Changes in / [352494a:37c7d5e] in sasmodels

Files:

• doc/ref/index.rst (modified) (1 diff)
• sasmodels/models/core_shell_parallelepiped.py (modified) (5 diffs)
• sasmodels/models/fractal.c (modified) (2 diffs)
• sasmodels/models/fractal.py (modified) (4 diffs)
• sasmodels/resolution.py (modified) (7 diffs)

doc/ref/index.rst

@@ -10 +10 @@
    refs.rst
    gpu/gpu_computations.rst
-   gpu/opencl_installation.rst
    magnetism/magnetism.rst
    sesans/sans_to_sesans.rst

sasmodels/models/core_shell_parallelepiped.py

@@ -4 +4 @@
 
 Calculates the form factor for a rectangular solid with a core-shell structure.
-The thickness and the scattering length density of the shell or 
-"rim" can be different on each (pair) of faces. However at this time
-the model does **NOT** actually calculate a c face rim despite the presence of
-the parameter.
-
-.. note::
-   This model was originally ported from NIST IGOR macros. However,t is not
-   yet fully understood by the SasView developers and is currently review.
+**The thickness and the scattering length density of the shell or "rim"
+can be different on all three (pairs) of faces.**
 
 The form factor is normalized by the particle volume $V$ such that
@@ -41 +35 @@
     V = ABC + 2t_ABC + 2t_BAC + 2t_CAB
 
-**meaning that there are "gaps" at the corners of the solid.**  Again note that
-$t_C = 0$ currently. 
+**meaning that there are "gaps" at the corners of the solid.**
 
 The intensity calculated follows the :ref:`parallelepiped` model, with the
@@ -48 +41 @@
 amplitudes of the core and shell, in the same manner as a core-shell model.
 
+.. math::
+
+    F_{a}(Q,\alpha,\beta)=
+    \Bigg(\frac{sin(Q(L_A+2t_A)/2sin\alpha sin\beta)}{Q(L_A+2t_A)/2sin\alpha
+    sin\beta)}
+    - \frac{sin(QL_A/2sin\alpha sin\beta)}{QL_A/2sin\alpha sin\beta)} \Bigg)
+    + \frac{sin(QL_B/2sin\alpha sin\beta)}{QL_B/2sin\alpha sin\beta)}
+    + \frac{sin(QL_C/2sin\alpha sin\beta)}{QL_C/2sin\alpha sin\beta)}
 
 .. note::
@@ -92 +93 @@
     detector plane.
 
+Validation
+----------
+
+The model uses the form factor calculations implemented in a c-library provided
+by the NIST Center for Neutron Research (Kline, 2006).
+
 References
 ----------
@@ -106 +113 @@
 
 * **Author:** NIST IGOR/DANSE **Date:** pre 2010
-* **Converted to sasmodels by:** Miguel Gonzales **Date:** February 26, 2016
-* **Last Modified by:** Wojciech Potrzebowski **Date:** January 11, 2017
-* **Currently Under review by:** Paul Butler
+* **Last Modified by:** Paul Butler **Date:** September 30, 2016
+* **Last Reviewed by:** Miguel Gonzales **Date:** March 21, 2016
 """
 

sasmodels/models/fractal.c

@@ -1 +1 @@
 #define INVALID(p) (p.fractal_dim < 0.0)
-
- static double
- form_volume(double radius)
- {
-     return M_4PI_3 * cube(radius);
- }
 
 static double
@@ -19 +13 @@
 
     //calculate P(q) for the spherical subunits
-    const double pq = square(form_volume(radius) * (sld_block-sld_solvent)
-                      *sas_3j1x_x(q*radius));
+    const double V = M_4PI_3*cube(radius);
+    const double pq = V * square((sld_block-sld_solvent)*sas_3j1x_x(q*radius));
 
     // scale to units cm-1 sr-1 (assuming data on absolute scale)

sasmodels/models/fractal.py

@@ -20 +20 @@
 .. math::
 
-    P(q)&= F(qR_0)^2 \\
-    F(q)&= \frac{3 (\sin x - x \cos x)}{x^3} \\
-    V_\text{particle} &= \frac{4}{3}\ \pi R_0 \\
+    P(q)&= F(qR_0)^2
+
+    F(q)&= \frac{3 (\sin x - x \cos x)}{x^3}
+
+    V_\text{particle} &= \frac{4}{3}\ \pi R_0
+
     S(q) &= 1 + \frac{D_f\  \Gamma\!(D_f-1)}{[1+1/(q \xi)^2\  ]^{(D_f -1)/2}}
     \frac{\sin[(D_f-1) \tan^{-1}(q \xi) ]}{(q R_0)^{D_f}}
@@ -29 +32 @@
 is the fractal dimension, representing the self similarity of the structure. 
 Note that S(q) here goes negative if $D_f$ is too large, and the Gamma function 
-diverges at $D_f=0$ and $D_f=1$.
+diverges at $D_f$=0 and $D_f$=1.  
 
