Index: src/sas/sasgui/perspectives/fitting/media/sm_help.rst
===================================================================
--- src/sas/sasgui/perspectives/fitting/media/sm_help.rst	(revision 6aad2e8b144e92be94b299c8ae7f4f418c094f1d)
+++ src/sas/sasgui/perspectives/fitting/media/sm_help.rst	(revision 5ed76f8390eac56ff5d3779fd9c454d0c8fe1dc0)
@@ -20,14 +20,14 @@
 ==================
 
-Sometimes the instrumental geometry used to acquire the experimental data has 
-an impact on the clarity of features in the reduced scattering curve. For 
-example, peaks or fringes might be slightly broadened. This is known as 
-*Q resolution smearing*. To compensate for this effect one can either try and 
-remove the resolution contribution - a process called *desmearing* - or add the 
-resolution contribution into a model calculation/simulation (which by definition 
-will be exact) to make it more representative of what has been measured 
+Sometimes the instrumental geometry used to acquire the experimental data has
+an impact on the clarity of features in the reduced scattering curve. For
+example, peaks or fringes might be slightly broadened. This is known as
+*Q resolution smearing*. To compensate for this effect one can either try and
+remove the resolution contribution - a process called *desmearing* - or add the
+resolution contribution into a model calculation/simulation (which by definition
+will be exact) to make it more representative of what has been measured
 experimentally - a process called *smearing*. SasView will do the latter.
 
-Both smearing and desmearing rely on functions to describe the resolution 
+Both smearing and desmearing rely on functions to describe the resolution
 effect. SasView provides three smearing algorithms:
 
@@ -36,5 +36,5 @@
 *  *2D Smearing*
 
-SasView also has an option to use Q resolution data (estimated at the time of 
+SasView also has an option to use $Q$ resolution data (estimated at the time of
 data reduction) supplied in a reduced data file: the *Use dQ data* radio button.
 
@@ -43,6 +43,6 @@
 dQ Smearing
 -----------
- 
-If this option is checked, SasView will assume that the supplied dQ values 
+
+If this option is checked, SasView will assume that the supplied $dQ$ values
 represent the standard deviations of Gaussian functions.
 
@@ -65,7 +65,7 @@
 **[Equation 1]**
 
-The functions |inlineimage004| and |inlineimage005|
-refer to the slit width weighting function and the slit height weighting 
-determined at the given *q* point, respectively. It is assumed that the weighting
+The functions $W_v(v)$ and $W_u(u)$
+refer to the slit width weighting function and the slit height weighting
+determined at the given $q$ point, respectively. It is assumed that the weighting
 function is described by a rectangular function, such that
 
@@ -80,8 +80,9 @@
 **[Equation 3]**
 
-so that |inlineimage008| |inlineimage009| for |inlineimage010| and *u*\ .
-
-Here |inlineimage011| and |inlineimage012| stand for
-the slit height (FWHM/2) and the slit width (FWHM/2) in *q* space.
+so that $\Delta q_\alpha = \int_0^\infty d\alpha W_\alpha(\alpha)$
+for $\alpha = v$ and $u$.
+
+Here $\Delta q_u$ and $\Delta q_v$ stand for
+the slit height (FWHM/2) and the slit width (FWHM/2) in $q$ space.
 
 This simplifies the integral in Equation 1 to
@@ -91,15 +92,16 @@
 **[Equation 4]**
 
-which may be solved numerically, depending on the nature of |inlineimage011| and |inlineimage012| .
+which may be solved numerically, depending on the nature of
+$\Delta q_u$ and $\Delta q_v$.
 
 Solution 1
 ^^^^^^^^^^
 
-**For** |inlineimage012| **= 0 and** |inlineimage011| **= constant.**
+**For $\Delta q_v= 0$ and $\Delta q_u = \text{constant}$.**
 
 .. image:: sm_image016.png
 
-For discrete *q* values, at the *q* values of the data points and at the *q*
-values extended up to *q*\ :sub:`N`\ = *q*\ :sub:`i` + |inlineimage011| the smeared
+For discrete $q$ values, at the $q$ values of the data points and at the $q$
+values extended up to $q_N = q_i + \Delta q_u$ the smeared
 intensity can be approximately calculated as
 
@@ -108,23 +110,23 @@
 **[Equation 5]**
 
-where |inlineimage018| = 0 for *I*\ :sub:`s` when *j* < *i* or *j* > *N-1*.
+where |inlineimage018| = 0 for $I_s$ when $j < i$ or $j > N-1$.
 
 Solution 2
 ^^^^^^^^^^
 
-**For** |inlineimage012| **= constant and** |inlineimage011| **= 0.**
+**For $\Delta q_v = \text{constant}$ and $\Delta q_u= 0$.**
 
 Similar to Case 1
 
-|inlineimage019| for *q*\ :sub:`p` = *q*\ :sub:`i` - |inlineimage012| and *q*\ :sub:`N` = *q*\ :sub:`i` + |inlineimage012|
+|inlineimage019| for $q_p = q_i - \Delta q_v$ and $q_N = q_i + \Delta q_v$
 
 **[Equation 6]**
 
-where |inlineimage018| = 0 for *I*\ :sub:`s` when *j* < *p* or *j* > *N-1*.
+where |inlineimage018| = 0 for $I_s$ when $j < p$ or $j > N-1$.
 
 Solution 3
 ^^^^^^^^^^
 
-**For** |inlineimage011| **= constant and** |inlineimage011| **= constant.**
+**For $\Delta q_u = \text{constant}$ and $\Delta q_v = \text{constant}$.**
 
 In this case, the best way is to perform the integration of Equation 1
@@ -142,7 +144,6 @@
 **[Equation 7]**
 
-for *q*\ :sub:`p` = *q*\ :sub:`i` - |inlineimage012| and *q*\ :sub:`N` = *q*\ :sub:`i` + |inlineimage012|
-
-where |inlineimage018| = 0 for *I*\ :sub:`s` when *j* < *p* or *j* > *N-1*.
+for *q_p = q_i - \Delta q_v$ and $q_N = q_i + \Delta q_v$
+where |inlineimage018| = 0 for *I_s$ when $j < p$ or $j > N-1$.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
@@ -175,16 +176,17 @@
 **[Equation 9]**
 
-In Equation 9, *x*\ :sub:`0` = *q* cos(|theta|), *y*\ :sub:`0` = *q* sin(|theta|), and
-the primed axes, are all in the coordinate rotated by an angle |theta| about
-the z-axis (see the figure below) so that *x'*\ :sub:`0` = *x*\ :sub:`0` cos(|theta|) +
-*y*\ :sub:`0` sin(|theta|) and *y'*\ :sub:`0` = -*x*\ :sub:`0` sin(|theta|) +
-*y*\ :sub:`0` cos(|theta|). Note that the rotation angle is zero for a x-y symmetric
-elliptical Gaussian distribution. The *A* is a normalization factor.
+In Equation 9, $x_0 = q \cos(\theta)$, $y_0 = q \sin(\theta)$, and
+the primed axes, are all in the coordinate rotated by an angle $\theta$ about
+the z-axis (see the figure below) so that
+$x'_0 = x_0 \cos(\theta) + y_0 \sin(\theta)$ and
+$y'_0 = -x_0 \sin(\theta) + y_0 \cos(\theta)$.
+Note that the rotation angle is zero for a $xy$ symmetric
+elliptical Gaussian distribution. The $A$ is a normalization factor.
 
 .. image:: sm_image023.png
 
-Now we consider a numerical integration where each of the bins in |theta| and *R* are
-*evenly* (this is to simplify the equation below) distributed by |bigdelta|\ |theta|
-and |bigdelta|\ R, respectively, and it is further assumed that *I(x',y')* is constant
+Now we consider a numerical integration where each of the bins in $\theta$ and $R$ are
+*evenly* (this is to simplify the equation below) distributed by $\Delta \theta$
+and $\Delta R$, respectively, and it is further assumed that $I(x',y')$ is constant
 within the bins. Then
 
@@ -194,6 +196,6 @@
 
 Since the weighting factor on each of the bins is known, it is convenient to
-transform *x'-y'* back to *x-y* coordinates (by rotating it by -|theta| around the
-*z* axis).
+transform $x'y'$ back to $xy$ coordinates (by rotating it by $-\theta$ around the
+$z$ axis).
 
 Then, for a polar symmetric smear
@@ -207,5 +209,5 @@
 .. image:: sm_image026.png
 
-while for a *x-y* symmetric smear
+while for a $xy$ symmetric smear
 
 .. image:: sm_image027.png
@@ -225,9 +227,9 @@
 -------------------------
 
-In all the cases above, the weighting matrix *W* is calculated on the first call
-to a smearing function, and includes ~60 *q* values (finely and evenly binned)
-below (>0) and above the *q* range of data in order to smear all data points for
-a given model and slit/pinhole size. The *Norm*  factor is found numerically with the
-weighting matrix and applied on the computation of *I*\ :sub:`s`.
+In all the cases above, the weighting matrix $W$ is calculated on the first call
+to a smearing function, and includes ~60 $q$ values (finely and evenly binned)
+below (>0) and above the $q$ range of data in order to smear all data points for
+a given model and slit/pinhole size. The $Norm$  factor is found numerically with the
+weighting matrix and applied on the computation of $I_s$.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
