+        frame[0, :] = np.median(frame[0:10, :], axis=0)
+        frame[-1, :] = np.median(frame[-10:, :], axis=0)
+        img = np.pad(frame, nspecpad, mode='edge')
+        slitmask_pad = np.pad(slitmask, nspecpad, mode='edge')
+        # Grab the pixels to be included in the fit
+        wpix = np.where(slitmask_pad == -1)
+
+        # Get some starting parameters (these were determined by fitting spectra,
+        # and should be close to the final fitted values to reduce computational time)
+        # Note :: These values need to be originally based on data that uses 1x1 binning,
+        # and are now scaled here according to the binning of the current data to be analysed.
+        # if tbl['dispname'] == 'BH2':
+        #     x0 = [sigmx, sigmy1, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
+        # elif tbl['dispname'] == 'BM':
+        #     x0 = [sigmx, sigmy1, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
+        if tbl['dispname'] == 'BL':
+            x0 = np.array([3.55797869e+02/specbin, 2.38333349e+02/spatbin,  # kernel widths
+                           2.52262744e+01/specbin, 1.97022975e+02/spatbin,  # pixel offsets
+                           9.41621820e-01, 1.02049301e-01, -2.10895495e-01, 2.36542387e-01,
+                           -9.83331029e-02])
+        else:
+            msgs.warn(f"Initial scattered light model parameters have not been setup for grating {tbl['dispname']}")
+            sigmx = 400.0 / specbin  # This is the spectral direction
+            sigmy = 200.0 / spatbin  # This is the spatial direction
+            shft_spec = -60.0 / specbin  # Shift of the scattered light in the spectral direction
+            shft_spat = 0.0 / spatbin  # Shift of the scattered light in the spatial direction
+            zoom = 1.0  # Zoom factor of the scattered light
+            term0, term1, term2, term3 = 0.1, -0.2, 0.3, -0.1  # Polynomial coefficients
+            x0 = [sigmx, sigmy, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
+
+        # Compute the best-fitting model parameters
+        msgs.info("Computing best-fitting model parameters of the scattered light")
+        res_lsq = least_squares(resid, x0,
+                                bounds=([1, 1, -200 / specbin, -200 / spatbin, 0, -10, -10, -10, -10],
+                                        [600 / specbin, 600 / spatbin, 200 / specbin, 200 / spatbin, 2, 10, 10, 10,
+                                         10]),
+                                args=(wpix, img), verbose=2)
+        # Store if this is a successful fit
+        success = res_lsq.success
+        if success:
+            msgs.info("Generating best-fitting scattered light model")
+            scatt_img = scattered_light_model(res_lsq.x, img)[nspecpad:-nspecpad, nspecpad:-nspecpad]
+        else:
+            msgs.warn("Scattered light model fitting failed")
+            scatt_img = np.zeros_like(frame)
+        return scatt_img, res_lsq.x, success
 
     def fit_2d_det_response(self, det_resp, gpmask):
         r"""
@@ -1311,7 +1363,6 @@ def is_nasmask(hdr):
         """
         return 'Mask' in hdr['RNASNAM']
 
-
     def get_rawimage(self, raw_file, det):
         """
         Read a raw KCRM data frame
@@ -1391,3 +1442,58 @@ def get_rawimage(self, raw_file, det):
 
         # Return
         return detpar, raw_img, hdu, exptime, rawdatasec_img, oscansec_img
+
+
+def scattered_light_model(param, img, kernel='gaussian'):
+    """ Model used to calculate the scattered light
+
+    Parameters
+    ----------
+    param : `numpy.ndarray`_
+        Model parameters that determine the scattered light based on the input img.
+        For KCWI there are 9 model parameters that need to be input, and these are:
+
+        * param[0] = Kernel width in the spectral direction
+        * param[1] = Kernel width in the spatial direction
+        * param[2] = Pixel shift of the scattered light in the spectral direction
+        * param[3] = Pixel shift of the scattered light in the spatial direction
+        * param[4] = Zoom factor of the scattered light (~1)
+        * param[5] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**0)
+        * param[6] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**1)
+        * param[7] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**2)
+        * param[8] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**3)
+    img : `numpy.ndarray`_
+        Raw image that you want to compute the scattered light model.
+        shape is (nspec, nspat)
+
+    Returns
+    -------
+    model : `numpy.ndarray`_
+        Model of the scattered light for the input
+    """
+    # For clarity, unpack the parameters
+    assert param.size == 9  # For KCWI, the scattered light model requires 9 model parameters
+    sigmx, sigmy, shft_spec, shft_spat, zoom = param[0], param[1], param[2], param[3], param[4]
+    term0, term1, term2, term3 = param[5], param[6], param[7], param[8]
+    # Generate a 2D smoothing kernel
+    if kernel == 'gaussian':
+        # Gaussian
+        subkrnx = np.exp(-0.5*(np.arange(int(6*sigmx))-3*sigmx)**2/sigmx**2)
+        subkrny = np.exp(-0.5*(np.arange(int(6*sigmy))-3*sigmy)**2/sigmy**2)
+    elif kernel == 'lorentzian':
+        # Lorentzian
+        subkrnx = sigmx / ((np.arange(int(10*sigmx))-5*sigmx)**2 + sigmx**2)
+        subkrny = sigmy / ((np.arange(int(10*sigmy))-5*sigmy)**2 + sigmy**2)
+    else:
+        msgs.error(f"Unknown kernel: {kernel}")
+    kernel = np.outer(subkrnx, subkrny)
+    kernel /= np.sum(kernel)
+    # Make a grid of coordinates
+    specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
+    spat, spec = np.meshgrid(spatvec, specvec/(specvec.size-1))
+    # Convolve the input image (note: most of the time is spent here)
+    # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
+    # scale_img = (term0 + term1 * spec + term2*spec**2 + term3*spec**3) * signal.fftconvolve(img, kernel, mode='same')
+    scale_img = (term0 + term1 * spec + term2*spec**2 + term3*spec**3) * signal.oaconvolve(img, kernel, mode='same')
+    spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
+    return spl(zoom*(specvec+shft_spec), zoom*(spatvec+shft_spat))
\ No newline at end of file

From 9fcbe986c6a82401acd5c49b8dc576a7f14eaefc Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 16:37:42 +0100
Subject: [PATCH 05/67] scattered light cleanup

---
 pypeit/calibrations.py               |   7 +-
 pypeit/images/rawimage.py            |   9 +-
 pypeit/scattlight.py                 |   4 +-
 pypeit/spectrographs/keck_kcwi.py    | 136 +++++++++++++++------------
 pypeit/spectrographs/spectrograph.py |  44 +++++++--
 5 files changed, 120 insertions(+), 80 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index 095eb4e68a..3bd318253e 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -593,9 +593,10 @@ def get_scattlight(self):
 
         spatbin = parse.parse_binning(binning)[1]
         pad = self.par['scattlight']['pad'] // spatbin
-        slitid_img_init = self.slits.slit_img(pad=pad, initial=True, flexure=None)
+        offslitmask = self.slits.slit_img(pad=pad, initial=True, flexure=None)==-1
 
-        model, modelpar, success = self.spectrograph.scattered_light(scattlightImage.image, slitid_img_init, self.fitstbl[scatt_idx[0]])
+        model, modelpar, success = self.spectrograph.scattered_light(scattlightImage.image, offslitmask,
+                                                                     self.fitstbl[scatt_idx[0]])
 
         if not success:
             # Something went awry
@@ -610,7 +611,7 @@ def get_scattlight(self):
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
                                                       pad=self.par['scattlight']['pad'],
-                                                      scattlight_raw=scattlightImage,
+                                                      scattlight_raw=scattlightImage.image,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
 
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index eb1f3dc737..8d13124c2a 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1132,12 +1132,13 @@ def subtract_scattlight(self, scattlight):
             msgs.warn("The scattered light has already been subtracted from the image!")
             return
 
+        if scattlight.scattlight_param is None:
+            msgs.warn("Scattered light parameters are not set. Cannot perform scattered light subtraction.")
+            return
+
         # Loop over the images
         for ii in range(self.nimg):
-            binning = self.detector[0].binning
-            # TODO :: Need to pass in scattlight or scattlight.scattlight_param
-            embed()
-            scatt_img = self.spectrograph.scattered_light(self.image[ii, ...], binning)
+            scatt_img = self.spectrograph.scattered_light_model(scattlight.scattlight_param, self.image[ii, ...])
             self.image[ii, ...] -= scatt_img
         self.steps[step] = True
 
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index a852d7867e..8ca9f223fa 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -73,11 +73,9 @@ def _validate(self):
         Validate the slit traces.
         """
         # Allow the object to be empty
-        if self.scattlight_model is None:
+        if self.scattlight_param is None:
             return
 
-        self.nspec, self.nslits = self.left_init.shape
-
         if self.PYP_SPEC is None:
             self.PYP_SPEC = 'unknown'
 
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index c9ab441f66..4cd3d8f023 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1032,7 +1032,68 @@ def get_rawimage(self, raw_file, det):
         # Return
         return detpar, raw_img, hdu, exptime, rawdatasec_img, oscansec_img
 
-    def scattered_light(self, frame, slitmask, tbl, nspecpad=300):
+    def scattered_light_model(self, param, img, kernel='gaussian'):
+        """ Model used to calculate the scattered light
+
+        Parameters
+        ----------
+        param : `numpy.ndarray`_
+            Model parameters that determine the scattered light based on the input img.
+            For KCWI there are 9 model parameters that need to be input, and these are:
+
+            * param[0] = Kernel width in the spectral direction
+            * param[1] = Kernel width in the spatial direction
+            * param[2] = Pixel shift of the scattered light in the spectral direction
+            * param[3] = Pixel shift of the scattered light in the spatial direction
+            * param[4] = Zoom factor of the scattered light (~1)
+            * param[5] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**0)
+            * param[6] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**1)
+            * param[7] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**2)
+            * param[8] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**3)
+        img : `numpy.ndarray`_
+            Raw image that you want to compute the scattered light model.
+            shape is (nspec, nspat)
+            Model used to calculate the scattered light. This function is used to
+            generate a model of the scattered light, based on a set of model parameters
+            that have been optimized using self.scattered_light().
+        kernel : :obj:`str`_, optional
+            The shape of the kernel to use. The allowed values for KCWI are 'gaussian' and 'lorentzian',
+            but the default value ('gaussian') is known to provide the best fit.
+
+        Returns
+        -------
+        model : `numpy.ndarray`_
+            Model of the scattered light for the input
+        """
+        # For clarity, unpack the parameters
+        assert param.size == 9  # For KCWI, the scattered light model requires 9 model parameters
+        sigmx, sigmy, shft_spec, shft_spat, zoom = param[0], param[1], param[2], param[3], param[4]
+        term0, term1, term2, term3 = param[5], param[6], param[7], param[8]
+        # Generate a 2D smoothing kernel
+        if kernel == 'gaussian':
+            # Gaussian
+            subkrnx = np.exp(-0.5 * (np.arange(int(6 * sigmx)) - 3 * sigmx) ** 2 / sigmx ** 2)
+            subkrny = np.exp(-0.5 * (np.arange(int(6 * sigmy)) - 3 * sigmy) ** 2 / sigmy ** 2)
+        elif kernel == 'lorentzian':
+            # Lorentzian
+            subkrnx = sigmx / ((np.arange(int(10 * sigmx)) - 5 * sigmx) ** 2 + sigmx ** 2)
+            subkrny = sigmy / ((np.arange(int(10 * sigmy)) - 5 * sigmy) ** 2 + sigmy ** 2)
+        else:
+            msgs.error(f"Unknown kernel: {kernel}")
+        kernel = np.outer(subkrnx, subkrny)
+        kernel /= np.sum(kernel)
+        # Make a grid of coordinates
+        specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
+        spat, spec = np.meshgrid(spatvec, specvec / (specvec.size - 1))
+        # Convolve the input image (note: most of the time is spent here)
+        # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
+        # scale_img = (term0 + term1 * spec + term2*spec**2 + term3*spec**3) * signal.fftconvolve(img, kernel, mode='same')
+        scale_img = (term0 + term1 * spec + term2 * spec ** 2 + term3 * spec ** 3) * signal.oaconvolve(img, kernel,
+                                                                                                       mode='same')
+        spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
+        return spl(zoom * (specvec + shft_spec), zoom * (spatvec + shft_spat))
+
+    def scattered_light(self, frame, offslitmask, tbl, detpad=300):
         """ Calculate a model of the scattered light of the input frame.
 
         For KCWI, the main contributor to the scattered light is referred to as the "narcissistic ghost"
@@ -1045,7 +1106,13 @@ def scattered_light(self, frame, slitmask, tbl, nspecpad=300):
         ----------
         frame : `numpy.ndarray`_
             Raw 2D data frame to be used to compute the scattered light.
-        TODO :: complete docstring
+        offslitmask : `numpy.ndarray`_
+            A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
+        tbl : :class:`~pypeit.metadata.PypeItMetaData`_
+            One row of the fitstbl PypeItMetaData that contains metadata about the file being used to
+            optimize the scattered light model parameters.
+        detpad : :obj:`int`_, optional
+            Number of pixels to pad to each of the detector edges to reduce edge effects.
 
         Returns
         -------
@@ -1070,7 +1137,7 @@ def resid(param, wpix, img):
             resid : `numpy.ndarray`_
                 A 1D vector of the residuals
             """
-            return img[wpix] - scattered_light_model(param, img)[wpix]
+            return img[wpix] - self.scattered_light_model(param, img)[wpix]
 
         # Grab the binning for convenience
         specbin, spatbin = parse.parse_binning(tbl['binning'])
@@ -1079,10 +1146,10 @@ def resid(param, wpix, img):
         # Do a median filter near the edges
         frame[0, :] = np.median(frame[0:10, :], axis=0)
         frame[-1, :] = np.median(frame[-10:, :], axis=0)
-        img = np.pad(frame, nspecpad, mode='edge')
-        slitmask_pad = np.pad(slitmask, nspecpad, mode='edge')
+        img = np.pad(frame, detpad, mode='edge')
+        offslitmask_pad = np.pad(offslitmask, detpad, mode='edge')
         # Grab the pixels to be included in the fit
-        wpix = np.where(slitmask_pad == -1)
+        wpix = np.where(offslitmask_pad)
 
         # Get some starting parameters (these were determined by fitting spectra,
         # and should be close to the final fitted values to reduce computational time)
@@ -1118,7 +1185,7 @@ def resid(param, wpix, img):
         success = res_lsq.success
         if success:
             msgs.info("Generating best-fitting scattered light model")
-            scatt_img = scattered_light_model(res_lsq.x, img)[nspecpad:-nspecpad, nspecpad:-nspecpad]
+            scatt_img = self.scattered_light_model(res_lsq.x, img)[detpad:-detpad, detpad:-detpad]
         else:
             msgs.warn("Scattered light model fitting failed")
             scatt_img = np.zeros_like(frame)
@@ -1442,58 +1509,3 @@ def get_rawimage(self, raw_file, det):
 
         # Return
         return detpar, raw_img, hdu, exptime, rawdatasec_img, oscansec_img
-
-
-def scattered_light_model(param, img, kernel='gaussian'):
-    """ Model used to calculate the scattered light
-
-    Parameters
-    ----------
-    param : `numpy.ndarray`_
-        Model parameters that determine the scattered light based on the input img.
-        For KCWI there are 9 model parameters that need to be input, and these are:
-
-        * param[0] = Kernel width in the spectral direction
-        * param[1] = Kernel width in the spatial direction
-        * param[2] = Pixel shift of the scattered light in the spectral direction
-        * param[3] = Pixel shift of the scattered light in the spatial direction
-        * param[4] = Zoom factor of the scattered light (~1)
-        * param[5] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**0)
-        * param[6] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**1)
-        * param[7] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**2)
-        * param[8] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**3)
-    img : `numpy.ndarray`_
-        Raw image that you want to compute the scattered light model.
-        shape is (nspec, nspat)
-
-    Returns
-    -------
-    model : `numpy.ndarray`_
-        Model of the scattered light for the input
-    """
-    # For clarity, unpack the parameters
-    assert param.size == 9  # For KCWI, the scattered light model requires 9 model parameters
-    sigmx, sigmy, shft_spec, shft_spat, zoom = param[0], param[1], param[2], param[3], param[4]
-    term0, term1, term2, term3 = param[5], param[6], param[7], param[8]
-    # Generate a 2D smoothing kernel
-    if kernel == 'gaussian':
-        # Gaussian
-        subkrnx = np.exp(-0.5*(np.arange(int(6*sigmx))-3*sigmx)**2/sigmx**2)
-        subkrny = np.exp(-0.5*(np.arange(int(6*sigmy))-3*sigmy)**2/sigmy**2)
-    elif kernel == 'lorentzian':
-        # Lorentzian
-        subkrnx = sigmx / ((np.arange(int(10*sigmx))-5*sigmx)**2 + sigmx**2)
-        subkrny = sigmy / ((np.arange(int(10*sigmy))-5*sigmy)**2 + sigmy**2)
-    else:
-        msgs.error(f"Unknown kernel: {kernel}")
-    kernel = np.outer(subkrnx, subkrny)
-    kernel /= np.sum(kernel)
-    # Make a grid of coordinates
-    specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
-    spat, spec = np.meshgrid(spatvec, specvec/(specvec.size-1))
-    # Convolve the input image (note: most of the time is spent here)
-    # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
-    # scale_img = (term0 + term1 * spec + term2*spec**2 + term3*spec**3) * signal.fftconvolve(img, kernel, mode='same')
-    scale_img = (term0 + term1 * spec + term2*spec**2 + term3*spec**3) * signal.oaconvolve(img, kernel, mode='same')
-    spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
-    return spl(zoom*(specvec+shft_spec), zoom*(spatvec+shft_spat))
\ No newline at end of file
diff --git a/pypeit/spectrographs/spectrograph.py b/pypeit/spectrographs/spectrograph.py
index f7c87028e8..ce8907b6e1 100644
--- a/pypeit/spectrographs/spectrograph.py
+++ b/pypeit/spectrographs/spectrograph.py
@@ -1891,21 +1891,50 @@ def calc_pattern_freq(self, frame, rawdatasec_img, oscansec_img, hdu):
         patt_freqs : :obj:`list`
             List of pattern frequencies.
         """
-        msgs.info("Pattern noise removal is not implemented for spectrograph {0:s}".format(self.name))
+        msgs.warn(f"Pattern noise removal is not implemented for spectrograph {self.name}")
         return []
 
-    def scattered_light(self, frame, binning):
+    def scattered_light_model(self, param, img, kernel='gaussian'):
+        """ Model used to calculate the scattered light. This function is used to
+        generate a model of the scattered light, based on a set of model parameters
+        that have been optimized using self.scattered_light().
+
+        Parameters
+        ----------
+        param : `numpy.ndarray`_
+            Model parameters that determine the scattered light based on the input img.
+            Every spectrograph is allowed a different number of model parameters.
+        img : `numpy.ndarray`_
+            Raw image that you want to compute the scattered light model.
+            shape is (nspec, nspat)
+        kernel : :obj:`str`_, optional
+            The shape of the kernel to use. The allowed values depend on the spectrograph
+            scattered light model.
+
+        Returns
+        -------
+        model : `numpy.ndarray`_
+            Model of the scattered light for the input
         """
-        Calculate a model of the scattered light of the input frame.
+        msgs.warn(f"Scattered light subtraction is not setup for {self.name}")
+        return np.zeros_like(img)
+
+    def scattered_light(self, frame, offslitmask, tbl, detpad=300):
+        """ Calculate the scattered light model parameters of the input frame.
+        This function is used to optimize the model parameters. See also
+        self.scattered_light_model()
 
         Parameters
         ----------
         frame : `numpy.ndarray`_
             Raw 2D data frame to be used to compute the scattered light.
-        binning : str, `numpy.ndarray`_, tuple
-            Binning of the frame (e.g. '2x1' refers to a binning of 2 in the spectral
-            direction, and a binning of 1 in the spatial direction). For the supported
-            formats, refer to :func:`~pypeit.core.parse.parse_binning`.
+        offslitmask : `numpy.ndarray`_
+            A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
+        tbl : :class:`~pypeit.metadata.PypeItMetaData`_
+            One row of the fitstbl PypeItMetaData that contains metadata about the file being used to
+            optimize the scattered light model parameters.
+        detpad : :obj:`int`_, optional
+            Number of pixels to pad to each of the detector edges to reduce edge effects.
 
         Returns
         -------
@@ -1926,4 +1955,3 @@ def __repr__(self):
         txt += '>'
         return txt
 
-

From e911a71bff5233c446625f16bd8780765224a180 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 20:56:48 +0100
Subject: [PATCH 06/67] add to config

---
 setup.cfg | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.cfg b/setup.cfg
index 5f0e25ee7e..4de6a91269 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -98,6 +98,7 @@ console_scripts =
     pypeit_chk_for_calibs = pypeit.scripts.chk_for_calibs:ChkForCalibs.entry_point
     pypeit_chk_noise_1dspec = pypeit.scripts.chk_noise_1dspec:ChkNoise1D.entry_point
     pypeit_chk_noise_2dspec = pypeit.scripts.chk_noise_2dspec:ChkNoise2D.entry_point
+    pypeit_chk_scattlight = pypeit.scripts.chk_scattlight:ChkScattLight.entry_point
     pypeit_coadd_1dspec = pypeit.scripts.coadd_1dspec:CoAdd1DSpec.entry_point
     pypeit_coadd_2dspec = pypeit.scripts.coadd_2dspec:CoAdd2DSpec.entry_point
     pypeit_coadd_datacube = pypeit.scripts.coadd_datacube:CoAddDataCube.entry_point

From 0f026322f446306e7b7bfaf90ef23d0d3d3a0ba8 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 20:57:14 +0100
Subject: [PATCH 07/67] add script

---
 pypeit/scattlight.py             | 19 +++++++--
 pypeit/scripts/chk_scattlight.py | 72 ++++++++++++++++++++++++++++++++
 2 files changed, 88 insertions(+), 3 deletions(-)
 create mode 100644 pypeit/scripts/chk_scattlight.py

diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 8ca9f223fa..7e7391efbd 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -15,6 +15,7 @@
 from pypeit import datamodel
 from pypeit import calibframe
 from pypeit.display import display
+from pypeit.spectrographs.util import load_spectrograph
 
 
 class ScatteredLight(calibframe.CalibFrame):
@@ -97,10 +98,12 @@ def get_model(self, image):
         if self.scattlight_param is None:
             msgs.warn("No scattered light parameters are available")
             return np.zeros_like(image)
+        # Load the spectrograph
+        spec = load_spectrograph(self.PYP_SPEC)
         # Return the model of the scattered light
-        return self.spec.scattered_light_model(self.scattlight_param, image)
+        return spec.scattered_light_model(self.scattlight_param, image)
 
-    def show(self, image=None, slits=None, wcs_match=True):
+    def show(self, image=None, slits=None, mask=False, wcs_match=True):
         """ Display the master scattered light frame, the model, and data-model.
 
         Parameters
@@ -110,13 +113,23 @@ def show(self, image=None, slits=None, wcs_match=True):
             the master Scattered Light frame wil be displayed by default
         slits : :class:`~pypeit.slittrace.SlitTraceSet`, optional
             The current slit traces
+        mask : :obj:`bool`_
+            If True, the slits will be masked to show only the scattered light regions
         wcs_match : :obj:`bool`, optional
             Use a reference image for the WCS and match all image in other channels to it.
         """
+        offslitmask = slits.slit_img(pad=0, initial=True, flexure=None) == -1 if mask else 1
+
         # Prepare the frames
         _data = self.scattlight_raw if image is None else image
         _model = self.scattlight_model if image is None else self.get_model(image)
-        _resid = _data - _model
+        _resid = (_data - _model)
+
+        # Fold in the mask
+        _data *= offslitmask
+        _model *= offslitmask
+        _resid *= offslitmask
+
         modmax = np.max(_model)
         image_list = zip([_data, _model, _resid],
                              ['data', 'model', 'data-model'],
diff --git a/pypeit/scripts/chk_scattlight.py b/pypeit/scripts/chk_scattlight.py
new file mode 100644
index 0000000000..f5a0377769
--- /dev/null
+++ b/pypeit/scripts/chk_scattlight.py
@@ -0,0 +1,72 @@
+"""
+This script displays the flat images in an RC Ginga window.
+
+.. include common links, assuming primary doc root is up one directory
+.. include:: ../include/links.rst
+"""
+
+from pypeit.scripts import scriptbase
+from pypeit import msgs
+from pypeit.pypmsgs import PypeItError, PypeItDataModelError
+from pypeit.images.detector_container import DetectorContainer
+from pypeit import io
+
+class ChkScattLight(scriptbase.ScriptBase):
+
+    @classmethod
+    def get_parser(cls, width=None):
+        parser = super().get_parser(description='Display the scattered light image in a Ginga viewer',
+                                    width=width)
+        parser.add_argument('file', type=str,
+                            help='PypeIt Scattered Light file [e.g. ScatteredLight_A_0_DET01.fits.gz]')
+        parser.add_argument('slits', type=str,
+                            help='Slits calibration file [e.g. Slits_A_0_DET01.fits.gz]')
+        parser.add_argument('--spec2d', type=str, default=None,
+                            help='PypeIt science spec2d file')
+        parser.add_argument('--det', default='1', type=str,
+                            help='Detector name or number.  If a number, the name is constructed '
+                                 'assuming the reduction is for a single detector.  If a string, '
+                                 'it must match the name of the detector object (e.g., DET01 for '
+                                 'a detector, MSC01 for a mosaic).')
+        parser.add_argument('--mask', type=bool, default=False,
+                            help='If True, the detector pixels that are considered on the slit will be '
+                                 'masked to highlight the scattered light regions')
+        parser.add_argument("--type", type=str, default='all',
+                            help="Which flats to display. Must be one of: pixel, illum, all")
+        return parser
+
+    @staticmethod
+    def main(args):
+
+        from pypeit import scattlight, spec2dobj, slittrace
+
+        # Parse the detector name
+        try:
+            det = int(args.det)
+        except:
+            detname = args.det
+        else:
+            detname = DetectorContainer.get_name(det)
+
+        # Load scattered light calibration frame
+        ScattLightImage = scattlight.ScatteredLight.from_file(args.file)
+
+        # Load slits information
+        slits = slittrace.SlitTraceSet.from_file(args.slits)
+
+        # Load the alternate file if requested
+        display_frame = None  # The default is to display the frame used to calculate the scattered light model
+        if args.spec2d is not None:
+            msgs.error("displaying the spec2d scattered light is not currently supported")
+            try:
+                # TODO :: the spec2d file may have already had the scattered light removed, so this is not correct.
+                spec2D = spec2dobj.Spec2DObj.from_file(args.spec2d, detname, chk_version=False)
+            except PypeItDataModelError:
+                msgs.warn(f"Error loading spec2d file {args.spec2d} - attempting to load science image from fits")
+                spec2D = None
+
+            # Now set the frame to be displayed
+            display_frame = io.fits_open(args.spec2d)[detname+'-SCIIMG'].data if spec2D is None else spec2D.sciimg
+
+        # Show
+        ScattLightImage.show(image=display_frame, slits=slits, mask=args.mask)

From 1045b14d72551ab13896dd7800e22bee01186c1d Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 20:57:26 +0100
Subject: [PATCH 08/67] fix display

---
 pypeit/calibrations.py        | 24 ++++++++++++++++--------
 pypeit/images/buildimage.py   |  6 ++++--
 pypeit/images/combineimage.py |  7 ++++---
 pypeit/pypeit.py              |  2 ++
 4 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index 3bd318253e..e616821add 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -183,6 +183,7 @@ def __init__(self, fitstbl, par, spectrograph, caldir, qadir=None,
         self.msbpm = None
         self.wv_calib = None
         self.slits = None
+        self.msscattlight = None
 
         self.wavetilts = None
         self.flatimages = None
@@ -323,8 +324,8 @@ def get_arc(self):
         self.msarc = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                     self.par['arcframe'], raw_files,
                                                     bias=self.msbias, bpm=self.msbpm,
-                                                    dark=self.msdark, calib_dir=self.calib_dir,
-                                                    setup=setup, calib_id=calib_id)
+                                                    dark=self.msdark, scattlight=self.msscattlight,
+                                                    calib_dir=self.calib_dir, setup=setup, calib_id=calib_id)
         # Save the result
         self.msarc.to_file()
         # Return it
@@ -367,6 +368,7 @@ def get_tiltimg(self):
                                                      self.par['tiltframe'], raw_files,
                                                      bias=self.msbias, bpm=self.msbpm,
                                                      dark=self.msdark, slits=self.slits,
+                                                     scattlight=self.msscattlight,
                                                      calib_dir=self.calib_dir, setup=setup,
                                                      calib_id=calib_id)
         # Save the result
@@ -416,7 +418,8 @@ def get_align(self):
                                                  self.par['alignframe'], raw_files,
                                                  bias=self.msbias, bpm=self.msbpm,
                                                  dark=self.msdark, calib_dir=self.calib_dir,
-                                                 setup=setup, calib_id=calib_id)
+                                                 setup=setup, calib_id=calib_id,
+                                                 scattlight=self.msscattlight)
 
         # Instantiate
         # TODO: From JFH: Do we need the bpm here?  Check that this was in the previous code.
@@ -713,7 +716,8 @@ def get_flats(self):
             pixel_flat = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                         self.par['pixelflatframe'],
                                                         raw_pixel_files, dark=self.msdark,
-                                                        bias=self.msbias, bpm=self.msbpm)
+                                                        bias=self.msbias, bpm=self.msbpm,
+                                                        scattlight=self.msscattlight)
             if len(raw_lampoff_files) > 0:
                 # Reset the BPM
                 self.get_bpm(frame=raw_lampoff_files[0])
@@ -724,7 +728,7 @@ def get_flats(self):
                                                               self.par['lampoffflatsframe'],
                                                               raw_lampoff_files,
                                                               dark=self.msdark, bias=self.msbias,
-                                                              bpm=self.msbpm)
+                                                              bpm=self.msbpm, scattlight=self.msscattlight)
                 pixel_flat = pixel_flat.sub(lampoff_flat)
 
             # Initialise the pixel flat
@@ -747,7 +751,7 @@ def get_flats(self):
                 msgs.prindent(f'{Path(f).name}')
             illum_flat = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                         self.par['illumflatframe'], raw_illum_files,
-                                                        dark=self.msdark, bias=self.msbias,
+                                                        dark=self.msdark, bias=self.msbias, scattlight=self.msscattlight,
                                                         flatimages=self.flatimages, bpm=self.msbpm)
             if len(raw_lampoff_files) > 0:
                 msgs.info('Subtracting lamp off flats using files: ')
@@ -758,7 +762,9 @@ def get_flats(self):
                                                                   self.par['lampoffflatsframe'],
                                                                   raw_lampoff_files,
                                                                   dark=self.msdark,
-                                                                  bias=self.msbias, bpm=self.msbpm)
+                                                                  bias=self.msbias,
+                                                                  scattlight=self.msscattlight,
+                                                                  bpm=self.msbpm)
                 illum_flat = illum_flat.sub(lampoff_flat)
 
             # Initialise the pixel flat
@@ -867,6 +873,7 @@ def get_slits(self):
         traceImage = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                     self.par['traceframe'], raw_trace_files,
                                                     bias=self.msbias, bpm=self.msbpm,
+                                                    scattlight=self.msscattlight,
                                                     dark=self.msdark, calib_dir=self.calib_dir,
                                                     setup=setup, calib_id=calib_id)
         if len(raw_lampoff_files) > 0:
@@ -880,7 +887,8 @@ def get_slits(self):
             lampoff_flat = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                           self.par['lampoffflatsframe'],
                                                           raw_lampoff_files, dark=self.msdark,
-                                                          bias=self.msbias, bpm=self.msbpm)
+                                                          bias=self.msbias, scattlight=self.msscattlight,
+                                                          bpm=self.msbpm)
             traceImage = traceImage.sub(lampoff_flat)
 
         edges = edgetrace.EdgeTraceSet(traceImage, self.spectrograph, self.par['slitedges'],
diff --git a/pypeit/images/buildimage.py b/pypeit/images/buildimage.py
index d30183cafc..b9ecfa9339 100644
--- a/pypeit/images/buildimage.py
+++ b/pypeit/images/buildimage.py
@@ -157,7 +157,7 @@ def construct_file_name(cls, calib_key, calib_dir=None, basename=None):
 
 
 def buildimage_fromlist(spectrograph, det, frame_par, file_list, bias=None, bpm=None, dark=None,
-                        flatimages=None, maxiters=5, ignore_saturation=True, slits=None,
+                        scattlight=None, flatimages=None, maxiters=5, ignore_saturation=True, slits=None,
                         mosaic=None, calib_dir=None, setup=None, calib_id=None):
     """
     Perform basic image processing on a list of images and combine the results.
@@ -192,6 +192,8 @@ def buildimage_fromlist(spectrograph, det, frame_par, file_list, bias=None, bpm=
         dark (:class:`~pypeit.images.buildimage.DarkImage`, optional):
             Dark-current image; passed directly to
             :func:`~pypeit.images.rawimage.RawImage.process` for all images.
+        scattlight (:class:`~pypeit.scattlight.ScatteredLight`, optional):
+            Scattered light model to be used to determine scattered light.
         flatimages (:class:`~pypeit.flatfield.FlatImages`, optional):
             Flat-field images for flat fielding; passed directly to
             :func:`~pypeit.images.rawimage.RawImage.process` for all images.
@@ -247,7 +249,7 @@ def buildimage_fromlist(spectrograph, det, frame_par, file_list, bias=None, bpm=
 
     # Do it
     combineImage = combineimage.CombineImage(spectrograph, det, frame_par['process'], file_list)
-    pypeitImage = combineImage.run(bias=bias, bpm=bpm, dark=dark, flatimages=flatimages,
+    pypeitImage = combineImage.run(bias=bias, bpm=bpm, dark=dark, flatimages=flatimages, scattlight=scattlight,
                                    sigma_clip=frame_par['process']['clip'],
                                    sigrej=frame_par['process']['comb_sigrej'],
                                    maxiters=maxiters, ignore_saturation=ignore_saturation,
diff --git a/pypeit/images/combineimage.py b/pypeit/images/combineimage.py
index 8432e77585..ffb0b20247 100644
--- a/pypeit/images/combineimage.py
+++ b/pypeit/images/combineimage.py
@@ -62,9 +62,8 @@ def __init__(self, spectrograph, det, par, files):
         if self.nfiles == 0:
             msgs.error('CombineImage requires a list of files to instantiate')
 
-    def run(self, bias=None, flatimages=None, ignore_saturation=False, sigma_clip=True,
-            bpm=None, sigrej=None, maxiters=5, slits=None, dark=None, scattlight=None,
-            combine_method='mean', mosaic=False):
+    def run(self, bias=None, scattlight=None, flatimages=None, ignore_saturation=False, sigma_clip=True,
+            bpm=None, sigrej=None, maxiters=5, slits=None, dark=None, combine_method='mean', mosaic=False):
         r"""
         Process and combine all images.
 
