r20466 - /branches/relax_disp/auto_analyses/relax_disp.py -- July 23, 2013 - 17:58

Author: bugman
Date: Tue Jul 23 17:58:18 2013
New Revision: 20466

URL: http://svn.gna.org/viewcvs/relax?rev=20466&view=rev
Log:
Added the new pre_run_dir argument to the relaxation dispersion auto-analysis.

This is to enable clustered optimisation.  This specifies a directory 
containing a completed
analysis.  The parameters from this previous run will be used as the starting 
point for optimisation
of the clustered analysis.


Modified:
    branches/relax_disp/auto_analyses/relax_disp.py

Modified: branches/relax_disp/auto_analyses/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/relax_disp/auto_analyses/relax_disp.py?rev=20466&r1=20465&r2=20466&view=diff
==============================================================================
--- branches/relax_disp/auto_analyses/relax_disp.py (original)
+++ branches/relax_disp/auto_analyses/relax_disp.py Tue Jul 23 17:58:18 2013
@@ -47,7 +47,7 @@
     opt_func_tol = 1e-25
     opt_max_iterations = int(1e7)
 
-    def __init__(self, pipe_name=None, pipe_bundle=None, results_dir=None, 
models=[MODEL_R2EFF], grid_inc=11, mc_sim_num=500, modsel='AIC', 
mc_sim_all_models=False):
+    def __init__(self, pipe_name=None, pipe_bundle=None, results_dir=None, 
models=[MODEL_R2EFF], grid_inc=11, mc_sim_num=500, modsel='AIC', 
pre_run_dir=None, mc_sim_all_models=False):
         """Perform a full relaxation dispersion analysis for the given list 
of models.
 
         @keyword pipe_name:         The name of the data pipe containing all 
of the data for the analysis.
@@ -64,6 +64,8 @@
         @type mc_sim_num:           int
         @keyword modsel:            The model selection technique to use in 
the analysis to determine which model is the best for each spin cluster.  
This can currently be one of 'AIC', 'AICc', and 'BIC'.
         @type modsel:               str
+        @keyword pre_run_dir:       The optional directory containing the 
dispersion auto-analysis results from a previous run.  The optimised 
parameters from these previous results will be used as the starting point for 
optimisation rather than performing a grid search.  This is essential for 
when large spin clusters are specified, as a grid search becomes 
prohibitively expensive with clusters of three or more spins.  At some point 
a RelaxError will occur because the grid search is impossibly large.  For the 
cluster specific parameters, i.e. the populations of the states and the 
exchange parameters, an average value will be used as the starting point.  
For all other parameters, the R20 values for each spin and magnetic field, as 
well as the parameters related to the chemical shift difference dw, the 
optimised values of the previous run will be directly copied.
+        @type pre_run_dir:          None or str
         @keyword mc_sim_all_models: A flag which if True will cause Monte 
Carlo simulations to be performed for each individual model.  Otherwise Monte 
Carlo simulations will be reserved for the final model.
         @type mc_sim_all_models:    bool
         """
@@ -86,6 +88,7 @@
         self.grid_inc = grid_inc
         self.mc_sim_num = mc_sim_num
         self.modsel = modsel
+        self.pre_run_dir = pre_run_dir
         self.mc_sim_all_models = mc_sim_all_models
 
         # No results directory, so default to the current directory.
@@ -212,12 +215,18 @@
         @type model:    str
         """
 
-        # Nested model simplification.
-        nested = self.nesting(model=model)
-
-        # Grid search.
-        if not nested:
-            self.interpreter.grid_search(inc=self.grid_inc)
+        # Use pre-run results as the optimisation starting point.
+        if self.pre_run_dir:
+            self.pre_run_parameters(model=model)
+
+        # Otherwise use the normal nesting check and grid search if not 
nested.
+        else:
+            # Nested model simplification.
+            nested = self.nesting(model=model)
+
+            # Grid search.
+            if not nested:
+                self.interpreter.grid_search(inc=self.grid_inc)
 
         # Minimise.
         self.interpreter.minimise('simplex', func_tol=self.opt_func_tol, 
max_iter=self.opt_max_iterations, constraints=True)
@@ -231,6 +240,28 @@
             self.interpreter.monte_carlo.error_analysis()
 
 
+    def pre_run_parameters(self, model=None):
+        """Copy parameters from an earlier analysis.
+
+        @keyword model: The model to be optimised.
+        @type model:    str
+        """
+
+        # Create a temporary data pipe for the previous run.
+        self.interpreter.pipe.create(pipe_name='pre', pipe_type='relax_disp')
+
+        # Load the previous results.
+        path = self.pre_run_dir + sep + model
+        self.interpreter.results.read(file='results', dir=path)
+
+        # Copy the parameters.
+        self.interpreter.relax_disp.parameter_copy(pipe_from='pre', 
pipe_to=model)
+
+        # Finally, switch back to the original data pipe and delete the 
temporary one.
+        self.interpreter.pipe.switch(pipe_name=model)
+        self.interpreter.pipe.delete(pipe_name='pre')
+
+
     def run(self):
         """Execute the auto-analysis."""