Changeset 783c1b5 in sasview for src/sas/sasgui/perspectives/fitting

Timestamp:

Aug 22, 2017 2:55:25 PM (7 years ago)

Author:

GitHub <noreply@…>

Parents:

f001bc9 (diff), d24e41d (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

git-author:

Paul Butler <butlerpd@…> (08/22/17 14:55:25)

git-committer:

GitHub <noreply@…> (08/22/17 14:55:25)

Message:

Merge d24e41d0373baae3f4b6c5d0d232d990ff61920b into f001bc9e4b9d1ac0d4712117442f0c949334dbc4

Location:

src/sas/sasgui/perspectives/fitting

Files:

• fitting.py (modified) (2 diffs)
• model_thread.py (modified) (3 diffs)

src/sas/sasgui/perspectives/fitting/fitting.py

@@ -1742 +1742 @@
             @param unsmeared_error: data error, rescaled to unsmeared model
         """
-
         number_finite = np.count_nonzero(np.isfinite(y))
         np.nan_to_num(y)
@@ -1759 +1758 @@
                                       dy=unsmeared_error)
         # Comment this out until we can get P*S models with correctly populated parameters
-        #if sq_model is not None and pq_model is not None:
-        #    self.create_theory_1D(x, sq_model, page_id, model, data, state,
-        #                          data_description=model.name + " S(q)",
-        #                          data_id=str(page_id) + " " + data.name + " S(q)")
-        #    self.create_theory_1D(x, pq_model, page_id, model, data, state,
-        #                          data_description=model.name + " P(q)",
-        #                          data_id=str(page_id) + " " + data.name + " P(q)")
+        if sq_model is not None and pq_model is not None:
+            self.create_theory_1D(x, sq_model, page_id, model, data, state,
+                                  data_description=model.name + " S(q)",
+                                  data_id=str(page_id) + " " + data.name + " S(q)")
+            self.create_theory_1D(x, pq_model, page_id, model, data, state,
+                                  data_description=model.name + " P(q)",
+                                  data_id=str(page_id) + " " + data.name + " P(q)")
 
         current_pg = self.fit_panel.get_page_by_id(page_id)

src/sas/sasgui/perspectives/fitting/model_thread.py

@@ -71 +71 @@
                     (self.data.qy_data * self.data.qy_data))
 
-        # For theory, qmax is based on 1d qmax 
+        # For theory, qmax is based on 1d qmax
         # so that must be mulitified by sqrt(2) to get actual max for 2d
         index_model = (self.qmin <= radius) & (radius <= self.qmax)
@@ -91 +91 @@
                 self.data.qy_data[index_model]
             ])
-        output = np.zeros(len(self.data.qx_data))
+        # Initialize output to NaN so masked elements do not get plotted.
+        output = np.empty_like(self.data.qx_data)
         # output default is None
         # This method is to distinguish between masked
         #point(nan) and data point = 0.
-        output = output / output
+        output[:] = np.NaN
         # set value for self.mask==True, else still None to Plottools
         output[index_model] = value
@@ -198 +199 @@
             output[index] = self.model.evalDistribution(self.data.x[index])
 
+        x=self.data.x[index]
+        y=output[index]
         sq_values = None
         pq_values = None
-        s_model = None
-        p_model = None
         if isinstance(self.model, MultiplicationModel):
             s_model = self.model.s_model
             p_model = self.model.p_model
-        elif hasattr(self.model, "get_composition_models"):
-            p_model, s_model = self.model.get_composition_models()
-
-        if p_model is not None and s_model is not None:
-            sq_values = np.zeros((len(self.data.x)))
-            pq_values = np.zeros((len(self.data.x)))
-            sq_values[index] = s_model.evalDistribution(self.data.x[index])
-            pq_values[index] = p_model.evalDistribution(self.data.x[index])
+            sq_values = s_model.evalDistribution(x)
+            pq_values = p_model.evalDistribution(x)
+        elif hasattr(self.model, "calc_composition_models"):
+            results = self.model.calc_composition_models(x)
+            if results is not None:
+                sq_values = results[0]
+                pq_values = results[1]
+
 
         elapsed = time.time() - self.starttime
 
-        self.complete(x=self.data.x[index], y=output[index],
+        self.complete(x=x, y=y,
                       page_id=self.page_id,
                       state=self.state,