r21434 - /trunk/target_functions/n_state_model.py -- November 13, 2013 - 18:30

Author: bugman
Date: Wed Nov 13 18:30:44 2013
New Revision: 21434

URL: http://svn.gna.org/viewcvs/relax?rev=21434&view=rev
Log:
Fixes for the N-state model target function gradient and Hessian for the data 
structure changes.

The unit vector data structure is no longer a pure numpy array but a mixed 
list and array structure
to handle pseudo-atoms.


Modified:
    trunk/target_functions/n_state_model.py

Modified: trunk/target_functions/n_state_model.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/target_functions/n_state_model.py?rev=21434&r1=21433&r2=21434&view=diff
==============================================================================
--- trunk/target_functions/n_state_model.py (original)
+++ trunk/target_functions/n_state_model.py Wed Nov 13 18:30:44 2013
@@ -980,7 +980,7 @@
                     # Calculate the RDC for state c (this is the pc partial 
derivative).
                     for j in range(self.num_interatom):
                         if self.rdc_flag[align_index] and not 
self.missing_rdc[align_index, j]:
-                            self.drdc_theta[param_index, align_index, j] = 
rdc_tensor(self.dip_const[j], self.dip_vect[j, c], self.A[align_index])
+                            self.drdc_theta[param_index, align_index, j] = 
rdc_tensor(self.dip_const[j], self.dip_vect[j][c], self.A[align_index])
 
                     # Calculate the PCS for state c (this is the pc partial 
derivative).
                     for j in range(self.num_spins):
@@ -1155,11 +1155,11 @@
                     # Calculate the RDC Hessian component.
                     for j in range(self.num_interatom):
                         if self.fixed_tensors[align_index] and 
self.rdc_flag[align_index] and not self.missing_rdc[align_index, j]:
-                            self.d2rdc_theta[pc_index, align_index*5+0, 
align_index, j] = self.d2rdc_theta[align_index*5+0, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j, c], self.dA[0])
-                            self.d2rdc_theta[pc_index, align_index*5+1, 
align_index, j] = self.d2rdc_theta[align_index*5+1, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j, c], self.dA[1])
-                            self.d2rdc_theta[pc_index, align_index*5+2, 
align_index, j] = self.d2rdc_theta[align_index*5+2, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j, c], self.dA[2])
-                            self.d2rdc_theta[pc_index, align_index*5+3, 
align_index, j] = self.d2rdc_theta[align_index*5+3, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j, c], self.dA[3])
-                            self.d2rdc_theta[pc_index, align_index*5+4, 
align_index, j] = self.d2rdc_theta[align_index*5+4, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j, c], self.dA[4])
+                            self.d2rdc_theta[pc_index, align_index*5+0, 
align_index, j] = self.d2rdc_theta[align_index*5+0, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j][c], self.dA[0])
+                            self.d2rdc_theta[pc_index, align_index*5+1, 
align_index, j] = self.d2rdc_theta[align_index*5+1, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j][c], self.dA[1])
+                            self.d2rdc_theta[pc_index, align_index*5+2, 
align_index, j] = self.d2rdc_theta[align_index*5+2, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j][c], self.dA[2])
+                            self.d2rdc_theta[pc_index, align_index*5+3, 
align_index, j] = self.d2rdc_theta[align_index*5+3, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j][c], self.dA[3])
+                            self.d2rdc_theta[pc_index, align_index*5+4, 
align_index, j] = self.d2rdc_theta[align_index*5+4, pc_index, align_index, j] 
= rdc_tensor(self.dip_const[j], self.dip_vect[j][c], self.dA[4])
 
                     # Calculate the PCS Hessian component.
                     for j in range(self.num_spins):