Changeset 6c2d1a1 in sasview for DataLoader/smearing_2d.py

Timestamp:

May 24, 2010 3:09:43 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:

9bc499b6

Parents:

11a7e11

Message:

put more doc

File:

• DataLoader/smearing_2d.py (modified) (4 diffs)

DataLoader/smearing_2d.py

@@ -26 +26 @@
     def __init__(self, data=None,model=None,index=None,limit=LIMIT,accuracy='Low'):
         """
-            Assumption: equally spaced bins of increasing q-values.
+            Assumption: equally spaced bins in dq_r, dq_phi space.
             
             @param data: 2d data used to set the smearing parameters
@@ -102 +102 @@
     def get_value(self):
         """
-            Over sampling of r_nbins times phi_nbins, calculate Gaussian weights, then find semared intensity
-            # For the default vaues, this is equivalent (but speed optimized by a factor of ten)to the following:
+            Over sampling of r_nbins times phi_nbins, calculate Gaussian weights, then find smeared intensity
+            # For the default values, this is equivalent (but speed optimized by a factor of ten)to the following:
             =====================================================================================
             ## Remove the singular points if exists
@@ -112 +112 @@
                     n = (phi)*5+(r)
                     r = r+0.25
-                    dphi = phi*2.0*math.pi/4.0 + numpy.arctan(self.qy_data[index_model]/self.qx_data[index_model])
+                    dphi = phi*2.0*math.pi/4.0 + numpy.arctan(self.qy_data[index_model]/self.dqy_data[index_model]/self.qx_data[index_model]*/self.dqx_data[index_model])
                     dq = r*numpy.sqrt( self.dqx_data[index_model]*self.dqx_data[index_model] \
                         + self.dqy_data[index_model]*self.dqy_data[index_model] )
-                    #integrant of r*math.exp(-0.5*r*r) dr at each bins
+                    #integrant of math.exp(-0.5*r*r) r dr at each bins : The integration may not need.
                     weight_res[n] = math.exp(-0.5*((r-0.25)*(r-0.25)))-math.exp(-0.5*((r-0.25)*(r-0.25)))
                     #if phi !=0 and r != 0:
                     qx_res=numpy.append(qx_res,self.qx_data[index_model]+ dq*math.cos(dphi))
                     qy_res=numpy.append(qy_res,self.qy_data[index_model]+ dq*math.sin(dphi))
+            ## Then compute I(qx_res,qy_res) and do weighted averaging. 
             =====================================================================================
         """
@@ -145 +146 @@
         dphi = phi*2.0*math.pi/nphi
         dphi = dphi.repeat(nr)
-        ## Transform to polar coordinate and set dphi at each data points ; 1d array
+        ## Transform to polar coordinate, and set dphi at each data points ; 1d array
         dphi = dphi.repeat(len_data)+numpy.arctan(self.qy_data*self.dqx_data/self.qx_data/self.dqy_data).repeat(n_bins).reshape(len_data,n_bins).transpose().flatten()
-        ## Find Gaussian weight for each dq bins: The weight depends only on r-direction
+        ## Find Gaussian weight for each dq bins: The weight depends only on r-direction (The integration may not need)
         weight_res = numpy.exp(-0.5*((r-bin_size/2.0)*(r-bin_size/2.0)))-numpy.exp(-0.5*((r+bin_size/2.0)*(r+bin_size/2.0)))
         weight_res = weight_res.repeat(nphi).reshape(nr,nphi).transpose().flatten()