Changeset a3f125f0 in sasview for src/sas/models/qsmearing.py

Timestamp:

Mar 30, 2015 10:51:41 AM (9 years ago)

Author:

Paul Kienzle <pkienzle@…>

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, release_4.0.1, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

edfc8ac

Parents:

d5419f7f

Message:

refactor resolution calculation to enable sasmodels resolution calcuator for pinhole smearing, but don't enable it yet

File:

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

src/sas/models/qsmearing.py

@@ -59 +59 @@
     if _found_resolution == True:
         return QSmearer(data1D, model)
+        #return pinhole_smear(data1D, model)
 
     # Look for slit smearing data
@@ -81 +82 @@
     # If we found slit smearing data, return a slit smearer
     if _found_slit == True:
-        return PySlitSmearer(data1D, model)
+        #return SlitSmearer(data1D, model)
+        return slit_smear(data1D, model)
     return None
             
@@ -198 +200 @@
                 iq_in[len(iq_in) - 1] = iq_in_high[0]
             # Append the extrapolated points to the data points
-            if self.nbins_low > 0:                             
+            if self.nbins_low > 0:
                 iq_in_temp = numpy.append(iq_in_low, iq_in)
             if self.nbins_high > 0:
@@ -586 +588 @@
 
 
-from .resolution import Slit1D
-class PySlitSmearer(object):
-    def __init__(self, data1D, model = None):
+from .resolution import Slit1D, Pinhole1D
+class PySmear(object):
+    """
+    Wrapper for pure python sasmodels resolution functions.
+    """
+    def __init__(self, resolution, model):
         self.model = model
-
-        q = data1D.x
-        width = data1D.dxw if data1D.dxw is not None else 0
-        height = data1D.dxl if data1D.dxl is not None else 0
-        # TODO: width and height seem to be reversed
-        self.resolution = Slit1D(q, height, width)
-
-    def __call__(self, iq_in, first_bin=0, last_bin=None):
-        if last_bin is None or last_bin >= len(iq_in):
-            last_bin = len(iq_in) - 1
+        self.resolution = resolution
+        self.offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
+
+    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 = numpy.empty_like(q_calc)
-        iq_calc[:first_bin] = 0
-        iq_calc[first_bin:last_bin+1] = iq_in
-        if last_bin < len(q_calc)-1:
-            iq_calc[last_bin:] = self.model.evalDistribution(q_calc[last_bin:])
-        iq_out = self.resolution.apply(iq_calc)
-        return iq_out[first_bin:last_bin+1]
+        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):
         """
-
-        :param q_min: minimum q-value to smear
-        :param q_max: maximum q-value to smear
-
-        """
-        # assume the data values are sorted
-        first = numpy.searchsorted(self.resolution.q, q_min)
-        # assume that the resolution is much larger than the q range
-        last = len(self.resolution.q)-1
-        return first, last
+        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 = numpy.searchsorted(q, q_min)
+        last = numpy.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)