r22565 - /trunk/auto_analyses/relax_disp.py -- March 27, 2014 - 16:46

Author: bugman
Date: Thu Mar 27 16:46:45 2014
New Revision: 22565

URL: http://svn.gna.org/viewcvs/relax?rev=22565&view=rev
Log:
Added the new exp_mc_sim_num argument to the relaxation dispersion 
auto-analysis.

This is in preparation for fixing bug #21869 (https://gna.org/bugs/?21869).

This argument allows for a different number of Monte Carlo simulations for 
the 'R2eff' model when
exponential curves are fit.  It will mainly be useful in the test suite to 
improve the accuracy of
the R2eff errors, while still running a low number of simulations for the 
other models to allow
optimisation to be quick.


Modified:
    trunk/auto_analyses/relax_disp.py

Modified: trunk/auto_analyses/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/auto_analyses/relax_disp.py?rev=22565&r1=22564&r2=22565&view=diff
==============================================================================
--- trunk/auto_analyses/relax_disp.py   (original)
+++ trunk/auto_analyses/relax_disp.py   Thu Mar 27 16:46:45 2014
@@ -48,7 +48,7 @@
     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', 
pre_run_dir=None, insignificance=0.0, numeric_only=False, 
mc_sim_all_models=False, eliminate=True):
+    def __init__(self, pipe_name=None, pipe_bundle=None, results_dir=None, 
models=[MODEL_R2EFF], grid_inc=11, mc_sim_num=500, exp_mc_sim_num=None, 
modsel='AIC', pre_run_dir=None, insignificance=0.0, numeric_only=False, 
mc_sim_all_models=False, eliminate=True):
         """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.
@@ -63,6 +63,9 @@
         @type grid_inc:             int
         @keyword mc_sim_num:        The number of Monte Carlo simulations to 
be used for error analysis at the end of the analysis.
         @type mc_sim_num:           int
+        @keyword exp_mc_sim_num:    The number of Monte Carlo simulations 
for the error analysis in the 'R2eff' model when exponential curves are 
fitted.  This defaults to the value of the mc_sim_num argument when not 
given.  For the 2-point fixed-time calculation for the 'R2eff' model, this 
argument is ignored.
+        @type exp_mc_sim_num:       int or None
+        @type
         @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.
@@ -94,6 +97,7 @@
         self.models = models
         self.grid_inc = grid_inc
         self.mc_sim_num = mc_sim_num
+        self.exp_mc_sim_num = exp_mc_sim_num
         self.modsel = modsel
         self.pre_run_dir = pre_run_dir
         self.insignificance = insignificance
@@ -375,7 +379,10 @@
 
         # Monte Carlo simulations.
         if self.mc_sim_all_models or len(self.models) < 2 or model == 
'R2eff':
-            self.interpreter.monte_carlo.setup(number=self.mc_sim_num)
+            if model == 'R2eff' and self.exp_mc_sim_num != None:
+                
self.interpreter.monte_carlo.setup(number=self.exp_mc_sim_num)
+            else:
+                self.interpreter.monte_carlo.setup(number=self.mc_sim_num)
             self.interpreter.monte_carlo.create_data()
             self.interpreter.monte_carlo.initial_values()
             self.interpreter.minimise('simplex', func_tol=self.opt_func_tol, 
max_iter=self.opt_max_iterations, constraints=True)