Changes in / [6c6aa83:b39debba] in sasview

Location:

src/sas

Files:

• sascalc/data_util/qsmearing.py (modified) (2 diffs)
• sascalc/fit/AbstractFitEngine.py (modified) (1 diff)
• sasgui/guiframe/gui_manager.py (modified) (2 diffs)
• sasgui/perspectives/fitting/basepage.py (modified) (5 diffs)
• sasgui/perspectives/fitting/fitpage.py (modified) (1 diff)
• sasgui/perspectives/fitting/model_thread.py (modified) (4 diffs)

src/sas/sascalc/data_util/qsmearing.py

@@ -41 +41 @@
         elif data.dqx_data == None or data.dqy_data == None:
             return None
-        return Pinhole2D(data)
+        return PySmear2D(data, model)
 
     if  not hasattr(data, "dx") and not hasattr(data, "dxl")\
@@ -142 +142 @@
     width = data.dx if data.dx is not None else 0
     return PySmear(Pinhole1D(q, width), model)
+
+
+class PySmear2D(object):
+    """
+    Q smearing class for SAS 2d pinhole data
+    """
+
+    def __init__(self, data=None, model=None):
+        self.data = data
+        self.model = model
+        self.accuracy = 'Low'
+        self.limit = 3.0
+        self.index = None
+        self.coords = 'polar'
+        self.smearer = True
+
+    def set_accuracy(self, accuracy='Low'):
+        """
+        Set accuracy.
+
+        :param accuracy:  string
+        """
+        self.accuracy = accuracy
+
+    def set_smearer(self, smearer=True):
+        """
+        Set whether or not smearer will be used
+
+        :param smearer: smear object
+
+        """
+        self.smearer = smearer
+
+    def set_data(self, data=None):
+        """
+        Set data.
+
+        :param data: DataLoader.Data_info type
+        """
+        self.data = data
+
+    def set_model(self, model=None):
+        """
+        Set model.
+
+        :param model: sas.models instance
+        """
+        self.model = model
+
+    def set_index(self, index=None):
+        """
+        Set index.
+
+        :param index: 1d arrays
+        """
+        self.index = index
+
+    def get_value(self):
+        """
+        Over sampling of r_nbins times phi_nbins, calculate Gaussian weights,
+        then find smeared intensity
+        """
+        if self.smearer:
+            res = Pinhole2D(data=self.data, index=self.index,
+                            nsigma=3.0, accuracy=self.accuracy,
+                            coords=self.coords)
+            val = self.model.evalDistribution(res.q_calc)
+            return res.apply(val)
+        else:
+            index = self.index if self.index is not None else slice(None)
+            qx_data = self.data.qx_data[index]
+            qy_data = self.data.qy_data[index]
+            q_calc = [qx_data, qy_data]
+            val = self.model.evalDistribution(q_calc)
+            return val
+

src/sas/sascalc/fit/AbstractFitEngine.py

@@ -359 +359 @@
         if self.smearer != None:
             fn.set_index(self.idx)
-            # Get necessary data from self.data and set the data for smearing
-            fn.get_data()
-
             gn = fn.get_value()
         else:

src/sas/sasgui/guiframe/gui_manager.py

@@ -1995 +1995 @@
                 wx.PostEvent(self,
                              StatusEvent(status="Completed saving."))
-            except:
+            except Exception:
                 msg = "Error occurred while saving: "
+                msg += traceback.format_exc()
                 msg += "To save, the application panel should have a data set.."
                 wx.PostEvent(self, StatusEvent(status=msg))
@@ -2045 +2046 @@
                 logging.warning(msg)
                 wx.PostEvent(self, StatusEvent(status=msg, info="error"))
-        except:
+        except Exception:
             msg = "Error occurred while saving: "
+            msg += traceback.format_exc()
             msg += "To save, at least one application panel "
             msg += "should have a data set.."

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

@@ -1041 +1041 @@
                         disp_model = POLYDISPERSITY_MODELS['array']()
                         if hasattr(state, "values") and \
-                                 self.disp_cb_dict[item].GetValue() is True:
+                                 self.disp_cb_dict[item].GetValue():
                             if len(state.values) > 0:
                                 self.values = state.values
@@ -1541 +1541 @@
                         index_data = ((self.qmin_x <= self.data.x) &
                                       (self.data.x <= self.qmax_x))
-                        val = self.data.x[index_data is True]
-                        val = len(val) if isinstance(val, list) else 1
-                        self.Npts_fit.SetValue(str(val))
-
+                        val = str(len(self.data.x[index_data]))
+                        self.Npts_fit.SetValue(val)
                     else:
                         # No data in the panel
@@ -2147 +2145 @@
                 flag = False
             else:
-                self.Npts_fit.SetValue(str(len(index_data[index_data is True])))
+                self.Npts_fit.SetValue(str(len(index_data[index_data])))
                 self.fitrange = True
 
@@ -2182 +2180 @@
                 flag = False
             else:
-                val = index_data[index_data is True]
-                val = len(val) if isinstance(val, list) else 1
-                self.Npts_fit.SetValue(str(val))
+                self.Npts_fit.SetValue(str(len(index_data[index_data])))
                 self.fitrange = True
 
@@ -2628 +2624 @@
             Layout after self._draw_model
         """
-        if ON_MAC is True:
+        if ON_MAC:
             time.sleep(1)
 

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

@@ -1618 +1618 @@
                 return
         # check if it is pinhole smear and get min max if it is.
-        if data.dx is not None and not numpy.any(data.dx):
+        if data.dx is not None and numpy.any(data.dx):
             self.smear_type = "Pinhole"
             self.dq_l = data.dx[0]

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

@@ -82 +82 @@
             fn.set_model(self.model)
             fn.set_index(index_model)
-            # Get necessary data from self.data and set the data for smearing
-            fn.get_data()
             # Calculate smeared Intensity
             #(by Gaussian averaging): DataLoader/smearing2d/Smearer2D()
@@ -89 +87 @@
         else:
             # calculation w/o smearing
-            value = self.model.evalDistribution(\
-                [self.data.qx_data[index_model],
-                 self.data.qy_data[index_model]])
+            value = self.model.evalDistribution([
+                self.data.qx_data[index_model],
+                self.data.qy_data[index_model]
+            ])
         output = numpy.zeros(len(self.data.qx_data))
         # output default is None
@@ -198 +197 @@
             output[index] = self.model.evalDistribution(self.data.x[index])
 
-        sq_model = None
-        pq_model = None
+        sq_values = None
+        pq_values = None
+        s_model = None
+        p_model = None
         if isinstance(self.model, MultiplicationModel):
-            sq_model = numpy.zeros((len(self.data.x)))
-            pq_model = numpy.zeros((len(self.data.x)))
-            sq_model[index] = self.model.s_model.evalDistribution(self.data.x[index])
-            pq_model[index] = self.model.p_model.evalDistribution(self.data.x[index])
+            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 = 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])
 
         elapsed = time.time() - self.starttime
@@ -221 +228 @@
                       unsmeared_data=unsmeared_data,
                       unsmeared_error=unsmeared_error,
-                      pq_model=pq_model,
-                      sq_model=sq_model)
+                      pq_model=pq_values,
+                      sq_model=sq_values)
 
     def results(self):