@@ -137,6 +136,8 @@ def run(self, bias=None, flatimages=None, ignore_saturation=False, sigma_clip=Tr
             bias (:class:`~pypeit.images.buildimage.BiasImage`, optional):
                 Bias image for bias subtraction; passed directly to
                 :func:`~pypeit.images.rawimage.RawImage.process` for all images.
+            scattlight (:class:`~pypeit.scattlight.ScatteredLight`, optional):
+                Scattered light model to be used to determine scattered light.
             flatimages (:class:`~pypeit.flatfield.FlatImages`, optional):
                 Flat-field images for flat fielding; passed directly to
                 :func:`~pypeit.images.rawimage.RawImage.process` for all images.
diff --git a/pypeit/pypeit.py b/pypeit/pypeit.py
index 69d9908ab1..bc2f2f33f4 100644
--- a/pypeit/pypeit.py
+++ b/pypeit/pypeit.py
@@ -759,6 +759,7 @@ def objfind_one(self, frames, det, bg_frames=None, std_outfile=None):
             self.spectrograph, det, frame_par,
             sci_files, bias=self.caliBrate.msbias, bpm=self.caliBrate.msbpm,
             dark=self.caliBrate.msdark,
+            scattlight=self.caliBrate.msscattlight,
             flatimages=self.caliBrate.flatimages,
             slits=self.caliBrate.slits,  # For flexure correction
             ignore_saturation=False)
@@ -770,6 +771,7 @@ def objfind_one(self, frames, det, bg_frames=None, std_outfile=None):
                                                    bpm=self.caliBrate.msbpm,
                                                    bias=self.caliBrate.msbias,
                                                    dark=self.caliBrate.msdark,
+                                                   scattlight=self.caliBrate.msscattlight,
                                                    flatimages=self.caliBrate.flatimages,
                                                    slits=self.caliBrate.slits,
                                                    ignore_saturation=False)

From 718263849b5e0ad998c0f2af4597fc1e8e66ccc0 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 21:01:37 +0100
Subject: [PATCH 09/67] scattlight

---
 doc/releases/1.14.1dev.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/releases/1.14.1dev.rst b/doc/releases/1.14.1dev.rst
index 53aae548b7..a39f1a9c4c 100644
--- a/doc/releases/1.14.1dev.rst
+++ b/doc/releases/1.14.1dev.rst
@@ -24,6 +24,8 @@ Script Changes
 
 - When making datacubes, the user can select a separate frame to use for the sky subtraction.
   In this case, it is the processed data that will be used for sky subtraction (akin to nodding).
+- A new script has been written (``chk_scattlight``) to check the generation of the scattered
+  light calibration frame model.
 
 Datamodel Changes
 -----------------

From eacde5f0a365aa119d0e72abb47c9070a4de4ae2 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 13 Oct 2023 21:35:26 +0100
Subject: [PATCH 10/67] frame specific scattlight

---
 pypeit/images/rawimage.py        | 18 +++++++++++++++---
 pypeit/scripts/chk_scattlight.py |  2 +-
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 8d13124c2a..2d76ae5499 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -643,7 +643,7 @@ def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, sl
 
         #   - Subtract scattered light... this needs to be done before flatfielding.
         if self.par['subtract_scattlight']:
-            self.subtract_scattlight(scattlight)
+            self.subtract_scattlight(scattlight, slits)
 
         # Flat-field the data.  This propagates the flat-fielding corrections to
         # the variance.  The returned bpm is propagated to the PypeItImage
@@ -1118,7 +1118,7 @@ def subtract_continuum(self, force=False):
         #cont = ndimage.median_filter(self.image, size=(1,101,3), mode='reflect')
         self.steps[step] = True
 
-    def subtract_scattlight(self, scattlight):
+    def subtract_scattlight(self, scattlight, slits):
         """
         Analyze and subtract the scattered light from the image.
 
@@ -1138,7 +1138,19 @@ def subtract_scattlight(self, scattlight):
 
         # Loop over the images
         for ii in range(self.nimg):
-            scatt_img = self.spectrograph.scattered_light_model(scattlight.scattlight_param, self.image[ii, ...])
+            # Calculate a model specific for this frame
+            spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
+            # TODO :: Need to pass in the fitstbl here... currently this is a dummy value
+            tbl = dict(binning=self.detector[0]['binning'], dispname='BL')
+            pad = scattlight.pad // spatbin
+            offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
+            scatt_img, _, success = self.spectrograph.scattered_light(self.image[ii, ...], offslitmask, tbl)
+            # If failure, revert back to the Scattered Light calibration frame model parameters
+            if not success:
+                msgs.warn("Scattered light model failed - using predefined model parameters")
+                # Use predefined model parameters
+                scatt_img = self.spectrograph.scattered_light_model(scattlight.scattlight_param, self.image[ii, ...])
+            # Subtract the scattered light model from the image
             self.image[ii, ...] -= scatt_img
         self.steps[step] = True
 
diff --git a/pypeit/scripts/chk_scattlight.py b/pypeit/scripts/chk_scattlight.py
index f5a0377769..426c8503c4 100644
--- a/pypeit/scripts/chk_scattlight.py
+++ b/pypeit/scripts/chk_scattlight.py
@@ -59,7 +59,7 @@ def main(args):
         if args.spec2d is not None:
             msgs.error("displaying the spec2d scattered light is not currently supported")
             try:
-                # TODO :: the spec2d file may have already had the scattered light removed, so this is not correct.
+                # TODO :: the spec2d file may have already had the scattered light removed, so this is not correct. This script only works when the scattered light is turned off for the spec2d file
                 spec2D = spec2dobj.Spec2DObj.from_file(args.spec2d, detname, chk_version=False)
             except PypeItDataModelError:
                 msgs.warn(f"Error loading spec2d file {args.spec2d} - attempting to load science image from fits")

From 552c667580e9b338c5745f1e780dee4b5365d198 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 19 Oct 2023 08:22:28 +0100
Subject: [PATCH 11/67] rm arg

---
 pypeit/scripts/chk_scattlight.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/pypeit/scripts/chk_scattlight.py b/pypeit/scripts/chk_scattlight.py
index 426c8503c4..f7446f17ae 100644
--- a/pypeit/scripts/chk_scattlight.py
+++ b/pypeit/scripts/chk_scattlight.py
@@ -31,8 +31,6 @@ def get_parser(cls, width=None):
         parser.add_argument('--mask', type=bool, default=False,
                             help='If True, the detector pixels that are considered on the slit will be '
                                  'masked to highlight the scattered light regions')
-        parser.add_argument("--type", type=str, default='all',
-                            help="Which flats to display. Must be one of: pixel, illum, all")
         return parser
 
     @staticmethod

From acc6510f72954106e1ac2efe438777bbfd2ad712 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:20:14 +0100
Subject: [PATCH 12/67] add scattlight doc

---
 doc/scripts/build_datacontainer_datamodels.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/doc/scripts/build_datacontainer_datamodels.py b/doc/scripts/build_datacontainer_datamodels.py
index d1f0f786f4..e065e6a8a4 100644
--- a/doc/scripts/build_datacontainer_datamodels.py
+++ b/doc/scripts/build_datacontainer_datamodels.py
@@ -67,6 +67,7 @@ def build_datamodel_tbl(obj):
     from pypeit.flatfield import FlatImages
     from pypeit.manual_extract import ManualExtractionObj
     from pypeit.onespec import OneSpec
+    from pypeit.scattlight import ScatteredLight
     from pypeit.sensfunc import SensFunc
     from pypeit.slittrace import SlitTraceSet
     from pypeit.spec2dobj import Spec2DObj
@@ -86,7 +87,7 @@ def build_datamodel_tbl(obj):
 
     from pypeit.images import buildimage
 
-    datacontainers = [Alignments, EdgeTraceSet, FlatImages, ManualExtractionObj, OneSpec,
+    datacontainers = [Alignments, EdgeTraceSet, FlatImages, ManualExtractionObj, OneSpec, ScatteredLight,
                       SensFunc, SlitTraceSet, Spec2DObj, SpecObj, TracePCA, WaveCalib, WaveTilts,
                       bspline, DataCube, PypeItFit, MultiSlitFlexure, Telluric, DetectorContainer,
                       Mosaic, PypeItImage, WaveFit]

From 981a006a8993604f008596d19c83068a8ecbf185 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:20:29 +0100
Subject: [PATCH 13/67] add binning/dispname

---
 pypeit/spectrographs/keck_kcwi.py    | 15 ++++++++-------
 pypeit/spectrographs/spectrograph.py |  9 +++++----
 2 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 4cd3d8f023..76882b369f 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1093,7 +1093,7 @@ def scattered_light_model(self, param, img, kernel='gaussian'):
         spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
         return spl(zoom * (specvec + shft_spec), zoom * (spatvec + shft_spat))
 
-    def scattered_light(self, frame, offslitmask, tbl, detpad=300):
+    def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpad=300):
         """ Calculate a model of the scattered light of the input frame.
 
         For KCWI, the main contributor to the scattered light is referred to as the "narcissistic ghost"
@@ -1108,9 +1108,10 @@ def scattered_light(self, frame, offslitmask, tbl, detpad=300):
             Raw 2D data frame to be used to compute the scattered light.
         offslitmask : `numpy.ndarray`_
             A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
-        tbl : :class:`~pypeit.metadata.PypeItMetaData`_
-            One row of the fitstbl PypeItMetaData that contains metadata about the file being used to
-            optimize the scattered light model parameters.
+        binning : :obj:`str`_, optional
+            Comma-separated binning along the spectral and spatial directions; e.g., ``2,1``
+        dispname : :obj:`str`_, optional
+            Name of the disperser
         detpad : :obj:`int`_, optional
             Number of pixels to pad to each of the detector edges to reduce edge effects.
 
@@ -1140,7 +1141,7 @@ def resid(param, wpix, img):
             return img[wpix] - self.scattered_light_model(param, img)[wpix]
 
         # Grab the binning for convenience
-        specbin, spatbin = parse.parse_binning(tbl['binning'])
+        specbin, spatbin = parse.parse_binning(binning)
 
         # First pad the edges to minimize edge effects
         # Do a median filter near the edges
@@ -1159,13 +1160,13 @@ def resid(param, wpix, img):
         #     x0 = [sigmx, sigmy1, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
         # elif tbl['dispname'] == 'BM':
         #     x0 = [sigmx, sigmy1, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
-        if tbl['dispname'] == 'BL':
+        if dispname == 'BL':
             x0 = np.array([3.55797869e+02/specbin, 2.38333349e+02/spatbin,  # kernel widths
                            2.52262744e+01/specbin, 1.97022975e+02/spatbin,  # pixel offsets
                            9.41621820e-01, 1.02049301e-01, -2.10895495e-01, 2.36542387e-01,
                            -9.83331029e-02])
         else:
-            msgs.warn(f"Initial scattered light model parameters have not been setup for grating {tbl['dispname']}")
+            msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname}")
             sigmx = 400.0 / specbin  # This is the spectral direction
             sigmy = 200.0 / spatbin  # This is the spatial direction
             shft_spec = -60.0 / specbin  # Shift of the scattered light in the spectral direction
diff --git a/pypeit/spectrographs/spectrograph.py b/pypeit/spectrographs/spectrograph.py
index ce8907b6e1..4dd4a9e980 100644
--- a/pypeit/spectrographs/spectrograph.py
+++ b/pypeit/spectrographs/spectrograph.py
@@ -1919,7 +1919,7 @@ def scattered_light_model(self, param, img, kernel='gaussian'):
         msgs.warn(f"Scattered light subtraction is not setup for {self.name}")
         return np.zeros_like(img)
 
-    def scattered_light(self, frame, offslitmask, tbl, detpad=300):
+    def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpad=300):
         """ Calculate the scattered light model parameters of the input frame.
         This function is used to optimize the model parameters. See also
         self.scattered_light_model()
@@ -1930,9 +1930,10 @@ def scattered_light(self, frame, offslitmask, tbl, detpad=300):
             Raw 2D data frame to be used to compute the scattered light.
         offslitmask : `numpy.ndarray`_
             A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
-        tbl : :class:`~pypeit.metadata.PypeItMetaData`_
-            One row of the fitstbl PypeItMetaData that contains metadata about the file being used to
-            optimize the scattered light model parameters.
+        binning : :obj:`str`_, optional
+            Comma-separated binning along the spectral and spatial directions; e.g., ``2,1``
+        dispname : :obj:`str`_, optional
+            Name of the disperser
         detpad : :obj:`int`_, optional
             Number of pixels to pad to each of the detector edges to reduce edge effects.
 

From bf9bb1f27d67683fe1dea964ec31811c3f19b5c5 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:20:49 +0100
Subject: [PATCH 14/67] add binning/dispname

---
 pypeit/images/rawimage.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 2d76ae5499..d231d927ef 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -518,7 +518,7 @@ def process(self, par, bpm=None, scattlight=None, flatimages=None, bias=None, sl
         if self.par['use_darkimage'] and dark is None:
             msgs.error('No dark available for dark subtraction!')
         if self.par['subtract_scattlight'] and scattlight is None:
-            msgs.error('Scattered light subtraction requested, but scattered light model provided.')
+            msgs.error('Scattered light subtraction requested, but scattered light model not provided.')
         if self.par['spat_flexure_correct'] and slits is None:
             msgs.error('Spatial flexure correction requested but no slits provided.')
         if self.use_flat and flatimages is None:
@@ -1136,15 +1136,17 @@ def subtract_scattlight(self, scattlight, slits):
             msgs.warn("Scattered light parameters are not set. Cannot perform scattered light subtraction.")
             return
 
+        # Obtain some information that is needed for the scattered light
+        binning = self.detector[0]['binning']
+        dispname = self.spectrograph.get_meta_value(self.spectrograph.get_headarr(self.filename), 'dispname')
         # Loop over the images
         for ii in range(self.nimg):
             # Calculate a model specific for this frame
             spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
-            # TODO :: Need to pass in the fitstbl here... currently this is a dummy value
-            tbl = dict(binning=self.detector[0]['binning'], dispname='BL')
             pad = scattlight.pad // spatbin
             offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
-            scatt_img, _, success = self.spectrograph.scattered_light(self.image[ii, ...], offslitmask, tbl)
+            scatt_img, _, success = self.spectrograph.scattered_light(self.image[ii, ...], offslitmask,
+                                                                      binning=binning, dispname=dispname)
             # If failure, revert back to the Scattered Light calibration frame model parameters
             if not success:
                 msgs.warn("Scattered light model failed - using predefined model parameters")

From 9b74cf7c7e053eb770d7df0f7413cc13a5bd87e9 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:21:00 +0100
Subject: [PATCH 15/67] cleanup scattlight

---
 pypeit/calibrations.py    | 23 +++++++++--------------
 pypeit/core/framematch.py |  2 +-
 pypeit/scattlight.py      | 12 +++++++++---
 3 files changed, 19 insertions(+), 18 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index e616821add..ae27fc67d9 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -324,8 +324,8 @@ def get_arc(self):
         self.msarc = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                     self.par['arcframe'], raw_files,
                                                     bias=self.msbias, bpm=self.msbpm,
-                                                    dark=self.msdark, scattlight=self.msscattlight,
-                                                    calib_dir=self.calib_dir, setup=setup, calib_id=calib_id)
+                                                    dark=self.msdark, calib_dir=self.calib_dir,
+                                                    setup=setup, calib_id=calib_id)
         # Save the result
         self.msarc.to_file()
         # Return it
@@ -368,7 +368,6 @@ def get_tiltimg(self):
                                                      self.par['tiltframe'], raw_files,
                                                      bias=self.msbias, bpm=self.msbpm,
                                                      dark=self.msdark, slits=self.slits,
-                                                     scattlight=self.msscattlight,
                                                      calib_dir=self.calib_dir, setup=setup,
                                                      calib_id=calib_id)
         # Save the result
@@ -418,8 +417,7 @@ def get_align(self):
                                                  self.par['alignframe'], raw_files,
                                                  bias=self.msbias, bpm=self.msbpm,
                                                  dark=self.msdark, calib_dir=self.calib_dir,
-                                                 setup=setup, calib_id=calib_id,
-                                                 scattlight=self.msscattlight)
+                                                 setup=setup, calib_id=calib_id)
 
         # Instantiate
         # TODO: From JFH: Do we need the bpm here?  Check that this was in the previous code.
@@ -549,9 +547,6 @@ def get_scattlight(self):
         Returns:
             :class:`~pypeit.scattlight.ScatteredLight`: The processed calibration image including the model.
         """
-        # Initialise to None
-        self.msscattlight = None
-
         # Check for existing data
         if not self._chk_objs(['msbpm', 'slits']):
             msgs.warn('Must have the bpm and the slits defined to make a scattered light image!  '
@@ -614,19 +609,19 @@ def get_scattlight(self):
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
                                                       pad=self.par['scattlight']['pad'],
-                                                      scattlight_raw=scattlightImage.image,
+                                                      scattlight_raw=scattlightImage,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
 
         # TODO :: Should we go back and recalculate the slit edges once the scattered light is known?
 
-        # Show the result if requested
-        if self.show:
-            self.msscattlight.show()
-
         if self.msscattlight is not None:
-            self.msscattlight.set_paths(self.calib_dir, setup, calib_id, detname)
+            # Show the result if requested
+            if self.show:
+                self.msscattlight.show()
+
             # Save the master scattered light model
+            self.msscattlight.set_paths(self.calib_dir, setup, calib_id, detname)
             self.msscattlight.to_file()
 
         return self.msscattlight
diff --git a/pypeit/core/framematch.py b/pypeit/core/framematch.py
index 795772d0b2..4125f7011e 100644
--- a/pypeit/core/framematch.py
+++ b/pypeit/core/framematch.py
@@ -32,7 +32,7 @@ def __init__(self):
                 ('lampoffflats', 'Flat-field exposure with lamps off used to remove '
                                  'persistence from lamp on flat exposures and/or thermal emission '
                                  'from the telescope and dome'),
-                  ('scattlight', 'Frame (usually similar to pixelflat) used to determine scattered light model'),
+                  ('scattlight', 'Frame (ideally with lots of counts) used to determine the scattered light model'),
                      ('science', 'On-sky observation of a primary target'),
                     ('standard', 'On-sky observation of a flux calibrator'),
                        ('trace', 'High-count exposure used to trace slit positions'),
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 7e7391efbd..659936ac4f 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -16,6 +16,7 @@
 from pypeit import calibframe
 from pypeit.display import display
 from pypeit.spectrographs.util import load_spectrograph
+from pypeit.images.buildimage import ScatteredLightImage
 
 
 class ScatteredLight(calibframe.CalibFrame):
@@ -49,9 +50,14 @@ class ScatteredLight(calibframe.CalibFrame):
                  'nspat': dict(otype=int, descr='Number of pixels in the image spatial direction.'),
                  'binning': dict(otype=str, descr='Binning in PypeIt orientation (not the original)'),
                  'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges.'),
-                 'scattlight_raw': dict(otype=np.ndarray, atype=np.floating, descr='Processed, combined scattered light image'),
-                 'scattlight_model': dict(otype=np.ndarray, atype=np.floating, descr='Model of the scattered light in scattlight_raw'),
-                 'scattlight_param': dict(otype=np.ndarray, atype=np.floating, descr='Model parameters that define the scattered light model')}
+                 'scattlight_raw': dict(otype=ScatteredLightImage,
+                                  descr='Image used to construct the edge traces; see '
+                                        ':class:`~pypeit.images.buildimage.ScatteredLightImage` and '
+                                        ':class:`~pypeit.images.pypeitimage.PypeItImage`.'),
+                 'scattlight_model': dict(otype=np.ndarray, atype=np.floating,
+                                          descr='Model of the scattered light in scattlight_raw'),
+                 'scattlight_param': dict(otype=np.ndarray, atype=np.floating,
+                                          descr='Model parameters that define the scattered light model')}
     """Provides the class data model."""
 
     # TODO: Allow tweaked edges to be arguments?

From e2d35dc4d71961a627e94b22627b4ea73ab87a4b Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:28:18 +0100
Subject: [PATCH 16/67] add chk_scattlight

---
 pypeit/scripts/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pypeit/scripts/__init__.py b/pypeit/scripts/__init__.py
index 410cc4f6d7..8da41d5f5b 100644
--- a/pypeit/scripts/__init__.py
+++ b/pypeit/scripts/__init__.py
@@ -12,6 +12,7 @@
 from pypeit.scripts import chk_for_calibs
 from pypeit.scripts import chk_noise_1dspec
 from pypeit.scripts import chk_noise_2dspec
+from pypeit.scripts import chk_scattlight
 from pypeit.scripts import chk_wavecalib
 from pypeit.scripts import coadd_1dspec
 from pypeit.scripts import coadd_2dspec

From e45f09e1267c999bc211ca58826930671bea77ab Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:28:31 +0100
Subject: [PATCH 17/67] display scattlight

---
 pypeit/display/display.py | 28 +++++++++++++++++++++++++++-
 pypeit/scattlight.py      | 32 +-------------------------------
 2 files changed, 28 insertions(+), 32 deletions(-)

diff --git a/pypeit/display/display.py b/pypeit/display/display.py
index dbd0c0cda3..2b53dc466f 100644
--- a/pypeit/display/display.py
+++ b/pypeit/display/display.py
@@ -636,6 +636,32 @@ def show_tilts(viewer, ch, tilt_traces, yoff=0., xoff=0., points=True, nspec=Non
     canvas.add('constructedcanvas', canvas_list)
 
 
+def show_scattered_light(image_list, slits=None, wcs_match=True):
+    """
+    Interface to ginga to show the quality of the Scattered Light subtraction
 
-
+    Parameters
+    ----------
+    image_list : zip
+        A zip of the images to show, their names, and the scales
+    slits : :class:`~pypeit.slittrace.SlitTraceSet`, optional
+        The current slit traces
+    wcs_match : :obj:`bool`, optional
+        Use a reference image for the WCS and match all image in other channels to it.
+    """
+    connect_to_ginga(raise_err=True, allow_new=True)
+    if slits is not None:
+        left, right, mask = slits.select_edges()
+        gpm = mask == 0
+    # Loop me
+    clear = True
+    for img, name, cut in image_list:
+        if img is None:
+            continue
+        viewer, ch = show_image(img, chname=name, cuts=cut, wcs_match=wcs_match, clear=clear)
+        if slits is not None:
+            show_slits(viewer, ch, left[:, gpm], right[:, gpm], slit_ids=slits.spat_id[gpm])
+        # Turn off clear
+        if clear:
+            clear = False
 
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 659936ac4f..1aca6eb132 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -142,34 +142,4 @@ def show(self, image=None, slits=None, mask=False, wcs_match=True):
                              [(0.0, modmax), (0.0, modmax), (-modmax/2,modmax/2)])
 
         # Display frames
-        show_scattered_light(image_list, slits=slits, wcs_match=wcs_match)
-
-
-def show_scattered_light(image_list, slits=None, wcs_match=True):
-    """
-    Interface to ginga to show the quality of the Scattered Light subtraction
-
-    Parameters
-    ----------
-    image_list : zip
-        A zip of the images to show, their names, and the scales
-    slits : :class:`~pypeit.slittrace.SlitTraceSet`, optional
-        The current slit traces
-    wcs_match : :obj:`bool`, optional
-        Use a reference image for the WCS and match all image in other channels to it.
-    """
-    display.connect_to_ginga(raise_err=True, allow_new=True)
-    if slits is not None:
-        left, right, mask = slits.select_edges()
-        gpm = mask == 0
-    # Loop me
-    clear = True
-    for img, name, cut in image_list:
-        if img is None:
-            continue
-        viewer, ch = display.show_image(img, chname=name, cuts=cut, wcs_match=wcs_match, clear=clear)
-        if slits is not None:
-            display.show_slits(viewer, ch, left[:, gpm], right[:, gpm], slit_ids=slits.spat_id[gpm])
-        # Turn off clear
-        if clear:
-            clear = False
+        display.show_scattered_light(image_list, slits=slits, wcs_match=wcs_match)

From 2c04eebe8f1dfc4e980eb5a779f7890b0425423d Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:35:44 +0100
Subject: [PATCH 18/67] doc update

---
 doc/spectrographs/keck_kcwi.rst   | 15 ++++++++++-----
 pypeit/spectrographs/keck_kcwi.py |  2 +-
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/doc/spectrographs/keck_kcwi.rst b/doc/spectrographs/keck_kcwi.rst
index 40e58c07e9..ba9f097fe6 100644
--- a/doc/spectrographs/keck_kcwi.rst
+++ b/doc/spectrographs/keck_kcwi.rst
@@ -112,11 +112,16 @@ Scattered Light Removal
 KCWI suffers from mild scattered light (at the level of ~1 percent),
 and this appears to be worse near regions of the detector where there
 is brighter illumination. We are currently working towards building a
-full model of the scattered light. For the moment, PypeIt uses a robust
-piecewise polynomial to model the scattered light that is detected on
-the left of slice 1, the unilluminated region between slices 12 and 13,
-and the right of slice 24. The model is smooth and continuous, and is
-determined for each spectral pixel. By default, the scattered light is
+full model of the scattered light. For KCWI, the main contributor to
+the scattered light is referred to as the "narcissistic ghost" by
+Morrissey et al. (2018), ApJ, 864, 93. This scattered light is thought
+to be a reflection off the detector that travels back through the optical
+system. Some fraction gets sent back out to space, while the remainder
+comes back through the optical system and a fuzzy version of this is
+re-imaged onto the detector. The current KCWI scattered light model is
+designed to account for these effects. To generate a scattered light model,
+it's a good idea to use a frame that has a lot of counts (e.g. a flatfield
+frame, or a standard star). By default, the scattered light is
 subtracted from the science frame, the pixel flat, and the illumination
 flat. To turn off the scattered light subtraction, you can add the
 following lines to your :ref:`pypeit_file`:
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 76882b369f..0621e5796c 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -351,7 +351,7 @@ def check_frame_type(self, ftype, fitstbl, exprng=None):
                     & self.lamps(fitstbl, 'off') & (fitstbl['hatch'] == 'Open')
         if ftype == 'bias':
             return good_exp & (fitstbl['idname'] == 'BIAS')
-        if ftype in ['pixelflat', 'scattlight']:
+        if ftype in ['pixelflat', 'scattlight']:  # Scattered light needs lots of counts - so set it to the pixelflat by default
             # Use internal lamp
             return good_exp & (fitstbl['idname'] == 'FLATLAMP') & (fitstbl['calpos'] == 'Mirror') \
                     & self.lamps(fitstbl, 'cont_noarc')

From cdcef0beae98b732d4c19386f8f97e38e4d7f478 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:41:18 +0100
Subject: [PATCH 19/67] doc update

---
 pypeit/spectrographs/keck_kcwi.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 0621e5796c..30949971eb 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1066,7 +1066,9 @@ def scattered_light_model(self, param, img, kernel='gaussian'):
             Model of the scattered light for the input
         """
         # For clarity, unpack the parameters
-        assert param.size == 9  # For KCWI, the scattered light model requires 9 model parameters
+        if param.size != 9:
+            msgs.error("For KCWI, the scattered light model requires 9 model parameters")
+        # Extract the parameters into more conveniently named variables
         sigmx, sigmy, shft_spec, shft_spat, zoom = param[0], param[1], param[2], param[3], param[4]
         term0, term1, term2, term3 = param[5], param[6], param[7], param[8]
         # Generate a 2D smoothing kernel
@@ -1119,6 +1121,10 @@ def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpa
         -------
         scatt_img : `numpy.ndarray`_
             A 2D image of the scattered light determined from the input frame
+        modelpar : `numpy.ndarray`_
+            A 1D array containing the best-fitting model parameters
+        success : :obj:`bool`_
+            True if the fit was successful, False otherwise
         """
         def resid(param, wpix, img):
             """ Residual function used to optimize the model parameters

From 260eb6ded46f0fff612eb225b32bb27f644e1651 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 13:44:14 +0100
Subject: [PATCH 20/67] doc update

---
 pypeit/spectrographs/keck_kcwi.py    | 4 +++-
 pypeit/spectrographs/spectrograph.py | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 30949971eb..ea29fa7aac 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1120,7 +1120,9 @@ def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpa
         Returns
         -------
         scatt_img : `numpy.ndarray`_
-            A 2D image of the scattered light determined from the input frame
+            A 2D image of the scattered light determined from the input frame.
+            Alternatively, if a constant value is used, a constant floating point
+            value can be returned as well.
         modelpar : `numpy.ndarray`_
             A 1D array containing the best-fitting model parameters
         success : :obj:`bool`_
diff --git a/pypeit/spectrographs/spectrograph.py b/pypeit/spectrographs/spectrograph.py
index 4dd4a9e980..ee4baffc3d 100644
--- a/pypeit/spectrographs/spectrograph.py
+++ b/pypeit/spectrographs/spectrograph.py
@@ -1943,6 +1943,10 @@ def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpa
             A 2D image of the scattered light determined from the input frame.
             Alternatively, if a constant value is used, a constant floating point
             value can be returned as well.
+        modelpar : `numpy.ndarray`_
+            A 1D array containing the best-fitting model parameters
+        success : :obj:`bool`_
+            True if the fit was successful, False otherwise
         """
         msgs.info("Scattered light removal is not implemented for spectrograph {0:s}".format(self.name))
         return 0.0

From 7bef3d33b1017d3fad2c38a4e56a6fa2954dc22d Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sat, 21 Oct 2023 19:33:40 +0100
Subject: [PATCH 21/67] fix binning/dispname

---
 pypeit/calibrations.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index ae27fc67d9..defe5532cc 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -583,6 +583,7 @@ def get_scattlight(self):
         self.get_bpm(frame=raw_scattlight_files[0])
 
         binning = self.fitstbl[scatt_idx[0]]['binning']
+        dispname = self.fitstbl[scatt_idx[0]]['dispname']
         scattlightImage = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                          self.par['scattlightframe'], raw_scattlight_files,
                                                          bias=self.msbias, bpm=self.msbpm,
@@ -591,10 +592,10 @@ def get_scattlight(self):
 
         spatbin = parse.parse_binning(binning)[1]
         pad = self.par['scattlight']['pad'] // spatbin
-        offslitmask = self.slits.slit_img(pad=pad, initial=True, flexure=None)==-1
+        offslitmask = self.slits.slit_img(pad=pad, initial=True, flexure=None) == -1
 
         model, modelpar, success = self.spectrograph.scattered_light(scattlightImage.image, offslitmask,
-                                                                     self.fitstbl[scatt_idx[0]])
+                                                                     binning=binning, dispname=dispname)
 
         if not success:
             # Something went awry

From ce1334b237afec20a323f0c84e670c04664af225 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sun, 22 Oct 2023 21:39:52 +0100
Subject: [PATCH 22/67] fix scattlight

---
 pypeit/calibrations.py            |  2 +-
 pypeit/scattlight.py              |  2 +-
 pypeit/spectrographs/keck_kcwi.py | 56 +++++++++++++++++++++++--------
 3 files changed, 44 insertions(+), 16 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index defe5532cc..f19864279d 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -610,7 +610,7 @@ def get_scattlight(self):
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
                                                       pad=self.par['scattlight']['pad'],
-                                                      scattlight_raw=scattlightImage,
+                                                      scattlight_raw=scattlightImage.image,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
 
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 1aca6eb132..fb8fa801e1 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -50,7 +50,7 @@ class ScatteredLight(calibframe.CalibFrame):
                  'nspat': dict(otype=int, descr='Number of pixels in the image spatial direction.'),
                  'binning': dict(otype=str, descr='Binning in PypeIt orientation (not the original)'),
                  'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges.'),
-                 'scattlight_raw': dict(otype=ScatteredLightImage,
+                 'scattlight_raw': dict(otype=np.ndarray, atype=np.floating,
                                   descr='Image used to construct the edge traces; see '
                                         ':class:`~pypeit.images.buildimage.ScatteredLightImage` and '
                                         ':class:`~pypeit.images.pypeitimage.PypeItImage`.'),
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index ea29fa7aac..b781ce0880 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1095,7 +1095,7 @@ def scattered_light_model(self, param, img, kernel='gaussian'):
         spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
         return spl(zoom * (specvec + shft_spec), zoom * (spatvec + shft_spat))
 
-    def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpad=300):
+    def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpad=300, debug=False):
         """ Calculate a model of the scattered light of the input frame.
 
         For KCWI, the main contributor to the scattered light is referred to as the "narcissistic ghost"
@@ -1116,6 +1116,8 @@ def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpa
             Name of the disperser
         detpad : :obj:`int`_, optional
             Number of pixels to pad to each of the detector edges to reduce edge effects.
+        debug : :obj:`bool`_, optional
+            If True, debug the final model fit that's been output
 
         Returns
         -------
@@ -1146,7 +1148,8 @@ def resid(param, wpix, img):
             resid : `numpy.ndarray`_
                 A 1D vector of the residuals
             """
-            return img[wpix] - self.scattered_light_model(param, img)[wpix]
+            model = self.scattered_light_model(param, img)
+            return (img[wpix] - model[wpix])*utils.inverse(np.sqrt(model[wpix]))
 
         # Grab the binning for convenience
         specbin, spatbin = parse.parse_binning(binning)
@@ -1155,8 +1158,8 @@ def resid(param, wpix, img):
         # Do a median filter near the edges
         frame[0, :] = np.median(frame[0:10, :], axis=0)
         frame[-1, :] = np.median(frame[-10:, :], axis=0)
-        img = np.pad(frame, detpad, mode='edge')
-        offslitmask_pad = np.pad(offslitmask, detpad, mode='edge')
+        img = np.pad(frame, detpad, mode='edge')  # Model should be generated on padded data
+        offslitmask_pad = np.pad(offslitmask, detpad, mode='constant', constant_values=0)  # but don't include padded data in the fit
         # Grab the pixels to be included in the fit
         wpix = np.where(offslitmask_pad)
 
@@ -1164,20 +1167,26 @@ def resid(param, wpix, img):
         # and should be close to the final fitted values to reduce computational time)
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
-        # if tbl['dispname'] == 'BH2':
-        #     x0 = [sigmx, sigmy1, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
-        # elif tbl['dispname'] == 'BM':
-        #     x0 = [sigmx, sigmy1, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
-        if dispname == 'BL':
-            x0 = np.array([3.55797869e+02/specbin, 2.38333349e+02/spatbin,  # kernel widths
-                           2.52262744e+01/specbin, 1.97022975e+02/spatbin,  # pixel offsets
-                           9.41621820e-01, 1.02049301e-01, -2.10895495e-01, 2.36542387e-01,
-                           -9.83331029e-02])
+        if dispname == 'BH2':
+            x0 = np.array([3.49128562e+02/specbin, 2.45833322e+02/spatbin,  # kernel widths
+                           -1.26958208e+02/specbin, 1.49033883e+01/spatbin,  # pixel offsets
+                           1.00229122e+00,  # Zoom factor
+                           1.87534428e-01, -2.75679127e-01, 4.08198109e-01, -1.87826458e-01])  # Polynomial terms
+        elif dispname == 'BM':
+            x0 = np.array([3.50037423e+02/specbin, 2.38662990e+02/spatbin,  # kernel widths
+                           -3.82935541e+01/specbin, -2.04645954e+01/spatbin,  # pixel offsets
+                           1.01042546e+00,  # Zoom factor
+                           1.15251491e-01, -1.59419953e-01,  1.30877795e-01, -1.84324642e-02])  # Polynomial terms
+        elif dispname == 'BL':
+            x0 = np.array([3.47000453e+02/specbin, 2.12462524e+02/spatbin,  # kernel widths
+                           -4.35179357e+01/specbin, 8.21150855e+00/spatbin,  # pixel offsets
+                           9.99651562e-01,  # Zoom factor
+                           1.18828616e-01, -3.22338051e-01, 5.07102665e-01, -2.85286699e-01])  # Polynomial terms
         else:
             msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname}")
             sigmx = 400.0 / specbin  # This is the spectral direction
             sigmy = 200.0 / spatbin  # This is the spatial direction
-            shft_spec = -60.0 / specbin  # Shift of the scattered light in the spectral direction
+            shft_spec = 0.0 / specbin  # Shift of the scattered light in the spectral direction
             shft_spat = 0.0 / spatbin  # Shift of the scattered light in the spatial direction
             zoom = 1.0  # Zoom factor of the scattered light
             term0, term1, term2, term3 = 0.1, -0.2, 0.3, -0.1  # Polynomial coefficients
@@ -1198,6 +1207,25 @@ def resid(param, wpix, img):
         else:
             msgs.warn("Scattered light model fitting failed")
             scatt_img = np.zeros_like(frame)
+        if debug:
+            # Do some checks on the results
+            embed()
+            scatt_img_alt = self.scattered_light_model(x0, img)[detpad:-detpad, detpad:-detpad]
+            vmin, vmax = 0, np.max(scatt_img_alt)
+            vmin, vmax = -3, 10.0
+            plt.subplot(231)
+            plt.imshow(frame, vmin=vmin, vmax=vmax)
+            plt.subplot(232)
+            plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
+            plt.subplot(233)
+            plt.imshow(frame-scatt_img, vmin=vmin, vmax=vmax)
+            plt.subplot(234)
+            plt.imshow(frame, vmin=vmin, vmax=vmax)
+            plt.subplot(235)
+            plt.imshow(scatt_img_alt, vmin=vmin, vmax=vmax)
+            plt.subplot(236)
+            plt.imshow(frame-scatt_img_alt, vmin=vmin, vmax=vmax)
+            plt.show()
         return scatt_img, res_lsq.x, success
 
     def fit_2d_det_response(self, det_resp, gpmask):

From b7d351183c52b89ab3546a93b8cd29a40b6cc7c3 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sun, 22 Oct 2023 21:40:45 +0100
Subject: [PATCH 23/67] Store full ScattLight

---
 pypeit/calibrations.py | 2 +-
 pypeit/scattlight.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index f19864279d..defe5532cc 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -610,7 +610,7 @@ def get_scattlight(self):
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
                                                       pad=self.par['scattlight']['pad'],
-                                                      scattlight_raw=scattlightImage.image,
+                                                      scattlight_raw=scattlightImage,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
 
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index fb8fa801e1..1aca6eb132 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -50,7 +50,7 @@ class ScatteredLight(calibframe.CalibFrame):
                  'nspat': dict(otype=int, descr='Number of pixels in the image spatial direction.'),
                  'binning': dict(otype=str, descr='Binning in PypeIt orientation (not the original)'),
                  'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges.'),
-                 'scattlight_raw': dict(otype=np.ndarray, atype=np.floating,
+                 'scattlight_raw': dict(otype=ScatteredLightImage,
                                   descr='Image used to construct the edge traces; see '
                                         ':class:`~pypeit.images.buildimage.ScatteredLightImage` and '
                                         ':class:`~pypeit.images.pypeitimage.PypeItImage`.'),

From 1010c8e56c23eea56ea643812a37e1798547f3d0 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Mon, 23 Oct 2023 10:31:33 +0100
Subject: [PATCH 24/67] update msgs

---
 pypeit/calibrations.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index defe5532cc..9b80a9825d 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -575,7 +575,7 @@ def get_scattlight(self):
 
         # Scattered light model does not exist or we're not reusing it.
         # Need to build everything from scratch.  Start with the trace image.
-        msgs.info('Creating edge tracing calibration frame using files: ')
+        msgs.info('Creating scattered light calibration frame using files: ')
         for f in raw_scattlight_files:
             msgs.prindent(f'{Path(f).name}')
 

From 2d56affc6efdff8ba8921351787da1f10b5dc0ac Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Mon, 23 Oct 2023 21:30:01 +0100
Subject: [PATCH 25/67] scattered light method

---
 pypeit/par/pypeitpar.py | 27 ++++++++++++++++++++++++---
 1 file changed, 24 insertions(+), 3 deletions(-)

diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index ed7169eb4c..6990c42d6d 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -219,7 +219,7 @@ def __init__(self, trim=None, apply_gain=None, orient=None,
                  dark_expscale=None,
                  empirical_rn=None, shot_noise=None, noise_floor=None,
                  use_pixelflat=None, use_illumflat=None, use_specillum=None,
-                 use_pattern=None, subtract_scattlight=None, subtract_continuum=None,
+                 use_pattern=None, subtract_scattlight=None, scattlight_method=None, subtract_continuum=None,
                  spat_flexure_correct=None):
 
         # Grab the parameter names and values from the function
@@ -305,6 +305,20 @@ def __init__(self, trim=None, apply_gain=None, orient=None,
                                        'be set to True for spectrographs that have dedicated methods to subtract ' \
                                        'scattered light. For all other cases, this parameter should be False.'
 
+        defaults['scattlight_method'] = 'model'
+        options['scattlight_method'] = ProcessImagesPar.valid_scattlight_methods()
+        dtypes['scattlight_method'] = str
+        descr['scattlight_method'] = 'Method used to fit the overscan. ' \
+                                     'Options are: {0}'.format(', '.join(options['scattlight_method'])) + '.' + \
+                                     '\'model\' will the scattered light model parameters derived from a ' \
+                                     'user-specified frame during their reduction (note, you will need to make sure ' \
+                                     'that you set appropriate scattlight frames in your .pypeit file for this option). ' \
+                                     '\'frame\' will use each individual frame to determine the scattered light ' \
+                                     'that affects this frame. ' \
+                                     '\'archive\' will use an archival model parameter solution for the scattered ' \
+                                     'light (note that this option is not currently available for all spectrographs).'
+
+
         defaults['empirical_rn'] = False
         dtypes['empirical_rn'] = bool
         descr['empirical_rn'] = 'If True, use the standard deviation in the overscan region to ' \
@@ -437,8 +451,8 @@ def __init__(self, trim=None, apply_gain=None, orient=None,
     def from_dict(cls, cfg):
         k = np.array([*cfg.keys()])
         parkeys = ['trim', 'apply_gain', 'orient', 'use_biasimage', 'subtract_continuum', 'subtract_scattlight',
-                   'use_pattern', 'use_overscan', 'overscan_method', 'overscan_par', 'use_darkimage',
-                   'dark_expscale', 'spat_flexure_correct', 'use_illumflat', 'use_specillum',
+                   'scattlight_method', 'use_pattern', 'use_overscan', 'overscan_method', 'overscan_par',
+                   'use_darkimage', 'dark_expscale', 'spat_flexure_correct', 'use_illumflat', 'use_specillum',
                    'empirical_rn', 'shot_noise', 'noise_floor', 'use_pixelflat', 'combine',
                    'satpix', #'calib_setup_and_bit',
                    'n_lohi', 'mask_cr',
@@ -461,6 +475,13 @@ def valid_overscan_methods():
         """
         return ['polynomial', 'savgol', 'median']
 
+    @staticmethod
+    def valid_scattlight_methods():
+        """
+        Return the valid overscan methods.
+        """
+        return ['model', 'frame', 'archive']
+
     @staticmethod
     def valid_combine_methods():
         """

From f8f4bd88f3ad69ba018d60294b7fac5209c58cec Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Mon, 23 Oct 2023 21:30:52 +0100
Subject: [PATCH 26/67] add core methods

---
 pypeit/core/scattlight.py | 185 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 185 insertions(+)
 create mode 100644 pypeit/core/scattlight.py

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
new file mode 100644
index 0000000000..4058450f7f
--- /dev/null
+++ b/pypeit/core/scattlight.py
@@ -0,0 +1,185 @@
+""" Module for sky subtraction
+
+.. include common links, assuming primary doc root is up one directory
+.. include:: ../include/links.rst
+"""
+import numpy as np
+
+import matplotlib.pyplot as plt
+from scipy.optimize import least_squares
+from scipy import signal, interpolate
+
+from IPython import embed
+
+from pypeit import msgs
+
+
+def scattered_light_model(param, img, kernel='gaussian'):
+    """ Model used to calculate the scattered light.
+
+    The current model to generate the scattered light is a shifted, scaled, and blurred version of the
+    data recorded on the detector. We allow for a shift in the spatial and spectral direction, and the
+    blurring kernel can have different widths in the spectral and spatial directions. Finally, the model
+    includes a polynomial correction in the spectral direction to account for a change in the spectral
+    efficiency of the scattered light relative to the detector image.
+
+    Parameters
+    ----------
+    param : `numpy.ndarray`_
+        Model parameters that determine the scattered light based on the input img.
+        The first 5 parameters are the same for all spectrographs. The sixth and all
+        subsequent parameters in this list are polynomial corrections to the spectral
+        direction. Here are the individual parameters:
+
+        * param[0] = Kernel width in the spectral direction
+        * param[1] = Kernel width in the spatial direction
+        * param[2] = Pixel shift of the scattered light in the spectral direction
+        * param[3] = Pixel shift of the scattered light in the spatial direction
+        * param[4] = Zoom factor of the scattered light (~1)
+        * param[5:] = Polynomial scaling coefficients in the spectral direction
+    img : `numpy.ndarray`_
+        Raw image that you want to compute the scattered light model.
+        shape is (nspec, nspat)
+        Model used to calculate the scattered light. This function is used to
+        generate a model of the scattered light, based on a set of model parameters
+        that have been optimized using self.scattered_light().
+    kernel : :obj:`str`_, optional
+        The shape of the kernel to use. The allowed values are 'gaussian' and 'lorentzian',
+        but the default value ('gaussian') is currently believed to provide the best fit.
+
+    Returns
+    -------
+    model : `numpy.ndarray`_
+        Model of the scattered light for the input
+    """
+    # Extract the parameters into more conveniently named variables
+    sigmx, sigmy, shft_spec, shft_spat, zoom = param[0], param[1], param[2], param[3], param[4]
+    polyterms = param[5:]
+    # Generate a 2D smoothing kernel
+    if kernel == 'gaussian':
+        # Gaussian
+        subkrnx = np.exp(-0.5 * (np.arange(int(6 * sigmx)) - 3 * sigmx) ** 2 / sigmx ** 2)
+        subkrny = np.exp(-0.5 * (np.arange(int(6 * sigmy)) - 3 * sigmy) ** 2 / sigmy ** 2)
+    elif kernel == 'lorentzian':
+        # Lorentzian
+        subkrnx = sigmx / ((np.arange(int(10 * sigmx)) - 5 * sigmx) ** 2 + sigmx ** 2)
+        subkrny = sigmy / ((np.arange(int(10 * sigmy)) - 5 * sigmy) ** 2 + sigmy ** 2)
+    else:
+        msgs.error(f"Unknown kernel: {kernel}")
+    kernel = np.outer(subkrnx, subkrny)
+    kernel /= np.sum(kernel)
+    # Make a grid of coordinates
+    specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
+    spat, spec = np.meshgrid(spatvec, specvec / (specvec.size - 1))
+    # Generate the polynomial efficiency scaling in the spectral direction
+    polyscale = np.zeros_like(img)
+    for pp in range(polyterms.size):
+        polyscale += polyterms[pp] * spec ** pp
+    # Convolve the input image (note: most of the time is spent here)
+    # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
+    # scale_img = polyscale * signal.fftconvolve(img, kernel, mode='same')
+    scale_img = polyscale * signal.oaconvolve(img, kernel, mode='same')
+    spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
+    return spl(zoom * (specvec + shft_spec), zoom * (spatvec + shft_spat))
+
+
+def scattlight_resid(param, wpix, img):
+    """ Residual function used to optimize the model parameters
+
+    Parameters
+    ----------
+    param : `numpy.ndarray`_
+        1D array of model parameters to use for the fitting function.
+    wpix : tuple
+        A tuple containing the x,y coordinates of the pixels in img
+        to be used for computing the residual.
+    img : `numpy.ndarray`_
+        Data image to be used to compute the residual. Shape is (nspec, nspat)
+
+    Returns
+    -------
+    resid : `numpy.ndarray`_
+        A 1D vector of the residuals
+    """
+    model = scattered_light_model(param, img)
+    return img[wpix] - model[wpix]
+
+
+def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False):
+    """ Calculate a model of the scattered light of the input frame.
+
+    Parameters
+    ----------
+    frame : `numpy.ndarray`_
+        Raw 2D data frame to be used to compute the scattered light.
+    bpm : `numpy.ndarray`_
+        2D boolean array indicating the bad pixels (True=bad)
+    offslitmask : `numpy.ndarray`_
+        A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
+    x0 : `numpy.ndarray`_
+        A 1D array containing the best-fitting model parameters
+    bounds : :obj:`tuple`_
+        A tuple of two elements, containing two `np.ndarray`_ of the same length as x0. These
+        two arrays contain the lower (first element of the tuple) and upper (second element of the tuple)
+        bounds to consider on the scattered light model parameters.
+    detpad : :obj:`int`_, optional
+        Number of pixels to pad to each of the detector edges to reduce edge effects.
+    debug : :obj:`bool`_, optional
+        If True, debug the final model fit that's been output
+
+    Returns
+    -------
+    scatt_img : `numpy.ndarray`_
+        A 2D image of the scattered light determined from the input frame.
+        Alternatively, if a constant value is used, a constant floating point
+        value can be returned as well.
+    modelpar : `numpy.ndarray`_
+        A 1D array containing the best-fitting model parameters
+    success : :obj:`bool`_
+        True if the fit was successful, False otherwise
+    """
+
+    # Grab a copy of the input frame, and do some pre-processing on it
+    _frame = frame.copy()
+
+    # First pad the edges to minimize edge effects
+    # Do a median filter near the edges
+    _frame[0, :] = np.median(_frame[0:10, :], axis=0)
+    _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
+    img = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
+    offslitmask_pad = np.pad(offslitmask * np.logical_not(bpm), detpad, mode='constant',
+                             constant_values=0)  # but don't include padded data in the fit
+    # Grab the pixels to be included in the fit
+    wpix = np.where(offslitmask_pad)
+
+    # Compute the best-fitting model parameters
+    msgs.info("Computing best-fitting model parameters of the scattered light")
+    res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, img), verbose=2)
+    # Store if this is a successful fit
+    success = res_lsq.success
+    if success:
+        msgs.info("Generating best-fitting scattered light model")
+        scatt_img = scattered_light_model(res_lsq.x, img)[detpad:-detpad, detpad:-detpad]
+    else:
+        msgs.warn("Scattered light model fitting failed")
+        scatt_img = np.zeros_like(_frame)
+    if debug:
+        # Do some checks on the results
+        embed()
+        scatt_img_alt = scattered_light_model(x0, img)[detpad:-detpad, detpad:-detpad]
+        vmin, vmax = 0, np.max(scatt_img_alt)
+        vmin, vmax = -3, 10.0
+        plt.subplot(231)
+        plt.imshow(_frame, vmin=vmin, vmax=vmax)
+        plt.subplot(232)
+        plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
+        plt.subplot(233)
+        plt.imshow(_frame - scatt_img, vmin=vmin, vmax=vmax)
+        plt.subplot(234)
+        plt.imshow(_frame, vmin=vmin, vmax=vmax)
+        plt.subplot(235)
+        plt.imshow(scatt_img_alt, vmin=vmin, vmax=vmax)
+        plt.subplot(236)
+        plt.imshow(_frame - scatt_img_alt, vmin=vmin, vmax=vmax)
+        plt.show()
+    return scatt_img, res_lsq.x, success

From b78c95d602a5fbc5b0f17f7e1db3f3be0515b29a Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Mon, 23 Oct 2023 21:43:41 +0100
Subject: [PATCH 27/67] core scattlight

---
 pypeit/calibrations.py               |  12 +-
 pypeit/core/scattlight.py            |   7 +-
 pypeit/images/rawimage.py            |  49 +++++---
 pypeit/scattlight.py                 |   2 +-
 pypeit/spectrographs/keck_esi.py     |  38 +++++++
 pypeit/spectrographs/keck_kcwi.py    | 161 +++------------------------
 pypeit/spectrographs/spectrograph.py |  71 +++++-------
 7 files changed, 128 insertions(+), 212 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index 9b80a9825d..b1e055e04c 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -29,6 +29,7 @@
 from pypeit.metadata import PypeItMetaData
 from pypeit.core import framematch
 from pypeit.core import parse
+from pypeit.core import scattlight as core_scattlight
 from pypeit.par import pypeitpar
 from pypeit.spectrographs.spectrograph import Spectrograph
 from pypeit import io
@@ -594,8 +595,11 @@ def get_scattlight(self):
         pad = self.par['scattlight']['pad'] // spatbin
         offslitmask = self.slits.slit_img(pad=pad, initial=True, flexure=None) == -1
 
-        model, modelpar, success = self.spectrograph.scattered_light(scattlightImage.image, offslitmask,
-                                                                     binning=binning, dispname=dispname)
+        # Get starting parameters for the scattered light model
+        x0, bounds = self.spectrograph.scattered_light_archive(binning, dispname)
+        # Perform a fit to the scattered light
+        model, modelpar, success = core_scattlight.scattered_light(scattlightImage.image, self.msbpm, offslitmask,
+                                                                   x0, bounds)
 
         if not success:
             # Something went awry
@@ -610,7 +614,7 @@ def get_scattlight(self):
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
                                                       pad=self.par['scattlight']['pad'],
-                                                      scattlight_raw=scattlightImage,
+                                                      scattlight_raw=scattlightImage.image,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
 
@@ -712,6 +716,7 @@ def get_flats(self):
             pixel_flat = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                         self.par['pixelflatframe'],
                                                         raw_pixel_files, dark=self.msdark,
+                                                        slits=self.slits,
                                                         bias=self.msbias, bpm=self.msbpm,
                                                         scattlight=self.msscattlight)
             if len(raw_lampoff_files) > 0:
@@ -723,6 +728,7 @@ def get_flats(self):
                 lampoff_flat = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                               self.par['lampoffflatsframe'],
                                                               raw_lampoff_files,
+                                                              slits=self.slits,
                                                               dark=self.msdark, bias=self.msbias,
                                                               bpm=self.msbpm, scattlight=self.msscattlight)
                 pixel_flat = pixel_flat.sub(lampoff_flat)
diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 4058450f7f..0e57bcb6b7 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -154,7 +154,8 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
 
     # Compute the best-fitting model parameters
     msgs.info("Computing best-fitting model parameters of the scattered light")
-    res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, img), verbose=2)
+    res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, img), verbose=2, ftol=1.0E-4)
+
     # Store if this is a successful fit
     success = res_lsq.success
     if success:
@@ -166,15 +167,15 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     if debug:
         # Do some checks on the results
         embed()
+        from matplotlib import pyplot as plt
         scatt_img_alt = scattered_light_model(x0, img)[detpad:-detpad, detpad:-detpad]
         vmin, vmax = 0, np.max(scatt_img_alt)
-        vmin, vmax = -3, 10.0
         plt.subplot(231)
         plt.imshow(_frame, vmin=vmin, vmax=vmax)
         plt.subplot(232)
         plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
         plt.subplot(233)
-        plt.imshow(_frame - scatt_img, vmin=vmin, vmax=vmax)
+        plt.imshow(_frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
         plt.subplot(234)
         plt.imshow(_frame, vmin=vmin, vmax=vmax)
         plt.subplot(235)
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index d231d927ef..a30c19c809 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -19,6 +19,7 @@
 from pypeit.core import procimg
 from pypeit.core import flat
 from pypeit.core import flexure
+from pypeit.core import scattlight
 from pypeit.core.mosaic import build_image_mosaic
 from pypeit.images import pypeitimage
 from pypeit import utils
@@ -1118,12 +1119,12 @@ def subtract_continuum(self, force=False):
         #cont = ndimage.median_filter(self.image, size=(1,101,3), mode='reflect')
         self.steps[step] = True
 
-    def subtract_scattlight(self, scattlight, slits):
+    def subtract_scattlight(self, msscattlight, slits):
         """
         Analyze and subtract the scattered light from the image.
 
         This is primarily a wrapper for
-        :func:`~pypeit.spectrographs.spectrograph.scattered_light`.
+        :func:`~pypeit.core.scattered_light`.
 
         """
         step = inspect.stack()[0][3]
@@ -1132,7 +1133,7 @@ def subtract_scattlight(self, scattlight, slits):
             msgs.warn("The scattered light has already been subtracted from the image!")
             return
 
-        if scattlight.scattlight_param is None:
+        if self.par["scattlight_method"] == "model" and msscattlight.scattlight_param is None:
             msgs.warn("Scattered light parameters are not set. Cannot perform scattered light subtraction.")
             return
 
@@ -1141,17 +1142,39 @@ def subtract_scattlight(self, scattlight, slits):
         dispname = self.spectrograph.get_meta_value(self.spectrograph.get_headarr(self.filename), 'dispname')
         # Loop over the images
         for ii in range(self.nimg):
-            # Calculate a model specific for this frame
-            spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
-            pad = scattlight.pad // spatbin
-            offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
-            scatt_img, _, success = self.spectrograph.scattered_light(self.image[ii, ...], offslitmask,
-                                                                      binning=binning, dispname=dispname)
-            # If failure, revert back to the Scattered Light calibration frame model parameters
-            if not success:
-                msgs.warn("Scattered light model failed - using predefined model parameters")
+            if self.par["scattlight_method"] == "model":
                 # Use predefined model parameters
-                scatt_img = self.spectrograph.scattered_light_model(scattlight.scattlight_param, self.image[ii, ...])
+                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...])
+            elif self.par["scattlight_method"] == "archive":
+                # Use archival model parameters
+                modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
+                if modpar is None:
+                    msgs.error(f"{self.spectrograph.name} does not have archival scattered light parameters. Please "
+                               f"set 'scattlight_method' to another option.")
+                scatt_img = scattlight.scattered_light_model(modpar, self.image[ii, ...])
+            elif self.par["scattlight_method"] == "frame":
+                # Calculate a model specific for this frame
+                spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
+                pad = msscattlight.pad // spatbin
+                offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
+                # Get starting parameters for the scattered light model
+                x0, bounds = self.spectrograph.scattered_light_archive(binning, dispname)
+                # Perform a fit to the scattered light
+                scatt_img, _, success = scattlight.scattered_light(self.image[ii, ...], self.bpm, offslitmask,
+                                                                   x0, bounds)
+                # If failure, revert back to the Scattered Light calibration frame model parameters
+                if not success:
+                    if msscattlight is not None:
+                        msgs.warn("Scattered light model failed - using predefined model parameters")
+                        scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...])
+                    else:
+                        msgs.warn("Scattered light model failed - using archival model parameters")
+                        # Use archival model parameters
+                        modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
+                        scatt_img = scattlight.scattered_light_model(modpar, self.image[ii, ...])
+            else:
+                msgs.warn("Scattered light not performed")
+                scatt_img = np.zeros(self.image[ii, ...].shape)
             # Subtract the scattered light model from the image
             self.image[ii, ...] -= scatt_img
         self.steps[step] = True
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 1aca6eb132..fb8fa801e1 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -50,7 +50,7 @@ class ScatteredLight(calibframe.CalibFrame):
                  'nspat': dict(otype=int, descr='Number of pixels in the image spatial direction.'),
                  'binning': dict(otype=str, descr='Binning in PypeIt orientation (not the original)'),
                  'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges.'),
-                 'scattlight_raw': dict(otype=ScatteredLightImage,
+                 'scattlight_raw': dict(otype=np.ndarray, atype=np.floating,
                                   descr='Image used to construct the edge traces; see '
                                         ':class:`~pypeit.images.buildimage.ScatteredLightImage` and '
                                         ':class:`~pypeit.images.pypeitimage.PypeItImage`.'),
diff --git a/pypeit/spectrographs/keck_esi.py b/pypeit/spectrographs/keck_esi.py
index c7292ceb5b..7e400666c4 100644
--- a/pypeit/spectrographs/keck_esi.py
+++ b/pypeit/spectrographs/keck_esi.py
@@ -306,6 +306,44 @@ def bpm(self, filename, det, shape=None, msbias=None):
         # Return
         return bpm_img
 
+    def scattered_light_archive(self, binning, dispname):
+        """Archival model parameters for the scattered light. These are based on best fits to currently available data.
+
+        Parameters
+        ----------
+        binning : :obj:`str`_, optional
+            Comma-separated binning along the spectral and spatial directions; e.g., ``2,1``
+        dispname : :obj:`str`_, optional
+            Name of the disperser
+
+        Returns
+        -------
+        x0 : `numpy.ndarray`_
+            A 1D array containing the best-fitting model parameters
+        bounds : :obj:`tuple`_
+            A tuple of two elements, containing two `np.ndarray`_ of the same length as x0. These
+            two arrays contain the lower (first element of the tuple) and upper (second element of the tuple)
+            bounds to consider on the scattered light model parameters.
+        """
+        # Grab the binning for convenience
+        specbin, spatbin = parse.parse_binning(binning)
+
+        # Get some starting parameters (these were determined by fitting spectra,
+        # and should be close to the final fitted values to reduce computational time)
+        # Note :: These values need to be originally based on data that uses 1x1 binning,
+        # and are now scaled here according to the binning of the current data to be analysed.
+        x0 = np.array([3.16544600e+02/specbin, 2.05443943e+02/spatbin,  # kernel widths
+                       -9.23121908e+01/specbin, 6.09000452e+01/spatbin,  # pixel offsets
+                       9.94913292e-01,  # Zoom factor
+                       2.23905249e-01, -1.86171132e-01, 8.55479705e-02, -1.26763813e-02])  # Polynomial terms
+
+        # Now set the bounds of the fitted parameters
+        bounds = ([1, 1, -200/specbin, -200/spatbin, 0, -10, -10, -10, -10],
+                  [600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 10, 10, 10, 10])
+
+        # Return the best-fitting archival parameters and the bounds
+        return x0, bounds
+
     @property
     def norders(self):
         """
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index b781ce0880..4846f8550b 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -13,8 +13,7 @@
 from astropy.io import fits
 from astropy.time import Time
 from astropy.coordinates import EarthLocation
-from scipy.optimize import curve_fit, least_squares
-from scipy import signal, interpolate
+from scipy.optimize import curve_fit
 
 from pypeit import msgs
 from pypeit import telescopes
@@ -1032,71 +1031,8 @@ def get_rawimage(self, raw_file, det):
         # Return
         return detpar, raw_img, hdu, exptime, rawdatasec_img, oscansec_img
 
-    def scattered_light_model(self, param, img, kernel='gaussian'):
-        """ Model used to calculate the scattered light
-
-        Parameters
-        ----------
-        param : `numpy.ndarray`_
-            Model parameters that determine the scattered light based on the input img.
-            For KCWI there are 9 model parameters that need to be input, and these are:
-
-            * param[0] = Kernel width in the spectral direction
-            * param[1] = Kernel width in the spatial direction
-            * param[2] = Pixel shift of the scattered light in the spectral direction
-            * param[3] = Pixel shift of the scattered light in the spatial direction
-            * param[4] = Zoom factor of the scattered light (~1)
-            * param[5] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**0)
-            * param[6] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**1)
-            * param[7] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**2)
-            * param[8] = Polynomial scaling coefficient in the spectral direction (coefficient of spec_pixel**3)
-        img : `numpy.ndarray`_
-            Raw image that you want to compute the scattered light model.
-            shape is (nspec, nspat)
-            Model used to calculate the scattered light. This function is used to
-            generate a model of the scattered light, based on a set of model parameters
-            that have been optimized using self.scattered_light().
-        kernel : :obj:`str`_, optional
-            The shape of the kernel to use. The allowed values for KCWI are 'gaussian' and 'lorentzian',
-            but the default value ('gaussian') is known to provide the best fit.
-
-        Returns
-        -------
-        model : `numpy.ndarray`_
-            Model of the scattered light for the input
-        """
-        # For clarity, unpack the parameters
-        if param.size != 9:
-            msgs.error("For KCWI, the scattered light model requires 9 model parameters")
-        # Extract the parameters into more conveniently named variables
-        sigmx, sigmy, shft_spec, shft_spat, zoom = param[0], param[1], param[2], param[3], param[4]
-        term0, term1, term2, term3 = param[5], param[6], param[7], param[8]
-        # Generate a 2D smoothing kernel
-        if kernel == 'gaussian':
-            # Gaussian
-            subkrnx = np.exp(-0.5 * (np.arange(int(6 * sigmx)) - 3 * sigmx) ** 2 / sigmx ** 2)
-            subkrny = np.exp(-0.5 * (np.arange(int(6 * sigmy)) - 3 * sigmy) ** 2 / sigmy ** 2)
-        elif kernel == 'lorentzian':
-            # Lorentzian
-            subkrnx = sigmx / ((np.arange(int(10 * sigmx)) - 5 * sigmx) ** 2 + sigmx ** 2)
-            subkrny = sigmy / ((np.arange(int(10 * sigmy)) - 5 * sigmy) ** 2 + sigmy ** 2)
-        else:
-            msgs.error(f"Unknown kernel: {kernel}")
-        kernel = np.outer(subkrnx, subkrny)
-        kernel /= np.sum(kernel)
-        # Make a grid of coordinates
-        specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
-        spat, spec = np.meshgrid(spatvec, specvec / (specvec.size - 1))
-        # Convolve the input image (note: most of the time is spent here)
-        # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
-        # scale_img = (term0 + term1 * spec + term2*spec**2 + term3*spec**3) * signal.fftconvolve(img, kernel, mode='same')
-        scale_img = (term0 + term1 * spec + term2 * spec ** 2 + term3 * spec ** 3) * signal.oaconvolve(img, kernel,
-                                                                                                       mode='same')
-        spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
-        return spl(zoom * (specvec + shft_spec), zoom * (spatvec + shft_spat))
-
-    def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpad=300, debug=False):
-        """ Calculate a model of the scattered light of the input frame.
+    def scattered_light_archive(self, binning, dispname):
+        """ Archival model parameters for the scattered light. These are based on best fits to currently available data.
 