 **Polydispersity on the radius is provided for.**
@@ -44 +47 @@
 ----------
 
-.. [#] J Teixeira, *J. Appl. Cryst.*, 21 (1988) 781-785
+J Teixeira, *J. Appl. Cryst.*, 21 (1988) 781-785
 
-Authorship and Verification
-----------------------------
+**Author:** NIST IGOR/DANSE **on:** pre 2010
 
-* **Author:** NIST IGOR/DANSE **Date:** pre 2010
-* **Converted to sasmodels by:** Paul Butler **Date:** March 19, 2016
-* **Last Modified by:** Paul Butler **Date:** March 12, 2017
-* **Last Reviewed by:** Paul Butler **Date:** March 12, 2017
+**Last Modified by:** Paul Butler **on:** March 20, 2016
+
+**Last Reviewed by:** Paul Butler **on:** March 20, 2016
 
 """
@@ -83 +84 @@
 parameters = [["volfraction", "", 0.05, [0.0, 1], "",
                "volume fraction of blocks"],
-              ["radius",    "Ang",  5.0, [0.0, inf], "volume",
+              ["radius",    "Ang",  5.0, [0.0, inf], "",
                "radius of particles"],
               ["fractal_dim",      "",  2.0, [0.0, 6.0], "",

sasmodels/resolution.py

@@ -17 +17 @@
 
 MINIMUM_RESOLUTION = 1e-8
-MINIMUM_ABSOLUTE_Q = 0.02  # relative to the minimum q in the data
+
+
+# When extrapolating to -q, what is the minimum positive q relative to q_min
+# that we wish to calculate?
+MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION = 0.01
 
 class Resolution(object):
@@ -78 +82 @@
         self.q_calc = (pinhole_extend_q(q, q_width, nsigma=nsigma)
                        if q_calc is None else np.sort(q_calc))
-
-        # Protect against models which are not defined for very low q.  Limit
-        # the smallest q value evaluated (in absolute) to 0.02*min
-        cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q)
-        self.q_calc = self.q_calc[abs(self.q_calc) >= cutoff]
-
-        # Build weight matrix from calculated q values
         self.weight_matrix = pinhole_resolution(self.q_calc, self.q,
                                 np.maximum(q_width, MINIMUM_RESOLUTION))
-        self.q_calc = abs(self.q_calc)
 
     def apply(self, theory):
@@ -127 +123 @@
         self.q_calc = slit_extend_q(q, qx_width, qy_width) \
             if q_calc is None else np.sort(q_calc)
-
-        # Protect against models which are not defined for very low q.  Limit
-        # the smallest q value evaluated (in absolute) to 0.02*min
-        cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q)
-        self.q_calc = self.q_calc[abs(self.q_calc) >= cutoff]
-
-        # Build weight matrix from calculated q values
         self.weight_matrix = \
             slit_resolution(self.q_calc, self.q, qx_width, qy_width)
-        self.q_calc = abs(self.q_calc)
 
     def apply(self, theory):
@@ -165 +153 @@
     # neither trapezoid nor Simpson's rule improved the accuracy.
     edges = bin_edges(q_calc)
-    #edges[edges < 0.0] = 0.0 # clip edges below zero
+    edges[edges < 0.0] = 0.0 # clip edges below zero
     cdf = erf((edges[:, None] - q[None, :]) / (sqrt(2.0)*q_width)[None, :])
     weights = cdf[1:] - cdf[:-1]
@@ -298 +286 @@
     # The current algorithm is a midpoint rectangle rule.
     q_edges = bin_edges(q_calc) # Note: requires q > 0
-    #q_edges[q_edges < 0.0] = 0.0 # clip edges below zero
+    q_edges[q_edges < 0.0] = 0.0 # clip edges below zero
     weights = np.zeros((len(q), len(q_calc)), 'd')
 
@@ -404 +392 @@
     interval.
 
-    Note that extrapolated values may be negative.
+    if *q_min* is zero or less then *q[0]/10* is used instead.
     """
     q = np.sort(q)
     if q_min + 2*MINIMUM_RESOLUTION < q[0]:
+        if q_min <= 0: q_min = q_min*MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION
         n_low = np.ceil((q[0]-q_min) / (q[1]-q[0])) if q[1] > q[0] else 15
         q_low = np.linspace(q_min, q[0], n_low+1)[:-1]
@@ -459 +448 @@
         log_delta_q = log(10.) / points_per_decade
     if q_min < q[0]:
-        if q_min < 0: q_min = q[0]*MINIMUM_ABSOLUTE_Q
+        if q_min < 0: q_min = q[0]*MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION
         n_low = log_delta_q * (log(q[0])-log(q_min))
         q_low = np.logspace(log10(q_min), log10(q[0]), np.ceil(n_low)+1)[:-1]