r26318 - /branches/frame_order_cleanup/auto_analyses/frame_order.py -- October 20, 2014 - 16:23

Author: bugman
Date: Mon Oct 20 16:23:08 2014
New Revision: 26318

URL: http://svn.gna.org/viewcvs/relax?rev=26318&view=rev
Log:
The intermediate results storing can now be turned off in the frame order 
auto-analysis.

The new store_intermediate Boolean argument has been added to the analysis to 
allow the storage of
these results to be turned on or off.


Modified:
    branches/frame_order_cleanup/auto_analyses/frame_order.py

Modified: branches/frame_order_cleanup/auto_analyses/frame_order.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/frame_order_cleanup/auto_analyses/frame_order.py?rev=26318&r1=26317&r2=26318&view=diff
==============================================================================
--- branches/frame_order_cleanup/auto_analyses/frame_order.py   (original)
+++ branches/frame_order_cleanup/auto_analyses/frame_order.py   Mon Oct 20 
16:23:08 2014
@@ -369,7 +369,7 @@
     # Debugging and test suite variables.
     _final_state = True
 
-    def __init__(self, data_pipe_full=None, data_pipe_subset=None, 
pipe_bundle=None, results_dir=None, pre_run_dir=None, opt_rigid=None, 
opt_subset=None, opt_full=None, opt_mc=None, mc_sim_num=500, 
models=MODEL_LIST_NONREDUNDANT, brownian_step_size=2.0, brownian_snapshot=10, 
brownian_total=1000, rigid_grid_split=False):
+    def __init__(self, data_pipe_full=None, data_pipe_subset=None, 
pipe_bundle=None, results_dir=None, pre_run_dir=None, opt_rigid=None, 
opt_subset=None, opt_full=None, opt_mc=None, mc_sim_num=500, 
models=MODEL_LIST_NONREDUNDANT, brownian_step_size=2.0, brownian_snapshot=10, 
brownian_total=1000, rigid_grid_split=False, store_intermediate=True):
         """Perform the full frame order analysis.
 
         @param data_pipe_full:          The name of the data pipe containing 
all of the RDC and PCS data.
@@ -402,6 +402,8 @@
         @type brownian_total:           int
         @keyword rigid_grid_split:      A flag which if True will cause the 
grid search for the rigid model to be split so that the rotation is optimised 
first followed by the translation.  When combined with grid zooming, this can 
save optimisation time.  However it may result in the global minimum being 
missed.
         @type rigid_grid_split:         bool
+        @keyword store_intermediate:    A flag which if True will cause all 
intermediate optimisation results to be stored in the 'intermediate_results' 
directory.  These can then be used for studying the optimisation settings or 
loaded for subsequent analyses.
+        @type store_intermediate:       bool
         """
 
         # Execution lock.
@@ -425,6 +427,7 @@
             self.brownian_snapshot = brownian_snapshot
             self.brownian_total = brownian_total
             self.rigid_grid_split = rigid_grid_split
+            self.store_intermediate = store_intermediate
 
             # Re-order the models to enable the parameter nesting protocol.
             self.models = self.reorder_models(models)
@@ -968,9 +971,10 @@
                     self.interpreter.minimise.grid_search(inc=incs)
 
                     # Store the intermediate results and statistics.
-                    self.results_output(model=model, 
dir=model_directory(model, base_dir=intermediate_dir), results_file=True, 
simulation=False)
-                    count_sobol_points(dir=intermediate_dir, force=True)
-                    summarise(dir=intermediate_dir, force=True)
+                    if self.store_intermediate:
+                        self.results_output(model=model, 
dir=model_directory(model, base_dir=intermediate_dir), results_file=True, 
simulation=False)
+                        count_sobol_points(dir=intermediate_dir, force=True)
+                        summarise(dir=intermediate_dir, force=True)
 
                 # Minimise (for the PCS data subset and full RDC set).
                 for i in opt.loop_min():
@@ -993,9 +997,10 @@
                     
self.interpreter.minimise.execute(min_algor=opt.get_min_algor(i), 
func_tol=func_tol, max_iter=max_iter)
 
                     # Store the intermediate results.
-                    self.results_output(model=model, 
dir=model_directory(model, base_dir=intermediate_dir), results_file=True, 
simulation=False)
-                    count_sobol_points(dir=intermediate_dir, force=True)
-                    summarise(dir=intermediate_dir, force=True)
+                    if self.store_intermediate:
+                        self.results_output(model=model, 
dir=model_directory(model, base_dir=intermediate_dir), results_file=True, 
simulation=False)
+                        count_sobol_points(dir=intermediate_dir, force=True)
+                        summarise(dir=intermediate_dir, force=True)
 
             # Printout.
             subsubtitle(file=sys.stdout, text="Optimisation using the full 
data set")
@@ -1037,9 +1042,10 @@
                     
self.interpreter.minimise.execute(min_algor=opt.get_min_algor(i), 
func_tol=func_tol, max_iter=max_iter)
 
                     # Store the intermediate results.
-                    self.results_output(model=model, 
dir=model_directory(model, base_dir=intermediate_dir), results_file=True, 
simulation=False)
-                    count_sobol_points(dir=intermediate_dir, force=True)
-                    summarise(dir=intermediate_dir, force=True)
+                    if self.store_intermediate:
+                        self.results_output(model=model, 
dir=model_directory(model, base_dir=intermediate_dir), results_file=True, 
simulation=False)
+                        count_sobol_points(dir=intermediate_dir, force=True)
+                        summarise(dir=intermediate_dir, force=True)
 
             # Results printout.
             self.print_results()