diff --git a/specreduce/background.py b/specreduce/background.py
index ac1f08c..242eaf6 100644
--- a/specreduce/background.py
+++ b/specreduce/background.py
@@ -35,12 +35,12 @@ class Background(_ImageParser):
     traces : List, `specreduce.tracing.Trace`, int, float
         Individual or list of trace object(s) (or integers/floats to define
         FlatTraces) to extract the background. If None, a ``FlatTrace`` at the
-        center of the image (according to `disp_axis`) will be used.
+        center of the image (according to ``disp_axis``) will be used.
     width : float
         width of extraction aperture in pixels
     statistic: string
-        statistic to use when computing the background.  'average' will
-        account for partial pixel weights, 'median' will include all partial
+        statistic to use when computing the background.  ``average`` will
+        account for partial pixel weights, ``median`` will include all partial
         pixels.
     disp_axis : int
         dispersion axis
@@ -50,9 +50,9 @@ class Background(_ImageParser):
         [default: 0]
     mask_treatment : string, optional
         The method for handling masked or non-finite data. Choice of ``filter``,
-        ``omit``, or ``zero-fill``. If ``filter`` is chosen, masked and non-finite
+        ``omit``, or ``zero-fill``. If `filter` is chosen, masked and non-finite
         data will not contribute to the background statistic that is calculated
-        in each column along `disp_axis`. If `omit` is chosen, columns along
+        in each column along ``disp_axis``. If ``omit`` is chosen, columns along
         disp_axis with any masked/non-finite data values will be fully masked
         (i.e, 2D mask is collapsed to 1D and applied). If ``zero-fill`` is chosen,
         masked/non-finite data will be replaced with 0.0 in the input image,
@@ -245,12 +245,12 @@ def two_sided(cls, image, trace_object, separation, **kwargs):
         crossdisp_axis : int
             cross-dispersion axis
         mask_treatment : string
-            The method for handling masked or non-finite data. Choice of `filter`,
-            `omit`, or `zero-fill`. If `filter` is chosen, masked/non-finite data
+            The method for handling masked or non-finite data. Choice of ``filter``,
+            ``omit`, or ``zero-fill``. If `filter` is chosen, masked/non-finite data
             will be filtered during the fit to each bin/column (along disp. axis) to
-            find the peak. If `omit` is chosen, columns along disp_axis with any
+            find the peak. If ``omit`` is chosen, columns along disp_axis with any
             masked/non-finite data values will be fully masked (i.e, 2D mask is
-            collapsed to 1D and applied). If `zero-fill` is chosen, masked/non-finite
+            collapsed to 1D and applied). If ``zero-fill`` is chosen, masked/non-finite
             data will be replaced with 0.0 in the input image, and the mask will then
             be dropped. For all three options, the input mask (optional on input
             NDData object) will be combined with a mask generated from any non-finite
@@ -294,12 +294,12 @@ def one_sided(cls, image, trace_object, separation, **kwargs):
         crossdisp_axis : int
             cross-dispersion axis
         mask_treatment : string
-            The method for handling masked or non-finite data. Choice of `filter`,
-            `omit`, or `zero-fill`. If `filter` is chosen, masked/non-finite data
+            The method for handling masked or non-finite data. Choice of ``filter``,
+            ``omit``, or ``zero-fill``. If `filter` is chosen, masked/non-finite data
             will be filtered during the fit to each bin/column (along disp. axis) to
-            find the peak. If `omit` is chosen, columns along disp_axis with any
+            find the peak. If ``omit`` is chosen, columns along disp_axis with any
             masked/non-finite data values will be fully masked (i.e, 2D mask is
-            collapsed to 1D and applied). If `zero-fill` is chosen, masked/non-finite
+            collapsed to 1D and applied). If ``zero-fill`` is chosen, masked/non-finite
             data will be replaced with 0.0 in the input image, and the mask will then
             be dropped. For all three options, the input mask (optional on input
             NDData object) will be combined with a mask generated from any non-finite
diff --git a/specreduce/extract.py b/specreduce/extract.py
index 8d4f1bc..1fea55a 100644
--- a/specreduce/extract.py
+++ b/specreduce/extract.py
@@ -148,12 +148,12 @@ class BoxcarExtract(SpecreduceOperation):
     crossdisp_axis
         cross-dispersion axis
     mask_treatment
-        The method for handling masked or non-finite data. Choice of `filter`,
-        `omit`, or `zero-fill`. If `filter` is chosen, the mask is ignored
-        and the non-finite data will passed to the extraction as is. If `omit`
+        The method for handling masked or non-finite data. Choice of ``filter``,
+        ``omit``, or ``zero-fill``. If `filter` is chosen, the mask is ignored
+        and the non-finite data will passed to the extraction as is. If ``omit``
         is chosen, columns along disp_axis with any masked or non-finite data
         values will be fully masked (i.e, 2D mask is collapsed to 1D and applied).
-        If `zero-fill` is chosen, masked and non-finite data will be replaced
+        If ``zero-fill`` is chosen, masked and non-finite data will be replaced
         with 0.0 in the input image, and the mask will then be dropped.
         For all three options, the input mask (optional on input NDData object)
         will be combined with a mask generated from any non-finite values in the
@@ -248,8 +248,9 @@ class HorneExtract(SpecreduceOperation):
     extraction - by default, a 1D gaussian is fit and as a uniform profile
     across the spectrum. Alternativley, the ``self profile`` option may be
     chosen - when this option is chosen, the spatial profile will be sampled
-    at various locations (set by <>) and interpolated between to produce a
-    smoothly varying spatial profile across the spectrum.
+    (using a default of 10 sample bins, but can be modified with
+    ``spatial_profile``) and interpolated between to produce a smoothly varying
+    spatial profile across the spectrum.
 
     If using the Gaussian option for the spatial profile, a background profile
     may be fit (but not subtracted) simultaneously to the data. By default,
diff --git a/specreduce/tracing.py b/specreduce/tracing.py
index 5f36792..361e4ef 100644
--- a/specreduce/tracing.py
+++ b/specreduce/tracing.py
@@ -258,10 +258,10 @@ class FitTrace(Trace, _ImageParser):
         ``max``: Saves the position with the maximum flux in each bin.
         [default: ``max``]
     mask_treatment : string, optional
-        The method for handling masked or non-finite data. Choice of `filter` or
-        `omit`. If `filter` is chosen, masked/non-finite data will be filtered
+        The method for handling masked or non-finite data. Choice of ``filter`` or
+        ``omit``. If `filter` is chosen, masked/non-finite data will be filtered
         during the fit to each bin/column (along disp. axis) to find the peak.
-        If `omit` is chosen, columns along disp_axis with any masked/non-finite
+        If ``omit`` is chosen, columns along disp_axis with any masked/non-finite
         data values will be fully masked (i.e, 2D mask is collapsed to 1D and applied).
         For both options, the input mask (optional on input NDData object) will
         be combined with a mask generated from any non-finite values in the image