r6985 - /branches/rdc_analysis/maths_fns/n_state_model.py -- July 25, 2008 - 14:57

Author: bugman
Date: Fri Jul 25 14:57:42 2008
New Revision: 6985

URL: http://svn.gna.org/viewcvs/relax?rev=6985&view=rev
Log:
No need to multiply by 1.0 to return a copy at the end - the gradient is 
reset at the start.


Modified:
    branches/rdc_analysis/maths_fns/n_state_model.py

Modified: branches/rdc_analysis/maths_fns/n_state_model.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/maths_fns/n_state_model.py?rev=6985&r1=6984&r2=6985&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/n_state_model.py (original)
+++ branches/rdc_analysis/maths_fns/n_state_model.py Fri Jul 25 14:57:42 2008
@@ -537,17 +537,12 @@
             for k in xrange(self.total_num_params):
                 self.dchi2[k] = self.dchi2[k] + dchi2_element(self.Dij[i], 
self.Dij_theta[i], self.dDij_theta[k, i], self.sigma_ij[i])
 
-        # Debugging print out.
-        for k in xrange(self.total_num_params):
-            print "\nParam: " + `k`
-            print self.dchi2[k]
-
         # Diagonal scaling.
         if self.scaling_flag:
             self.dchi2 = dot(self.dchi2, self.scaling_matrix)
 
         # Return a copy of the gradient.
-        return self.dchi2 * 1.0
+        return self.dchi2
 
 
     def d2func_population(self, params):