Changeset 1423ddb in sasmodels

Timestamp:

Mar 31, 2019 12:47:58 AM (6 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master

Children:

f64b154

Parents:

9fac5f5

Message:

update beta approx docs. Refs sasview/sasview#1164.

File:

• doc/guide/fitting_sq.rst (modified) (1 diff)

doc/guide/fitting_sq.rst

@@ -16 +16 @@
 .. figure:: p_and_s_buttons.png
 
-**Product models**, or $P@S$ models for short, multiply the structure factor
-$S(Q)$ by the form factor $P(Q)$, modulated by the **effective radius** of the
-form factor.
-
-Many of the parameters in $P@S$ models take on specific meanings so that they
-can be handled correctly inside SasView:
+**Product models**, or $P@S$ models for short, multiply the form factor
+$P(Q)$ by the structure factor $S(Q)$, modulated by the **effective radius**
+of the form factor.
+
+
+Scattering at vector $\mathbf Q$ for an individual particle with
+shape parameters $\mathbf\xi$ and contrast $\rho_c(\mathbf r, \mathbf\xi)$
+is computed from the square of the amplitude, $F(\mathbf Q, \mathbf\xi)$, as
+
+.. math::
+    I(\mathbf Q) = F(\mathbf Q, \mathbf\xi) F^*(\mathbf Q, \mathbf\xi)
+        \big/ V(\mathbf\xi)
+
+with particle volume $V(\mathbf \xi)$ and
+
+.. math::
+    F(\mathbf Q, \mathbf\xi) = \int_{\mathbb R^3} \rho_c(\mathbf r, \mathbf\xi)
+        e^{i \mathbf Q \cdot \mathbf r} \,\mathrm d \mathbf r
+
+The 1-D scattering pattern for monodisperse particles uses the orientation
+average in spherical coordinates,
+
+.. math::
+    I(Q) = n \langle F F^*\rangle = \frac{n}{4\pi}
+    \int_{\theta=0}^{\pi} \int_{\phi=0}^{2\pi}
+    F F^* \sin(\theta) \,\mathrm d\phi \mathrm d\theta
+
+where $F(\mathbf Q,\mathbf\xi)$ uses
+$\mathbf Q = [Q \sin\theta\cos\phi, Q \sin\theta\sin\phi, Q \cos\theta]^T$.
+A $u$-substitution may be used, with $\alpha = \cos \theta$,
+$\surd(1 - \alpha^2) = \sin \theta$, and
+$\mathrm d\alpha = -\sin\theta\,\mathrm d\theta$.
+Here,
+
+.. math:: n = V_f/V(\mathbf\xi)
+
+is the number density of scatterers estimated from the volume fraction
+of particles in solution. In this formalism, each incoming
+wave interacts with exactly one particle before being scattered into the
+detector. All interference effects are within the particle itself.
+The detector accumulates counts in proportion to the relative probability
+at each pixel. The extension to heterogeneous systems is simply a matter of
+adding the scattering patterns in proportion to the number density of each
+particle. That is, given shape parameters $\mathbf\xi$ with probability
+$P_\mathbf{\xi}$,
+
+.. math::
+
+    I(Q) = \int_\Xi n(\mathbf\xi) \langle F F^* \rangle \,\mathrm d\xi
+         = V_f\frac{\int_\Xi P_\mathbf{\xi} \langle F F^* \rangle
+         \,\mathrm d\mathbf\xi}{\int_\Xi P_\mathbf\xi V(\mathbf\xi)\,\mathrm d\mathbf\xi}
+
+This approximation is valid in the dilute limit, where particles are
+sufficiently far apart that the interaction between them can be ignored.
+
+As concentration increases, a structure factor term $S(Q)$ can be included,
+giving the monodisperse approximation for the interaction between particles,
+with
+
+.. math:: I(Q) = n \langle F F^* \rangle S(Q)
+
+For particles without spherical symmetry, the decoupling approximation (DA)
+is more accurate, with
+
+.. math::
+
+    I(Q) = n [\langle F F^* \rangle
+        + \langle F \rangle \langle F \rangle^* (S(Q) - 1)]
+
+Or equivalently,
+
+.. math:: I(Q) = P(Q)[1 + \beta\,(S(Q) - 1)]
+
+with form factor $P(Q) = n \langle F F^* \rangle$ and
+$\beta = \langle F \rangle \langle F \rangle^* \big/ \langle F F^* \rangle$.
+These approximations can be extended to heterogeneous systems using averages
+over size, $\langle \cdot \rangle_\mathbf\xi = \int_\Xi P_\mathbf\xi \langle\cdot\rangle\,\mathrm d\mathbf\xi \big/ \int_\Xi P_\mathbf\xi \,\mathrm d\mathbf\xi$ and setting
+$n = V_f\big/\langle V \rangle_\mathbf\xi$.
+Further improvements can be made using the local monodisperse
+approximation (LMA) or using partial structure factors, as described
+in \cite{bresler_sasfit:_2015}.
+
+Many parameters are common amongst $P@S$ models, and take on specific meanings:
 
