Index: src/sas/sascalc/data_util/qsmearing.py
===================================================================
--- src/sas/sascalc/data_util/qsmearing.py	(revision 235f514ba93b57fa63e44309f182acef0b98bb55)
+++ 	(revision )
@@ -1,253 +1,0 @@
-"""
-    Handle Q smearing
-"""
-#####################################################################
-#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. 
-#See the license text in license.txt
-#copyright 2008, University of Tennessee
-######################################################################
-import math
-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 .nxsunit import Converter
-
-def smear_selection(data, model = None):
-    """
-    Creates the right type of smearer according
-    to the data.
-    The canSAS format has a rule that either
-    slit smearing data OR resolution smearing data
-    is available.
-
-    For the present purpose, we choose the one that
-    has none-zero data. If both slit and resolution
-    smearing arrays are filled with good data
-    (which should not happen), then we choose the
-    resolution smearing data.
-
-    :param data: Data1D object
-    :param model: sas.model instance
-    """
-    # 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 is None:
-            return None
-        elif data.dqx_data is None or data.dqy_data is None:
-            return None
-        return PySmear2D(data)
-    # This checks for 1D data with smearing info in the data itself (again, fail is likely; no exceptions)
-    if  not hasattr(data, "dx") and not hasattr(data, "dxl")\
-         and not hasattr(data, "dxw"):
-        return None
-
-    # 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 np.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:
-            raise ValueError('one or more of your dx values are negative, please check the data file!')
-
-    if _found_sesans:
-        # Pre-compute the Hankel matrix (H)
-        SElength = Converter(data._xunit)(data.x, "A")
-
-        theta_max = Converter("radians")(data.sample.zacceptance)[0]
-        q_max = 2 * np.pi / np.max(data.source.wavelength) * np.sin(theta_max)
-        zaccept = Converter("1/A")(q_max, "1/" + data.source.wavelength_unit),
-
-        Rmax = 10000000
-        hankel = SesansTransform(data.x, SElength,
-                                 data.source.wavelength,
-                                 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):
-
-        # Check that we have non-zero data
-        if data.dx[0] > 0.0:
-            _found_resolution = True
-            #print "_found_resolution",_found_resolution
-            #print "data1D.dx[0]",data1D.dx[0],data1D.dxl[0]
-    # If we found resolution smearing data, return a QSmearer
-    if _found_resolution == True:
-         return pinhole_smear(data, model)
-
-    # Look for slit smearing data
-    _found_slit = False
-    if data.dxl is not None and len(data.dxl) == len(data.x) \
-        and data.dxw is not None and len(data.dxw) == len(data.x):
-
-        # Check that we have non-zero data
-        if data.dxl[0] > 0.0 or data.dxw[0] > 0.0:
-            _found_slit = True
-
-        # Sanity check: all data should be the same as a function of Q
-        for item in data.dxl:
-            if data.dxl[0] != item:
-                _found_resolution = False
-                break
-
-        for item in data.dxw:
-            if data.dxw[0] != item:
-                _found_resolution = False
-                break
-    # If we found slit smearing data, return a slit smearer
-    if _found_slit == True:
-        return slit_smear(data, model)
-    return None
-
-
-class PySmear(object):
-    """
-    Wrapper for pure python sasmodels resolution functions.
-    """
-    def __init__(self, resolution, model, offset=None):
-        self.model = model
-        self.resolution = resolution
-        if offset is None:
-            offset = np.searchsorted(self.resolution.q_calc, self.resolution.q[0])
-        self.offset = offset
-
-    def apply(self, iq_in, first_bin=0, last_bin=None):
-        """
-        Apply the resolution function to the data.
-        Note that this is called with iq_in matching data.x, but with
-        iq_in[first_bin:last_bin] set to theory values for these bins,
-        and the remainder left undefined.  The first_bin, last_bin values
-        should be those returned from get_bin_range.
-        The returned value is of the same length as iq_in, with the range
-        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]
-        smeared = self.resolution.apply(iq_calc)
-        return smeared
-    __call__ = apply
-
-    def get_bin_range(self, q_min=None, q_max=None):
-        """
-        For a given q_min, q_max, find the corresponding indices in the data.
-        Returns first, last.
-        Note that these are indexes into q from the data, not the q_calc
-        needed by the resolution function.  Note also that these are the
-        indices, not the range limits.  That is, the complete range will be
-        q[first:last+1].
-        """
-        q = self.resolution.q
-        first = np.searchsorted(q, q_min)
-        last = np.searchsorted(q, q_max)
-        return first, min(last,len(q)-1)
-
-def slit_smear(data, model=None):
-    q = data.x
-    width = data.dxw if data.dxw is not None else 0
-    height = data.dxl if data.dxl is not None else 0
-    # TODO: width and height seem to be reversed
-    return PySmear(Slit1D(q, height, width), model)
-
-def pinhole_smear(data, model=None):
-    q = data.x
-    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
-
