Changeset 3d58247 in sasmodels

Timestamp:

Jan 12, 2018 8:57:34 AM (7 years ago)

Author:

dirk

Branches:

master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

92d330fd

Parents:

3c44c34

Message:

uniform distribution ignores nsigma

Files:

• doc/guide/pd/pd_uniform.jpg (modified) (previous)
• doc/guide/pd/polydispersity.rst (modified) (5 diffs)
• sasmodels/weights.py (modified) (2 diffs)

doc/guide/pd/polydispersity.rst

@@ -71 +71 @@
 where $\bar x$ is the mean of the distribution, $w$ is the half-width, and
 *Norm* is a normalization factor which is determined during the numerical
-calculation.
+calculation. 
 
 Note that the standard deviation and the half width $w$ are different!
@@ -86 +86 @@
 
     Rectangular distribution.
+    
+
 
 Uniform Distribution
@@ -102 +104 @@
     where $\bar x$ is the mean of the distribution, $\sigma$ is the half-width, and
     *Norm* is a normalization factor which is determined during the numerical
-    calculation.
+    calculation. 
 
     Note that the polydispersity is given by
@@ -111 +113 @@
 
         Uniform distribution.
+        
+The value $N_\sigma$ is ignored for this distribution.        
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
@@ -196 +200 @@
 
 Note that larger values of PD might need larger number of points and $N_\sigma$.
-For example, at PD=0.7 and radius=60 |Ang|, Npts>=160 and Nsigmas>=15 at least.
+For example, at PD=0.7 and radius=60 |Ang|, Npts>=160 and $N_\sigma$>=15 at least.
 
 .. figure:: pd_schulz.jpg

sasmodels/weights.py

@@ -106 +106 @@
     """
     type = "uniform"
-    default = dict(npts=35, width=0, nsigmas=1)
-    def _weights(self, center, sigma, lb, ub):
-        x = self._linspace(center, sigma, lb, ub)
-        x = x[np.fabs(x-center) <= np.fabs(sigma)]
+    default = dict(npts=35, width=0, nsigmas=None)
+    def _weights(self, center, sigma, lb, ub):
+        x = np.linspace(center-sigma, center+sigma, self.npts)
+        x = x[(x >= lb) & (x <= ub)]
         return x, np.ones_like(x)
 
@@ -123 +123 @@
     default = dict(npts=35, width=0, nsigmas=1.73205)
     def _weights(self, center, sigma, lb, ub):
-        x = self._linspace(center, sigma, lb, ub)
-        x = x[np.fabs(x-center) <= np.fabs(sigma)*sqrt(3.0)]
-        return x, np.ones_like(x)
-
+         x = self._linspace(center, sigma, lb, ub)
+         x = x[np.fabs(x-center) <= np.fabs(sigma)*sqrt(3.0)]
+         return x, np.ones_like(x)
 
 class LogNormalDispersion(Dispersion):