Changeset 342a506 in sasview

Timestamp:

Mar 1, 2011 5:54:15 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:

b1a65b6

Parents:

9f391af

Message:

Added x_err and more correction on determining q values, some utest are updates but many more to go

Location:

DataLoader

Files:

• manipulations.py (modified) (9 diffs)
• test/utest_averaging.py (modified) (2 diffs)

DataLoader/manipulations.py

@@ -430 +430 @@
         q_data = data2D.q_data[numpy.isfinite(data2D.data)]
         err_data = data2D.err_data[numpy.isfinite(data2D.data)]
-   
+        dq_data = None
+        if data2D.dqx_data != None and data2D.dqy_data != None:
+            dq_data = data2D.dqx_data[numpy.isfinite(data2D.data)] * \
+                        data2D.dqx_data[numpy.isfinite(data2D.data)]
+            dq_data += data2D.dqy_data[numpy.isfinite(data2D.data)] * \
+                        data2D.dqy_data[numpy.isfinite(data2D.data)]
+            dq_data = numpy.sqrt(dq_data)
+            
         q_data_max = numpy.max(q_data)
 
@@ -444 +451 @@
         y  = numpy.zeros(nbins)
         err_y = numpy.zeros(nbins)
+        err_x = numpy.zeros(nbins)
         y_counts = numpy.zeros(nbins)
 
@@ -474 +482 @@
             else:
                 err_y[i_q] += frac * frac * err_data[npt] * err_data[npt]
+            if dq_data != None:
+                err_x[i_q] += frac * frac * dq_data[npt] * dq_data[npt]
+            else:
+                err_x = None
             y_counts[i_q]  += frac
         
-        ## x should be the center value of each bins        
-        x = qbins + self.bin_width / 2  
-        
+        # We take the center of ring area, not radius.  
+        # This is more accurate than taking the radial center of ring.
+        x = (qbins + self.bin_width) * (qbins + self.bin_width)
+        x += qbins * qbins
+        x = x / 2.0
+        x = numpy.sqrt(x)
         # Average the sums       
         for n in range(nbins):
             if err_y[n] < 0: err_y[n] = -err_y[n]
             err_y[n] = math.sqrt(err_y[n])
-                      
+            if err_x != None:
+                err_x[n] = math.sqrt(err_x[n])
+            
         err_y = err_y / y_counts
+        err_y[err_y==0] = numpy.average(err_y)
         y    = y / y_counts
         idx = (numpy.isfinite(y)) & (numpy.isfinite(x)) 
-        
+        if err_x != None:
+            d_x = err_x[idx] / y_counts[idx]
+        else:
+            d_x = None
+
         if not idx.any(): 
             msg = "Average Error: No points inside ROI to average..." 
             raise ValueError, msg
+        
         #elif len(y[idx])!= nbins:
         #    print "resulted",nbins- len(y[idx])
         #,"empty bin(s) due to tight binning..."
-        return Data1D(x=x[idx], y=y[idx], dy=err_y[idx])
+        return Data1D(x=x[idx], y=y[idx], dy=err_y[idx], dx=d_x)
     
 
@@ -722 +745 @@
         qx_data = data2D.qx_data[numpy.isfinite(data2D.data)] 
         qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
-        
+        dq_data = None
+        # dx (smear) data
+        if data2D.dqx_data != None and data2D.dqy_data != None:
+            dq_data = data2D.dqx_data[numpy.isfinite(data2D.data)] * \
+                        data2D.dqx_data[numpy.isfinite(data2D.data)]
+            dq_data += data2D.dqy_data[numpy.isfinite(data2D.data)] * \
+                        data2D.dqy_data[numpy.isfinite(data2D.data)]
+            dq_data = numpy.sqrt(dq_data)
+            
         #set space for 1d outputs
         x        = numpy.zeros(self.nbins)
         y        = numpy.zeros(self.nbins)
-        y_err    = numpy.zeros(self.nbins)         
+        y_err    = numpy.zeros(self.nbins)   
+        x_err    = numpy.zeros(self.nbins)      
         y_counts = numpy.zeros(self.nbins)
                       
@@ -797 +829 @@
             y[i_bin] += frac * data_n
 
-            if err_data == None or err_data[n] == 0.0:
+            if err_data[n] == None or err_data[n] == 0.0:
                 if data_n < 0:
                     data_n = -data_n
@@ -803 +835 @@
             else:
                 y_err[i_bin] += frac * frac * err_data[n] * err_data[n]
+                
+            if dq_data != None:
+                x_err[i_bin] += frac * frac * dq_data[n] * dq_data[n]
+            else:
+                x_err = None
             y_counts[i_bin] += frac
    
@@ -809 +846 @@
             y[i] = y[i] / y_counts[i]
             y_err[i] = math.sqrt(y_err[i]) / y_counts[i]
-            
+            if x_err != None:
+                x_err[i] = math.sqrt(x_err[i]) / y_counts[i]
             # The type of averaging: phi,q2, or q
             # Calculate x[i]should be at the center of the bin
@@ -816 +854 @@
                     (1.0 * i + 0.5) + self.phi_min
             else:
-                x[i] = (self.r_max - self.r_min) / self.nbins * \
-                    (1.0 * i + 0.5) + self.r_min
+                # We take the center of ring area, not radius.  
+                # This is more accurate than taking the radial center of ring.
+                delta_r = (self.r_max - self.r_min) / self.nbins
+                r_inner = self.r_min + delta_r * i
+                r_outer = r_inner + delta_r
+                x[i] = math.sqrt((r_inner * r_inner + r_outer * r_outer) / 2)
                 
+        y_err[y_err==0] = numpy.average(y_err)        
         idx = (numpy.isfinite(y) & numpy.isfinite(y_err))
-        
+        if x_err != None:
+            d_x = x_err[idx]
+        else:
+            d_x = None
         if not idx.any():
             msg = "Average Error: No points inside sector of ROI to average..." 
@@ -827 +873 @@
         #    print "resulted",self.nbins- len(y[idx]),
         #"empty bin(s) due to tight binning..."
-        return Data1D(x=x[idx], y=y[idx], dy=y_err[idx])
+        return Data1D(x=x[idx], y=y[idx], dy=y_err[idx], dx=d_x)
                 
 class SectorPhi(_Sector):

DataLoader/test/utest_averaging.py

@@ -71 +71 @@
             Test sector averaging
         """
-        r = SectorPhi(r_min=self.qmin, r_max=3*self.qmin, phi_min=0, phi_max=math.pi*2.0)
-        r.nbins_phi = 20
-        o = r(self.data)
-        for i in range(20):
+        r = SectorPhi(r_min=self.qmin, r_max=3*self.qmin, 
+                      phi_min=0, phi_max=math.pi*2.0)
+        r.nbins_phi = 20
+        o = r(self.data)
+        for i in range(7):
             self.assertEqual(o.y[i], 1.0)
             
@@ -81 +82 @@
         """
         """
-        phi_max = 1.5
-        r = SectorPhi(r_min=self.qmin, r_max=3*self.qmin, phi_min=0, phi_max=phi_max)
+        phi_max = math.pi * 1.5
+        r = SectorPhi(r_min=self.qmin, r_max=3*self.qmin, 
+                      phi_min=0, phi_max=phi_max)
         self.assertEqual(r.phi_max, phi_max)
         r.nbins_phi = 20
         o = r(self.data)
         self.assertEqual(r.phi_max, phi_max)
-        for i in range(20):
+        for i in range(17):
             self.assertEqual(o.y[i], 1.0)