r14843 - /1.3/maths_fns/n_state_model.py -- October 12, 2011 - 16:47

Author: bugman
Date: Wed Oct 12 16:47:53 2011
New Revision: 14843

URL: http://svn.gna.org/viewcvs/relax?rev=14843&view=rev
Log:
Fixes for the population N-state model for when subsets of alignment tensors 
are fixed.

This is in the function, gradient, and Hessian code.


Modified:
    1.3/maths_fns/n_state_model.py

Modified: 1.3/maths_fns/n_state_model.py
URL: 
http://svn.gna.org/viewcvs/relax/1.3/maths_fns/n_state_model.py?rev=14843&r1=14842&r2=14843&view=diff
==============================================================================
--- 1.3/maths_fns/n_state_model.py (original)
+++ 1.3/maths_fns/n_state_model.py Wed Oct 12 16:47:53 2011
@@ -626,6 +626,7 @@
             self.paramag_info()
 
         # Loop over each alignment.
+        index = 0
         for i in xrange(self.num_align):
             # Create tensor i from the parameters.
             if self.fixed_tensors[i]:
@@ -645,6 +646,10 @@
                     if not self.missing_deltaij[i, j]:
                         self.deltaij_theta[i, j] = 
ave_pcs_tensor(self.pcs_const[i, j], self.paramag_unit_vect[j], self.N, 
self.A[i], weights=self.probs)
 
+            # Skip the rest if the tensor is fixed.
+            if self.fixed_tensors[i]:
+                continue
+
             # Calculate and sum the single alignment chi-squared value (for 
the RDC).
             if self.rdc_flag:
                 chi2_sum = chi2_sum + chi2(self.Dij[i], self.Dij_theta[i], 
self.rdc_sigma_ij[i])
@@ -652,6 +657,9 @@
             # Calculate and sum the single alignment chi-squared value (for 
the PCS).
             if self.pcs_flag:
                 chi2_sum = chi2_sum + chi2(self.deltaij[i], 
self.deltaij_theta[i], self.pcs_sigma_ij[i])
+
+            # Increment the index.
+            index += 1
 
         # Return the chi-squared value.
         return chi2_sum
@@ -1041,7 +1049,12 @@
         self.dchi2 = self.dchi2 * 0.0
 
         # Loop over each alignment.
+        index = 0
         for i in xrange(self.num_align):
+            # Fixed tensor, so skip.
+            if self.fixed_tensors[i]:
+                continue
+
             # Construct the Amn partial derivative components.
             for j in xrange(self.num_spins):
                 # RDC.
@@ -1085,6 +1098,9 @@
                 if self.pcs_flag:
                     self.dchi2[k] = self.dchi2[k] + 
dchi2_element(self.deltaij[i], self.deltaij_theta[i], self.ddeltaij_theta[k, 
i], self.pcs_sigma_ij[i])
 
+            # Increment the index.
+            index += 0
+
         # Diagonal scaling.
         if self.scaling_flag:
             self.dchi2 = dot(self.dchi2, self.scaling_matrix)
@@ -1425,7 +1441,12 @@
         self.d2chi2 = self.d2chi2 * 0.0
 
         # Loop over each alignment.
+        index = 0
         for i in xrange(self.num_align):
+            # Fixed tensor, so skip.
+            if self.fixed_tensors[i]:
+                continue
+
             # Construct the pc-Amn second partial derivative Hessian 
components.
             for c in xrange(self.N - 1):
                 # Index in the parameter array.
@@ -1449,6 +1470,9 @@
                         self.d2deltaij_theta[pc_index, i*5+3, i, j] = 
self.d2deltaij_theta[i*5+3, pc_index, i, j] = pcs_tensor(self.pcs_const[i, j, 
c], self.paramag_unit_vect[j, c], self.dA[3])
                         self.d2deltaij_theta[pc_index, i*5+4, i, j] = 
self.d2deltaij_theta[i*5+4, pc_index, i, j] = pcs_tensor(self.pcs_const[i, j, 
c], self.paramag_unit_vect[j, c], self.dA[4])
 
+            # Increment the index.
+            index += 0
+
         # Loop over each alignment.
         for i in xrange(self.num_align):
             # Construct the chi-squared Hessian element for parameters j and 
k, alignment i.
@@ -1568,7 +1592,12 @@
         self.d2chi2 = self.d2chi2 * 0.0
 
         # Loop over each alignment.
+        index = 0
         for i in xrange(self.num_align):
+            # Fixed tensor, so skip.
+            if self.fixed_tensors[i]:
+                continue
+
             # Construct the chi-squared Hessian element for parameters j and 
k, alignment i.
             for j in xrange(self.total_num_params):
                 for k in xrange(self.total_num_params):
@@ -1580,6 +1609,9 @@
                     if self.pcs_flag:
                         self.d2chi2[j, k] = self.d2chi2[j, k] + 
d2chi2_element(self.deltaij[i], self.deltaij_theta[i], self.ddeltaij_theta[j, 
i], self.ddeltaij_theta[k, i], self.zero_hessian, self.pcs_sigma_ij[i])
 
+            # Increment the index.
+            index += 0
+
         # Diagonal scaling.
         if self.scaling_flag:
             self.d2chi2 = dot(self.d2chi2, self.scaling_matrix)