         For KCWI, the main contributor to the scattered light is referred to as the "narcissistic ghost"
         by Morrissey et al. (2018), ApJ, 864, 93. This scattered light is thought to be a reflection off the
@@ -1106,63 +1042,23 @@ def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpa
 
         Parameters
         ----------
-        frame : `numpy.ndarray`_
-            Raw 2D data frame to be used to compute the scattered light.
-        offslitmask : `numpy.ndarray`_
-            A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
         binning : :obj:`str`_, optional
             Comma-separated binning along the spectral and spatial directions; e.g., ``2,1``
         dispname : :obj:`str`_, optional
             Name of the disperser
-        detpad : :obj:`int`_, optional
-            Number of pixels to pad to each of the detector edges to reduce edge effects.
-        debug : :obj:`bool`_, optional
-            If True, debug the final model fit that's been output
 
         Returns
         -------
-        scatt_img : `numpy.ndarray`_
-            A 2D image of the scattered light determined from the input frame.
-            Alternatively, if a constant value is used, a constant floating point
-            value can be returned as well.
-        modelpar : `numpy.ndarray`_
+        x0 : `numpy.ndarray`_
             A 1D array containing the best-fitting model parameters
-        success : :obj:`bool`_
-            True if the fit was successful, False otherwise
+        bounds : :obj:`tuple`_
+            A tuple of two elements, containing two `np.ndarray`_ of the same length as x0. These
+            two arrays contain the lower (first element of the tuple) and upper (second element of the tuple)
+            bounds to consider on the scattered light model parameters.
         """
-        def resid(param, wpix, img):
-            """ Residual function used to optimize the model parameters
-
-            Parameters
-            ----------
-            param : `numpy.ndarray`_
-                1D array of model parameters to use for the fitting function.
-            wpix : tuple
-                A tuple containing the x,y coordinates of the pixels in img
-                to be used for computing the residual.
-            img : `numpy.ndarray`_
-                Data image to be used to compute the residual. Shape is (nspec, nspat)
-
-            Returns
-            -------
-            resid : `numpy.ndarray`_
-                A 1D vector of the residuals
-            """
-            model = self.scattered_light_model(param, img)
-            return (img[wpix] - model[wpix])*utils.inverse(np.sqrt(model[wpix]))
-
         # Grab the binning for convenience
         specbin, spatbin = parse.parse_binning(binning)
 
-        # First pad the edges to minimize edge effects
-        # Do a median filter near the edges
-        frame[0, :] = np.median(frame[0:10, :], axis=0)
-        frame[-1, :] = np.median(frame[-10:, :], axis=0)
-        img = np.pad(frame, detpad, mode='edge')  # Model should be generated on padded data
-        offslitmask_pad = np.pad(offslitmask, detpad, mode='constant', constant_values=0)  # but don't include padded data in the fit
-        # Grab the pixels to be included in the fit
-        wpix = np.where(offslitmask_pad)
-
         # Get some starting parameters (these were determined by fitting spectra,
         # and should be close to the final fitted values to reduce computational time)
         # Note :: These values need to be originally based on data that uses 1x1 binning,
@@ -1192,41 +1088,12 @@ def resid(param, wpix, img):
             term0, term1, term2, term3 = 0.1, -0.2, 0.3, -0.1  # Polynomial coefficients
             x0 = [sigmx, sigmy, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
 
-        # Compute the best-fitting model parameters
-        msgs.info("Computing best-fitting model parameters of the scattered light")
-        res_lsq = least_squares(resid, x0,
-                                bounds=([1, 1, -200 / specbin, -200 / spatbin, 0, -10, -10, -10, -10],
-                                        [600 / specbin, 600 / spatbin, 200 / specbin, 200 / spatbin, 2, 10, 10, 10,
-                                         10]),
-                                args=(wpix, img), verbose=2)
-        # Store if this is a successful fit
-        success = res_lsq.success
-        if success:
-            msgs.info("Generating best-fitting scattered light model")
-            scatt_img = self.scattered_light_model(res_lsq.x, img)[detpad:-detpad, detpad:-detpad]
-        else:
-            msgs.warn("Scattered light model fitting failed")
-            scatt_img = np.zeros_like(frame)
-        if debug:
-            # Do some checks on the results
-            embed()
-            scatt_img_alt = self.scattered_light_model(x0, img)[detpad:-detpad, detpad:-detpad]
-            vmin, vmax = 0, np.max(scatt_img_alt)
-            vmin, vmax = -3, 10.0
-            plt.subplot(231)
-            plt.imshow(frame, vmin=vmin, vmax=vmax)
-            plt.subplot(232)
-            plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
-            plt.subplot(233)
-            plt.imshow(frame-scatt_img, vmin=vmin, vmax=vmax)
-            plt.subplot(234)
-            plt.imshow(frame, vmin=vmin, vmax=vmax)
-            plt.subplot(235)
-            plt.imshow(scatt_img_alt, vmin=vmin, vmax=vmax)
-            plt.subplot(236)
-            plt.imshow(frame-scatt_img_alt, vmin=vmin, vmax=vmax)
-            plt.show()
-        return scatt_img, res_lsq.x, success
+        # Now set the bounds of the fitted parameters
+        bounds = ([1, 1, -200/specbin, -200/spatbin, 0, -10, -10, -10, -10],
+                  [600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 10, 10, 10, 10])
+
+        # Return the best-fitting archival parameters and the bounds
+        return x0, bounds
 
     def fit_2d_det_response(self, det_resp, gpmask):
         r"""
diff --git a/pypeit/spectrographs/spectrograph.py b/pypeit/spectrographs/spectrograph.py
index ee4baffc3d..dbb9c4c46d 100644
--- a/pypeit/spectrographs/spectrograph.py
+++ b/pypeit/spectrographs/spectrograph.py
@@ -1894,62 +1894,43 @@ def calc_pattern_freq(self, frame, rawdatasec_img, oscansec_img, hdu):
         msgs.warn(f"Pattern noise removal is not implemented for spectrograph {self.name}")
         return []
 
-    def scattered_light_model(self, param, img, kernel='gaussian'):
-        """ Model used to calculate the scattered light. This function is used to
-        generate a model of the scattered light, based on a set of model parameters
-        that have been optimized using self.scattered_light().
+    def scattered_light_archive(self, binning, dispname):
+        """ Archival model parameters for the scattered light. These are based on best fits to currently available data.
 
         Parameters
         ----------
-        param : `numpy.ndarray`_
-            Model parameters that determine the scattered light based on the input img.
-            Every spectrograph is allowed a different number of model parameters.
-        img : `numpy.ndarray`_
-            Raw image that you want to compute the scattered light model.
-            shape is (nspec, nspat)
-        kernel : :obj:`str`_, optional
-            The shape of the kernel to use. The allowed values depend on the spectrograph
-            scattered light model.
-
-        Returns
-        -------
-        model : `numpy.ndarray`_
-            Model of the scattered light for the input
-        """
-        msgs.warn(f"Scattered light subtraction is not setup for {self.name}")
-        return np.zeros_like(img)
-
-    def scattered_light(self, frame, offslitmask, binning=None, dispname=None, detpad=300):
-        """ Calculate the scattered light model parameters of the input frame.
-        This function is used to optimize the model parameters. See also
-        self.scattered_light_model()
-
-        Parameters
-        ----------
-        frame : `numpy.ndarray`_
-            Raw 2D data frame to be used to compute the scattered light.
-        offslitmask : `numpy.ndarray`_
-            A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
         binning : :obj:`str`_, optional
             Comma-separated binning along the spectral and spatial directions; e.g., ``2,1``
         dispname : :obj:`str`_, optional
             Name of the disperser
-        detpad : :obj:`int`_, optional
-            Number of pixels to pad to each of the detector edges to reduce edge effects.
 
         Returns
         -------
-        scatt_img : `numpy.ndarray`_, float
-            A 2D image of the scattered light determined from the input frame.
-            Alternatively, if a constant value is used, a constant floating point
-            value can be returned as well.
-        modelpar : `numpy.ndarray`_
+        x0 : `numpy.ndarray`_
             A 1D array containing the best-fitting model parameters
-        success : :obj:`bool`_
-            True if the fit was successful, False otherwise
-        """
-        msgs.info("Scattered light removal is not implemented for spectrograph {0:s}".format(self.name))
-        return 0.0
+        bounds : :obj:`tuple`_
+            A tuple of two elements, containing two `np.ndarray`_ of the same length as x0. These
+            two arrays contain the lower (first element of the tuple) and upper (second element of the tuple)
+            bounds to consider on the scattered light model parameters.
+        """
+        # Grab the binning for convenience
+        specbin, spatbin = parse.parse_binning(binning)
+
+        msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname} of {self.name}")
+        sigmx = 400.0 / specbin  # This is the spectral direction
+        sigmy = 200.0 / spatbin  # This is the spatial direction
+        shft_spec = 0.0 / specbin  # Shift of the scattered light in the spectral direction
+        shft_spat = 0.0 / spatbin  # Shift of the scattered light in the spatial direction
+        zoom = 1.0  # Zoom factor of the scattered light
+        term0, term1, term2, term3 = 0.1, -0.2, 0.3, -0.1  # Polynomial coefficients
+        x0 = [sigmx, sigmy, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
+
+        # Now set the bounds of the fitted parameters
+        bounds = ([1, 1, -200/specbin, -200/spatbin, 0, -10, -10, -10, -10],
+                  [600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 10, 10, 10, 10])
+
+        # Return the best-fitting archival parameters and the bounds
+        return x0, bounds
 
     def __repr__(self):
         """Return a string representation of the instance."""

From 3443e334ec7e5437923fb15fee0702c3e3f40fb8 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Mon, 23 Oct 2023 21:44:29 +0100
Subject: [PATCH 28/67] rm import

---
 pypeit/core/scattlight.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 0e57bcb6b7..6ca464fa5f 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -5,7 +5,6 @@
 """
 import numpy as np
 
-import matplotlib.pyplot as plt
 from scipy.optimize import least_squares
 from scipy import signal, interpolate
 

From f9ae3c4915b775b69cbd5e7dd7ca85c9d1eb9a63 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 24 Oct 2023 22:18:05 +0100
Subject: [PATCH 29/67] add docs for scattlight

---
 doc/calibrations/align.rst      |   4 +-
 doc/calibrations/scattlight.rst | 123 ++++++++++++++++++++++++++++++++
 2 files changed, 125 insertions(+), 2 deletions(-)
 create mode 100644 doc/calibrations/scattlight.rst

diff --git a/doc/calibrations/align.rst b/doc/calibrations/align.rst
index dd242a0da2..6511ab4773 100644
--- a/doc/calibrations/align.rst
+++ b/doc/calibrations/align.rst
@@ -47,14 +47,14 @@ the spatial alignment.
 Trouble Shooting
 ================
 
-If your image appears to be in err, here are the things to consider:
+If your image appears to be in error, here are the things to consider:
 
  - Is one or more of your input alignment frames junk?
 
  - Check each of your input alignment frames. Some might be mislabeled.
 
  - If some alignments are apparently missing, you might want to check
-   that the slits are currently traced.
+   that the slits are correctly traced.
 
 Current Alignments Data Model
 =============================
diff --git a/doc/calibrations/scattlight.rst b/doc/calibrations/scattlight.rst
new file mode 100644
index 0000000000..1fe0f6fb05
--- /dev/null
+++ b/doc/calibrations/scattlight.rst
@@ -0,0 +1,123 @@
+
+.. include:: ../include/links.rst
+
+.. _scattlight:
+
+===========================
+Scattered light subtraction
+===========================
+
+Overview
+========
+
+This document describes how PypeIt implements a scattered light subtraction.
+Scattered light usually affects data at the few percent level or even less.
+So, before you implement a scattered light correction for your data, convince
+yourself that this is really needed for your science case. There is currently
+one algorithm that can be applied to all spectrographs, although this model
+has only been tested with Keck/KCWI and Keck/ESI data, and may not be optimal
+for your instrument setup, so inspect the outputs to be sure that you are not
+adding artefacts to your data.
+
+Overview
+========
+
+This file describes the model fitting procedure and how to check the results of your
+scattered light fits. The input image used should generally be a bright object that
+does not fill the slit. A bright standard star or a series of alignment frames are
+a good choice.
+
+The image is written to disk as a multi-extension FITS file
+prefixed by ``ScatteredLight`` in the ``Calibrations/`` folder.
+See :ref:`calib-naming` for the naming convention.
+
+Model Implementation
+====================
+
+The scattered light model is derived from regions of the
+detector that are outside of the slit boundaries. The slit edges are padded (assuming 1x1 binning)
+by a default integer value so that bleeding light from the edges of the slits minimally
+impacts the scattered light modelling procedure. If you would like to change the default amount
+of padding, you can set the number of padded pixels by the following parameter (the following
+example will pad the slits by 10 detector pixels, assuming 1x1 binning):
+
+.. code-block:: ini
+
+    [calibrations]
+        [[scattlight]]
+            pad = 10
+
+Note: if your data are binned by 2 in the spatial direction, then setting ``pad=10`` would actually
+pad the slit edges by 5 pixels.
+
+The current scattered light model involves a 9 parameter fit to the inter-slit regions. The model
+utilises the observed 2D (trimmed+oriented+debiased) frame to estimate the scattered light. The
+scattered light model is essentially a blurred, shifted, flux-scaled, and zoomed version of the
+detector image. The amount of blurring and shifting in the spatial and spectral direction are done
+separately (2 parameters each), and there is a single parameter to estimate the zoom factor. The
+flux-scaling is a cubic polynomial (4 parameters) to the spectral direction. Once the best-fitting
+model parameters are determined, these can be applied to any other frame to estimate the scattered
+light contribution of this frame.
+
+Alternatively, the scattered light can be determined for each frame independently (i.e. you can
+derive a separate set of 9 model parameters for each frame that you wish to perform a scattered
+light correction). To set this option, you can add the following arguments to your pypeit file:
+
+.. code-block:: ini
+
+    [calibrations]
+        [[FRAMETYPE]]
+            [[[process]]]
+            scattlight_method = frame
+
+You can also set ``scattlight_method = archive``, which will use pre-determined archival values
+for the spectrograph that you are trying to reduce data for. However, note that not all spectrographs
+have this feature implemented, so this is not a good idea in general.
+
+Inspecting
+==========
+
+There is a dedicated PypeIt script to check the scattered light model fit:
+
+.. code-block:: console
+
+    pypeit_chk_scattlight Calibrations/ScatteredLight_A_0_DET01.fits.gz Calibrations/Slits_A_0_DET01.fits.gz
+
+Where the first and second arguments are the ``ScatteredLight`` and ``Slits`` calibration frames.
+A three panel ginga window will be opened that shows:
+(1) the data frame used to determine the scattered light model parameters;
+(2) the model of the scattered light; and
+(3) The model subtracted from the data (i.e. data-model).
+All panels are re-oriented so that vertical is the spectral dimension with
+blue at the bottom, following the PypeIt :ref:`pypeit-orientation` convention.
+
+Here is an screen shot of a ``ginga`` view for an example from
+the ``keck_kcwi`` spectrograph.
+
+.. image:: ../figures/scatteredlight_image.png
+
+Trouble Shooting
+================
+
+If your scattered light model appears to be in error, here are some things to consider:
+
+ - Does the scattered light input frame have enough pixels between the
+   slits to accurately pin down the scattered light contribution?
+
+ - Check your chosen scattered light frame. It might be mislabeled. It's
+   often best to choose one really good scattered light frame, unless you
+   are confident that all frames labelled ``scattlight`` closely resemble
+   one another
+
+ - Check your slit tracing. If some slits are apparently missing, you might
+   want to check that the slits are correctly traced.
+
+Current Scattered Light Data Model
+==================================
+
+The datamodel written to disk is:
+
+.. include:: ../include/datamodel_scattlight.rst
+
+The `astropy.io.fits.BinTableHDU`_ contains all the data from the underlying
+:class:`~pypeit.scattlight.ScatteredLight` class.  For details see its datamodel.

From 12f1ff01cc56f32fe883b9522ad75ae2023d4fd4 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 26 Oct 2023 14:31:32 +0100
Subject: [PATCH 30/67] add kernel

---
 pypeit/scattlight.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index fb8fa801e1..429cbe1d42 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -15,7 +15,7 @@
 from pypeit import datamodel
 from pypeit import calibframe
 from pypeit.display import display
-from pypeit.spectrographs.util import load_spectrograph
+from pypeit.core import scattlight
 from pypeit.images.buildimage import ScatteredLightImage
 
 
@@ -49,7 +49,8 @@ class ScatteredLight(calibframe.CalibFrame):
                  'nspec': dict(otype=int, descr='Number of pixels in the image spectral direction.'),
                  'nspat': dict(otype=int, descr='Number of pixels in the image spatial direction.'),
                  'binning': dict(otype=str, descr='Binning in PypeIt orientation (not the original)'),
-                 'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges.'),
+                 'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges'),
+                 'kernel': dict(otype=str, descr='Kernel used to generate scattered light image'),
                  'scattlight_raw': dict(otype=np.ndarray, atype=np.floating,
                                   descr='Image used to construct the edge traces; see '
                                         ':class:`~pypeit.images.buildimage.ScatteredLightImage` and '
@@ -64,7 +65,7 @@ class ScatteredLight(calibframe.CalibFrame):
     # TODO: May want nspat to be a required argument.
     # The INIT must contain every datamodel item or risk fail on I/O when it is a nested container
     def __init__(self, pypeline=None, detname=None, nspec=None, nspat=None, PYP_SPEC=None, binning=None, pad=0,
-                 scattlight_raw=None, scattlight_model=None, scattlight_param=None):
+                 kernel=None, scattlight_raw=None, scattlight_model=None, scattlight_param=None):
 
         # Instantiate the DataContainer
         args, _, _, values = inspect.getargvalues(inspect.currentframe())
@@ -104,10 +105,8 @@ def get_model(self, image):
         if self.scattlight_param is None:
             msgs.warn("No scattered light parameters are available")
             return np.zeros_like(image)
-        # Load the spectrograph
-        spec = load_spectrograph(self.PYP_SPEC)
         # Return the model of the scattered light
-        return spec.scattered_light_model(self.scattlight_param, image)
+        return scattlight.scattered_light_model(self.scattlight_param, image, kernel=self.kernel)
 
     def show(self, image=None, slits=None, mask=False, wcs_match=True):
         """ Display the master scattered light frame, the model, and data-model.

From 0e4cd4714730d196efc054a28b6e832ccd0207ca Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 26 Oct 2023 14:33:17 +0100
Subject: [PATCH 31/67] add kernel

---
 pypeit/calibrations.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index b1e055e04c..9274835624 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -614,6 +614,7 @@ def get_scattlight(self):
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
                                                       pad=self.par['scattlight']['pad'],
+                                                      kernel=self.par['scattlight']['kernel'],
                                                       scattlight_raw=scattlightImage.image,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)

From 41398c0767818ff053b90700dfc538fd880a5a1f Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 26 Oct 2023 22:03:22 +0100
Subject: [PATCH 32/67] docstring

---
 pypeit/core/scattlight.py | 160 +++++++++++++++++++++++++-------------
 1 file changed, 107 insertions(+), 53 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 6ca464fa5f..e43cf91af9 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -13,7 +13,7 @@
 from pypeit import msgs
 
 
-def scattered_light_model(param, img, kernel='gaussian'):
+def scattered_light_model(param, img):
     """ Model used to calculate the scattered light.
 
     The current model to generate the scattered light is a shifted, scaled, and blurred version of the
@@ -30,21 +30,24 @@ def scattered_light_model(param, img, kernel='gaussian'):
         subsequent parameters in this list are polynomial corrections to the spectral
         direction. Here are the individual parameters:
 
-        * param[0] = Kernel width in the spectral direction
-        * param[1] = Kernel width in the spatial direction
-        * param[2] = Pixel shift of the scattered light in the spectral direction
-        * param[3] = Pixel shift of the scattered light in the spatial direction
-        * param[4] = Zoom factor of the scattered light (~1)
-        * param[5:] = Polynomial scaling coefficients in the spectral direction
+        * param[0] = Gaussian kernel width in the spectral direction
+        * param[1] = Gaussian kernel width in the spatial direction
+        * param[2] = Lorentzian kernel width in the spectral direction
+        * param[3] = Lorentzian kernel width in the spatial direction
+        * param[4] = Pixel shift of the scattered light in the spectral direction
+        * param[5] = Pixel shift of the scattered light in the spatial direction
+        * param[6] = Zoom factor of the scattered light (~1)
+        * param[7] = Kernel angle
+        * param[8] = Relative importance of Gaussian vs Lorentzian.
+                     0 < value < 1 means Lorentzian is weighted more
+                     value > 1 means Gaussian is weighted more.
+        * param[9:] = Polynomial scaling coefficients
     img : `numpy.ndarray`_
         Raw image that you want to compute the scattered light model.
         shape is (nspec, nspat)
         Model used to calculate the scattered light. This function is used to
         generate a model of the scattered light, based on a set of model parameters
         that have been optimized using self.scattered_light().
-    kernel : :obj:`str`_, optional
-        The shape of the kernel to use. The allowed values are 'gaussian' and 'lorentzian',
-        but the default value ('gaussian') is currently believed to provide the best fit.
 
     Returns
     -------
@@ -52,37 +55,74 @@ def scattered_light_model(param, img, kernel='gaussian'):
         Model of the scattered light for the input
     """
     # Extract the parameters into more conveniently named variables
-    sigmx, sigmy, shft_spec, shft_spat, zoom = param[0], param[1], param[2], param[3], param[4]
-    polyterms = param[5:]
-    # Generate a 2D smoothing kernel
-    if kernel == 'gaussian':
-        # Gaussian
-        subkrnx = np.exp(-0.5 * (np.arange(int(6 * sigmx)) - 3 * sigmx) ** 2 / sigmx ** 2)
-        subkrny = np.exp(-0.5 * (np.arange(int(6 * sigmy)) - 3 * sigmy) ** 2 / sigmy ** 2)
-    elif kernel == 'lorentzian':
-        # Lorentzian
-        subkrnx = sigmx / ((np.arange(int(10 * sigmx)) - 5 * sigmx) ** 2 + sigmx ** 2)
-        subkrny = sigmy / ((np.arange(int(10 * sigmy)) - 5 * sigmy) ** 2 + sigmy ** 2)
-    else:
-        msgs.error(f"Unknown kernel: {kernel}")
-    kernel = np.outer(subkrnx, subkrny)
-    kernel /= np.sum(kernel)
+    sigmx_g, sigmy_g, sigmx_l, sigmy_l = param[0], param[1], param[2], param[3]
+    shft_spec, shft_spat, zoom = param[4], param[5], param[6]
+    kern_angle, kern_scale = param[7], param[8]
+    polyterms = param[9:]
+
     # Make a grid of coordinates
     specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
-    spat, spec = np.meshgrid(spatvec, specvec / (specvec.size - 1))
+    spat, spec = np.meshgrid(spatvec/(spatvec.size-1), specvec/(specvec.size - 1))
     # Generate the polynomial efficiency scaling in the spectral direction
-    polyscale = np.zeros_like(img)
-    for pp in range(polyterms.size):
-        polyscale += polyterms[pp] * spec ** pp
+    polyscale = polyterms[0] + polyterms[1]*spec + polyterms[2]*spat + polyterms[3]*spec*spat
+    # polyscale = np.zeros_like(img)
+    # for pp in range(polyterms_spec.size):
+    #     polyscale += polyterms_spec[pp] * spec ** pp
+    # for pp in range(polyterms_spat.size):
+    #     polyscale += polyterms_spat[pp] * spat ** pp
+
+    # Generate a 2D smoothing kernel, composed of a 2D Gaussian and a 2D Lorentzian
+    sigmx, sigmy = max(sigmx_g, sigmx_l), max(sigmy_g, sigmy_l),
+    xkern, ykern = np.meshgrid(np.arange(int(10 * sigmx)) - 5 * sigmx,
+                               np.arange(int(10 * sigmy)) - 5 * sigmy)
+    # Rotate the kernel
+    xkernrot = (xkern * np.cos(kern_angle) - ykern * np.sin(kern_angle))
+    ykernrot = (xkern * np.sin(kern_angle) + ykern * np.cos(kern_angle))
+    # Create and normalise the Gaussian kernel
+    kernel_gaussian = np.exp(-((xkernrot/sigmx_g)**2 + (ykernrot/sigmy_g)**2))
+    kernel_gaussian /= np.sum(kernel_gaussian)
+    # Create and normalise the Lorenztian kernel
+    kernel_lorentzian = 1 / ((xkernrot/sigmx_l) ** 2 + (ykernrot/sigmy_l) ** 2 + 1)
+    kernel_lorentzian /= np.sum(kernel_lorentzian)
+    # Add the individual kernels into a single kernel. Arbitrarily scale the Gaussian kernel (either is fine).
+    # The point of this is to make it so either a lorentzian, gaussian, or something in-between can be
+    # used as the kernel, making it more flexible for different spectrographs.
+    kernel = kernel_lorentzian + kern_scale * kernel_gaussian
+    kernel /= np.sum(kernel)
+
     # Convolve the input image (note: most of the time is spent here)
     # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
-    # scale_img = polyscale * signal.fftconvolve(img, kernel, mode='same')
-    scale_img = polyscale * signal.oaconvolve(img, kernel, mode='same')
+    scale_img = polyscale * signal.fftconvolve(img, kernel, mode='same')
+    # scale_img = polyscale * signal.oaconvolve(img, kernel, mode='same')
     spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
     return spl(zoom * (specvec + shft_spec), zoom * (spatvec + shft_spat))
 
 
-def scattlight_resid(param, wpix, img):
+def evaluate(param, frame, detpad=300):
+    """ Evaluate the scattered light model, allowing for some image padding to minimise edge effects
+
+    Parameters
+    ----------
+    param : `numpy.ndarray`_
+        Model parameters that determine the scattered light based on the input img.
+        See `scattered_light_model()`_
+    img : `numpy.ndarray`_
+        Raw image that you want to compute the scattered light model.
+        shape is (nspec, nspat)
+
+    Returns
+    -------
+    model : `numpy.ndarray`_
+        Model of the scattered light for the input
+    """
+    _frame = frame.copy()
+    _frame[0, :] = np.median(_frame[0:10, :], axis=0)
+    _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
+    img = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
+    return scattered_light_model(param, img)[detpad:-detpad, detpad:-detpad]
+
+
+def scattlight_resid(param, wpix, img, modimg):
     """ Residual function used to optimize the model parameters
 
     Parameters
@@ -100,7 +140,7 @@ def scattlight_resid(param, wpix, img):
     resid : `numpy.ndarray`_
         A 1D vector of the residuals
     """
-    model = scattered_light_model(param, img)
+    model = scattered_light_model(param, modimg)
     return img[wpix] - model[wpix]
 
 
@@ -137,12 +177,7 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     success : :obj:`bool`_
         True if the fit was successful, False otherwise
     """
-
-    # Grab a copy of the input frame, and do some pre-processing on it
     _frame = frame.copy()
-
-    # First pad the edges to minimize edge effects
-    # Do a median filter near the edges
     _frame[0, :] = np.median(_frame[0:10, :], axis=0)
     _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
     img = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
@@ -151,30 +186,49 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     # Grab the pixels to be included in the fit
     wpix = np.where(offslitmask_pad)
 
-    # Compute the best-fitting model parameters
-    msgs.info("Computing best-fitting model parameters of the scattered light")
-    res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, img), verbose=2, ftol=1.0E-4)
-
-    # Store if this is a successful fit
-    success = res_lsq.success
-    if success:
-        msgs.info("Generating best-fitting scattered light model")
-        scatt_img = scattered_light_model(res_lsq.x, img)[detpad:-detpad, detpad:-detpad]
-    else:
-        msgs.warn("Scattered light model fitting failed")
-        scatt_img = np.zeros_like(_frame)
+    # Iterate on the scattered light
+    scatt_img = 0.0
+    niter = 1
+    for ii in range(niter):
+        # Grab a copy of the input frame, and do some pre-processing on it
+        _frame = frame.copy()-scatt_img
+
+        # First pad the edges to minimize edge effects
+        # Do a median filter near the edges
+        _frame[0, :] = np.median(_frame[0:10, :], axis=0)
+        _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
+        modimg = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
+
+        # Compute the best-fitting model parameters
+        msgs.info(f"ITERATION {ii+1}: Computing best-fitting model parameters of the scattered light")
+        res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, img, modimg),
+                                verbose=2, ftol=1.0E-4)
+
+        # Store if this is a successful fit
+        success = res_lsq.success
+        if success:
+            msgs.info("Generating best-fitting scattered light model")
+            scatt_img = scattered_light_model(res_lsq.x, modimg)[detpad:-detpad, detpad:-detpad]
+        else:
+            msgs.warn("Scattered light model fitting failed")
+            scatt_img = np.zeros_like(_frame)
+            break
     if debug:
         # Do some checks on the results
         embed()
-        from matplotlib import pyplot as plt
         scatt_img_alt = scattered_light_model(x0, img)[detpad:-detpad, detpad:-detpad]
-        vmin, vmax = 0, np.max(scatt_img_alt)
+        from matplotlib import pyplot as plt
+        vmin, vmax = 0, 40.0#np.max(scatt_img_alt)
+        plt.imshow(frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
+        plt.show()
+        print(res_lsq.x)
+
         plt.subplot(231)
         plt.imshow(_frame, vmin=vmin, vmax=vmax)
         plt.subplot(232)
         plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
         plt.subplot(233)
-        plt.imshow(_frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
+        plt.imshow(frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
         plt.subplot(234)
         plt.imshow(_frame, vmin=vmin, vmax=vmax)
         plt.subplot(235)