 * *scale*:
 
-  In simple $P(Q)$ models **scale** often represents the volume fraction of
-  material.
-  
-  In $P@S$ models **scale** should be set to 1.0, as the $P@S$ model contains a
-  **volfraction** parameter.
+    Overall model scale factor.
+
+    To compute number density $n$ the volume fraction $V_f$ is needed.  In
+    most $P(Q)$ models $V_f$ is not defined and **scale** is used instead.
+    Some $P(Q)$ models, such as *vesicle*, do define **volfraction** and so
+    can leave **scale** at 1.0.
+
+    The structure factor model $S(Q)$ has **volfraction**.  This is also used
+    as the volume fraction for the form factor model $P(Q)$, replacing the
+    **volfraction** parameter if it exists in $P$. This means that
+    $P@S$ models can leave **scale** at 1.0.
+
+    If the volume fraction required for $S(Q)$ is *not* the volume fraction
+    needed to compute the number density for $P(Q)$, then leave
+    **volfraction** as the volume fraction for $S(Q)$ and use
+    **scale** to define the volume fraction for $P(Q)$ as
+    $V_f$ = **scale**  $\cdot$  **volfraction**.  This situation may
+    occur in a mixed phase system where the effective volume
+    fraction needed to compute the structure is much higher than the
+    true volume fraction.
 
 * *volfraction*:
 
-  The volume fraction of material.
-
-  For hollow shapes, **volfraction** still represents the volume fraction of
-  material but the $S(Q)$ calculation needs the volume fraction *enclosed by*
-  *the shape.* SasView scales the user-specified volume fraction by the ratio
-  form:shell computed from the average form volume and average shell volume
-  returned from the $P(Q)$ calculation (the original volfraction is divided
-  by the shell volume to compute the number density, and then $P@S$ is scaled
-  by that to get the absolute scaling on the final $I(Q)$).
+    The volume fraction of material.
+
+    For hollow shapes, **volfraction** still represents the volume fraction of
+    material but the $S(Q)$ calculation needs the volume fraction *enclosed by*
+    *the shape.*  Thus the user-specified **volfraction** is scaled by the ratio
+    form:shell computed from the average form volume and average shell volume
+    returned from the $P(Q)$ calculation when calculating $S(Q)$.  The original
+    **volfraction** is divided by the shell volume to compute the number
+    density $n$ used in $P@S$ to get the absolute scaling on the final $I(Q)$.
 
 * *radius_effective*:
 
