Changeset 7182d96 in sasview for Invariant

Timestamp:

Jun 8, 2010 12:54:45 PM (14 years ago)

Author:

Jae Cho <jhjcho@…>

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:

76f2d63

Parents:

fca90f82

Message:

added Q* cal for smeared data

File:

• Invariant/invariant.py (modified) (10 diffs)

Invariant/invariant.py

@@ -361 +361 @@
         self._background = background
         self._scale = scale
-        
+        # slit height for smeared data
+        self._smeared = None
         # The data should be private
         self._data = self._get_data(data)
+        # get the dxl if the data is smeared: This is done only once on init.
+        if data.dxl != None and data.dxl.all() >0:
+            # assumes constant dxl
+            self._smeared = data.dxl[0]
       
         # Since there are multiple variants of Q*, you should force the
@@ -407 +412 @@
             (min(new_data.dy)==0 and max(new_data.dy)==0):
             new_data.dy = numpy.ones(len(new_data.x))  
-        
         return  new_data
      
@@ -438 +442 @@
     
             q_star = x0**2 *y0 *dx0 +x1**2 *y1 *dx1 
-                        + ..+ xn**2 *yn *dxn 
+                        + ..+ xn**2 *yn *dxn    for non smeared data
+                        
+            q_star = dxl0 *x0 *y0 *dx0 +dxl1 *x1 *y1 *dx1 
+                        + ..+ dlxn *xn *yn *dxn    for smeared data
                         
             where n >= len(data.x)-1
+            dxl = slit height dQl
             dxi = 1/2*(xi+1 - xi) + (xi - xi-1)
             dx0 = (x1 - x0)/2
@@ -454 +462 @@
             raise ValueError, msg
         else:
+            # Take care of smeared data
+            if self._smeared is None:
+                gx = data.x * data.x
+            # assumes that len(x) == len(dxl).
+            else:               
+                gx = data.dxl * data.x 
+                
             n = len(data.x)- 1
             #compute the first delta q
@@ -460 +475 @@
             dxn = (data.x[n] - data.x[n-1])/2
             sum = 0
-            sum += data.x[0] * data.x[0] * data.y[0] * dx0
-            sum += data.x[n] * data.x[n] * data.y[n] * dxn
+            sum += gx[0] * data.y[0] * dx0
+            sum += gx[n] * data.y[n] * dxn
             
             if len(data.x) == 2:
@@ -469 +484 @@
                 for i in xrange(1, n-1):
                     dxi = (data.x[i+1] - data.x[i-1])/2
-                    sum += data.x[i] * data.x[i] * data.y[i] * dxi
+                    sum += gx[i] * data.y[i] * dxi
                 return sum
             
@@ -501 +516 @@
             else:
                 dy = data.dy
-                
+            # Take care of smeared data
+            if self._smeared is None:
+                gx = data.x * data.x
+            # assumes that len(x) == len(dxl).
+            else:
+                gx = data.dxl * data.x
+  
             n = len(data.x) - 1
             #compute the first delta
@@ -508 +529 @@
             dxn= (data.x[n] - data.x[n-1])/2
             sum = 0
-            sum += (data.x[0] * data.x[0] * dy[0] * dx0)**2
-            sum += (data.x[n] * data.x[n] * dy[n] * dxn)**2
+            sum += (gx[0] * dy[0] * dx0)**2
+            sum += (gx[n] * dy[n] * dxn)**2
             if len(data.x) == 2:
                 return math.sqrt(sum)
@@ -516 +537 @@
                 for i in xrange(1, n-1):
                     dxi = (data.x[i+1] - data.x[i-1])/2
-                    sum += (data.x[i] * data.x[i] * dy[i] * dxi)**2
+                    sum += (gx[i] * dy[i] * dxi)**2
                 return math.sqrt(sum)
         
@@ -533 +554 @@
          
         result_data = LoaderData1D(x=q, y=iq, dy=diq)
+        if self._smeared != None:
+            result_data.dxl = self._smeared * numpy.ones(len(q))
         return result_data