From 2087f7c84e79776205a0b25509299d5e53796164 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 26 Oct 2023 22:03:33 +0100
Subject: [PATCH 33/67] update ESI scattlight params

---
 pypeit/spectrographs/keck_esi.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/pypeit/spectrographs/keck_esi.py b/pypeit/spectrographs/keck_esi.py
index 7e400666c4..4bb0d61f75 100644
--- a/pypeit/spectrographs/keck_esi.py
+++ b/pypeit/spectrographs/keck_esi.py
@@ -131,6 +131,10 @@ def default_pypeit_par(cls):
         par['reduce']['findobj']['maxnumber_std'] = 1  # Slit is narrow so allow one object per order
         par['reduce']['extraction']['model_full_slit'] = True  # local sky subtraction operates on entire slit
 
+        # Scattered light
+        par['calibrations']['pixelflatframe']['process']['subtract_scattlight'] = True
+        par['calibrations']['illumflatframe']['process']['subtract_scattlight'] = True
+        par['scienceframe']['process']['subtract_scattlight'] = True
 
         # Always flux calibrate, starting with default parameters
         # Do not correct for flexure
@@ -332,14 +336,17 @@ def scattered_light_archive(self, binning, dispname):
         # and should be close to the final fitted values to reduce computational time)
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
-        x0 = np.array([3.16544600e+02/specbin, 2.05443943e+02/spatbin,  # kernel widths
-                       -9.23121908e+01/specbin, 6.09000452e+01/spatbin,  # pixel offsets
-                       9.94913292e-01,  # Zoom factor
-                       2.23905249e-01, -1.86171132e-01, 8.55479705e-02, -1.26763813e-02])  # Polynomial terms
+        x0 = np.array([2.66062476e+02/specbin, 1.57687605e+02/spatbin,  # Gaussian kernel widths
+                       2.65852665e+02/specbin, 1.77129333e+02/spatbin,  # Lorentzian kernel widths
+                       -1.38348682e+02/specbin, 7.70314399e+01/spatbin,  # pixel offsets
+                       1.01701653e+00,  # Zoom factor
+                       9.46809475e-01,  # kernel angle
+                       5.24572902e-02,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                       2.98836062e-01, -2.48646829e-01, -1.31752297e-01, 2.25868369e-01])  # Polynomial terms
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, -200/specbin, -200/spatbin, 0, -10, -10, -10, -10],
-                  [600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 10, 10, 10, 10])
+        bounds = ([1, 1, 1, 1, -200/specbin, -200/spatbin, 0, -2*np.pi, 0.0, -10, -10, -10, -10],
+                  [600/specbin, 600/spatbin, 600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 2*np.pi, 1000.0, 10, 10, 10, 10])
 
         # Return the best-fitting archival parameters and the bounds
         return x0, bounds

From e84eb1274854171db35e45ab985d2fa0c8c4be14 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 26 Oct 2023 22:04:16 +0100
Subject: [PATCH 34/67] rm kernel

---
 pypeit/core/scattlight.py | 4 +---
 pypeit/scattlight.py      | 5 ++---
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index e43cf91af9..d1dd171365 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -26,9 +26,7 @@ def scattered_light_model(param, img):
     ----------
     param : `numpy.ndarray`_
         Model parameters that determine the scattered light based on the input img.
-        The first 5 parameters are the same for all spectrographs. The sixth and all
-        subsequent parameters in this list are polynomial corrections to the spectral
-        direction. Here are the individual parameters:
+        Here is a list of the individual parameter meanings:
 
         * param[0] = Gaussian kernel width in the spectral direction
         * param[1] = Gaussian kernel width in the spatial direction
diff --git a/pypeit/scattlight.py b/pypeit/scattlight.py
index 429cbe1d42..5fab7a9b55 100644
--- a/pypeit/scattlight.py
+++ b/pypeit/scattlight.py
@@ -50,7 +50,6 @@ class ScatteredLight(calibframe.CalibFrame):
                  'nspat': dict(otype=int, descr='Number of pixels in the image spatial direction.'),
                  'binning': dict(otype=str, descr='Binning in PypeIt orientation (not the original)'),
                  'pad': dict(otype=int, descr='Integer number of pixels to mask beyond the slit edges'),
-                 'kernel': dict(otype=str, descr='Kernel used to generate scattered light image'),
                  'scattlight_raw': dict(otype=np.ndarray, atype=np.floating,
                                   descr='Image used to construct the edge traces; see '
                                         ':class:`~pypeit.images.buildimage.ScatteredLightImage` and '
@@ -65,7 +64,7 @@ class ScatteredLight(calibframe.CalibFrame):
     # TODO: May want nspat to be a required argument.
     # The INIT must contain every datamodel item or risk fail on I/O when it is a nested container
     def __init__(self, pypeline=None, detname=None, nspec=None, nspat=None, PYP_SPEC=None, binning=None, pad=0,
-                 kernel=None, scattlight_raw=None, scattlight_model=None, scattlight_param=None):
+                 scattlight_raw=None, scattlight_model=None, scattlight_param=None):
 
         # Instantiate the DataContainer
         args, _, _, values = inspect.getargvalues(inspect.currentframe())
@@ -106,7 +105,7 @@ def get_model(self, image):
             msgs.warn("No scattered light parameters are available")
             return np.zeros_like(image)
         # Return the model of the scattered light
-        return scattlight.scattered_light_model(self.scattlight_param, image, kernel=self.kernel)
+        return scattlight.scattered_light_model(self.scattlight_param, image)
 
     def show(self, image=None, slits=None, mask=False, wcs_match=True):
         """ Display the master scattered light frame, the model, and data-model.

From 3773c58a4e3b6419ac87816c74018c5e81107841 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 26 Oct 2023 22:06:41 +0100
Subject: [PATCH 35/67] update scattlight

---
 pypeit/calibrations.py               |  1 -
 pypeit/images/rawimage.py            | 27 +++++++++++++++++++++++----
 pypeit/par/pypeitpar.py              |  2 +-
 pypeit/spectrographs/spectrograph.py | 18 +++++++++---------
 4 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index 9274835624..b1e055e04c 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -614,7 +614,6 @@ def get_scattlight(self):
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
                                                       pad=self.par['scattlight']['pad'],
-                                                      kernel=self.par['scattlight']['kernel'],
                                                       scattlight_raw=scattlightImage.image,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index a30c19c809..9d07521a4c 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1140,18 +1140,37 @@ def subtract_scattlight(self, msscattlight, slits):
         # Obtain some information that is needed for the scattered light
         binning = self.detector[0]['binning']
         dispname = self.spectrograph.get_meta_value(self.spectrograph.get_headarr(self.filename), 'dispname')
+
         # Loop over the images
         for ii in range(self.nimg):
+            # Mask bad pixels
+            _frame = self.image[ii, ...] * np.logical_not(self.bpm[ii, ...])
+            # Apply the requested method for the scattered light
             if self.par["scattlight_method"] == "model":
                 # Use predefined model parameters
-                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...])
+                scatt_img = scattlight.evaluate(msscattlight.scattlight_param, _frame)
+                debug = False  # RJC requests to keep this here for debugging
+                if debug:
+                    spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
+                    pad = msscattlight.pad // spatbin
+                    offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
+                    from matplotlib import pyplot as plt
+                    _frame = self.image[ii, ...]
+                    vmin, vmax = 0, np.max(scatt_img)
+                    plt.subplot(131)
+                    plt.imshow(_frame*offslitmask, vmin=vmin, vmax=vmax)
+                    plt.subplot(132)
+                    plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
+                    plt.subplot(133)
+                    plt.imshow((_frame - scatt_img)*offslitmask, vmin=-vmax / 2, vmax=vmax / 2)
+                    plt.show()
             elif self.par["scattlight_method"] == "archive":
                 # Use archival model parameters
                 modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
                 if modpar is None:
                     msgs.error(f"{self.spectrograph.name} does not have archival scattered light parameters. Please "
                                f"set 'scattlight_method' to another option.")
-                scatt_img = scattlight.scattered_light_model(modpar, self.image[ii, ...])
+                scatt_img = scattlight.scattered_light_model(modpar, _frame)
             elif self.par["scattlight_method"] == "frame":
                 # Calculate a model specific for this frame
                 spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
@@ -1166,12 +1185,12 @@ def subtract_scattlight(self, msscattlight, slits):
                 if not success:
                     if msscattlight is not None:
                         msgs.warn("Scattered light model failed - using predefined model parameters")
-                        scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...])
+                        scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, _frame)
                     else:
                         msgs.warn("Scattered light model failed - using archival model parameters")
                         # Use archival model parameters
                         modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
-                        scatt_img = scattlight.scattered_light_model(modpar, self.image[ii, ...])
+                        scatt_img = scattlight.scattered_light_model(modpar, _frame)
             else:
                 msgs.warn("Scattered light not performed")
                 scatt_img = np.zeros(self.image[ii, ...].shape)
diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index 6990c42d6d..f49143a212 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -478,7 +478,7 @@ def valid_overscan_methods():
     @staticmethod
     def valid_scattlight_methods():
         """
-        Return the valid overscan methods.
+        Return the valid scattered light methods.
         """
         return ['model', 'frame', 'archive']
 
diff --git a/pypeit/spectrographs/spectrograph.py b/pypeit/spectrographs/spectrograph.py
index dbb9c4c46d..d973a5e1bb 100644
--- a/pypeit/spectrographs/spectrograph.py
+++ b/pypeit/spectrographs/spectrograph.py
@@ -1917,17 +1917,17 @@ def scattered_light_archive(self, binning, dispname):
         specbin, spatbin = parse.parse_binning(binning)
 
         msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname} of {self.name}")
-        sigmx = 400.0 / specbin  # This is the spectral direction
-        sigmy = 200.0 / spatbin  # This is the spatial direction
-        shft_spec = 0.0 / specbin  # Shift of the scattered light in the spectral direction
-        shft_spat = 0.0 / spatbin  # Shift of the scattered light in the spatial direction
-        zoom = 1.0  # Zoom factor of the scattered light
-        term0, term1, term2, term3 = 0.1, -0.2, 0.3, -0.1  # Polynomial coefficients
-        x0 = [sigmx, sigmy, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
+        x0 = np.array([200/specbin, 100/spatbin,  # Gaussian kernel widths
+                       200/specbin, 100/spatbin,  # Lorentzian kernel widths
+                       0.0/specbin, 0.0/spatbin,  # pixel offsets
+                       1.0,  # Zoom factor
+                       0.0,  # kernel angle
+                       0.0,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                       0.1, 0.0, 0.0, 0.0])  # Polynomial terms
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, -200/specbin, -200/spatbin, 0, -10, -10, -10, -10],
-                  [600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 10, 10, 10, 10])
+        bounds = ([1, 1, 1, 1, -200/specbin, -200/spatbin, 0, -2*np.pi, 0.0, -10, -10, -10, -10],
+                  [600/specbin, 600/spatbin, 600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 2*np.pi, 1000.0, 10, 10, 10, 10])
 
         # Return the best-fitting archival parameters and the bounds
         return x0, bounds

From ea608a8ff6a4db9e33f864678067a7cfa40236cd Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 27 Oct 2023 21:58:32 +0100
Subject: [PATCH 36/67] update docs

---
 doc/calibrations/scattlight.rst | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/doc/calibrations/scattlight.rst b/doc/calibrations/scattlight.rst
index 1fe0f6fb05..3fb4e3bf0b 100644
--- a/doc/calibrations/scattlight.rst
+++ b/doc/calibrations/scattlight.rst
@@ -50,17 +50,20 @@ example will pad the slits by 10 detector pixels, assuming 1x1 binning):
 Note: if your data are binned by 2 in the spatial direction, then setting ``pad=10`` would actually
 pad the slit edges by 5 pixels.
 
-The current scattered light model involves a 9 parameter fit to the inter-slit regions. The model
+The current scattered light model involves a 13 parameter fit to the inter-slit regions. The model
 utilises the observed 2D (trimmed+oriented+debiased) frame to estimate the scattered light. The
-scattered light model is essentially a blurred, shifted, flux-scaled, and zoomed version of the
-detector image. The amount of blurring and shifting in the spatial and spectral direction are done
-separately (2 parameters each), and there is a single parameter to estimate the zoom factor. The
-flux-scaling is a cubic polynomial (4 parameters) to the spectral direction. Once the best-fitting
+scattered light model is essentially a blurred, shifted, flux-scaled, rotated, and zoomed version of the
+detector image. The amount of blurring in the spatial and spectral direction include contributions from
+a Gaussian and a Lorentzian kernel. The relative importance of these kernels is controlled by scaling
+parameter (1 paramerer), and each kernel has two widths each (4 parameters total), and the joint kernel
+is rotated (1 additional parameter). The scattered light image is than shifted in the spectral and spatial
+directions (2 additional parameters), and there is a single parameter to estimate the zoom factor. Finally,
+the scattered light flux is scaled by a low order 2D polynomial (4 parameters). Once the best-fitting
 model parameters are determined, these can be applied to any other frame to estimate the scattered
 light contribution of this frame.
 
 Alternatively, the scattered light can be determined for each frame independently (i.e. you can
-derive a separate set of 9 model parameters for each frame that you wish to perform a scattered
+derive a separate set of 13 model parameters for each frame that you wish to perform a scattered
 light correction). To set this option, you can add the following arguments to your pypeit file:
 
 .. code-block:: ini

From b0692e1664c084aa1809ce76f242a54723ce2595 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 27 Oct 2023 21:59:16 +0100
Subject: [PATCH 37/67] tmp kcwi update

---
 pypeit/spectrographs/keck_kcwi.py | 62 ++++++++++++++++++++-----------
 1 file changed, 40 insertions(+), 22 deletions(-)

diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 4846f8550b..e2440d7fed 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -215,7 +215,7 @@ def compound_meta(self, headarr, meta_key):
                 try:
                     hdrstr = 'TARGRA' if meta_key == 'ra' else 'TARGDEC'
                 except KeyError:
-                    hdrstr = ''
+                    msgs.error(f'Cannot determine the {meta_key} from the header')
             return headarr[0][hdrstr]
         elif meta_key == 'pressure':
             try:
@@ -1064,33 +1064,51 @@ def scattered_light_archive(self, binning, dispname):
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
         if dispname == 'BH2':
-            x0 = np.array([3.49128562e+02/specbin, 2.45833322e+02/spatbin,  # kernel widths
-                           -1.26958208e+02/specbin, 1.49033883e+01/spatbin,  # pixel offsets
-                           1.00229122e+00,  # Zoom factor
-                           1.87534428e-01, -2.75679127e-01, 4.08198109e-01, -1.87826458e-01])  # Polynomial terms
+            x0 = np.array([ 2.67186394e+02/specbin, 1.60743078e+02/spatbin,  # Gaussian kernel widths
+                            2.98999820e+02/specbin, 1.45068103e+02/spatbin,  # Lorentzian kernel widths
+                            -1.37376475e+02/specbin, 6.07508615e+01/spatbin,  # pixel offsets
+                            9.86766634e-01,  # Zoom factor
+                            1.41130604e+00,  # kernel angle
+                            4.19246820e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                            7.93912603e-02, -5.81933968e-02, -5.76201948e-03, -7.83543150e-04])  # Polynomial terms
         elif dispname == 'BM':
-            x0 = np.array([3.50037423e+02/specbin, 2.38662990e+02/spatbin,  # kernel widths
-                           -3.82935541e+01/specbin, -2.04645954e+01/spatbin,  # pixel offsets
-                           1.01042546e+00,  # Zoom factor
-                           1.15251491e-01, -1.59419953e-01,  1.30877795e-01, -1.84324642e-02])  # Polynomial terms
+            # This solution had Cost: 8.8951e+07 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=10
+
+       #      x0 = np.array([1.72694908e+02, 1.43224435e+01,
+       #                     1.55856081e+02,  1.18366623e+02,
+       # -6.32142819e+01,  1.52454051e+01,  9.96530165e-01,  1.30756297e+00,
+       #  7.81666600e-02,  8.25778311e-02, -2.91919118e-02, -3.24661854e-03,
+       #  4.32022859e-03])
+            x0 = np.array([ 60/specbin, 60/spatbin,  # Gaussian kernel widths
+                            60/specbin, 60/spatbin,  # Lorentzian kernel widths
+                            0/specbin, 0/spatbin,  # pixel offsets
+                            9.86766634e-01,  # Zoom factor
+                            1.41130604e+00,  # kernel angle
+                            4.19246820e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                            7.93912603e-02, -5.81933968e-02, -5.76201948e-03, -7.83543150e-04])  # Polynomial terms
         elif dispname == 'BL':
-            x0 = np.array([3.47000453e+02/specbin, 2.12462524e+02/spatbin,  # kernel widths
-                           -4.35179357e+01/specbin, 8.21150855e+00/spatbin,  # pixel offsets
-                           9.99651562e-01,  # Zoom factor
-                           1.18828616e-01, -3.22338051e-01, 5.07102665e-01, -2.85286699e-01])  # Polynomial terms
+            # This solution had Cost: 1.0184e+07 and was based on a 2x2 dataset using pixelflat as the scattlight frame, and assuming pad=10
+            x0 = np.array([1.45201474e+01/specbin, 1.34653515e+02/spatbin,  # Gaussian kernel widths
+                           1.32198875e+02/specbin, 1.60178190e+02/spatbin,  # Lorentzian kernel widths
+                           6.75891120e+01/specbin, 2.38622901e+01/spatbin,  # pixel offsets
+                           9.94430899e-01,  # Zoom factor
+                           2.02560248e-01,  # kernel angle
+                           1.90164026e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           7.00761436e-02, -3.67325245e-02, -2.55285293e-03, 2.41518440e-03])  # Polynomial terms
         else:
             msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname}")
-            sigmx = 400.0 / specbin  # This is the spectral direction
-            sigmy = 200.0 / spatbin  # This is the spatial direction
-            shft_spec = 0.0 / specbin  # Shift of the scattered light in the spectral direction
-            shft_spat = 0.0 / spatbin  # Shift of the scattered light in the spatial direction
-            zoom = 1.0  # Zoom factor of the scattered light
-            term0, term1, term2, term3 = 0.1, -0.2, 0.3, -0.1  # Polynomial coefficients
-            x0 = [sigmx, sigmy, shft_spec, shft_spat, zoom, term0, term1, term2, term3]
+            x0 = np.array([200/specbin, 100/spatbin,  # Gaussian kernel widths
+                           200/specbin, 100/spatbin,  # Lorentzian kernel widths
+                           0.0/specbin, 0.0/spatbin,  # pixel offsets
+                           1.0,  # Zoom factor
+                           0.0,  # kernel angle
+                           0.0,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           0.1, 0.0, 0.0, 0.0])  # Polynomial terms
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, -200/specbin, -200/spatbin, 0, -10, -10, -10, -10],
-                  [600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 10, 10, 10, 10])
+        bounds = ([1, 1, 1, 1, -200 / specbin, -200 / spatbin, 0, -2 * np.pi, 0.0, -10, -10, -10, -10],
+                  [600 / specbin, 600 / spatbin, 600 / specbin, 600 / spatbin, 200 / specbin, 200 / spatbin, 2,
+                   2 * np.pi, 1000.0, 10, 10, 10, 10])
 
         # Return the best-fitting archival parameters and the bounds
         return x0, bounds

From cd8d26527d23d28ad806271e2eff13b73f84099c Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 27 Oct 2023 21:59:24 +0100
Subject: [PATCH 38/67] debug

---
 pypeit/images/rawimage.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 9d07521a4c..73c3867770 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1149,19 +1149,26 @@ def subtract_scattlight(self, msscattlight, slits):
             if self.par["scattlight_method"] == "model":
                 # Use predefined model parameters
                 scatt_img = scattlight.evaluate(msscattlight.scattlight_param, _frame)
-                debug = False  # RJC requests to keep this here for debugging
+                debug = True  # RJC requests to keep this here for debugging
                 if debug:
+                    embed()
+                    tmp = msscattlight.scattlight_param.copy()
+                    tmp[:6] *= 2
+                    print("\n\n\n2x2 BINNING ASSUMED!!!\n\n\n")
+                    print(tmp)
                     spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
                     pad = msscattlight.pad // spatbin
                     offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
                     from matplotlib import pyplot as plt
                     _frame = self.image[ii, ...]
                     vmin, vmax = 0, np.max(scatt_img)
-                    plt.subplot(131)
+                    plt.subplot(221)
+                    plt.imshow(_frame, vmin=vmin, vmax=2*np.median(_frame))
+                    plt.subplot(222)
                     plt.imshow(_frame*offslitmask, vmin=vmin, vmax=vmax)
-                    plt.subplot(132)
+                    plt.subplot(223)
                     plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
-                    plt.subplot(133)
+                    plt.subplot(224)
                     plt.imshow((_frame - scatt_img)*offslitmask, vmin=-vmax / 2, vmax=vmax / 2)
                     plt.show()
             elif self.par["scattlight_method"] == "archive":

From 2d6bd231de8b7ea2bbd6167b77b16d22a83325c6 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sun, 29 Oct 2023 16:15:29 +0000
Subject: [PATCH 39/67] update scattlight docs

---
 doc/calibrations/scattlight.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/calibrations/scattlight.rst b/doc/calibrations/scattlight.rst
index 3fb4e3bf0b..c7c82b00ff 100644
--- a/doc/calibrations/scattlight.rst
+++ b/doc/calibrations/scattlight.rst
@@ -50,6 +50,8 @@ example will pad the slits by 10 detector pixels, assuming 1x1 binning):
 Note: if your data are binned by 2 in the spatial direction, then setting ``pad=10`` would actually
 pad the slit edges by 5 pixels.
 
+# TODO :: Make sure this description is up-to-date!
+
 The current scattered light model involves a 13 parameter fit to the inter-slit regions. The model
 utilises the observed 2D (trimmed+oriented+debiased) frame to estimate the scattered light. The
 scattered light model is essentially a blurred, shifted, flux-scaled, rotated, and zoomed version of the

From d504de05594c43b1fb2c9666096097624a9bdc60 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sun, 29 Oct 2023 16:15:58 +0000
Subject: [PATCH 40/67] new scattlight model

---
 pypeit/core/scattlight.py         | 113 +++++++++++-------------------
 pypeit/images/rawimage.py         |   5 +-
 pypeit/spectrographs/keck_esi.py  |  21 +++---
 pypeit/spectrographs/keck_kcwi.py |  17 +++--
 4 files changed, 69 insertions(+), 87 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index d1dd171365..f25605219b 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -10,10 +10,10 @@
 
 from IPython import embed
 
-from pypeit import msgs
+from pypeit import msgs, utils
 
 
-def scattered_light_model(param, img):
+def scattered_light_model(param, img, gpm):
     """ Model used to calculate the scattered light.
 
     The current model to generate the scattered light is a shifted, scaled, and blurred version of the
@@ -46,6 +46,9 @@ def scattered_light_model(param, img):
         Model used to calculate the scattered light. This function is used to
         generate a model of the scattered light, based on a set of model parameters
         that have been optimized using self.scattered_light().
+    gpm : `numpy.ndarray`_
+        Mask containing good pixels that should be included in the fit
+        shape is (nspec, nspat)
 
     Returns
     -------
@@ -54,9 +57,9 @@ def scattered_light_model(param, img):
     """
     # Extract the parameters into more conveniently named variables
     sigmx_g, sigmy_g, sigmx_l, sigmy_l = param[0], param[1], param[2], param[3]
-    shft_spec, shft_spat, zoom = param[4], param[5], param[6]
-    kern_angle, kern_scale = param[7], param[8]
-    polyterms = param[9:]
+    shft_spec, shft_spat, zoom_spec, zoom_spat = param[4], param[5], param[6], param[7]
+    kern_angle, kern_scale = param[8], param[9]
+    polyterms = param[10:]
 
     # Make a grid of coordinates
     specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
@@ -71,8 +74,8 @@ def scattered_light_model(param, img):
 
     # Generate a 2D smoothing kernel, composed of a 2D Gaussian and a 2D Lorentzian
     sigmx, sigmy = max(sigmx_g, sigmx_l), max(sigmy_g, sigmy_l),
-    xkern, ykern = np.meshgrid(np.arange(int(10 * sigmx)) - 5 * sigmx,
-                               np.arange(int(10 * sigmy)) - 5 * sigmy)
+    xkern, ykern = np.meshgrid(np.arange(int(6 * sigmx)) - 3 * sigmx,
+                               np.arange(int(6 * sigmy)) - 3 * sigmy)
     # Rotate the kernel
     xkernrot = (xkern * np.cos(kern_angle) - ykern * np.sin(kern_angle))
     ykernrot = (xkern * np.sin(kern_angle) + ykern * np.cos(kern_angle))
@@ -88,39 +91,18 @@ def scattered_light_model(param, img):
     kernel = kernel_lorentzian + kern_scale * kernel_gaussian
     kernel /= np.sum(kernel)
 
+    # Convolve the GPM, and use this to reduce edge effects
+    gpm_weights = signal.oaconvolve(gpm, kernel, mode='same')
+    # gpm_weights = signal.fftconvolve(gpm, kernel, mode='same')
     # Convolve the input image (note: most of the time is spent here)
     # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
-    scale_img = polyscale * signal.fftconvolve(img, kernel, mode='same')
-    # scale_img = polyscale * signal.oaconvolve(img, kernel, mode='same')
+    # scale_img = polyscale * signal.fftconvolve(img*gpm, kernel, mode='same') * utils.inverse(gpm_weights)
+    scale_img = polyscale * signal.oaconvolve(img*gpm, kernel, mode='same') * utils.inverse(gpm_weights)
     spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
-    return spl(zoom * (specvec + shft_spec), zoom * (spatvec + shft_spat))
-
-
-def evaluate(param, frame, detpad=300):
-    """ Evaluate the scattered light model, allowing for some image padding to minimise edge effects
-
-    Parameters
-    ----------
-    param : `numpy.ndarray`_
-        Model parameters that determine the scattered light based on the input img.
-        See `scattered_light_model()`_
-    img : `numpy.ndarray`_
-        Raw image that you want to compute the scattered light model.
-        shape is (nspec, nspat)
-
-    Returns
-    -------
-    model : `numpy.ndarray`_
-        Model of the scattered light for the input
-    """
-    _frame = frame.copy()
-    _frame[0, :] = np.median(_frame[0:10, :], axis=0)
-    _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
-    img = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
-    return scattered_light_model(param, img)[detpad:-detpad, detpad:-detpad]
+    return spl(zoom_spec * (specvec + shft_spec), zoom_spat * (spatvec + shft_spat))
 
 
-def scattlight_resid(param, wpix, img, modimg):
+def scattlight_resid(param, wpix, img, gpm):
     """ Residual function used to optimize the model parameters
 
     Parameters
@@ -132,13 +114,15 @@ def scattlight_resid(param, wpix, img, modimg):
         to be used for computing the residual.
     img : `numpy.ndarray`_
         Data image to be used to compute the residual. Shape is (nspec, nspat)
+    gpm : `numpy.ndarray`_
+        Good pixel mask to use for the fit. Shape is (nspec, nspat)
 
     Returns
     -------
     resid : `numpy.ndarray`_
         A 1D vector of the residuals
     """
-    model = scattered_light_model(param, modimg)
+    model = scattered_light_model(param, img, gpm)
     return img[wpix] - model[wpix]
 
 
@@ -159,8 +143,6 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
         A tuple of two elements, containing two `np.ndarray`_ of the same length as x0. These
         two arrays contain the lower (first element of the tuple) and upper (second element of the tuple)
         bounds to consider on the scattered light model parameters.
-    detpad : :obj:`int`_, optional
-        Number of pixels to pad to each of the detector edges to reduce edge effects.
     debug : :obj:`bool`_, optional
         If True, debug the final model fit that's been output
 
@@ -175,48 +157,37 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     success : :obj:`bool`_
         True if the fit was successful, False otherwise
     """
-    _frame = frame.copy()
+    gpm = np.logical_not(bpm)
+    _frame = frame * gpm
     _frame[0, :] = np.median(_frame[0:10, :], axis=0)
     _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
-    img = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
-    offslitmask_pad = np.pad(offslitmask * np.logical_not(bpm), detpad, mode='constant',
-                             constant_values=0)  # but don't include padded data in the fit
+    _frame_pad = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
+    gpm_pad = np.pad(gpm, detpad, mode='constant', constant_values=0)  # but don't include padded data in the fit
+    offslitmask_pad = np.pad(offslitmask * gpm, detpad, mode='constant', constant_values=0)  # but don't include padded data in the fit
     # Grab the pixels to be included in the fit
     wpix = np.where(offslitmask_pad)
 
-    # Iterate on the scattered light
-    scatt_img = 0.0
-    niter = 1
-    for ii in range(niter):
-        # Grab a copy of the input frame, and do some pre-processing on it
-        _frame = frame.copy()-scatt_img
-
-        # First pad the edges to minimize edge effects
-        # Do a median filter near the edges
-        _frame[0, :] = np.median(_frame[0:10, :], axis=0)
-        _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
-        modimg = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
-
-        # Compute the best-fitting model parameters
-        msgs.info(f"ITERATION {ii+1}: Computing best-fitting model parameters of the scattered light")
-        res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, img, modimg),
-                                verbose=2, ftol=1.0E-4)
-
-        # Store if this is a successful fit
-        success = res_lsq.success
-        if success:
-            msgs.info("Generating best-fitting scattered light model")
-            scatt_img = scattered_light_model(res_lsq.x, modimg)[detpad:-detpad, detpad:-detpad]
-        else:
-            msgs.warn("Scattered light model fitting failed")
-            scatt_img = np.zeros_like(_frame)
-            break
+    # Compute the best-fitting model parameters
+    msgs.info("Performing a least-squares fit to the scattered light")
+    res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, _frame_pad, gpm_pad),
+                            verbose=2, ftol=1.0E-3)
+
+    # Store if this is a successful fit
+    success = res_lsq.success
+    if success:
+        msgs.info("Generating best-fitting scattered light model")
+        scatt_img = scattered_light_model(res_lsq.x, _frame_pad, gpm_pad)[detpad:-detpad, detpad:-detpad]
+    else:
+        msgs.warn("Scattered light model fitting failed")
+        scatt_img = np.zeros_like(frame)
+
+    debug = True
     if debug:
         # Do some checks on the results
         embed()
-        scatt_img_alt = scattered_light_model(x0, img)[detpad:-detpad, detpad:-detpad]
+        scatt_img_alt = scattered_light_model(x0, _frame_pad, gpm_pad)[detpad:-detpad, detpad:-detpad]
         from matplotlib import pyplot as plt
-        vmin, vmax = 0, 40.0#np.max(scatt_img_alt)
+        vmin, vmax = 0, 40.0#np.max(scatt_img)#40
         plt.imshow(frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
         plt.show()
         print(res_lsq.x)
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 73c3867770..2e7829a21c 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1144,11 +1144,12 @@ def subtract_scattlight(self, msscattlight, slits):
         # Loop over the images
         for ii in range(self.nimg):
             # Mask bad pixels
-            _frame = self.image[ii, ...] * np.logical_not(self.bpm[ii, ...])
+            # _frame = self.image[ii, ...] * np.logical_not(self.bpm[ii, ...])
             # Apply the requested method for the scattered light
             if self.par["scattlight_method"] == "model":
                 # Use predefined model parameters
-                scatt_img = scattlight.evaluate(msscattlight.scattlight_param, _frame)
+                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...],
+                                                             np.logical_not(self.bpm[ii, ...]))
                 debug = True  # RJC requests to keep this here for debugging
                 if debug:
                     embed()
diff --git a/pypeit/spectrographs/keck_esi.py b/pypeit/spectrographs/keck_esi.py
index 4bb0d61f75..71f0e85052 100644
--- a/pypeit/spectrographs/keck_esi.py
+++ b/pypeit/spectrographs/keck_esi.py
@@ -19,6 +19,7 @@
 from pypeit.spectrographs import spectrograph
 from pypeit.images import detector_container
 
+
 class KeckESISpectrograph(spectrograph.Spectrograph):
     """
     Child to handle Keck/ESI specific code
@@ -71,7 +72,7 @@ def get_detector_par(self, det, hdu=None):
             mincounts       = -1e10,
             numamplifiers   = 2,
             gain            = np.atleast_1d([1.3, 1.3]), 
-            ronoise         = np.atleast_1d([2.5,2.5]), 
+            ronoise         = np.atleast_1d([2.5, 2.5]),
             )
         return detector_container.DetectorContainer(**detector_dict)
 
