r11254 - /1.3/maths_fns/n_state_model.py -- June 29, 2010 - 09:45

Author: bugman
Date: Tue Jun 29 09:45:17 2010
New Revision: 11254

URL: http://svn.gna.org/viewcvs/relax?rev=11254&view=rev
Log:
The alignment tensor optimisation is now optional within the population 
N-state model target fn.


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=11254&r1=11253&r2=11254&view=diff
==============================================================================
--- 1.3/maths_fns/n_state_model.py (original)
+++ 1.3/maths_fns/n_state_model.py Tue Jun 29 09:45:17 2010
@@ -60,6 +60,12 @@
             - red_data, the alignment tensors in 5D form in a rank-1 array.
             - red_errors, the alignment tensor errors in 5D form in a rank-1 
array.  This data is not obligatory.
             - full_in_ref_frame, an array of flags specifying if the tensor 
in the reference frame is the full or reduced tensor.
+
+
+        The population N-state model
+        ============================
+
+        In this model, populations are optimised for each state.  
Additionally the alignment tensors for anisotropic data can also be optimised 
if they have not been supplied (through the full_tensors arg).
 
 
         PCS base data
@@ -184,6 +190,21 @@
 
         # The flexible population or equal probability N-state models.
         elif model in ['population', 'fixed']:
+            # Fixed alignment tensors.
+            self.tensor_opt = True
+            if full_tensors != None:
+                # The optimisation flag.
+                self.tensor_opt = False
+
+                # Convert to numpy.
+                self.full_tensors = array(full_tensors, float64)
+
+                # Convert to 3x3 form.
+                self.num_tensors = len(self.full_tensors) / 5
+                self.A = zeros((self.num_tensors, 3, 3), float64)
+                for i in range(self.num_tensors):
+                    to_tensor(self.A[i], self.full_tensors[5*i:5*i+5])
+
             # Set the RDC and PCS flags (indicating the presence of data).
             self.rdc_flag = True
             self.pcs_flag = True
@@ -550,7 +571,8 @@
         # Loop over each alignment.
         for i in xrange(self.num_align):
             # Create tensor i from the parameters.
-            to_tensor(self.A[i], params[5*i:5*i + 5])
+            if self.tensor_opt:
+                to_tensor(self.A[i], params[5*i:5*i + 5])
 
             # Loop over the spin systems j.
             for j in xrange(self.num_spins):