r24022 - /branches/disp_spin_speed/target_functions/relax_disp.py -- June 17, 2014 - 12:20

Author: tlinnet
Date: Tue Jun 17 12:20:12 2014
New Revision: 24022

URL: http://svn.gna.org/viewcvs/relax?rev=24022&view=rev
Log:
Fix for structure cleaning and value replacing for model mmq CR72.

System test: test_korzhnev_2005_all_data
revealed how this should be done properly.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion 
models for Clustered analysis.

Modified:
    branches/disp_spin_speed/target_functions/relax_disp.py

Modified: branches/disp_spin_speed/target_functions/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/target_functions/relax_disp.py?rev=24022&r1=24021&r2=24022&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py     (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py     Tue Jun 17 
12:20:12 2014
@@ -1279,9 +1279,6 @@
         # Reshape R20 to per experiment, spin and frequency.
         self.r20_struct[:] = multiply.outer( R20.reshape(self.NE, self.NS, 
self.NM), self.no_nd_ones )
 
-        # Initialise.
-        chi2_sum = 0.0
-
         # Loop over the experiment types.
         for ei in range(self.num_exp):
 
@@ -1309,20 +1306,16 @@
             # Back calculate the R2eff values.
             r2eff_mmq_cr72(r20=r20, pA=pA, pB=pB, dw=aliased_dw, 
dwH=aliased_dwH, kex=kex, k_AB=k_AB, k_BA=k_BA, cpmg_frqs=self.cpmg_frqs[ei], 
inv_tcpmg=self.inv_relax_times[ei], tcp=self.tau_cpmg[ei], 
back_calc=self.back_calc[ei])
 
-            # Clean the data for all values, which is left over at the end 
of arrays.
-            self.back_calc[ei] = self.back_calc[ei]*self.disp_struct[ei]
-
-            # For all missing data points, set the back-calculated value to 
the measured values so that it has no effect on the chi-squared value.
-            if self.has_missing:
-                # Replace with values.
-                mask_replace_blank_ei = masked_equal(self.missing[ei], 1.0)
-                self.back_calc[ei][mask_replace_blank_ei.mask] = 
self.values[ei][mask_replace_blank_ei.mask]
-
-            # Calculate and return the chi-squared value.
-            chi2_sum += chi2_rankN(self.values[ei], self.back_calc[ei], 
self.errors[ei])
-
-        # Return the total chi-squared value.
-        return chi2_sum
+        # Clean the data for all values, which is left over at the end of 
arrays.
+        self.back_calc = self.back_calc*self.disp_struct
+
+        ## For all missing data points, set the back-calculated value to the 
measured values so that it has no effect on the chi-squared value.
+        if self.has_missing:
+            # Replace with values.
+            self.back_calc[self.mask_replace_blank.mask] = 
self.values[self.mask_replace_blank.mask]
+
+        ## Calculate the chi-squared statistic.
+        return chi2_rankN(self.values, self.back_calc, self.errors)
 
 
     def func_NOREX(self, params):