@@ -336,17 +337,21 @@ def scattered_light_archive(self, binning, dispname):
         # and should be close to the final fitted values to reduce computational time)
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
+       #  x0 = np.array([ 2.99899335e+02,  2.01422930e+02,  2.18511169e+02,  1.95963738e+02,
+       # -4.96314134e+01,  5.64219402e+01,  9.68966612e-01, -1.79304303e+00,
+       #  5.24082933e-01,  3.73084496e-01, -2.93388226e-01, -3.00799508e-01,
+       #  2.86810088e-01])
         x0 = np.array([2.66062476e+02/specbin, 1.57687605e+02/spatbin,  # Gaussian kernel widths
                        2.65852665e+02/specbin, 1.77129333e+02/spatbin,  # Lorentzian kernel widths
-                       -1.38348682e+02/specbin, 7.70314399e+01/spatbin,  # pixel offsets
-                       1.01701653e+00,  # Zoom factor
-                       9.46809475e-01,  # kernel angle
-                       5.24572902e-02,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                       -50/specbin, 50.0/spatbin,  # pixel offsets (spec, spat)
+                       1.0, 1.0,  # Zoom factor (spec, spat)
+                       0.0,  # kernel angle
+                       0.05,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
                        2.98836062e-01, -2.48646829e-01, -1.31752297e-01, 2.25868369e-01])  # Polynomial terms
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, 1, 1, -200/specbin, -200/spatbin, 0, -2*np.pi, 0.0, -10, -10, -10, -10],
-                  [600/specbin, 600/spatbin, 600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 2*np.pi, 1000.0, 10, 10, 10, 10])
+        bounds = ([1, 1, 1, 1, -200/specbin, -200/spatbin, 0, 0, -2*np.pi, 0.0, -10, -10, -10, -10],
+                  [600/specbin, 600/spatbin, 600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 2, 2*np.pi, 1000.0, 10, 10, 10, 10])
 
         # Return the best-fitting archival parameters and the bounds
         return x0, bounds
@@ -354,7 +359,7 @@ def scattered_light_archive(self, binning, dispname):
     @property
     def norders(self):
         """
-        Number of orders for this spectograph. Should only defined for
+        Number of orders for this spectrograph. Should only defined for
         echelle spectrographs, and it is undefined for the base class.
         """
         return 10   # 15-6
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index e2440d7fed..97d7a67270 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1067,7 +1067,7 @@ def scattered_light_archive(self, binning, dispname):
             x0 = np.array([ 2.67186394e+02/specbin, 1.60743078e+02/spatbin,  # Gaussian kernel widths
                             2.98999820e+02/specbin, 1.45068103e+02/spatbin,  # Lorentzian kernel widths
                             -1.37376475e+02/specbin, 6.07508615e+01/spatbin,  # pixel offsets
-                            9.86766634e-01,  # Zoom factor
+                            9.86766634e-01, 9.86766634e-01,  # Zoom factor
                             1.41130604e+00,  # kernel angle
                             4.19246820e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
                             7.93912603e-02, -5.81933968e-02, -5.76201948e-03, -7.83543150e-04])  # Polynomial terms
@@ -1088,12 +1088,17 @@ def scattered_light_archive(self, binning, dispname):
                             7.93912603e-02, -5.81933968e-02, -5.76201948e-03, -7.83543150e-04])  # Polynomial terms
         elif dispname == 'BL':
             # This solution had Cost: 1.0184e+07 and was based on a 2x2 dataset using pixelflat as the scattlight frame, and assuming pad=10
-            x0 = np.array([1.45201474e+01/specbin, 1.34653515e+02/spatbin,  # Gaussian kernel widths
+            #1.1357e+07
+            # array([1.46747486e+01, 1.33657850e+02, 1.65940119e+02, 1.63479652e+02,
+            #        1.06998939e+02, -2.10911731e+01, 9.72422938e-01, 1.02567767e+00,
+            #        1.57990253e+00, 5.66603960e-03, 5.08362982e-02, 1.83209211e-03,
+            #        -3.83980954e-03, -2.89908954e-02])
+            x0 = np.array([1.05201474e+02/specbin, 1.34653515e+02/spatbin,  # Gaussian kernel widths
                            1.32198875e+02/specbin, 1.60178190e+02/spatbin,  # Lorentzian kernel widths
                            6.75891120e+01/specbin, 2.38622901e+01/spatbin,  # pixel offsets
-                           9.94430899e-01,  # Zoom factor
+                           9.94430899e-01, 9.94430899e-01,  # Zoom factor
                            2.02560248e-01,  # kernel angle
-                           1.90164026e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           0.090164026e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
                            7.00761436e-02, -3.67325245e-02, -2.55285293e-03, 2.41518440e-03])  # Polynomial terms
         else:
             msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname}")
@@ -1106,8 +1111,8 @@ def scattered_light_archive(self, binning, dispname):
                            0.1, 0.0, 0.0, 0.0])  # Polynomial terms
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, 1, 1, -200 / specbin, -200 / spatbin, 0, -2 * np.pi, 0.0, -10, -10, -10, -10],
-                  [600 / specbin, 600 / spatbin, 600 / specbin, 600 / spatbin, 200 / specbin, 200 / spatbin, 2,
+        bounds = ([1, 1, 1, 1, -200 / specbin, -200 / spatbin, 0, 0, -2 * np.pi, 0.0, -10, -10, -10, -10],
+                  [600 / specbin, 600 / spatbin, 600 / specbin, 600 / spatbin, 200 / specbin, 200 / spatbin, 2, 2,
                    2 * np.pi, 1000.0, 10, 10, 10, 10])
 
         # Return the best-fitting archival parameters and the bounds

From 82d36afaf7ae2db778a077b5f08c6d927222da98 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Sun, 29 Oct 2023 20:37:02 +0000
Subject: [PATCH 41/67] ESI scattlight updates

---
 pypeit/core/scattlight.py        | 44 +++++++++++++-------------------
 pypeit/images/rawimage.py        |  5 ++--
 pypeit/spectrographs/keck_esi.py | 19 ++++++--------
 3 files changed, 28 insertions(+), 40 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index f25605219b..780078532f 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -6,14 +6,13 @@
 import numpy as np
 
 from scipy.optimize import least_squares
-from scipy import signal, interpolate
-
+from scipy import signal, interpolate, ndimage
 from IPython import embed
 
 from pypeit import msgs, utils
 
 
-def scattered_light_model(param, img, gpm):
+def scattered_light_model(param, img):
     """ Model used to calculate the scattered light.
 
     The current model to generate the scattered light is a shifted, scaled, and blurred version of the
@@ -46,9 +45,6 @@ def scattered_light_model(param, img, gpm):
         Model used to calculate the scattered light. This function is used to
         generate a model of the scattered light, based on a set of model parameters
         that have been optimized using self.scattered_light().
-    gpm : `numpy.ndarray`_
-        Mask containing good pixels that should be included in the fit
-        shape is (nspec, nspat)
 
     Returns
     -------
@@ -74,8 +70,8 @@ def scattered_light_model(param, img, gpm):
 
     # Generate a 2D smoothing kernel, composed of a 2D Gaussian and a 2D Lorentzian
     sigmx, sigmy = max(sigmx_g, sigmx_l), max(sigmy_g, sigmy_l),
-    xkern, ykern = np.meshgrid(np.arange(int(6 * sigmx)) - 3 * sigmx,
-                               np.arange(int(6 * sigmy)) - 3 * sigmy)
+    xkern, ykern = np.meshgrid(np.arange(int(10 * sigmx)) - 5 * sigmx,
+                               np.arange(int(10 * sigmy)) - 5 * sigmy)
     # Rotate the kernel
     xkernrot = (xkern * np.cos(kern_angle) - ykern * np.sin(kern_angle))
     ykernrot = (xkern * np.sin(kern_angle) + ykern * np.cos(kern_angle))
@@ -91,18 +87,12 @@ def scattered_light_model(param, img, gpm):
     kernel = kernel_lorentzian + kern_scale * kernel_gaussian
     kernel /= np.sum(kernel)
 
-    # Convolve the GPM, and use this to reduce edge effects
-    gpm_weights = signal.oaconvolve(gpm, kernel, mode='same')
-    # gpm_weights = signal.fftconvolve(gpm, kernel, mode='same')
-    # Convolve the input image (note: most of the time is spent here)
-    # oaconvolve is the fastest option when the kernel is much smaller dimensions than the image
-    # scale_img = polyscale * signal.fftconvolve(img*gpm, kernel, mode='same') * utils.inverse(gpm_weights)
-    scale_img = polyscale * signal.oaconvolve(img*gpm, kernel, mode='same') * utils.inverse(gpm_weights)
+    scale_img = polyscale * signal.oaconvolve(img, kernel, mode='same')
     spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
     return spl(zoom_spec * (specvec + shft_spec), zoom_spat * (spatvec + shft_spat))
 
 
-def scattlight_resid(param, wpix, img, gpm):
+def scattlight_resid(param, wpix, img):
     """ Residual function used to optimize the model parameters
 
     Parameters
@@ -114,15 +104,13 @@ def scattlight_resid(param, wpix, img, gpm):
         to be used for computing the residual.
     img : `numpy.ndarray`_
         Data image to be used to compute the residual. Shape is (nspec, nspat)
-    gpm : `numpy.ndarray`_
-        Good pixel mask to use for the fit. Shape is (nspec, nspat)
 
     Returns
     -------
     resid : `numpy.ndarray`_
         A 1D vector of the residuals
     """
-    model = scattered_light_model(param, img, gpm)
+    model = scattered_light_model(param, img)
     return img[wpix] - model[wpix]
 
 
@@ -157,35 +145,39 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     success : :obj:`bool`_
         True if the fit was successful, False otherwise
     """
+    # Convert the BPM to a GPM for convenience
     gpm = np.logical_not(bpm)
-    _frame = frame * gpm
+
+    # Replace bad pixels with the nearest (good) neighbour
+    ind = ndimage.distance_transform_edt(bpm, return_distances=False, return_indices=True)
+    _frame = frame[tuple(ind)]
+
+    # Pad the edges of the data
     _frame[0, :] = np.median(_frame[0:10, :], axis=0)
     _frame[-1, :] = np.median(_frame[-10:, :], axis=0)
     _frame_pad = np.pad(_frame, detpad, mode='edge')  # Model should be generated on padded data
-    gpm_pad = np.pad(gpm, detpad, mode='constant', constant_values=0)  # but don't include padded data in the fit
     offslitmask_pad = np.pad(offslitmask * gpm, detpad, mode='constant', constant_values=0)  # but don't include padded data in the fit
     # Grab the pixels to be included in the fit
     wpix = np.where(offslitmask_pad)
 
     # Compute the best-fitting model parameters
     msgs.info("Performing a least-squares fit to the scattered light")
-    res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, _frame_pad, gpm_pad),
-                            verbose=2, ftol=1.0E-3)
+    res_lsq = least_squares(scattlight_resid, x0, bounds=bounds, args=(wpix, _frame_pad),
+                            verbose=2, ftol=1.0E-4)
 
     # Store if this is a successful fit
     success = res_lsq.success
     if success:
         msgs.info("Generating best-fitting scattered light model")
-        scatt_img = scattered_light_model(res_lsq.x, _frame_pad, gpm_pad)[detpad:-detpad, detpad:-detpad]
+        scatt_img = scattered_light_model(res_lsq.x, _frame_pad)[detpad:-detpad, detpad:-detpad]
     else:
         msgs.warn("Scattered light model fitting failed")
         scatt_img = np.zeros_like(frame)
 
-    debug = True
     if debug:
         # Do some checks on the results
         embed()
-        scatt_img_alt = scattered_light_model(x0, _frame_pad, gpm_pad)[detpad:-detpad, detpad:-detpad]
+        scatt_img_alt = scattered_light_model(x0, _frame_pad)[detpad:-detpad, detpad:-detpad]
         from matplotlib import pyplot as plt
         vmin, vmax = 0, 40.0#np.max(scatt_img)#40
         plt.imshow(frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 2e7829a21c..b167d31214 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1148,9 +1148,8 @@ def subtract_scattlight(self, msscattlight, slits):
             # Apply the requested method for the scattered light
             if self.par["scattlight_method"] == "model":
                 # Use predefined model parameters
-                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...],
-                                                             np.logical_not(self.bpm[ii, ...]))
-                debug = True  # RJC requests to keep this here for debugging
+                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...])
+                debug = False  # RJC requests to keep this here for debugging
                 if debug:
                     embed()
                     tmp = msscattlight.scattlight_param.copy()
diff --git a/pypeit/spectrographs/keck_esi.py b/pypeit/spectrographs/keck_esi.py
index 71f0e85052..1b4e8e8f2b 100644
--- a/pypeit/spectrographs/keck_esi.py
+++ b/pypeit/spectrographs/keck_esi.py
@@ -337,17 +337,14 @@ def scattered_light_archive(self, binning, dispname):
         # and should be close to the final fitted values to reduce computational time)
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
-       #  x0 = np.array([ 2.99899335e+02,  2.01422930e+02,  2.18511169e+02,  1.95963738e+02,
-       # -4.96314134e+01,  5.64219402e+01,  9.68966612e-01, -1.79304303e+00,
-       #  5.24082933e-01,  3.73084496e-01, -2.93388226e-01, -3.00799508e-01,
-       #  2.86810088e-01])
-        x0 = np.array([2.66062476e+02/specbin, 1.57687605e+02/spatbin,  # Gaussian kernel widths
-                       2.65852665e+02/specbin, 1.77129333e+02/spatbin,  # Lorentzian kernel widths
-                       -50/specbin, 50.0/spatbin,  # pixel offsets (spec, spat)
-                       1.0, 1.0,  # Zoom factor (spec, spat)
-                       0.0,  # kernel angle
-                       0.05,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                       2.98836062e-01, -2.48646829e-01, -1.31752297e-01, 2.25868369e-01])  # Polynomial terms
+        # These parameters give a cost of 8.0833e+08 with the science frame used as scattlight (1x1 binning, pad=5)
+        x0 = np.array([2.63726939e+02/specbin, 1.57625683e+02/spatbin,  # Gaussian kernel widths
+                       2.63911253e+02/specbin, 1.74452723e+02/spatbin,  # Lorentzian kernel widths
+                       -1.38026314e+02/specbin, 7.72883842e+01/spatbin,  # pixel offsets
+                       9.59395069e-01, 1.04452747e+00,  # Zoom factor (spec, spat)
+                       6.00977118e-01,  # kernel angle
+                       7.70958895e-02,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                       3.41914530e-01, -3.15033809e-01, -1.15238092e-01,  1.80022435e-01])  # Polynomial terms
 
         # Now set the bounds of the fitted parameters
         bounds = ([1, 1, 1, 1, -200/specbin, -200/spatbin, 0, 0, -2*np.pi, 0.0, -10, -10, -10, -10],

From 1d6ea850ecaceb99155ea59211781b5064e6b3bd Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 31 Oct 2023 15:29:02 +0000
Subject: [PATCH 42/67] scattlight zoom updates

---
 pypeit/core/scattlight.py         | 38 +++++++++---------
 pypeit/images/rawimage.py         |  1 +
 pypeit/spectrographs/keck_esi.py  |  3 +-
 pypeit/spectrographs/keck_kcwi.py | 65 ++++++++++++++-----------------
 4 files changed, 51 insertions(+), 56 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 780078532f..9a36d16260 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -55,18 +55,17 @@ def scattered_light_model(param, img):
     sigmx_g, sigmy_g, sigmx_l, sigmy_l = param[0], param[1], param[2], param[3]
     shft_spec, shft_spat, zoom_spec, zoom_spat = param[4], param[5], param[6], param[7]
     kern_angle, kern_scale = param[8], param[9]
-    polyterms = param[10:]
+    polyterms_spat = param[10:12]
+    polyterms_spec = param[12:]
 
     # Make a grid of coordinates
     specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
     spat, spec = np.meshgrid(spatvec/(spatvec.size-1), specvec/(specvec.size - 1))
-    # Generate the polynomial efficiency scaling in the spectral direction
-    polyscale = polyterms[0] + polyterms[1]*spec + polyterms[2]*spat + polyterms[3]*spec*spat
-    # polyscale = np.zeros_like(img)
-    # for pp in range(polyterms_spec.size):
-    #     polyscale += polyterms_spec[pp] * spec ** pp
-    # for pp in range(polyterms_spat.size):
-    #     polyscale += polyterms_spat[pp] * spat ** pp
+    # Generate the polynomial efficiency scaling in the spatial direction
+    polyscale = spat*(polyterms_spat[0] + polyterms_spat[1]*spec)  # linear term and a spectral cross-term
+    # Now include the spectral direction
+    for pp in range(polyterms_spec.size):
+        polyscale += polyterms_spec[pp] * spec**pp
 
     # Generate a 2D smoothing kernel, composed of a 2D Gaussian and a 2D Lorentzian
     sigmx, sigmy = max(sigmx_g, sigmx_l), max(sigmy_g, sigmy_l),
@@ -149,6 +148,7 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     gpm = np.logical_not(bpm)
 
     # Replace bad pixels with the nearest (good) neighbour
+    msgs.info("Replacing bad pixels in the scattered light input")
     ind = ndimage.distance_transform_edt(bpm, return_distances=False, return_indices=True)
     _frame = frame[tuple(ind)]
 
@@ -174,27 +174,27 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
         msgs.warn("Scattered light model fitting failed")
         scatt_img = np.zeros_like(frame)
 
-    if debug:
+    if debug or True:
         # Do some checks on the results
         embed()
         scatt_img_alt = scattered_light_model(x0, _frame_pad)[detpad:-detpad, detpad:-detpad]
         from matplotlib import pyplot as plt
-        vmin, vmax = 0, 40.0#np.max(scatt_img)#40
+        vmin, vmax = 0, np.max(scatt_img)#40
         plt.imshow(frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
         plt.show()
         print(res_lsq.x)
 
-        plt.subplot(231)
+        plt.subplot(221)
         plt.imshow(_frame, vmin=vmin, vmax=vmax)
-        plt.subplot(232)
+        plt.subplot(222)
         plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
-        plt.subplot(233)
+        plt.subplot(223)
         plt.imshow(frame - scatt_img, vmin=-vmax/2, vmax=vmax/2)
-        plt.subplot(234)
-        plt.imshow(_frame, vmin=vmin, vmax=vmax)
-        plt.subplot(235)
-        plt.imshow(scatt_img_alt, vmin=vmin, vmax=vmax)
-        plt.subplot(236)
-        plt.imshow(_frame - scatt_img_alt, vmin=vmin, vmax=vmax)
+        plt.subplot(224)
+        plt.imshow(_frame, vmin=-vmax/2, vmax=vmax/2)
+        # plt.subplot(235)
+        # plt.imshow(scatt_img_alt, vmin=vmin, vmax=vmax)
+        # plt.subplot(236)
+        # plt.imshow(_frame - scatt_img_alt, vmin=vmin, vmax=vmax)
         plt.show()
     return scatt_img, res_lsq.x, success
diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index b167d31214..14cd06dc8f 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1141,6 +1141,7 @@ def subtract_scattlight(self, msscattlight, slits):
         binning = self.detector[0]['binning']
         dispname = self.spectrograph.get_meta_value(self.spectrograph.get_headarr(self.filename), 'dispname')
 
+        # TODO :: We really need to pass in the CR mask here...
         # Loop over the images
         for ii in range(self.nimg):
             # Mask bad pixels
diff --git a/pypeit/spectrographs/keck_esi.py b/pypeit/spectrographs/keck_esi.py
index 1b4e8e8f2b..0089a3780f 100644
--- a/pypeit/spectrographs/keck_esi.py
+++ b/pypeit/spectrographs/keck_esi.py
@@ -344,7 +344,8 @@ def scattered_light_archive(self, binning, dispname):
                        9.59395069e-01, 1.04452747e+00,  # Zoom factor (spec, spat)
                        6.00977118e-01,  # kernel angle
                        7.70958895e-02,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                       3.41914530e-01, -3.15033809e-01, -1.15238092e-01,  1.80022435e-01])  # Polynomial terms
+                       -1.15238092e-01, 1.80022435e-01,   # Polynomial terms (coefficients of "spat" and "spat*spec")
+                       3.41914530e-01, -3.15033809e-01])  # Polynomial terms (coefficients of spec**index)
 
         # Now set the bounds of the fitted parameters
         bounds = ([1, 1, 1, 1, -200/specbin, -200/spatbin, 0, 0, -2*np.pi, 0.0, -10, -10, -10, -10],
diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 97d7a67270..51cada0b59 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1064,42 +1064,35 @@ def scattered_light_archive(self, binning, dispname):
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
         if dispname == 'BH2':
-            x0 = np.array([ 2.67186394e+02/specbin, 1.60743078e+02/spatbin,  # Gaussian kernel widths
-                            2.98999820e+02/specbin, 1.45068103e+02/spatbin,  # Lorentzian kernel widths
-                            -1.37376475e+02/specbin, 6.07508615e+01/spatbin,  # pixel offsets
-                            9.86766634e-01, 9.86766634e-01,  # Zoom factor
-                            1.41130604e+00,  # kernel angle
-                            4.19246820e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                            7.93912603e-02, -5.81933968e-02, -5.76201948e-03, -7.83543150e-04])  # Polynomial terms
+            # This solution had Cost: 1.0626e+08 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=6
+            x0 = np.array([6.33925782e+01/specbin, 1.21677075e+02/spatbin,  # Gaussian kernel widths
+                           1.62497815e+02/specbin, 1.08146499e+02/spatbin,  # Lorentzian kernel widths
+                           1.25213594e+02/specbin, 1.01076028e+01/spatbin,  # pixel offsets
+                           8.98169722e-01, 9.95797850e-01,  # Zoom factor (spec, spat)
+                           1.11512749e-01,  # kernel angle
+                           3.51216118e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           5.00375324e-03, -7.01001263e-03,  # Polynomial terms (coefficients of "spat" and "spat*spec")
+                           1.07201611e-01, -2.47435669e-04, -1.94130583e-02])  # Polynomial terms (coefficients of spec**index)
         elif dispname == 'BM':
-            # This solution had Cost: 8.8951e+07 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=10
-
-       #      x0 = np.array([1.72694908e+02, 1.43224435e+01,
-       #                     1.55856081e+02,  1.18366623e+02,
-       # -6.32142819e+01,  1.52454051e+01,  9.96530165e-01,  1.30756297e+00,
-       #  7.81666600e-02,  8.25778311e-02, -2.91919118e-02, -3.24661854e-03,
-       #  4.32022859e-03])
-            x0 = np.array([ 60/specbin, 60/spatbin,  # Gaussian kernel widths
-                            60/specbin, 60/spatbin,  # Lorentzian kernel widths
-                            0/specbin, 0/spatbin,  # pixel offsets
-                            9.86766634e-01,  # Zoom factor
-                            1.41130604e+00,  # kernel angle
-                            4.19246820e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                            7.93912603e-02, -5.81933968e-02, -5.76201948e-03, -7.83543150e-04])  # Polynomial terms
+            # This solution had Cost: 5.5975e+07 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=6
+            x0 = np.array([5.90102917e+01/specbin, 3.18121981e+01/spatbin,  # Gaussian kernel widths
+                           1.67699475e+02/specbin, 1.76824113e+02/spatbin,  # Lorentzian kernel widths
+                           8.57232912e+01/specbin, 1.61878670e+01/spatbin,  # pixel offsets
+                           9.60669284e-01, 9.95367555e-01,  # Zoom factor (spec, spat)
+                           -8.54192838e-02,  # kernel angle
+                           2.78138895e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           -2.09025586e-03, 2.95085274e-03,  # Polynomial terms (coefficients of "spat" and "spat*spec")
+                           9.16987649e-02, -7.87655037e-02, 5.16153047e-02])  # Polynomial terms (coefficients of spec**index)
         elif dispname == 'BL':
-            # This solution had Cost: 1.0184e+07 and was based on a 2x2 dataset using pixelflat as the scattlight frame, and assuming pad=10
-            #1.1357e+07
-            # array([1.46747486e+01, 1.33657850e+02, 1.65940119e+02, 1.63479652e+02,
-            #        1.06998939e+02, -2.10911731e+01, 9.72422938e-01, 1.02567767e+00,
-            #        1.57990253e+00, 5.66603960e-03, 5.08362982e-02, 1.83209211e-03,
-            #        -3.83980954e-03, -2.89908954e-02])
-            x0 = np.array([1.05201474e+02/specbin, 1.34653515e+02/spatbin,  # Gaussian kernel widths
-                           1.32198875e+02/specbin, 1.60178190e+02/spatbin,  # Lorentzian kernel widths
-                           6.75891120e+01/specbin, 2.38622901e+01/spatbin,  # pixel offsets
-                           9.94430899e-01, 9.94430899e-01,  # Zoom factor
-                           2.02560248e-01,  # kernel angle
-                           0.090164026e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                           7.00761436e-02, -3.67325245e-02, -2.55285293e-03, 2.41518440e-03])  # Polynomial terms
+            # This solution had Cost: 7.2398e+06 and was based on a 2x2 dataset using pixelflat as the scattlight frame, and assuming pad=10
+            x0 = np.array([4.72573727e+01/specbin, 1.88577219e+01/spatbin,  # Gaussian kernel widths
+                           1.50198652e+02/specbin, 1.61086575e+02/spatbin,  # Lorentzian kernel widths
+                           8.33889721e+01/specbin, 5.57126288e+00/spatbin,  # pixel offsets
+                           9.76804617e-01, 9.99902593e-01,  # Zoom factor (spec, spat)
+                           0.0,  # kernel angle
+                           3.31643788e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           -4.60504065e-03, 3.78190619e-03,  # Polynomial terms (coefficients of "spat" and "spat*spec")
+                           7.44547223e-02, -4.45899682e-02, 1.97057130e-03])  # Polynomial terms (coefficients of spec**index)
         else:
             msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname}")
             x0 = np.array([200/specbin, 100/spatbin,  # Gaussian kernel widths
@@ -1111,9 +1104,9 @@ def scattered_light_archive(self, binning, dispname):
                            0.1, 0.0, 0.0, 0.0])  # Polynomial terms
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, 1, 1, -200 / specbin, -200 / spatbin, 0, 0, -2 * np.pi, 0.0, -10, -10, -10, -10],
+        bounds = ([1, 1, 1, 1, -200 / specbin, -200 / spatbin, 0, 0, -(10 / 180) * np.pi, 0.0, -10, -10, -10, -10, -10],
                   [600 / specbin, 600 / spatbin, 600 / specbin, 600 / spatbin, 200 / specbin, 200 / spatbin, 2, 2,
-                   2 * np.pi, 1000.0, 10, 10, 10, 10])
+                   (10 / 180) * np.pi, 1000.0, 10, 10, 10, 10, 10])
 
         # Return the best-fitting archival parameters and the bounds
         return x0, bounds

From c5590c9a07a67ff79b269c451012b7105e4568d1 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 31 Oct 2023 15:41:32 +0000
Subject: [PATCH 43/67] gpm scattlight

---
 pypeit/core/scattlight.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 9a36d16260..4d0f079de2 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -174,7 +174,7 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
         msgs.warn("Scattered light model fitting failed")
         scatt_img = np.zeros_like(frame)
 
-    if debug or True:
+    if debug:
         # Do some checks on the results
         embed()
         scatt_img_alt = scattered_light_model(x0, _frame_pad)[detpad:-detpad, detpad:-detpad]

From 21f864690477c1ea1156c5aa137654be23b4e472 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 2 Nov 2023 09:07:07 +0000
Subject: [PATCH 44/67] use bias

---
 pypeit/spectrographs/keck_kcwi.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 51cada0b59..f2700db391 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -289,7 +289,7 @@ def default_pypeit_par(cls):
         par['scienceframe']['process']['use_illumflat'] = True  # illumflat is applied when building the relative scale image in reduce.py, so should be applied to scienceframe too.
         par['scienceframe']['process']['use_specillum'] = True  # apply relative spectral illumination
         par['scienceframe']['process']['spat_flexure_correct'] = False  # don't correct for spatial flexure - varying spatial illumination profile could throw this correction off. Also, there's no way to do astrometric correction if we can't correct for spatial flexure of the contbars frames
-        par['scienceframe']['process']['use_biasimage'] = False
+        par['scienceframe']['process']['use_biasimage'] = True  # Need to use bias frames for KCWI, because the bias level varies monotonically with spatial and spectral direction
         par['scienceframe']['process']['use_darkimage'] = False
 
         # Don't do 1D extraction for 3D data - it's meaningless because the DAR correction must be performed on the 3D data.

From 0d6c09bff8bab4e0a4631e36d68d95dcb41a4445 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 2 Nov 2023 09:07:37 +0000
Subject: [PATCH 45/67] tmp edits

---
 pypeit/images/rawimage.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 14cd06dc8f..387d146a94 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1150,9 +1150,16 @@ def subtract_scattlight(self, msscattlight, slits):
             if self.par["scattlight_method"] == "model":
                 # Use predefined model parameters
                 scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...])
-                debug = False  # RJC requests to keep this here for debugging
+                debug = True  # RJC requests to keep this here for debugging
                 if debug:
                     embed()
+                    # import astropy.io.fits as fits
+                    # hdu = fits.PrimaryHDU([self.image[ii, ...]-scatt_img])
+                    # hdu.writeto(self.filename.replace(".fits.gz", "_proc_slsub.fits.gz"))
+                    # assert False
+                    # for ii in range(11):
+                    #     print("saving", ii)
+                    #     np.save("scattlight_test/slitmask_pad{0:02d}.npy".format(ii), slits.slit_img(pad=ii, initial=True, flexure=None) == -1)
                     tmp = msscattlight.scattlight_param.copy()
                     tmp[:6] *= 2
                     print("\n\n\n2x2 BINNING ASSUMED!!!\n\n\n")
@@ -1166,9 +1173,9 @@ def subtract_scattlight(self, msscattlight, slits):
                     plt.subplot(221)
                     plt.imshow(_frame, vmin=vmin, vmax=2*np.median(_frame))
                     plt.subplot(222)
-                    plt.imshow(_frame*offslitmask, vmin=vmin, vmax=vmax)
+                    plt.imshow(_frame*offslitmask, vmin=vmin, vmax=3*vmax)
                     plt.subplot(223)
-                    plt.imshow(scatt_img, vmin=vmin, vmax=vmax)
+                    plt.imshow(scatt_img, vmin=vmin, vmax=3*vmax)
                     plt.subplot(224)
                     plt.imshow((_frame - scatt_img)*offslitmask, vmin=-vmax / 2, vmax=vmax / 2)
                     plt.show()

From 23bad6b32e6ff1eacbc7f6a2fede2d60440348de Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 09:50:23 +0000
Subject: [PATCH 46/67] change par

---
 pypeit/calibrations.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index b1e055e04c..8fe1272583 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -592,7 +592,7 @@ def get_scattlight(self):
                                                          setup=setup, calib_id=calib_id)
 
         spatbin = parse.parse_binning(binning)[1]
-        pad = self.par['scattlight']['pad'] // spatbin
+        pad = self.par['scattlight_pad'] // spatbin
         offslitmask = self.slits.slit_img(pad=pad, initial=True, flexure=None) == -1
 
         # Get starting parameters for the scattered light model
@@ -613,7 +613,7 @@ def get_scattlight(self):
                                                       detname=scattlightImage.detector.name,
                                                       nspec=scattlightImage.shape[0], nspat=scattlightImage.shape[1],
                                                       binning=scattlightImage.detector.binning,
-                                                      pad=self.par['scattlight']['pad'],
+                                                      pad=self.par['scattlight_pad'],
                                                       scattlight_raw=scattlightImage.image,
                                                       scattlight_model=model,
                                                       scattlight_param=modelpar)

From af78f33189ed014ce5b1219319d623c4339f2133 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 19:55:37 +0000
Subject: [PATCH 47/67] update scattlight params

---
 pypeit/par/pypeitpar.py | 79 ++++++++++++++++++++++++-----------------
 1 file changed, 47 insertions(+), 32 deletions(-)

diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index be8748a8c5..a219d05fe4 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -219,7 +219,7 @@ def __init__(self, trim=None, apply_gain=None, orient=None,
                  dark_expscale=None,
                  empirical_rn=None, shot_noise=None, noise_floor=None,
                  use_pixelflat=None, use_illumflat=None, use_specillum=None,
-                 use_pattern=None, subtract_scattlight=None, scattlight_method=None, subtract_continuum=None,
+                 use_pattern=None, subtract_scattlight=None, scattlight=None, subtract_continuum=None,
                  spat_flexure_correct=None):
 
         # Grab the parameter names and values from the function
@@ -305,19 +305,9 @@ def __init__(self, trim=None, apply_gain=None, orient=None,
                                        'be set to True for spectrographs that have dedicated methods to subtract ' \
                                        'scattered light. For all other cases, this parameter should be False.'
 
