Changeset 804fefa in sasview for src/sas/sasgui/perspectives/fitting

Timestamp:

Oct 5, 2016 1:03:32 PM (8 years ago)

Author:

Mathieu Doucet <doucetm@…>

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:

6a0c75d1

Parents:

c807957

Message:

Add rescaled data (re #687)

Location:

src/sas/sasgui/perspectives/fitting

Files:

• fitting.py (modified) (4 diffs)
• model_thread.py (modified) (3 diffs)

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

@@ -1666 +1666 @@
 
     def create_theory_1D(self, x, y, page_id, model, data, state,
-                         data_description, data_id):
+                         data_description, data_id, dy=None):
         """
             Create a theory object associate with an existing Data1D
@@ -1680 +1680 @@
         """
         new_plot = Data1D(x=x, y=y)
-        new_plot.is_data = False
-        new_plot.dy = numpy.zeros(len(y))
-        new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
+        new_plot.is_data = dy is not None
+        new_plot.interactive = True
+        new_plot.dy = dy
+        new_plot.dx = None
+        new_plot.dxl = None
+        new_plot.dxw = None
+        # If this is a theory curve, pick the proper symbol to make it a curve
+        if not new_plot.is_data:
+            new_plot.symbol = GUIFRAME_ID.CURVE_SYMBOL_NUM
         _yaxis, _yunit = data.get_yaxis()
         _xaxis, _xunit = data.get_xaxis()
         new_plot.title = data.name
-
         new_plot.group_id = data.group_id
         if new_plot.group_id == None:
@@ -1713 +1718 @@
                     toggle_mode_on=False, state=None,
                     data=None, update_chisqr=True,
-                    source='model', plot_result=True, unsmeared_model=None):
+                    source='model', plot_result=True,
+                    unsmeared_model=None, unsmeared_data=None,
+                    unsmeared_error=None):
         """
             Complete plotting 1D data
             @param unsmeared_model: fit model, without smearing
+            @param unsmeared_data: data, rescaled to unsmeared model
         """
         try:
@@ -1724 +1732 @@
                                              data_id=str(page_id) + " " + data.name)
             if unsmeared_model is not None:
+                logging.error(str(unsmeared_error))
                 self.create_theory_1D(x, unsmeared_model, page_id, model, data, state,
                                       data_description=model.name + " unsmeared",
                                       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)
 
             current_pg = self.fit_panel.get_page_by_id(page_id)

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

@@ -167 +167 @@
 
         # If we use a smearer, also return the unsmeared model
-        output_unsmeared = None
+        unsmeared_output = None
+        unsmeared_data = None
+        unsmeared_error = None
         ##smearer the ouput of the plot
         if self.smearer is not None:
@@ -173 +175 @@
                                                              self.qmax)
             mask = self.data.x[first_bin:last_bin+1]
-            output_unsmeared = numpy.zeros((len(self.data.x)))
-            output_unsmeared[first_bin:last_bin+1] = self.model.evalDistribution(mask)
-            output = self.smearer(output_unsmeared, first_bin, last_bin)
-            output_unsmeared = output_unsmeared[index]
+            unsmeared_output = numpy.zeros((len(self.data.x)))
+            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
         else:
             output[index] = self.model.evalDistribution(self.data.x[index])
@@ -192 +206 @@
                       update_chisqr=self.update_chisqr,
                       source=self.source,
-                      unsmeared_model=output_unsmeared)
+                      unsmeared_model=unsmeared_output,
+                      unsmeared_data=unsmeared_data,
+                      unsmeared_error=unsmeared_error)
 
     def results(self):