Changeset f7bc948 in sasview

Timestamp:

Oct 8, 2016 2:13:22 PM (8 years ago)

Author:

ajj

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:

b61bd57

Parents:

24f6f4a

Message:

changes to qsmearing to try to accommodate sesans

File:

• src/sas/sascalc/data_util/qsmearing.py (modified) (4 diffs)

src/sas/sascalc/data_util/qsmearing.py

@@ -13 +13 @@
 import logging
 import sys
+import sasmodels.sesans
 
-from sasmodels.resolution import Slit1D, Pinhole1D
+from sasmodels.resolution import Slit1D, Pinhole1D, SESANS1D
 from sasmodels.resolution2d import Pinhole2D
-from sasmodels import sesans
 
 def smear_selection(data, model = None):
@@ -48 +48 @@
         return None
 
-    # Look for sesans
+    # Look for resolution smearing data
     _found_sesans = False
-    if hasattr(data,'lam') :
-        _found_sesans = True
-        logging.info("Found SESANS data!!")
+    if data.dx is not None and data.lam is not None:
+        if data.dx[0] > 0.0:
+            _found_sesans = True
 
-    # If we found sesans data, do the necessary jiggery pokery
     if _found_sesans == True:
         return sesans_smear(data, model)
 
-    # Look for resolution smearing data
     _found_resolution = False
     if data.dx is not None and len(data.dx) == len(data.x):
@@ -93 +91 @@
     if _found_slit == True:
         return slit_smear(data, model)
-
     return None
-
-def sesans_smear(data, model=None):
-    q = sesans.make_q(data.sample.zacceptance, data.Rmax)
-    index = slice(None, None)
-    res = None
-    if data.y is not None:
-        Iq, dIq = data.y, data.dy
-    else:
-        Iq, dIq = None, None
-    #self._theory = np.zeros_like(q)
-    q_vectors = [q]
-    q_mono = sesans.make_all_q(data)
-    Iq = model.evalDistribution(q_mono)
-
-    return sesans.transform(data, q, Iq, 0, 0)
 
 
@@ -169 +151 @@
     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 = 50000
+    q_calc = sesans.make_q(data.sample.z, Rmax)
+    return PySmear(SESANS1D(data,q_calc),model)