Changes in src/sas/sascalc/data_util/qsmearing.py [235f514:ba91f71] in sasview

File:

• src/sas/sascalc/data_util/qsmearing.py (modified) (8 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
 ######################################################################
+import numpy
 import math
 import logging
@@ -17 +18 @@
 
 from sasmodels.resolution import Slit1D, Pinhole1D
-from sasmodels.sesans import SesansTransform
+from sasmodels.sesans import SesansTransform, OrientedSesansTransform
 from sasmodels.resolution2d import Pinhole2D
 from .nxsunit import Converter
@@ -42 +43 @@
     # This checks for 2D data (does not throw exception because fail is common)
     if  data.__class__.__name__ not in ['Data1D', 'Theory1D']:
-        if data is None:
+        if data == None:
             return None
-        elif data.dqx_data is None or data.dqy_data is None:
+        elif data.dqx_data == None or data.dqy_data == None:
             return None
         return PySmear2D(data)
@@ -59 +60 @@
     if data.dx is not None and data.isSesans:
         #if data.dx[0] > 0.0:
-        if np.size(data.dx[data.dx <= 0]) == 0:
+        if numpy.size(data.dx[data.dx <= 0]) == 0:
             _found_sesans = True
         # if data.dx[0] <= 0.0:
-        if np.size(data.dx[data.dx <= 0]) > 0:
+        if numpy.size(data.dx[data.dx <= 0]) > 0:
             raise ValueError('one or more of your dx values are negative, please check the data file!')
 
@@ -71 +72 @@
         zaccept = Converter(qunits)(qmax, "1/A"),
         Rmax = 10000000
-        hankel = SesansTransform(data.x, SElength, zaccept, Rmax)
+        # data must have the isoriented flag here!
         # Then return the actual transform, as if it were a smearing function
-        return PySmear(hankel, model, offset=0)
+        if getattr(data, 'isoriented', False):
+            costransform = OrientedSesansTransform(data.x, SElength, zaccept, Rmax)
+            return PySmear(costransform, model, offset=0)
+        else:
+            hankel = SesansTransform(data.x, SElength, zaccept, Rmax)
+            return PySmear(hankel, model, offset=0)
 
     _found_resolution = False
@@ -120 +126 @@
         self.resolution = resolution
         if offset is None:
-            offset = np.searchsorted(self.resolution.q_calc, self.resolution.q[0])
+            offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
         self.offset = offset
 
@@ -133 +139 @@
         first_bin:last_bin set to the resolution smeared values.
         """
-        if last_bin is None: last_bin = len(iq_in)
-        start, end = first_bin + self.offset, last_bin + self.offset
         q_calc = self.resolution.q_calc
-        iq_calc = np.empty_like(q_calc)
-        if start > 0:
-            iq_calc[:start] = self.model.evalDistribution(q_calc[:start])
-        if end+1 < len(q_calc):
-            iq_calc[end+1:] = self.model.evalDistribution(q_calc[end+1:])
-        iq_calc[start:end+1] = iq_in[first_bin:last_bin+1]
+        if isinstance(q_calc, tuple):
+            # We are 2D -> 1D!
+            iq_calc = self.model.evalDistribution(q_calc)
+        else:
+            if last_bin is None: last_bin = len(iq_in)
+            start, end = first_bin + self.offset, last_bin + self.offset
+            iq_calc = numpy.empty_like(q_calc)
+            if start > 0:
+                iq_calc[:start] = self.model.evalDistribution(q_calc[:start])
+            if end+1 < len(q_calc):
+                iq_calc[end+1:] = self.model.evalDistribution(q_calc[end+1:])
+            iq_calc[start:end+1] = iq_in[first_bin:last_bin+1]
         smeared = self.resolution.apply(iq_calc)
         return smeared
@@ -156 +166 @@
         """
         q = self.resolution.q
-        first = np.searchsorted(q, q_min)
-        last = np.searchsorted(q, q_max)
+        first = numpy.searchsorted(q, q_min)
+        last = numpy.searchsorted(q, q_max)
         return first, min(last,len(q)-1)