Changeset 286c757 in sasview for src/sas/sasgui

Timestamp:

Oct 7, 2016 10:48:25 AM (8 years ago)

Author:

mathieu

Branches:

master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.1.1, release-4.1.2, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

1b1a1c1

Parents:

61780e3

Message:

Make sure we can compute models w/o data. Fixes #714

Location:

src/sas/sasgui/perspectives/fitting

Files:

• fitting.py (modified) (1 diff)
• model_thread.py (modified) (1 diff)

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

@@ -1740 +1740 @@
                                       data_id=str(page_id) + " " + data.name + " unsmeared")
 
-                self.create_theory_1D(x, unsmeared_data, page_id, model, data, state,
-                                      data_description="Data unsmeared",
-                                      data_id="Data  " + data.name + " unsmeared",
-                                      dy=unsmeared_error)
+                if unsmeared_data is not None and unsmeared_error is not None:
+                    self.create_theory_1D(x, unsmeared_data, page_id, model, data, state,
+                                          data_description="Data unsmeared",
+                                          data_id="Data  " + data.name + " unsmeared",
+                                          dy=unsmeared_error)
                 
             if sq_model is not None and pq_model is not None:

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

@@ -179 +179 @@
             unsmeared_output[first_bin:last_bin+1] = self.model.evalDistribution(mask)
             output = self.smearer(unsmeared_output, first_bin, last_bin)
-            
+
             # Rescale data to unsmeared model
-            unsmeared_data = numpy.zeros((len(self.data.x)))
-            unsmeared_error = numpy.zeros((len(self.data.x)))
-            unsmeared_data[first_bin:last_bin+1] = self.data.y[first_bin:last_bin+1]\
-                                                    * unsmeared_output[first_bin:last_bin+1]\
-                                                    / output[first_bin:last_bin+1]
-            unsmeared_error[first_bin:last_bin+1] = self.data.dy[first_bin:last_bin+1]\
-                                                    * unsmeared_output[first_bin:last_bin+1]\
-                                                    / output[first_bin:last_bin+1]
-            unsmeared_output=unsmeared_output[index]
-            unsmeared_data=unsmeared_data[index]
-            unsmeared_error=unsmeared_error
+            # Check that the arrays are compatible. If we only have a model but no data,
+            # the length of data.y will be zero.
+            if isinstance(self.data.y, numpy.ndarray) and output.shape == self.data.y.shape:
+                unsmeared_data = numpy.zeros((len(self.data.x)))
+                unsmeared_error = numpy.zeros((len(self.data.x)))
+                unsmeared_data[first_bin:last_bin+1] = self.data.y[first_bin:last_bin+1]\
+                                                        * unsmeared_output[first_bin:last_bin+1]\
+                                                        / output[first_bin:last_bin+1]
+                unsmeared_error[first_bin:last_bin+1] = self.data.dy[first_bin:last_bin+1]\
+                                                        * unsmeared_output[first_bin:last_bin+1]\
+                                                        / output[first_bin:last_bin+1]
+                unsmeared_output=unsmeared_output[index]
+                unsmeared_data=unsmeared_data[index]
+                unsmeared_error=unsmeared_error
         else:
             output[index] = self.model.evalDistribution(self.data.x[index])