Changes in src/sas/qtgui/Perspectives/Fitting/ModelThread.py [dcabba7:40975f8] in sasview

File:

• src/sas/qtgui/Perspectives/Fitting/ModelThread.py (modified) (2 diffs)

src/sas/qtgui/Perspectives/Fitting/ModelThread.py

@@ -101 +101 @@
         elapsed = time.time() - self.starttime
 
-        res = dict(image = output, data = self.data, page_id = self.page_id,
-            model = self.model, state = self.state,
-            toggle_mode_on = self.toggle_mode_on, elapsed = elapsed,
-            index = index_model, fid = self.fid,
-            qmin = self.qmin, qmax = self.qmax,
-            weight = self.weight, update_chisqr = self.update_chisqr,
-            source = self.source)
-
         if LocalConfig.USING_TWISTED:
-            return res
-        else:
-            self.completefn(res)
+            return (output,
+                    self.data,
+                    self.page_id,
+                    self.model,
+                    self.state,
+                    self.toggle_mode_on,
+                    elapsed,
+                    index_model,
+                    self.fid,
+                    self.qmin,
+                    self.qmax,
+                    self.weight,
+                    self.update_chisqr,
+                    self.source)
+        else:
+            self.completefn((output,
+                           self.data,
+                           self.page_id,
+                           self.model,
+                           self.state,
+                           self.toggle_mode_on,
+                           elapsed,
+                           index_model,
+                           self.fid,
+                           self.qmin,
+                           self.qmax,
+                           self.weight,
+                           #qstep=self.qstep,
+                           self.update_chisqr,
+                           self.source))
+
 
 class Calc1D(CalcThread):
@@ -195 +215 @@
             output[index] = self.model.evalDistribution(self.data.x[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, "calc_composition_models"):
-            results = self.model.calc_composition_models(self.data.x[index])
-            if results is not None:
-                pq_values, sq_values = results
-
-        if pq_values is None or sq_values is None:
-            if p_model is not None and s_model is not None:
-                sq_values = numpy.zeros((len(self.data.x)))
-                pq_values = numpy.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])
+        results_eval = {}
+        intermediate_results = getattr(self.model, "_intermediate_results", None)
+        if callable(intermediate_results):
+            # support for future sasmodels (beta approx support) - it returns a dict of intermediate results, keyed by
+            # name
+            results_eval = intermediate_results()
+        else:
+            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, "calc_composition_models"):
+                results = self.model.calc_composition_models(self.data.x[index])
+                if results is not None:
+                    pq_values, sq_values = results
+
+            if pq_values is None or sq_values is None:
+                if p_model is not None and s_model is not None:
+                    sq_values = numpy.zeros((len(self.data.x)))
+                    pq_values = numpy.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])
+
+            if pq_values is not None and sq_values is not None:
+                results_eval = {
+                    "P(Q)": pq_values,
+                    "S(Q)": sq_values
+                }
 
         elapsed = time.time() - self.starttime
 
-        res = dict(x = self.data.x[index], y = output[index],
-            page_id = self.page_id, state = self.state, weight = self.weight,
-            fid = self.fid, toggle_mode_on = self.toggle_mode_on,
-            elapsed = elapsed, index = index, model = self.model,
-            data = self.data, update_chisqr = self.update_chisqr,
-            source = self.source, unsmeared_output = unsmeared_output,
-            unsmeared_data = unsmeared_data, unsmeared_error = unsmeared_error,
-            pq_values = pq_values, sq_values = sq_values)
-
         if LocalConfig.USING_TWISTED:
-            return res
-        else:
-            self.completefn(res)
+            return (self.data.x[index], output[index],
+                    self.page_id,
+                    self.state,
+                    self.weight,
+                    self.fid,
+                    self.toggle_mode_on,
+                    elapsed, index, self.model,
+                    self.data,
+                    self.update_chisqr,
+                    self.source,
+                    unsmeared_output, unsmeared_data, unsmeared_error,
+                    results_eval)
+        else:
+            self.completefn((self.data.x[index], output[index],
+                        self.page_id,
+                        self.state,
+                        self.weight,
+                        self.fid,
+                        self.toggle_mode_on,
+                        elapsed, index, self.model,
+                        self.data,
+                        self.update_chisqr,
+                        self.source,
+                        unsmeared_output, unsmeared_data, unsmeared_error,
+                        results_eval))
 
     def results(self):