-  The radial distance determining the range of the $S(Q)$ interaction.
-  
-  This may, or may not, be the same as any "size" parameters describing the
-  form of the shape. For example, in a system containing freely-rotating
-  cylinders, the volume of space each cylinder requires to tumble will be
-  much larger than the volume of the cylinder itself. Thus the effective
-  radius will be larger than either the radius or half-length of the
-  cylinder. It may be sensible to tie or constrain **radius_effective** to one
-  or other of these "size" parameters.
-
-  If just part of the $S(Q)$ calculation, the value of **radius_effective** may
-  be polydisperse. If it is calculated by $P(Q)$, then it will be the weighted
-  average of the effective radii computed for the polydisperse shape
-  parameters.
+    The radial distance determining the range of the $S(Q)$ interaction.
+
+    This may be estimated from the "size" parameters $\mathbf \xi$ describing
+    the form of the shape.  For example, in a system containing freely-rotating
+    cylinders, the volume of space each cylinder requires to tumble will be
+    much larger than the volume of the cylinder itself.  Thus the effective
+    radius will be larger than either the radius or the half-length of the
+    cylinder.  It may be sensible to tie or constrain **radius_effective**
+    to one or other of these "size" parameters. **radius_effective** may
+    also be specified directly, independent of the estimate from $P(Q)$.
+
+    If it is calculated by $P(Q)$, **radius_effective** will be the
+    weighted average of the effective radii computed for the polydisperse
+    shape parameters, and that average used to compute $S(Q)$.  When
+    specified directly, the value of **radius_effective** may be
+    polydisperse, and $S(Q)$ will be averaged over a range of effective
+    radii.  Whether this makes any physical sense will depend on the system.
+
+* *radius_effective_mode*:
+
+    Selects the **radius_effective** value to use.
+
+    When **radius_effective_mode = 0** then the **radius_effective**
+    parameter in the $P@S$ model is used.  Otherwise
+    **radius_effective_mode = k** is the index into the list of
+    **radius_effective_modes** defined by the model indicating how the
+    effective radius should be computed from the parameters of the shape.
+    For example, the *ellipsoid* model defines the following::
+
+        1 => average curvature
+        2 => equivalent volume sphere
+        3 => min radius
+        4 => max radius
+
+    **radius_effective_mode** will only appear in the parameter table if
+    the model defines the list of modes, otherwise it will be set permanently
+    to 0 for user defined effective radius.
 
 * *structure_factor_mode*:
 
-  If the $P@S$ model supports the $\beta(Q)$ *decoupling correction* [1] then
-  **structure_factor_mode** will appear in the parameter table after the $S(Q)$
-  parameters.
-  
-  If **structure_factor_mode = 0** then the *local monodisperse approximation*
-  will be used, i.e.:
-
-    $I(Q)$ = $(scale$ / $volume)$ x $P(Q)$ x $S(Q)$ + $background$
-
-  If **structure_factor_mode = 1** then the $\beta(q)$ correction will be
-  used, i.e.:
-
-    $I(Q)$ = $(scale$ x $volfraction$ / $volume)$ x $( <F(Q)^2>$ + $<F(Q)>^2$ x $(S(Q)$ - $1) )$ + $background$
-
-    where $P(Q)$ = $<|F(Q)|^2>$.
-    
-  This is equivalent to:
-  
-    $I(Q)$ = $(scale$ / $volume)$ x $P(Q)$ x $( 1$ + $\beta(Q)$ x $(S(Q)$ - $1) )$ + $background$
-
-  The $\beta(Q)$ decoupling approximation has the effect of damping the
-  oscillations in the normal (local monodisperse) $S(Q)$. When $\beta(Q)$ = 1
-  the local monodisperse approximation is recovered.
-
-  More mode options may appear in future as more complicated operations are
-  added.
+    The type of structure factor calculation to use.
+
+    If the $P@S$ model supports the $\beta(Q)$ *decoupling correction* [1]
+    then **structure_factor_mode** will appear in the parameter table after
+    the $S(Q)$ parameters.
+
+    If **structure_factor_mode = 0** then the
+    *local monodisperse approximation* will be used, i.e.:
+
+    .. math::
+        I(Q) = \text{scale} \frac{V_f}{V} P(Q) S(Q) + \text{background}
+
+    where $P(Q) = \langle F(Q)^2 \rangle$.
+
+    If **structure_factor_mode = 1** then the $\beta(Q)$ correction will be
+    used, i.e.:
+
+    .. math::
+        I(Q) = \text{scale} \frac{V_f}{V} P(Q) [ 1 + \beta(Q) (S(Q) - 1) ]
+        + \text{background}
+
+    The $\beta(Q)$ decoupling approximation has the effect of damping the
+    oscillations in the normal (local monodisperse) $S(Q)$. When $\beta(Q) = 1$
+    the local monodisperse approximation is recovered.
+
+    More mode options may appear in future as more complicated operations are
+    added.
 
 References