-        defaults['scattlight_method'] = 'model'
-        options['scattlight_method'] = ProcessImagesPar.valid_scattlight_methods()
-        dtypes['scattlight_method'] = str
-        descr['scattlight_method'] = 'Method used to fit the overscan. ' \
-                                     'Options are: {0}'.format(', '.join(options['scattlight_method'])) + '.' + \
-                                     '\'model\' will the scattered light model parameters derived from a ' \
-                                     'user-specified frame during their reduction (note, you will need to make sure ' \
-                                     'that you set appropriate scattlight frames in your .pypeit file for this option). ' \
-                                     '\'frame\' will use each individual frame to determine the scattered light ' \
-                                     'that affects this frame. ' \
-                                     '\'archive\' will use an archival model parameter solution for the scattered ' \
-                                     'light (note that this option is not currently available for all spectrographs).'
-
+        defaults['scattlight'] = ScatteredLightPar()
+        dtypes['scattlight'] = [ParSet, dict]
+        descr['scattlight'] = 'Scattered light subtraction parameters.'
 
         defaults['empirical_rn'] = False
         dtypes['empirical_rn'] = bool
@@ -451,7 +441,7 @@ def __init__(self, trim=None, apply_gain=None, orient=None,
     def from_dict(cls, cfg):
         k = np.array([*cfg.keys()])
         parkeys = ['trim', 'apply_gain', 'orient', 'use_biasimage', 'subtract_continuum', 'subtract_scattlight',
-                   'scattlight_method', 'use_pattern', 'use_overscan', 'overscan_method', 'overscan_par',
+                   'scattlight', 'use_pattern', 'use_overscan', 'overscan_method', 'overscan_par',
                    'use_darkimage', 'dark_expscale', 'spat_flexure_correct', 'use_illumflat', 'use_specillum',
                    'empirical_rn', 'shot_noise', 'noise_floor', 'use_pixelflat', 'combine',
                    'satpix', #'calib_setup_and_bit',
@@ -475,13 +465,6 @@ def valid_overscan_methods():
         """
         return ['polynomial', 'savgol', 'median', 'odd_even']
 
-    @staticmethod
-    def valid_scattlight_methods():
-        """
-        Return the valid scattered light methods.
-        """
-        return ['model', 'frame', 'archive']
-
     @staticmethod
     def valid_combine_methods():
         """
@@ -1022,7 +1005,7 @@ class ScatteredLightPar(ParSet):
     see :ref:`parameters`.
     """
 
-    def __init__(self, pad=None):
+    def __init__(self, method=None, finecorr_pad=None, finecorr_order=None):
 
         # Grab the parameter names and values from the function
         # arguments
@@ -1039,9 +1022,33 @@ def __init__(self, pad=None):
         # Fill out parameter specifications.  Only the values that are
         # *not* None (i.e., the ones that are defined) need to be set
 
-        defaults['pad'] = 5
-        dtypes['pad'] = int
-        descr['pad'] = 'Number of unbinned pixels to extend the slit edges by when masking the slits.'
+        defaults['method'] = 'model'
+        options['method'] = ScatteredLightPar.valid_scattlight_methods()
+        dtypes['method'] = str
+        descr['method'] = 'Method used to fit the overscan. ' \
+                          'Options are: {0}'.format(', '.join(options['method'])) + '.' + \
+                          '\'model\' will the scattered light model parameters derived from a ' \
+                          'user-specified frame during their reduction (note, you will need to make sure ' \
+                          'that you set appropriate scattlight frames in your .pypeit file for this option). ' \
+                          '\'frame\' will use each individual frame to determine the scattered light ' \
+                          'that affects this frame. ' \
+                          '\'archive\' will use an archival model parameter solution for the scattered ' \
+                          'light (note that this option is not currently available for all spectrographs).'
+
+        defaults['finecorr'] = True
+        dtypes['finecorr'] = bool
+        descr['finecorr'] = 'If True, a fine correction to the scattered light will be performed. However, the ' \
+                            'fine correction will only be applied if the model/frame/archive correction is performed.'
+
+        defaults['finecorr_pad'] = 5
+        dtypes['finecorr_pad'] = int
+        descr['finecorr_pad'] = 'Number of unbinned pixels to extend the slit edges by when masking the slits for the' \
+                                'fine correction to the scattered light.'
+
+        defaults['finecorr_order'] = 2
+        dtypes['finecorr_order'] = int
+        descr['finecorr_order'] = 'Polynomial order to use for the fine correction to the scattered light ' \
+                                  'subtraction. It should be a low value.'
 
         # Instantiate the parameter set
         super(ScatteredLightPar, self).__init__(list(pars.keys()),
@@ -1055,7 +1062,7 @@ def __init__(self, pad=None):
     @classmethod
     def from_dict(cls, cfg):
         k = np.array([*cfg.keys()])
-        parkeys = ['pad']
+        parkeys = ['method', 'finecorr_pad', 'finecorr_order']
 
         badkeys = np.array([pk not in parkeys for pk in k])
         if np.any(badkeys):
@@ -1072,6 +1079,13 @@ def validate(self):
         """
         pass
 
+    @staticmethod
+    def valid_scattlight_methods():
+        """
+        Return the valid scattered light methods.
+        """
+        return ['model', 'frame', 'archive']
+
 
 class Coadd1DPar(ParSet):
     """
@@ -4151,7 +4165,8 @@ def __init__(self, calib_dir=None, bpm_usebias=None, biasframe=None, darkframe=N
                  arcframe=None, tiltframe=None, pixelflatframe=None, pinholeframe=None,
                  alignframe=None, alignment=None, traceframe=None, illumflatframe=None,
                  lampoffflatsframe=None, scattlightframe=None, skyframe=None, standardframe=None,
-                 scattlight=None, flatfield=None, wavelengths=None, slitedges=None, tilts=None, raise_chk_error=None):
+                 scattlight_pad=None, flatfield=None, wavelengths=None, slitedges=None, tilts=None,
+                 raise_chk_error=None):
 
 
         # Grab the parameter names and values from the function
@@ -4289,9 +4304,9 @@ def __init__(self, calib_dir=None, bpm_usebias=None, biasframe=None, darkframe=N
         dtypes['alignment'] = [ ParSet, dict ]
         descr['alignment'] = 'Define the procedure for the alignment of traces'
 
-        defaults['scattlight'] = ScatteredLightPar()
-        dtypes['scattlight'] = [ ParSet, dict ]
-        descr['scattlight'] = 'Define the procedure for modelling the scattered light'
+        defaults['scattlight_pad'] = 5
+        dtypes['scattlight_pad'] = int
+        descr['scattlight_pad'] = 'Number of unbinned pixels to extend the slit edges by when masking the slits.'
 
         defaults['flatfield'] = FlatFieldPar()
         dtypes['flatfield'] = [ ParSet, dict ]
@@ -4328,7 +4343,7 @@ def from_dict(cls, cfg):
         allkeys = parkeys + ['biasframe', 'darkframe', 'arcframe', 'tiltframe', 'pixelflatframe',
                              'illumflatframe', 'lampoffflatsframe', 'scattlightframe',
                              'pinholeframe', 'alignframe', 'alignment', 'traceframe', 'standardframe', 'skyframe',
-                             'scattlight', 'flatfield', 'wavelengths', 'slitedges', 'tilts']
+                             'scattlight_pad', 'flatfield', 'wavelengths', 'slitedges', 'tilts']
         badkeys = np.array([pk not in allkeys for pk in k])
         if np.any(badkeys):
             raise ValueError('{0} not recognized key(s) for CalibrationsPar.'.format(k[badkeys]))

From 6c41fdc1562a1f4ae7088af25797c0565f92a428 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 20:01:40 +0000
Subject: [PATCH 48/67] replace_bad function added

---
 pypeit/utils.py | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/pypeit/utils.py b/pypeit/utils.py
index 1df736426e..98832f0dc2 100644
--- a/pypeit/utils.py
+++ b/pypeit/utils.py
@@ -1360,6 +1360,31 @@ def find_nearest(array, values):
     return idxs
 
 
+def replace_bad(frame, bpm):
+    """ Find all bad pixels, and replace the bad pixels with the nearest good pixel
+
+    Parameters
+    ----------
+    frame : `numpy.ndarray`_
+        A frame that contains bad pixels that need to be replaced by the nearest good pixel
+    bpm : `numpy.ndarray`_
+        Boolean array (same shape as frame) indicating bad pixel values (bad=True)
+        that need to be replaced.
+
+    Returns
+    -------
+    _frame : `numpy.ndarray`_
+        A direct copy of the input frame, with the bad pixels replaced by the nearest good pixels.
+    """
+    # Do some checks on the inputs
+    if frame.shape != bpm.shape:
+        msgs.error("Input frame and BPM have different shapes")
+    # Replace bad pixels with the nearest (good) neighbour
+    msgs.info("Replacing bad pixels")
+    ind = scipy.ndimage.distance_transform_edt(bpm, return_distances=False, return_indices=True)
+    return frame[tuple(ind)]
+
+
 def yamlify(obj, debug=False):
     """
 

From bebec09df88d5493a7d5bd725d52a06736480d91 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 20:33:40 +0000
Subject: [PATCH 49/67] add fine correction

---
 pypeit/core/scattlight.py | 49 +++++++++++++++++++++++++++++++++++----
 1 file changed, 45 insertions(+), 4 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 4d0f079de2..1ca4c4d327 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -147,10 +147,8 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     # Convert the BPM to a GPM for convenience
     gpm = np.logical_not(bpm)
 
-    # Replace bad pixels with the nearest (good) neighbour
-    msgs.info("Replacing bad pixels in the scattered light input")
-    ind = ndimage.distance_transform_edt(bpm, return_distances=False, return_indices=True)
-    _frame = frame[tuple(ind)]
+    # Replace bad pixels with the nearest good pixel
+    _frame = utils.replace_bad(frame, bpm)
 
     # Pad the edges of the data
     _frame[0, :] = np.median(_frame[0:10, :], axis=0)
@@ -198,3 +196,46 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
         # plt.imshow(_frame - scatt_img_alt, vmin=vmin, vmax=vmax)
         plt.show()
     return scatt_img, res_lsq.x, success
+
+
+def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
+    """ Calculate a fine correction to the residual scattered light of the input frame.
+
+    Parameters
+    ----------
+    frame : `numpy.ndarray`_
+        Raw 2D data frame to be used to compute the fine correction of the scattered light.
+        This frame should be the raw frame, minus the first estimate of the scattered light
+        that has been derived from the `scattered_light_model()`_ function.
+    bpm : `numpy.ndarray`_
+        2D boolean array indicating the bad pixels (True=bad)
+    offslitmask : `numpy.ndarray`_
+        A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
+    polyord : :obj:`int`_, optional
+        Polynomial order to use for fitting the residual scattered light in the spatial direction.
+    debug : :obj:`bool`_, optional
+        If True, debug the final model fit that's been output
+
+    Returns
+    -------
+    scatt_img : `numpy.ndarray`_
+        A 2D image of the fine correction to the scattered light determined from the input frame.
+    """
+    # Convert the BPM to a GPM for convenience
+    gpm = np.logical_not(bpm)
+    # Define some useful variables
+    nspec, nspat = frame.shape
+    xspat = np.arange(nspat)
+    model = np.zeros_like(frame)
+    # Loop over the residual scattered light in the spectral direction and perform
+    # a low order polynomial fit to the scattered light in the spatial direction.
+    for yy in range(nspec):
+        ext = frame[yy, :]
+        gd = np.where(offslitmask[yy, :] & gpm[yy, :])
+        coeff = np.polyfit(xspat[gd], ext[gd], polyord)
+        model[yy, :] = np.polyval(coeff, xspat)
+    # Median filter in the spectral direction to smooth out irregularities in the fine correction
+    model_med = ndimage.median_filter(model, size=(50, 1))  # Median filter to get rid of CRs
+    scatt_light_fine = ndimage.gaussian_filter(model_med, sigma=10)  # Gaussian filter to smooth median filter
+    # Return the fine correction model of the scattered light
+    return scatt_light_fine
\ No newline at end of file

From ddb02a7522fc06d292bf0b9a2927802b0ea3d6a6 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 21:29:41 +0000
Subject: [PATCH 50/67] mask inter-slit regions

---
 pypeit/core/scattlight.py | 57 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 1ca4c4d327..69c23dd91f 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -198,6 +198,61 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     return scatt_img, res_lsq.x, success
 
 
+def mask_slit_regions(offslitmask, centrace, mask_regions=None):
+    """ Exclude some user-specified inter-slit regions for the fine correction to the scattered light determination
+
+    Parameters
+    ----------
+    offslitmask : `numpy.ndarray`_
+        A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
+    centrace : `numpy.ndarray`_
+        A 2D array, shape is (nspec, nslit), containing the central trace of each slit.
+    mask_regions : :obj:`int`, :obj:`list`, optional
+        A list of regions that should be excluded in the fine correction to the scattered light. A zero
+        value indicates that all pixels left of the first slit will be masked. A value of one indicates
+        that all pixels between the first and second slit will be masked, and so forth. A list of these
+        integers can be supplied. If None, no inter-slit regions will be excluded. If an integer is
+        specified, just one region will be excluded.
+
+    Returns
+    -------
+    good_mask : `numpy.ndarray`_
+        A 2D boolean array indicating the pixels that are suitable to use for the fine scattered light correction.
+    """
+    # Check if there are regions to be masked
+    if mask_regions is None:
+        msgs.warn("There are no inter-slit regions specified that need to be masked")
+        return offslitmask
+    elif isinstance(mask_regions, int):
+        # Convert this to a list
+        _mask_regions = [mask_regions]
+    else:
+        _mask_regions = mask_regions
+
+    # Grab the dimensions
+    nspec, nspat = offslitmask.shape
+    nslit = centrace.shape[1]
+
+    # Setup the pixel coordinates
+    spat = np.arange(nspat)
+
+    # Find the pixels in each slit, limited by the minimum and
+    # maximum spectral position.
+    good_mask = offslitmask.copy()
+    for ii in _mask_regions:
+        if ii == 0:  # All pixels to the left of the first slit
+            indx = spat[None, :] < centrace[:, ii, None]
+        elif ii == nslit:  # All pixels to the right of the last slit
+            indx = spat[None, :] > centrace[:, ii-1, None]
+        else:  # Everything else in between
+            indx = (spat[None, :] > centrace[:, ii-1, None]) \
+                   & (spat[None, :] < centrace[:, ii, None])
+        # Exclude these pixels
+        good_mask[indx] = False
+    # Return the mask of good inter-slit pixels
+    return good_mask
+
+
 def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
     """ Calculate a fine correction to the residual scattered light of the input frame.
 
@@ -223,10 +278,12 @@ def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
     """
     # Convert the BPM to a GPM for convenience
     gpm = np.logical_not(bpm)
+
     # Define some useful variables
     nspec, nspat = frame.shape
     xspat = np.arange(nspat)
     model = np.zeros_like(frame)
+
     # Loop over the residual scattered light in the spectral direction and perform
     # a low order polynomial fit to the scattered light in the spatial direction.
     for yy in range(nspec):

From 2a04dcb713715a0adc4662a94fbeae6c9a3eb633 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 21:40:37 +0000
Subject: [PATCH 51/67] cleanup

---
 pypeit/core/scattlight.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index 69c23dd91f..cf70abb249 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -276,12 +276,13 @@ def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
     scatt_img : `numpy.ndarray`_
         A 2D image of the fine correction to the scattered light determined from the input frame.
     """
+    msgs.info("Performing a fine correction to the scattered light")
     # Convert the BPM to a GPM for convenience
     gpm = np.logical_not(bpm)
 
     # Define some useful variables
     nspec, nspat = frame.shape
-    xspat = np.arange(nspat)
+    xspat = np.linspace(0, 1, nspat)
     model = np.zeros_like(frame)
 
     # Loop over the residual scattered light in the spectral direction and perform
@@ -294,5 +295,14 @@ def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
     # Median filter in the spectral direction to smooth out irregularities in the fine correction
     model_med = ndimage.median_filter(model, size=(50, 1))  # Median filter to get rid of CRs
     scatt_light_fine = ndimage.gaussian_filter(model_med, sigma=10)  # Gaussian filter to smooth median filter
+    if debug:
+        embed()
+        from matplotlib import pyplot as plt
+        vmin, vmax = -np.max(scatt_light_fine), np.max(scatt_light_fine)
+        plt.subplot(121)
+        plt.imshow(frame, vmin=vmin, vmax=vmax)
+        plt.subplot(122)
+        plt.imshow(frame-scatt_light_fine, vmin=vmin, vmax=vmax)
+        plt.show()
     # Return the fine correction model of the scattered light
-    return scatt_light_fine
\ No newline at end of file
+    return scatt_light_fine

From fae7db52d77f4fea0675cfffd851cfec99cccf3b Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 21:43:11 +0000
Subject: [PATCH 52/67] add finecorr mask

---
 pypeit/par/pypeitpar.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index a219d05fe4..c31e508ddc 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -1005,7 +1005,7 @@ class ScatteredLightPar(ParSet):
     see :ref:`parameters`.
     """
 
-    def __init__(self, method=None, finecorr_pad=None, finecorr_order=None):
+    def __init__(self, method=None, finecorr=None, finecorr_pad=None, finecorr_order=None, finecorr_mask=None):
 
         # Grab the parameter names and values from the function
         # arguments
@@ -1040,7 +1040,7 @@ def __init__(self, method=None, finecorr_pad=None, finecorr_order=None):
         descr['finecorr'] = 'If True, a fine correction to the scattered light will be performed. However, the ' \
                             'fine correction will only be applied if the model/frame/archive correction is performed.'
 
-        defaults['finecorr_pad'] = 5
+        defaults['finecorr_pad'] = 2
         dtypes['finecorr_pad'] = int
         descr['finecorr_pad'] = 'Number of unbinned pixels to extend the slit edges by when masking the slits for the' \
                                 'fine correction to the scattered light.'
@@ -1050,6 +1050,15 @@ def __init__(self, method=None, finecorr_pad=None, finecorr_order=None):
         descr['finecorr_order'] = 'Polynomial order to use for the fine correction to the scattered light ' \
                                   'subtraction. It should be a low value.'
 
+        defaults['finecorr_mask'] = None
+        dtypes['finecorr_mask'] = [int, list]
+        descr['finecorr_mask'] = 'A list containing the inter-slit regions that the user wishes to mask during ' \
+                                 'the fine correction to the scattered light. Each integer corresponds to an ' \
+                                 'inter-slit region. For example, "0" corresponds to all pixels left of the leftmost ' \
+                                 'slit, while a value of "1" corresponds to all pixels between the first and second ' \
+                                 'slit (counting from the left). It should be either a single integer value, or a ' \
+                                 'list of integer values.'
+
         # Instantiate the parameter set
         super(ScatteredLightPar, self).__init__(list(pars.keys()),
                                                 values=list(pars.values()),
@@ -1062,7 +1071,7 @@ def __init__(self, method=None, finecorr_pad=None, finecorr_order=None):
     @classmethod
     def from_dict(cls, cfg):
         k = np.array([*cfg.keys()])
-        parkeys = ['method', 'finecorr_pad', 'finecorr_order']
+        parkeys = ['method', 'finecorr', 'finecorr_pad', 'finecorr_order', 'finecorr_mask']
 
         badkeys = np.array([pk not in parkeys for pk in k])
         if np.any(badkeys):

From fe319234f43fa498659bc504e47b655dea377c1c Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 21:43:29 +0000
Subject: [PATCH 53/67] update default params

---
 pypeit/spectrographs/keck_kcwi.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index f2700db391..11cb698cbc 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -900,9 +900,13 @@ def default_pypeit_par(cls):
         par['scienceframe']['process']['use_pattern'] = True
         # Subtract scattered light, but only for the pixel and illum flats,
         # as well as and science/standard star data.
+        par['calibrations']['scattlight_pad'] = 6  # This is the unbinned number of pixels to pad
         par['calibrations']['pixelflatframe']['process']['subtract_scattlight'] = True
         par['calibrations']['illumflatframe']['process']['subtract_scattlight'] = True
         par['scienceframe']['process']['subtract_scattlight'] = True
+        par['scienceframe']['process']['scattlight']['finecorr_pad'] = 2
+        par['scienceframe']['process']['scattlight']['finecorr_order'] = 2
+        par['scienceframe']['process']['scattlight']['finecorr_mask'] = 12  # Mask the middle inter-slit region. It contains a strange scattered light feature that doesn't appear to affect any other inter-slit regions
 
         # Correct the illumflat for pixel-to-pixel sensitivity variations
         par['calibrations']['illumflatframe']['process']['use_pixelflat'] = True

From a37dd888700abf2acd375a7587ae98a739f9cdd5 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 3 Nov 2023 21:44:13 +0000
Subject: [PATCH 54/67] refactor scattered light

---
 pypeit/images/rawimage.py | 64 +++++++++++++++++++++++++++++----------
 1 file changed, 48 insertions(+), 16 deletions(-)

diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 4bb75013d8..26547f9a69 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1120,13 +1120,22 @@ def subtract_continuum(self, force=False):
         #cont = ndimage.median_filter(self.image, size=(1,101,3), mode='reflect')
         self.steps[step] = True
 
-    def subtract_scattlight(self, msscattlight, slits):
+    def subtract_scattlight(self, msscattlight, slits, debug=False):
         """
         Analyze and subtract the scattered light from the image.
 
         This is primarily a wrapper for
-        :func:`~pypeit.core.scattered_light`.
+        :func:`~pypeit.core.scattered_light_model`.
 
+
+        Parameters
+        ----------
+        msscattlight : :class:`~pypeit.scattlight.ScatteredLight`
+            Scattered light calibration frame
+        slits : :class:`~pypeit.slittrace.SlitTraceSet`
+            Slit edge information
+        debug : :obj:`bool`_, optional
+            If True, debug the computed scattered light image
         """
         step = inspect.stack()[0][3]
         if self.steps[step]:
@@ -1134,7 +1143,7 @@ def subtract_scattlight(self, msscattlight, slits):
             msgs.warn("The scattered light has already been subtracted from the image!")
             return
 
-        if self.par["scattlight_method"] == "model" and msscattlight.scattlight_param is None:
+        if self.par["scattlight"]["method"] == "model" and msscattlight.scattlight_param is None:
             msgs.warn("Scattered light parameters are not set. Cannot perform scattered light subtraction.")
             return
 
@@ -1142,16 +1151,24 @@ def subtract_scattlight(self, msscattlight, slits):
         binning = self.detector[0]['binning']
         dispname = self.spectrograph.get_meta_value(self.spectrograph.get_headarr(self.filename), 'dispname')
 
-        # TODO :: We really need to pass in the CR mask here...
         # Loop over the images
+        var = utils.inverse(self.build_ivar())  # Build a temporary variance frame to be used for the CR mask
+        spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
         for ii in range(self.nimg):
-            # Mask bad pixels
-            # _frame = self.image[ii, ...] * np.logical_not(self.bpm[ii, ...])
+            # Mask pixels affected by CR
+            crmask = procimg.lacosmic(self.image[ii, ...],# saturation=saturation, nonlinear=nonlinear,
+                                      bpm=self.bpm[ii, ...], varframe=var[ii, ...], maxiter=self.par['lamaxiter'],
+                                      grow=self.par['grow'], remove_compact_obj=self.par['rmcompact'],
+                                      sigclip=self.par['sigclip'], sigfrac=self.par['sigfrac'],
+                                      objlim=self.par['objlim'])
+            # Replace all bad pixels with the nearest good pixel
+            full_bpm = self.bpm[ii, ...] | crmask
+            _img = utils.replace_bad(self.image[ii, ...], full_bpm)
             # Apply the requested method for the scattered light
-            if self.par["scattlight_method"] == "model":
+            do_finecorr = self.par["scattlight"]["finecorr"]
+            if self.par["scattlight"]["method"] == "model":
                 # Use predefined model parameters
-                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, self.image[ii, ...])
-                debug = True  # RJC requests to keep this here for debugging
+                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, _img)
                 if debug:
                     embed()
                     # import astropy.io.fits as fits
@@ -1180,36 +1197,51 @@ def subtract_scattlight(self, msscattlight, slits):
                     plt.subplot(224)
                     plt.imshow((_frame - scatt_img)*offslitmask, vmin=-vmax / 2, vmax=vmax / 2)
                     plt.show()
-            elif self.par["scattlight_method"] == "archive":
+            elif self.par["scattlight"]["method"] == "archive":
                 # Use archival model parameters
                 modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
                 if modpar is None:
                     msgs.error(f"{self.spectrograph.name} does not have archival scattered light parameters. Please "
                                f"set 'scattlight_method' to another option.")
-                scatt_img = scattlight.scattered_light_model(modpar, _frame)
-            elif self.par["scattlight_method"] == "frame":
+                scatt_img = scattlight.scattered_light_model(modpar, _img)
+            elif self.par["scattlight"]["method"] == "frame":
                 # Calculate a model specific for this frame
-                spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
                 pad = msscattlight.pad // spatbin
                 offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
                 # Get starting parameters for the scattered light model
                 x0, bounds = self.spectrograph.scattered_light_archive(binning, dispname)
                 # Perform a fit to the scattered light
-                scatt_img, _, success = scattlight.scattered_light(self.image[ii, ...], self.bpm, offslitmask,
+                scatt_img, _, success = scattlight.scattered_light(self.image[ii, ...], full_bpm, offslitmask,
                                                                    x0, bounds)
                 # If failure, revert back to the Scattered Light calibration frame model parameters
                 if not success:
                     if msscattlight is not None:
                         msgs.warn("Scattered light model failed - using predefined model parameters")
-                        scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, _frame)
+                        scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, _img)
                     else:
                         msgs.warn("Scattered light model failed - using archival model parameters")
                         # Use archival model parameters
                         modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
-                        scatt_img = scattlight.scattered_light_model(modpar, _frame)
+                        scatt_img = scattlight.scattered_light_model(modpar, _img)
             else:
                 msgs.warn("Scattered light not performed")
                 scatt_img = np.zeros(self.image[ii, ...].shape)
+                do_finecorr = False
+            # Check if a fine correction to the scattered light should be applied
+            if do_finecorr:
+                pad = self.par['scattlight']['finecorr_pad'] // spatbin
+                offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
+                # Check if the user wishes to mask some inter-slit regions
+                if self.par['scattlight']['finecorr_mask'] is not None:
+                    # Get the central trace of each slit
+                    left, right, _ = slits.select_edges(initial=True, flexure=None)
+                    centrace = 0.5*(left+right)
+                    # Now mask user-defined inter-slit regions
+                    offslitmask = scattlight.mask_slit_regions(offslitmask, centrace,
+                                                               mask_regions=self.par['scattlight']['finecorr_mask'])
+                # Calculate the fine correction to the scattered light image, and add it to the full model
+                scatt_img += scattlight.fine_correction(_img-scatt_img, full_bpm, offslitmask,
+                                                        polyord=self.par['scattlight']['finecorr_order'])
             # Subtract the scattered light model from the image
             self.image[ii, ...] -= scatt_img
         self.steps[step] = True

From 1e4548f049ad6650b1f9e2419c48d80bcff3fbb8 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 10 Nov 2023 10:53:24 +0000
Subject: [PATCH 55/67] updated scattlight params

---
 pypeit/spectrographs/keck_kcwi.py | 90 ++++++++++++++++++-------------
 1 file changed, 53 insertions(+), 37 deletions(-)

diff --git a/pypeit/spectrographs/keck_kcwi.py b/pypeit/spectrographs/keck_kcwi.py
index 11cb698cbc..b3157e055d 100644
--- a/pypeit/spectrographs/keck_kcwi.py
+++ b/pypeit/spectrographs/keck_kcwi.py
@@ -1068,49 +1068,65 @@ def scattered_light_archive(self, binning, dispname):
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
         if dispname == 'BH2':
-            # This solution had Cost: 1.0626e+08 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=6
-            x0 = np.array([6.33925782e+01/specbin, 1.21677075e+02/spatbin,  # Gaussian kernel widths
-                           1.62497815e+02/specbin, 1.08146499e+02/spatbin,  # Lorentzian kernel widths
-                           1.25213594e+02/specbin, 1.01076028e+01/spatbin,  # pixel offsets
-                           8.98169722e-01, 9.95797850e-01,  # Zoom factor (spec, spat)
-                           1.11512749e-01,  # kernel angle
-                           3.51216118e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                           5.00375324e-03, -7.01001263e-03,  # Polynomial terms (coefficients of "spat" and "spat*spec")
-                           1.07201611e-01, -2.47435669e-04, -1.94130583e-02])  # Polynomial terms (coefficients of spec**index)
+            # This solution had Cost: 1.0393e+08 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=6
+            x0 = np.array([67.15200530737414 / specbin, 157.1074288810557 / spatbin,  # Gaussian kernel widths
+                           179.2999412601927 / specbin, 139.76705167365654 / spatbin,  # Lorentzian kernel widths
+                           4.562250837489429 / specbin, 5.054084609129999 / spatbin,  # pixel offsets
+                           0.9551212825380554, 0.9982713953567679,  # Zoom factor (spec, spat)
+                           24.967114476694526,  # constant flux offset
+                           0.09655699215523728,  # kernel angle
+                           0.5524841628998713,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           0.0035617904638365447, -0.004572414005692468, # Polynomial terms (coefficients of "spat" and "spat*spec")
+                           0.10339051745377138, -0.011285432228341519, -0.007042406129643602])  # Polynomial terms (coefficients of spec**index)
         elif dispname == 'BM':
-            # This solution had Cost: 5.5975e+07 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=6
-            x0 = np.array([5.90102917e+01/specbin, 3.18121981e+01/spatbin,  # Gaussian kernel widths
-                           1.67699475e+02/specbin, 1.76824113e+02/spatbin,  # Lorentzian kernel widths
-                           8.57232912e+01/specbin, 1.61878670e+01/spatbin,  # pixel offsets
-                           9.60669284e-01, 9.95367555e-01,  # Zoom factor (spec, spat)
-                           -8.54192838e-02,  # kernel angle
-                           2.78138895e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                           -2.09025586e-03, 2.95085274e-03,  # Polynomial terms (coefficients of "spat" and "spat*spec")
-                           9.16987649e-02, -7.87655037e-02, 5.16153047e-02])  # Polynomial terms (coefficients of spec**index)
+            # This solution had Cost: 4.8690e+07 and was based on a 1x1 dataset using pixelflat as the scattlight frame, and assuming pad=6
+            x0 = np.array([57.52686698670778 / specbin, 44.22645738529251 / spatbin,  # Gaussian kernel widths
+                           177.49996713255973 / specbin, 157.85206762558929 / spatbin,  # Lorentzian kernel widths
+                           1.5547056520696672 / specbin, 5.1916115942048915 / spatbin,  # pixel offsets
+                           0.9969235709861033, 0.9988876925628252,  # Zoom factor (spec, spat)
+                           5.117391743273053,  # constant flux offset
+                           0.1073126011932528,  # kernel angle
+                           0.37915677855016305,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           -0.0023288032996881753, 0.002167786497577728,  # Polynomial terms (coefficients of "spat" and "spat*spec")
+                           0.08981952806519802, -0.07364263035160445, 0.04799106653657783])  # Polynomial terms (coefficients of spec**index)
         elif dispname == 'BL':
-            # This solution had Cost: 7.2398e+06 and was based on a 2x2 dataset using pixelflat as the scattlight frame, and assuming pad=10
-            x0 = np.array([4.72573727e+01/specbin, 1.88577219e+01/spatbin,  # Gaussian kernel widths
-                           1.50198652e+02/specbin, 1.61086575e+02/spatbin,  # Lorentzian kernel widths
-                           8.33889721e+01/specbin, 5.57126288e+00/spatbin,  # pixel offsets
-                           9.76804617e-01, 9.99902593e-01,  # Zoom factor (spec, spat)
-                           0.0,  # kernel angle
-                           3.31643788e-01,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                           -4.60504065e-03, 3.78190619e-03,  # Polynomial terms (coefficients of "spat" and "spat*spec")
-                           7.44547223e-02, -4.45899682e-02, 1.97057130e-03])  # Polynomial terms (coefficients of spec**index)
+            # This solution had Cost: 7.0172e+06 and was based on a 2x2 dataset using pixelflat as the scattlight frame, and assuming pad=10
+            x0 = np.array([54.843502304988725 / specbin, 71.36603219575882 / spatbin,  # Gaussian kernel widths
+                           166.5990017834228 / specbin, 164.45188033168876 / spatbin,  # Lorentzian kernel widths
+                           -5.759623374637964 / specbin, 5.01392929142184 / spatbin,  # pixel offsets
+                           1.0017829374409521, 1.000312421855213,  # Zoom factor (spec, spat)
+                           4.429458755393496,  # constant flux offset
+                           -0.11853206385621386,  # kernel angle
+                           0.4961668294341919,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           -0.004790394657721825, 0.0032481886185675036,  # Polynomial terms (coefficients of "spat" and "spat*spec")
+                           0.07823077510724392, -0.0644638013233617, 0.01819438897935518])  # Polynomial terms (coefficients of spec**index)
         else:
             msgs.warn(f"Initial scattered light model parameters have not been setup for grating {dispname}")
-            x0 = np.array([200/specbin, 100/spatbin,  # Gaussian kernel widths
-                           200/specbin, 100/spatbin,  # Lorentzian kernel widths
-                           0.0/specbin, 0.0/spatbin,  # pixel offsets
-                           1.0,  # Zoom factor
-                           0.0,  # kernel angle
-                           0.0,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                           0.1, 0.0, 0.0, 0.0])  # Polynomial terms
+            x0 = np.array([54.843502304988725 / specbin, 71.36603219575882 / spatbin,  # Gaussian kernel widths
+                           166.5990017834228 / specbin, 164.45188033168876 / spatbin,  # Lorentzian kernel widths
+                           -5.759623374637964 / specbin, 5.01392929142184 / spatbin,  # pixel offsets
+                           1.0017829374409521, 1.000312421855213,  # Zoom factor (spec, spat)
+                           4.429458755393496,  # constant flux offset
+                           -0.11853206385621386,  # kernel angle
+                           0.4961668294341919,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                           -0.004790394657721825, 0.0032481886185675036,  # Polynomial terms (coefficients of "spat" and "spat*spec")
+                           0.07823077510724392, -0.0644638013233617, 0.01819438897935518])  # Polynomial terms (coefficients of spec**index)
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, 1, 1, -200 / specbin, -200 / spatbin, 0, 0, -(10 / 180) * np.pi, 0.0, -10, -10, -10, -10, -10],
-                  [600 / specbin, 600 / spatbin, 600 / specbin, 600 / spatbin, 200 / specbin, 200 / spatbin, 2, 2,
-                   (10 / 180) * np.pi, 1000.0, 10, 10, 10, 10, 10])
+        bounds = ([# Lower bounds:
+                      1, 1,  # Gaussian kernel widths
+                      1, 1,  # Lorentzian kernel widths
+                      -10 / specbin, -10 / spatbin,  # pixel offsets
+                      0, 0,  # Zoom factor (spec, spat)
+                      -1000, -(10 / 180) * np.pi, 0.0,  # constant flux offset, kernel angle, relative kernel scale
+                      -10, -10, -10, -10, -10],  # Polynomial terms
+                  # Upper bounds
+                     [600 / specbin, 600 / spatbin,  # Gaussian kernel widths
+                      600 / specbin, 600 / spatbin,  # Lorentzian kernel widths
+                      10 / specbin, 10 / spatbin,  # pixel offsets
+                      2, 2,  # Zoom factor (spec, spat)
+                      1000.0, +(10 / 180) * np.pi, 1000.0,  # constant flux offset, kernel angle, relative kernel scale
+                      10, 10, 10, 10, 10])  # Polynomial terms
 
