Changes in sasmodels/models/fcc_paracrystal.py [e7e9231:da7b26b] in sasmodels

File:

• sasmodels/models/fcc_paracrystal.py (modified) (5 diffs)

sasmodels/models/fcc_paracrystal.py

@@ -3 +3 @@
 #note - calculation requires double precision
 r"""
+.. warning:: This model and this model description are under review following 
+             concerns raised by SasView users. If you need to use this model, 
+             please email help@sasview.org for the latest situation. *The 
+             SasView Developers. September 2018.*
+
 Definition
 ----------
@@ -10 +15 @@
 negligible, and the size of the paracrystal is infinitely large.
 Paracrystalline distortion is assumed to be isotropic and characterized by
-a Gaussian distribution. 
+a Gaussian distribution.
 
 The scattering intensity $I(q)$ is calculated as
@@ -95 +100 @@
 
 Authorship and Verification
-----------------------------
+---------------------------
 
 * **Author:** NIST IGOR/DANSE **Date:** pre 2010
-* **Last Modified by:** Paul Butler **Date:** September 16, 2018
-* **Last Reviewed by:** Paul Butler **Date:** September 16, 2018
+* **Last Modified by:** Paul Butler **Date:** September 29, 2016
+* **Last Reviewed by:** Richard Heenan **Date:** March 21, 2016
 """
 
@@ -118 +123 @@
 # pylint: disable=bad-whitespace, line-too-long
 #             ["name", "units", default, [lower, upper], "type","description"],
-parameters = [["lattice_spacing", "Ang", 220, [-inf, inf], "", "Lattice spacing"],
-              ["lattice_distortion", "", 0.06, [-inf, inf], "", "Paracrystal distortion factor"],
+parameters = [["dnn", "Ang", 220, [-inf, inf], "", "Nearest neighbour distance"],
+              ["d_factor", "", 0.06, [-inf, inf], "", "Paracrystal distortion factor"],
               ["radius", "Ang", 40, [0, inf], "volume", "Particle radius"],
               ["sld", "1e-6/Ang^2", 4, [-inf, inf], "sld", "Particle scattering length density"],
@@ -134 +139 @@
     # copied from bcc_paracrystal
     radius = 10**np.random.uniform(1.3, 4)
-    lattice_distortion = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7
-    lattice_spacing_fraction = np.random.beta(a=10, b=1)
-    lattice_spacing = radius*4/np.sqrt(2)/lattice_spacing_fraction
+    d_factor = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7
+    dnn_fraction = np.random.beta(a=10, b=1)
+    dnn = radius*4/np.sqrt(2)/dnn_fraction
     pars = dict(
         #sld=1, sld_solvent=0, scale=1, background=1e-32,
-        latice_spacing=lattice_spacing,
-        lattice_distortion=d_factor,
+        dnn=dnn,
+        d_factor=d_factor,
         radius=radius,
     )