Changes in src/sas/sascalc/data_util/qsmearing.py [2ffe241:0d64713] in sasview

File:

• src/sas/sascalc/data_util/qsmearing.py (modified) (5 diffs)

src/sas/sascalc/data_util/qsmearing.py

@@ -13 +13 @@
 import logging
 import sys
-import numpy as np  # type: ignore
-from numpy import pi, exp # type:ignore
+
 from sasmodels.resolution import Slit1D, Pinhole1D
-from sasmodels.sesans import SesansTransform
 from sasmodels.resolution2d import Pinhole2D
-from src.sas.sascalc.data_util.nxsunit import Converter
 
 def smear_selection(data, model = None):
@@ -39 +36 @@
     # Sanity check. If we are not dealing with a SAS Data1D
     # object, just return None
-    # This checks for 2D data (does not throw exception because fail is common)
     if  data.__class__.__name__ not in ['Data1D', 'Theory1D']:
         if data == None:
@@ -45 +41 @@
         elif data.dqx_data == None or data.dqy_data == None:
             return None
-        return PySmear2D(data)
-    # This checks for 1D data with smearing info in the data itself (again, fail is likely; no exceptions)
+        return PySmear2D(data, model)
+
     if  not hasattr(data, "dx") and not hasattr(data, "dxl")\
          and not hasattr(data, "dxw"):
@@ -52 +48 @@
 
     # Look for resolution smearing data
-    # This is the code that checks for SESANS data; it looks for the file loader
-    # TODO: change other sanity checks to check for file loader instead of data structure?
-    _found_sesans = False
-    #if data.dx is not None and data.meta_data['loader']=='SESANS':
-    if data.dx is not None and data.isSesans:
-        #if data.dx[0] > 0.0:
-        if numpy.size(data.dx[data.dx <= 0]) == 0:
-            _found_sesans = True
-        # if data.dx[0] <= 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!')
-
-    if _found_sesans == True:
-        #Pre-compute the Hankel matrix (H)
-        qmax, qunits = data.sample.zacceptance
-        SElength = Converter(data._xunit)(data.x, "A")
-        zaccept = Converter(qunits)(qmax, "1/A"),
-        Rmax = 10000000
-        hankel = SesansTransform(data.x, SElength, zaccept, Rmax)
-        # Then return the actual transform, as if it were a smearing function
-        return PySmear(hankel, model, offset=0)
-
     _found_resolution = False
     if data.dx is not None and len(data.dx) == len(data.x):
@@ -115 +89 @@
     Wrapper for pure python sasmodels resolution functions.
     """
-    def __init__(self, resolution, model, offset=None):
+    def __init__(self, resolution, model):
         self.model = model
         self.resolution = resolution
-        if offset is None:
-            offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
-        self.offset = offset
+        self.offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
 
     def apply(self, iq_in, first_bin=0, last_bin=None):