Changeset 9f59333 in sasview for src/sas/sascalc/data_util/qsmearing.py

Timestamp:

Apr 10, 2017 9:45:27 AM (8 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

costrafo411

Children:

7b15990

Parents:

ba91f71 (diff), d26f025 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Message:

Merge branch 'master' into costrafo411

File:

• src/sas/sascalc/data_util/qsmearing.py (modified) (7 diffs)

src/sas/sascalc/data_util/qsmearing.py

@@ -9 +9 @@
 #copyright 2008, University of Tennessee
 ######################################################################
-import numpy
 import math
 import logging
@@ -43 +42 @@
     # This checks for 2D data (does not throw exception because fail is common)
     if  data.__class__.__name__ not in ['Data1D', 'Theory1D']:
-        if data == None:
+        if data is None:
             return None
-        elif data.dqx_data == None or data.dqy_data == None:
+        elif data.dqx_data is None or data.dqy_data is None:
             return None
         return PySmear2D(data)
@@ -60 +59 @@
     if data.dx is not None and data.isSesans:
         #if data.dx[0] > 0.0:
-        if numpy.size(data.dx[data.dx <= 0]) == 0:
+        if np.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:
+        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!')
 
@@ -126 +125 @@
         self.resolution = resolution
         if offset is None:
-            offset = numpy.searchsorted(self.resolution.q_calc, self.resolution.q[0])
+            offset = np.searchsorted(self.resolution.q_calc, self.resolution.q[0])
         self.offset = offset
 
@@ -140 +139 @@
         """
         q_calc = self.resolution.q_calc
-        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]
+        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
@@ -166 +159 @@
         """
         q = self.resolution.q
-        first = numpy.searchsorted(q, q_min)
-        last = numpy.searchsorted(q, q_max)
+        first = np.searchsorted(q, q_min)
+        last = np.searchsorted(q, q_max)
         return first, min(last,len(q)-1)
 
@@ -256 +249 @@
             val = self.model.evalDistribution(q_calc)
             return val
-