Changes in sasmodels/sasview_model.py [3bcb88c:9c1a59c] in sasmodels

File:

• sasmodels/sasview_model.py (modified) (3 diffs)

sasmodels/sasview_model.py

@@ -565 +565 @@
         parameters = self._model_info.parameters
         pairs = [self._get_weights(p) for p in parameters.call_parameters]
+        #weights.plot_weights(self._model_info, pairs)
         call_details, values, is_magnetic = make_kernel_args(calculator, pairs)
         #call_details.show()
@@ -618 +619 @@
             # remember them is kind of funky.
             # Note: can't seem to get disperser parameters from sasview
-            # (1) Could create a sasview model that has not yet # been
+            # (1) Could create a sasview model that has not yet been
             # converted, assign the disperser to one of its polydisperse
             # parameters, then retrieve the disperser parameters from the
-            # sasview model.  (2) Could write a disperser parameter retriever
-            # in sasview.  (3) Could modify sasview to use sasmodels.weights
-            # dispersers.
+            # sasview model.
+            # (2) Could write a disperser parameter retriever in sasview.
+            # (3) Could modify sasview to use sasmodels.weights dispersers.
             # For now, rely on the fact that the sasview only ever uses
             # new dispersers in the set_dispersion call and create a new
             # one instead of trying to assign parameters.
-            dispersion = weights.MODELS[dispersion.type]()
             self.dispersion[parameter] = dispersion.get_pars()
         else:
@@ -658 +658 @@
         elif par.polydisperse:
             dis = self.dispersion[par.name]
-            value, weight = weights.get_weights(
-                dis['type'], dis['npts'], dis['width'], dis['nsigmas'],
-                self.params[par.name], par.limits, par.relative_pd)
+            if dis['type'] == 'array':
+                value, weight = dis['values'], dis['weights']
+            else:
+                value, weight = weights.get_weights(
+                    dis['type'], dis['npts'], dis['width'], dis['nsigmas'],
+                    self.params[par.name], par.limits, par.relative_pd)
             return value, weight / np.sum(weight)
         else: