r18173 - /trunk/sample_scripts/model_free/diff_min.py -- January 07, 2013 - 11:08

Author: bugman
Date: Mon Jan  7 11:08:07 2013
New Revision: 18173

URL: http://svn.gna.org/viewcvs/relax?rev=18173&view=rev
Log:
Updated the diffusion tensor minimisation sample script as the code is very 
old and useless.


Modified:
    trunk/sample_scripts/model_free/diff_min.py

Modified: trunk/sample_scripts/model_free/diff_min.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/sample_scripts/model_free/diff_min.py?rev=18173&r1=18172&r2=18173&view=diff
==============================================================================
--- trunk/sample_scripts/model_free/diff_min.py (original)
+++ trunk/sample_scripts/model_free/diff_min.py Mon Jan  7 11:08:07 2013
@@ -19,7 +19,19 @@
 #                                                                            
 #
 
###############################################################################
 
-"""Script for diffusion tensor optimisation."""
+"""Demonstration script for diffusion tensor optimisation in a model-free 
analysis.
+
+Importantly, if a model-free analysis is to be based on this script, note 
that multiple rounds of executing this script will be required for each 
diffusion tensor.  To understand this concept of finding the universal 
solution of the optimisation minimum across multiple model-free spaces (or 
universes), please read:
+
+    d'Auvergne E. J., Gooley P. R. (2007). Set theory formulation of the 
model-free problem and the diffusion seeded model-free paradigm. Mol. 
Biosyst., 3(7), 483-494. (http://dx.doi.org/10.1039/b702202f)
+
+    d'Auvergne, E. J. and Gooley, P. R. (2008). Optimisation of NMR dynamic 
models II. A new methodology for the dual optimisation of the model-free 
parameters and the Brownian rotational diffusion tensor. J. Biomol. NMR, 
40(2), 121-133. (http://dx.doi.org/10.1007/s10858-007-9213-3).
+
+
+Also note that for a model-free analysis, you should look at the fully 
contained dauvergne_protocol.py sample script rather than here.
+
+This script requires an initial tensor estimate which can be found using 
either relax or other software packages.
+"""
 
 
 # Set the data pipe names (also the names of preset model-free models).
@@ -76,8 +88,25 @@
     model_free.select_model(model=name)
 
     # Minimise.
-    grid_search(inc=5)
+    grid_search(inc=21)
     minimise('newton')
+
+    # Model elimination.
+    eliminate()
+
+    # Write the results.
+    results.write(file='results', force=True)
+
+# Model selection.
+print("\n\n\n\n\n")
+print("####################")
+print("# Model selection. #")
+print("####################")
+print("\n\n\n")
+
+# Select the models and save the results.
+model_selection(method='AIC', modsel_pipe='aic')
+results.write(file='results', force=True)
 
 # Minimise the diffusion tensor parameters.
 print("\n\n\n\n\n")
@@ -86,23 +115,15 @@
 print("###########################################")
 print("\n\n\n")
 
-# Loop over the data pipes.
-for name in pipes:
-    # Switch to the data pipe.
-    pipe.switch(name)
+# Unfix all parameters.
+fix('all', fixed=False)
 
-    # Unfix the diffusion tensor.
-    fix('diff', fixed=0)
+# Minimise.
+grid_search(inc=5)
+minimise('newton')
 
-    # Fix all model-free paremeter values.
-    fix('all_res')
-
-    # Minimise.
-    grid_search(inc=5)
-    minimise('newton', max_iter=5000)
-
-    # Write the results.
-    results.write(file='results', force=True)
+# Write the results.
+results.write(file='results', dir='opt', force=True)
 
 # Save the program state.
 state.save('save', force=True)