         # Return the best-fitting archival parameters and the bounds
         return x0, bounds

From 345214b729488c9d28149131530896fc73c789f0 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 10 Nov 2023 10:53:47 +0000
Subject: [PATCH 56/67] add constant term

---
 pypeit/core/scattlight.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index cf70abb249..e7aab05667 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -54,9 +54,9 @@ def scattered_light_model(param, img):
     # Extract the parameters into more conveniently named variables
     sigmx_g, sigmy_g, sigmx_l, sigmy_l = param[0], param[1], param[2], param[3]
     shft_spec, shft_spat, zoom_spec, zoom_spat = param[4], param[5], param[6], param[7]
-    kern_angle, kern_scale = param[8], param[9]
-    polyterms_spat = param[10:12]
-    polyterms_spec = param[12:]
+    constant, kern_angle, kern_scale = param[8], param[9], param[10]
+    polyterms_spat = param[11:13]
+    polyterms_spec = param[13:]
 
     # Make a grid of coordinates
     specvec, spatvec = np.arange(img.shape[0]), np.arange(img.shape[1])
@@ -88,7 +88,7 @@ def scattered_light_model(param, img):
 
     scale_img = polyscale * signal.oaconvolve(img, kernel, mode='same')
     spl = interpolate.RectBivariateSpline(specvec, spatvec, scale_img, kx=1, ky=1)
-    return spl(zoom_spec * (specvec + shft_spec), zoom_spat * (spatvec + shft_spat))
+    return constant + spl(zoom_spec * (specvec + shft_spec), zoom_spat * (spatvec + shft_spat))
 
 
 def scattlight_resid(param, wpix, img):

From 930bd96a8371135f5be4c6841bc8f16aa02b09bc Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 10 Nov 2023 10:55:12 +0000
Subject: [PATCH 57/67] add scattlight debugging

---
 pypeit/images/rawimage.py | 44 ++++++++++++++++++++++++++-------------
 1 file changed, 29 insertions(+), 15 deletions(-)

diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index 26547f9a69..d4f029a044 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1127,7 +1127,6 @@ def subtract_scattlight(self, msscattlight, slits, debug=False):
         This is primarily a wrapper for
         :func:`~pypeit.core.scattered_light_model`.
 
-
         Parameters
         ----------
         msscattlight : :class:`~pypeit.scattlight.ScatteredLight`
@@ -1164,11 +1163,14 @@ def subtract_scattlight(self, msscattlight, slits, debug=False):
             # Replace all bad pixels with the nearest good pixel
             full_bpm = self.bpm[ii, ...] | crmask
             _img = utils.replace_bad(self.image[ii, ...], full_bpm)
+            # Get a copy of the best-fitting model parameters
+            this_modpar = msscattlight.scattlight_param.copy()
+            this_modpar[8] = 0.0  # This is the zero-level of the scattlight frame. The zero-level is determined by the finecorr
             # Apply the requested method for the scattered light
             do_finecorr = self.par["scattlight"]["finecorr"]
             if self.par["scattlight"]["method"] == "model":
                 # Use predefined model parameters
-                scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, _img)
+                scatt_img = scattlight.scattered_light_model(this_modpar, _img)
                 if debug:
                     embed()
                     # import astropy.io.fits as fits
@@ -1178,11 +1180,20 @@ def subtract_scattlight(self, msscattlight, slits, debug=False):
                     # for ii in range(11):
                     #     print("saving", ii)
                     #     np.save("scattlight_test/slitmask_pad{0:02d}.npy".format(ii), slits.slit_img(pad=ii, initial=True, flexure=None) == -1)
+                    specbin, spatbin = parse.parse_binning(self.detector[0]['binning'])
                     tmp = msscattlight.scattlight_param.copy()
-                    tmp[:6] *= 2
-                    print("\n\n\n2x2 BINNING ASSUMED!!!\n\n\n")
-                    print(tmp)
-                    spatbin = parse.parse_binning(self.detector[0]['binning'])[1]
+                    print("\n\n\n2x2 BINNING ASSUMED AS INPUT!!!\n\n\n")
+                    # Find a pretty way to print out the archive variables
+                    strprint = f"x0 = np.array([{tmp[0]*specbin} / specbin, {tmp[1]*spatbin} / spatbin,  # Gaussian kernel widths\n" + \
+                               f"               {tmp[2]*specbin} / specbin, {tmp[3]*spatbin} / spatbin,  # Lorentzian kernel widths\n" + \
+                               f"               {tmp[4]*specbin} / specbin, {tmp[5]*spatbin} / spatbin,  # pixel offsets\n" + \
+                               f"               {tmp[6]}, {tmp[7]},  # Zoom factor (spec, spat)\n" + \
+                               f"               {tmp[8]},  # constant flux offset\n" + \
+                               f"               {tmp[9]},  # kernel angle\n" + \
+                               f"               {tmp[10]},  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)\n" + \
+                               f"               {tmp[11]}, {tmp[12]},  # Polynomial terms (coefficients of \"spat\" and \"spat*spec\")\n" + \
+                               f"               {tmp[13]}, {tmp[14]}, {tmp[15]}])  # Polynomial terms (coefficients of spec**index)\n"
+                    print(strprint)
                     pad = msscattlight.pad // spatbin
                     offslitmask = slits.slit_img(pad=pad, initial=True, flexure=None) == -1
                     from matplotlib import pyplot as plt
@@ -1191,19 +1202,21 @@ def subtract_scattlight(self, msscattlight, slits, debug=False):
                     plt.subplot(221)
                     plt.imshow(_frame, vmin=vmin, vmax=2*np.median(_frame))
                     plt.subplot(222)
-                    plt.imshow(_frame*offslitmask, vmin=vmin, vmax=3*vmax)
+                    plt.imshow(_frame*offslitmask, vmin=-2*vmax, vmax=2*vmax)
                     plt.subplot(223)
-                    plt.imshow(scatt_img, vmin=vmin, vmax=3*vmax)
+                    plt.imshow(scatt_img*offslitmask, vmin=-2*vmax, vmax=2*vmax)
                     plt.subplot(224)
-                    plt.imshow((_frame - scatt_img)*offslitmask, vmin=-vmax / 2, vmax=vmax / 2)
+                    plt.imshow((_frame - scatt_img)*offslitmask, vmin=-2*vmax, vmax=2*vmax)
+                    # plt.imshow((_frame - scatt_img)*offslitmask, vmin=-vmax/5, vmax=vmax/5)
                     plt.show()
             elif self.par["scattlight"]["method"] == "archive":
                 # Use archival model parameters
-                modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
-                if modpar is None:
+                arx_modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
+                arx_modpar[8] = 0.0
+                if arx_modpar is None:
                     msgs.error(f"{self.spectrograph.name} does not have archival scattered light parameters. Please "
                                f"set 'scattlight_method' to another option.")
-                scatt_img = scattlight.scattered_light_model(modpar, _img)
+                scatt_img = scattlight.scattered_light_model(arx_modpar, _img)
             elif self.par["scattlight"]["method"] == "frame":
                 # Calculate a model specific for this frame
                 pad = msscattlight.pad // spatbin
@@ -1217,12 +1230,13 @@ def subtract_scattlight(self, msscattlight, slits, debug=False):
                 if not success:
                     if msscattlight is not None:
                         msgs.warn("Scattered light model failed - using predefined model parameters")
-                        scatt_img = scattlight.scattered_light_model(msscattlight.scattlight_param, _img)
+                        scatt_img = scattlight.scattered_light_model(this_modpar, _img)
                     else:
                         msgs.warn("Scattered light model failed - using archival model parameters")
                         # Use archival model parameters
-                        modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
-                        scatt_img = scattlight.scattered_light_model(modpar, _img)
+                        arx_modpar, _ = self.spectrograph.scattered_light_archive(binning, dispname)
+                        arx_modpar[8] = 0.0
+                        scatt_img = scattlight.scattered_light_model(arx_modpar, _img)
             else:
                 msgs.warn("Scattered light not performed")
                 scatt_img = np.zeros(self.image[ii, ...].shape)

From d33dbf35bc743afac5a2ba4c98c46be9ca77bd9d Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 10 Nov 2023 13:15:40 +0000
Subject: [PATCH 58/67] update ESI scattlight params

---
 pypeit/spectrographs/keck_esi.py | 35 ++++++++++++++++++++++----------
 1 file changed, 24 insertions(+), 11 deletions(-)

diff --git a/pypeit/spectrographs/keck_esi.py b/pypeit/spectrographs/keck_esi.py
index 0089a3780f..2cb933f313 100644
--- a/pypeit/spectrographs/keck_esi.py
+++ b/pypeit/spectrographs/keck_esi.py
@@ -337,19 +337,32 @@ def scattered_light_archive(self, binning, dispname):
         # and should be close to the final fitted values to reduce computational time)
         # Note :: These values need to be originally based on data that uses 1x1 binning,
         # and are now scaled here according to the binning of the current data to be analysed.
-        # These parameters give a cost of 8.0833e+08 with the science frame used as scattlight (1x1 binning, pad=5)
-        x0 = np.array([2.63726939e+02/specbin, 1.57625683e+02/spatbin,  # Gaussian kernel widths
-                       2.63911253e+02/specbin, 1.74452723e+02/spatbin,  # Lorentzian kernel widths
-                       -1.38026314e+02/specbin, 7.72883842e+01/spatbin,  # pixel offsets
-                       9.59395069e-01, 1.04452747e+00,  # Zoom factor (spec, spat)
-                       6.00977118e-01,  # kernel angle
-                       7.70958895e-02,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
-                       -1.15238092e-01, 1.80022435e-01,   # Polynomial terms (coefficients of "spat" and "spat*spec")
-                       3.41914530e-01, -3.15033809e-01])  # Polynomial terms (coefficients of spec**index)
+        # These parameters give a cost of 8.0517e+08 with the science frame used as scattlight (1x1 binning, pad=5)
+        x0 = np.array([272.33958742493064 / specbin, 115.501464689107 / spatbin,  # Gaussian kernel widths
+                       272.3418000034377 / specbin, 168.0591427733949 / spatbin,  # Lorentzian kernel widths
+                       -141.2552517318941 / specbin, 79.25936221285629 / spatbin,  # pixel offsets
+                       1.0877734248786808, 1.0562808322123667,  # Zoom factor (spec, spat)
+                       5.876311151022701,  # constant flux offset
+                       0.0444248025888341,  # kernel angle
+                       0.6090358292193677,  # Relative kernel scale (>1 means the kernel is more Gaussian, >0 but <1 makes the profile more lorentzian)
+                       0.135392229831296, -0.16167521454188258, # Polynomial terms (coefficients of "spat" and "spat*spec")
+                       0.06148093592863097, 0.10305719952486242])  # Polynomial terms (coefficients of spec**index)
 
         # Now set the bounds of the fitted parameters
-        bounds = ([1, 1, 1, 1, -200/specbin, -200/spatbin, 0, 0, -2*np.pi, 0.0, -10, -10, -10, -10],
-                  [600/specbin, 600/spatbin, 600/specbin, 600/spatbin, 200/specbin, 200/spatbin, 2, 2, 2*np.pi, 1000.0, 10, 10, 10, 10])
+        bounds = ([# Lower bounds:
+                      1, 1,  # Gaussian kernel widths
+                      1, 1,  # Lorentzian kernel widths
+                      -200 / specbin, -200 / spatbin,  # pixel offsets
+                      0, 0,  # Zoom factor (spec, spat)
+                      -1000, -(10 / 180) * np.pi, 0.0,  # constant flux offset, kernel angle, relative kernel scale
+                      -10, -10, -10, -10],  # Polynomial terms
+                  # Upper bounds
+                     [600 / specbin, 600 / spatbin,  # Gaussian kernel widths
+                      600 / specbin, 600 / spatbin,  # Lorentzian kernel widths
+                      200 / specbin, 200 / spatbin,  # pixel offsets
+                      2, 2,  # Zoom factor (spec, spat)
+                      1000.0, +(10 / 180) * np.pi, 1000.0,  # constant flux offset, kernel angle, relative kernel scale
+                      10, 10, 10, 10])  # Polynomial terms
 
         # Return the best-fitting archival parameters and the bounds
         return x0, bounds

From 939c1380920f875447684b379de1e540bde69258 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Fri, 10 Nov 2023 13:15:59 +0000
Subject: [PATCH 59/67] illumflat scattlight

---
 pypeit/calibrations.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pypeit/calibrations.py b/pypeit/calibrations.py
index 8fe1272583..e24e9b07c0 100644
--- a/pypeit/calibrations.py
+++ b/pypeit/calibrations.py
@@ -754,7 +754,7 @@ def get_flats(self):
             illum_flat = buildimage.buildimage_fromlist(self.spectrograph, self.det,
                                                         self.par['illumflatframe'], raw_illum_files,
                                                         dark=self.msdark, bias=self.msbias, scattlight=self.msscattlight,
-                                                        flatimages=self.flatimages, bpm=self.msbpm)
+                                                        slits=self.slits, flatimages=self.flatimages, bpm=self.msbpm)
             if len(raw_lampoff_files) > 0:
                 msgs.info('Subtracting lamp off flats using files: ')
                 for f in raw_lampoff_files:
@@ -765,11 +765,12 @@ def get_flats(self):
                                                                   raw_lampoff_files,
                                                                   dark=self.msdark,
                                                                   bias=self.msbias,
+                                                                  slits=self.slits,
                                                                   scattlight=self.msscattlight,
                                                                   bpm=self.msbpm)
                 illum_flat = illum_flat.sub(lampoff_flat)
 
-            # Initialise the pixel flat
+            # Initialise the illum flat
             illumFlatField = flatfield.FlatField(illum_flat, self.spectrograph,
                                                  self.par['flatfield'], self.slits, self.wavetilts,
                                                  self.wv_calib, spat_illum_only=True,

From e29b2da9f7398cd163aee9c22ade5080a6dc4891 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Mon, 20 Nov 2023 11:59:33 +0000
Subject: [PATCH 60/67] add doc files

---
 doc/api/pypeit.coadd3d.rst        | 8 ++++++++
 doc/api/pypeit.core.ref_index.rst | 8 ++++++++
 2 files changed, 16 insertions(+)
 create mode 100644 doc/api/pypeit.coadd3d.rst
 create mode 100644 doc/api/pypeit.core.ref_index.rst

diff --git a/doc/api/pypeit.coadd3d.rst b/doc/api/pypeit.coadd3d.rst
new file mode 100644
index 0000000000..f6d6dbc5ac
--- /dev/null
+++ b/doc/api/pypeit.coadd3d.rst
@@ -0,0 +1,8 @@
+pypeit.coadd3d module
+=====================
+
+.. automodule:: pypeit.coadd3d
+   :members:
+   :private-members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/doc/api/pypeit.core.ref_index.rst b/doc/api/pypeit.core.ref_index.rst
new file mode 100644
index 0000000000..8d1b6d7856
--- /dev/null
+++ b/doc/api/pypeit.core.ref_index.rst
@@ -0,0 +1,8 @@
+pypeit.core.ref\_index module
+=============================
+
+.. automodule:: pypeit.core.ref_index
+   :members:
+   :private-members:
+   :undoc-members:
+   :show-inheritance:

From 405afadaebfe0667f376f762bd76494ab88f4823 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 21 Nov 2023 21:22:26 +0000
Subject: [PATCH 61/67] add finecorr docs

---
 pypeit/par/pypeitpar.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index d911052193..ab0ef44c87 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -1068,7 +1068,8 @@ def __init__(self, method=None, finecorr=None, finecorr_pad=None, finecorr_order
                                  'inter-slit region. For example, "0" corresponds to all pixels left of the leftmost ' \
                                  'slit, while a value of "1" corresponds to all pixels between the first and second ' \
                                  'slit (counting from the left). It should be either a single integer value, or a ' \
-                                 'list of integer values.'
+                                 'list of integer values. The default (None) means that no inter-slit regions will ' \
+                                 'be masked.'
 
         # Instantiate the parameter set
         super(ScatteredLightPar, self).__init__(list(pars.keys()),

From b659bf1177ccf1e32e0d24a371833cf35886de61 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 21 Nov 2023 21:22:37 +0000
Subject: [PATCH 62/67] add debug notes

---
 pypeit/images/rawimage.py | 13 +++----------
 1 file changed, 3 insertions(+), 10 deletions(-)

diff --git a/pypeit/images/rawimage.py b/pypeit/images/rawimage.py
index d4f029a044..98d36f5ec3 100644
--- a/pypeit/images/rawimage.py
+++ b/pypeit/images/rawimage.py
@@ -1172,18 +1172,11 @@ def subtract_scattlight(self, msscattlight, slits, debug=False):
                 # Use predefined model parameters
                 scatt_img = scattlight.scattered_light_model(this_modpar, _img)
                 if debug:
-                    embed()
-                    # import astropy.io.fits as fits
-                    # hdu = fits.PrimaryHDU([self.image[ii, ...]-scatt_img])
-                    # hdu.writeto(self.filename.replace(".fits.gz", "_proc_slsub.fits.gz"))
-                    # assert False
-                    # for ii in range(11):
-                    #     print("saving", ii)
-                    #     np.save("scattlight_test/slitmask_pad{0:02d}.npy".format(ii), slits.slit_img(pad=ii, initial=True, flexure=None) == -1)
                     specbin, spatbin = parse.parse_binning(self.detector[0]['binning'])
                     tmp = msscattlight.scattlight_param.copy()
-                    print("\n\n\n2x2 BINNING ASSUMED AS INPUT!!!\n\n\n")
-                    # Find a pretty way to print out the archive variables
+                    # The following printout is the same format that is used in the spectrgraph files for archiving a good set
+                    # of starting parameter values for future reductions. The following printout is only useful for developers
+                    # that want to store a solution in a spectrograph file.
                     strprint = f"x0 = np.array([{tmp[0]*specbin} / specbin, {tmp[1]*spatbin} / spatbin,  # Gaussian kernel widths\n" + \
                                f"               {tmp[2]*specbin} / specbin, {tmp[3]*spatbin} / spatbin,  # Lorentzian kernel widths\n" + \
                                f"               {tmp[4]*specbin} / specbin, {tmp[5]*spatbin} / spatbin,  # pixel offsets\n" + \

From daee90f48eb435a9db021bf59517f8563e65f616 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 21 Nov 2023 21:26:23 +0000
Subject: [PATCH 63/67] cleanup scattered light

---
 pypeit/core/scattlight.py | 30 ++++++++++++++----------------
 1 file changed, 14 insertions(+), 16 deletions(-)

diff --git a/pypeit/core/scattlight.py b/pypeit/core/scattlight.py
index e7aab05667..e04ee2e5ad 100644
--- a/pypeit/core/scattlight.py
+++ b/pypeit/core/scattlight.py
@@ -119,11 +119,11 @@ def scattered_light(frame, bpm, offslitmask, x0, bounds, detpad=300, debug=False
     Parameters
     ----------
     frame : `numpy.ndarray`_
-        Raw 2D data frame to be used to compute the scattered light.
+        Raw 2D data frame (nspec, nspat) to be used to compute the scattered light.
     bpm : `numpy.ndarray`_
-        2D boolean array indicating the bad pixels (True=bad)
+        2D boolean array indicating the bad pixels (True=bad), same shape as frame
     offslitmask : `numpy.ndarray`_
-        A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
+        A boolean mask indicating the pixels that are on/off the slit (True = off the slit), same shape as frame
     x0 : `numpy.ndarray`_
         A 1D array containing the best-fitting model parameters
     bounds : :obj:`tuple`_
@@ -236,21 +236,20 @@ def mask_slit_regions(offslitmask, centrace, mask_regions=None):
     # Setup the pixel coordinates
     spat = np.arange(nspat)
 
-    # Find the pixels in each slit, limited by the minimum and
-    # maximum spectral position.
-    good_mask = offslitmask.copy()
+    # Loop through all user-specified inter-slit regions to mask
+    bad_mask = np.zeros_like(offslitmask)
     for ii in _mask_regions:
         if ii == 0:  # All pixels to the left of the first slit
-            indx = spat[None, :] < centrace[:, ii, None]
+            mask_pix = spat[None, :] < centrace[:, ii, None]
         elif ii == nslit:  # All pixels to the right of the last slit
-            indx = spat[None, :] > centrace[:, ii-1, None]
+            mask_pix = spat[None, :] > centrace[:, ii-1, None]
         else:  # Everything else in between
-            indx = (spat[None, :] > centrace[:, ii-1, None]) \
+            mask_pix = (spat[None, :] > centrace[:, ii-1, None]) \
                    & (spat[None, :] < centrace[:, ii, None])
         # Exclude these pixels
-        good_mask[indx] = False
+        bad_mask[mask_pix] = True
     # Return the mask of good inter-slit pixels
-    return good_mask
+    return offslitmask & np.logical_not(bad_mask)
 
 
 def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
@@ -259,13 +258,13 @@ def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
     Parameters
     ----------
     frame : `numpy.ndarray`_
-        Raw 2D data frame to be used to compute the fine correction of the scattered light.
+        Raw 2D data frame (nspec, nspat) to be used to compute the fine correction of the scattered light.
         This frame should be the raw frame, minus the first estimate of the scattered light
         that has been derived from the `scattered_light_model()`_ function.
     bpm : `numpy.ndarray`_
-        2D boolean array indicating the bad pixels (True=bad)
+        2D boolean array indicating the bad pixels (True=bad), same shape as frame
     offslitmask : `numpy.ndarray`_
-        A boolean mask indicating the pixels that are on/off the slit (True = off the slit)
+        A boolean mask indicating the pixels that are on/off the slit (True = off the slit), same shape as frame
     polyord : :obj:`int`_, optional
         Polynomial order to use for fitting the residual scattered light in the spatial direction.
     debug : :obj:`bool`_, optional
@@ -274,7 +273,7 @@ def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
     Returns
     -------
     scatt_img : `numpy.ndarray`_
-        A 2D image of the fine correction to the scattered light determined from the input frame.
+        A 2D image (nspec, nspat) of the fine correction to the scattered light determined from the input frame.
     """
     msgs.info("Performing a fine correction to the scattered light")
     # Convert the BPM to a GPM for convenience
@@ -296,7 +295,6 @@ def fine_correction(frame, bpm, offslitmask, polyord=2, debug=False):
     model_med = ndimage.median_filter(model, size=(50, 1))  # Median filter to get rid of CRs
     scatt_light_fine = ndimage.gaussian_filter(model_med, sigma=10)  # Gaussian filter to smooth median filter
     if debug:
-        embed()
         from matplotlib import pyplot as plt
         vmin, vmax = -np.max(scatt_light_fine), np.max(scatt_light_fine)
         plt.subplot(121)

From 3b379aca21c30b4b15e7d7a098e9291cbc47a030 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 21 Nov 2023 21:48:48 +0000
Subject: [PATCH 64/67] update docs

---
 doc/calibrations/scattlight.rst | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/doc/calibrations/scattlight.rst b/doc/calibrations/scattlight.rst
index c7c82b00ff..c691b5fa64 100644
--- a/doc/calibrations/scattlight.rst
+++ b/doc/calibrations/scattlight.rst
@@ -50,22 +50,26 @@ example will pad the slits by 10 detector pixels, assuming 1x1 binning):
 Note: if your data are binned by 2 in the spatial direction, then setting ``pad=10`` would actually
 pad the slit edges by 5 pixels.
 
-# TODO :: Make sure this description is up-to-date!
-
-The current scattered light model involves a 13 parameter fit to the inter-slit regions. The model
+The current scattered light model involves a 14+ parameter fit to the inter-slit regions. The model
 utilises the observed 2D (trimmed+oriented+debiased) frame to estimate the scattered light. The
 scattered light model is essentially a blurred, shifted, flux-scaled, rotated, and zoomed version of the
 detector image. The amount of blurring in the spatial and spectral direction include contributions from
-a Gaussian and a Lorentzian kernel. The relative importance of these kernels is controlled by scaling
-parameter (1 paramerer), and each kernel has two widths each (4 parameters total), and the joint kernel
-is rotated (1 additional parameter). The scattered light image is than shifted in the spectral and spatial
-directions (2 additional parameters), and there is a single parameter to estimate the zoom factor. Finally,
-the scattered light flux is scaled by a low order 2D polynomial (4 parameters). Once the best-fitting
-model parameters are determined, these can be applied to any other frame to estimate the scattered
-light contribution of this frame.
-
+a Gaussian and a Lorentzian kernel.
+The relative importance of these kernels is controlled by scaling parameter (1 paramerer),
+and each kernel has two widths each (one for each direction on the detector; 4 parameters total),
+and the joint kernel is rotated (1 additional parameter).
+The scattered light image is than shifted in the spectral and spatial directions (2 additional parameters),
+and there is a parameter to estimate the zoom factor in the spectral and spatial directions (2 parameters).
+Finally, the scattered light flux is scaled by a low order 2D polynomial, including:
+(i) 2 parameters in the spatial direction (a linear term and a cross-term)
+``par1 * spatial + par2 * spatial*spectral``; and
+(ii) N (where N>=1) parameters for the spectral dependence.
+``par3 + par4 * spectral + par5 * spectral^2 + ...``.
+
+Once the best-fitting model parameters are determined, these can be applied to any
+other frame to estimate the scattered light contribution of this frame.
 Alternatively, the scattered light can be determined for each frame independently (i.e. you can
-derive a separate set of 13 model parameters for each frame that you wish to perform a scattered
+derive a separate set of 14+ model parameters for each frame that you wish to perform a scattered
 light correction). To set this option, you can add the following arguments to your pypeit file:
 
 .. code-block:: ini

From dc80ca0052b438a9fb08cb4eaef48774d0d6768f Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Tue, 21 Nov 2023 22:00:56 +0000
Subject: [PATCH 65/67] update docs

---
 doc/calibrations/scattlight.rst      |   8 ++++++--
 doc/figures/scatteredlight_image.png | Bin 0 -> 1609787 bytes
 2 files changed, 6 insertions(+), 2 deletions(-)
 create mode 100644 doc/figures/scatteredlight_image.png

diff --git a/doc/calibrations/scattlight.rst b/doc/calibrations/scattlight.rst
index c691b5fa64..361d7b4dd4 100644
--- a/doc/calibrations/scattlight.rst
+++ b/doc/calibrations/scattlight.rst
@@ -100,8 +100,12 @@ A three panel ginga window will be opened that shows:
 All panels are re-oriented so that vertical is the spectral dimension with
 blue at the bottom, following the PypeIt :ref:`pypeit-orientation` convention.
 
-Here is an screen shot of a ``ginga`` view for an example from
-the ``keck_kcwi`` spectrograph.
+Here is an screen shot of a ``ginga`` view of an example from
+the ``keck_kcwi`` spectrograph (BL grating).
+The left image shows the raw data,
+the middle image shows the estimated scattered light, based on a model fit, and
+the right image shows the raw frame with the scattered light subtracted off.
+All images are shown on the same color scale.
 
 .. image:: ../figures/scatteredlight_image.png
 
diff --git a/doc/figures/scatteredlight_image.png b/doc/figures/scatteredlight_image.png
new file mode 100644
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From f098af8319125bf69209442fde31307f925fd259 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Wed, 22 Nov 2023 21:35:30 +0000
Subject: [PATCH 66/67] fix parset default bug

---
 pypeit/par/parset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pypeit/par/parset.py b/pypeit/par/parset.py
index 284917c799..df289c23be 100644
--- a/pypeit/par/parset.py
+++ b/pypeit/par/parset.py
@@ -487,7 +487,7 @@ def config_lines(par, section_name=None, section_comment=None, section_level=0,
                     lines += ParSet._config_comment(par.descr[k], component_indent)
             except:
                 pass
-            if not exclude_defaults or par[k] != par.default[k]:
+            if not exclude_defaults or par[k] != par.defaults[k]:
                 argvalue = ParSet._data_string(par[k])
                 if isinstance(par[k], list):
                     argvalue += ','

From 5d88464ba881ad35fe311ceda8eca176a41aa050 Mon Sep 17 00:00:00 2001
From: rcooke <rcooke.ast@gmail.com>
Date: Thu, 23 Nov 2023 19:29:23 +0000
Subject: [PATCH 67/67] fix scattlight

---
 pypeit/par/parset.py    | 2 +-
 pypeit/par/pypeitpar.py | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/pypeit/par/parset.py b/pypeit/par/parset.py
index df289c23be..284917c799 100644
--- a/pypeit/par/parset.py
+++ b/pypeit/par/parset.py
@@ -487,7 +487,7 @@ def config_lines(par, section_name=None, section_comment=None, section_level=0,
                     lines += ParSet._config_comment(par.descr[k], component_indent)
             except:
                 pass
-            if not exclude_defaults or par[k] != par.defaults[k]:
+            if not exclude_defaults or par[k] != par.default[k]:
                 argvalue = ParSet._data_string(par[k])
                 if isinstance(par[k], list):
                     argvalue += ','
diff --git a/pypeit/par/pypeitpar.py b/pypeit/par/pypeitpar.py
index ab0ef44c87..ac9c8251d4 100644
--- a/pypeit/par/pypeitpar.py
+++ b/pypeit/par/pypeitpar.py
@@ -456,6 +456,9 @@ def from_dict(cls, cfg):
         kwargs = {}
         for pk in parkeys:
             kwargs[pk] = cfg[pk] if pk in k else None
+        # Keywords that are ParSets
+        pk = 'scattlight'
+        kwargs[pk] = ScatteredLightPar.from_dict(cfg[pk]) if pk in k else None
         return cls(**kwargs)
 
     @staticmethod