Changes in src/sas/sascalc/data_util/qsmearing.py [f8aa738:392056d] in sasview

File:

• src/sas/sascalc/data_util/qsmearing.py (modified) (6 diffs)

src/sas/sascalc/data_util/qsmearing.py

@@ -5 +5 @@
 #This software was developed by the University of Tennessee as part of the
 #Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
-#project funded by the US National Science Foundation. 
+#project funded by the US National Science Foundation.
 #See the license text in license.txt
 #copyright 2008, University of Tennessee
@@ -13 +13 @@
 import logging
 import sys
+from sasmodels import sesans
 
-from sasmodels.resolution import Slit1D, Pinhole1D
+from sasmodels.resolution import Slit1D, Pinhole1D, SESANS1D
 from sasmodels.resolution2d import Pinhole2D
 
@@ -48 +49 @@
 
     # Look for resolution smearing data
+    _found_sesans = False
+    if data.dx is not None and data.meta_data['loader']=='SESANS':
+        if data.dx[0] > 0.0:
+            _found_sesans = True
+
+    if _found_sesans == True:
+        return sesans_smear(data, model)
+
     _found_resolution = False
     if data.dx is not None and len(data.dx) == len(data.x):
@@ -92 +101 @@
         self.model = model
         self.resolution = resolution
-        self.offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
+        if hasattr(self.resolution, 'data'):
+            if self.resolution.data.meta_data['loader'] == 'SESANS':
+                self.offset = 0
+            # This is default behaviour, for future resolution/transform functions this needs to be revisited.
+            else:
+                self.offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
+        else:
+            self.offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
+
+        #self.offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
 
     def apply(self, iq_in, first_bin=0, last_bin=None):
@@ -126 +144 @@
         q[first:last+1].
         """
+
         q = self.resolution.q
         first = numpy.searchsorted(q, q_min)
@@ -142 +161 @@
     width = data.dx if data.dx is not None else 0
     return PySmear(Pinhole1D(q, width), model)
+
+def sesans_smear(data, model=None):
+    #This should be calculated characteristic length scale
+    #Probably not a data prameter either
+    #Need function to calculate this based on model
+    #Here assume a number
+    Rmax = 1000000
+    q_calc = sesans.make_q(data.sample.zacceptance, Rmax)
+    return PySmear(SESANS1D(data,q_calc),model)