r6228 - /1.3/generic_fns/monte_carlo.py -- May 22, 2008 - 00:21

Author: bugman
Date: Thu May 22 00:02:56 2008
New Revision: 6228

URL: http://svn.gna.org/viewcvs/relax?rev=6228&view=rev
Log:
Updated the error_analysis() function to the new design.


Modified:
    1.3/generic_fns/monte_carlo.py

Modified: 1.3/generic_fns/monte_carlo.py
URL: 
http://svn.gna.org/viewcvs/relax/1.3/generic_fns/monte_carlo.py?rev=6228&r1=6227&r2=6228&view=diff
==============================================================================
--- 1.3/generic_fns/monte_carlo.py (original)
+++ 1.3/generic_fns/monte_carlo.py Thu May 22 00:02:56 2008
@@ -104,33 +104,39 @@
         pack_sim_data(spin, random)
 
 
-def error_analysis(run=None, prune=0.0):
+def error_analysis(prune=0.0):
     """Function for calculating errors from the Monte Carlo simulations.
 
     The standard deviation formula used to calculate the errors is the 
square root of the
-    bias-corrected variance, given by the formula:
-
-                   ____________________________
+    bias-corrected variance, given by the formula::
+
+                   __________________________
                   /   1
-        sd  =    /  ----- * sum({Xi - Xav}^2)]
+        sd  =    /  ----- * sum({Xi - Xav}^2)
                \/   n - 1
 
-    where:
-        n is the total number of simulations.
-        Xi is the parameter value for simulation i.
-        Xav is the mean parameter value for all simulations.
+    where
+        - n is the total number of simulations.
+        - Xi is the parameter value for simulation i.
+        - Xav is the mean parameter value for all simulations.
+
+
+    @keyword prune:     The amount to prune the upper and lower tails the 
distribution.  If set to
+                        0.0, no simulations will be pruned.  If set to 1.0, 
all simulations will be
+                        pruned.  This argument should be set 0.0 to avoid 
meaningless statistics.
+    @type prune:        float
     """
 
-    # Arguments.
-    self.run = run
-
-    # Test if the run exists.
-    if not self.run in relax_data_store.run_names:
-        raise RelaxNoPipeError, self.run
-
-    # Test if simulations have been set up.
-    if not hasattr(relax_data_store, 'sim_state'):
-        raise RelaxError, "Monte Carlo simulations for the run " + 
`self.run` + " have not been set up."
+    # Test if the current data pipe exists.
+    if not relax_data_store.current_pipe:
+        raise RelaxNoPipeError
+
+    # Alias the current data pipe.
+    cdp = relax_data_store[relax_data_store.current_pipe]
+
+    # Test if simulations have been set up.
+    if not hasattr(cdp, 'sim_state'):
+        raise RelaxError, "Monte Carlo simulations have not been set up."
 
     # Specific number of instances, return simulation chi2 array, return 
selected simulation array, return simulation parameter array, and set error 
functions.
     count_num_instances = get_specific_fn('num_instances', cdp.pipe_type)
@@ -141,20 +147,20 @@
     set_error = get_specific_fn('set_error', cdp.pipe_type)
 
     # Count the number of instances.
-    num_instances = count_num_instances(self.run)
+    num_instances = count_num_instances()
 
     # Loop over the instances.
     for instance in xrange(num_instances):
         # Get the selected simulation array.
-        select_sim = return_selected_sim(self.run, instance)
-
-        # Initialise an array of indecies to prune (an empty array means no 
prunning).
+        select_sim = return_selected_sim(instance)
+
+        # Initialise an array of indecies to prune (an empty array means no 
pruning).
         indecies_to_skip = []
 
         # Pruning.
         if prune > 0.0:
             # Get the array of simulation chi-squared values.
-            chi2_array = return_sim_chi2(self.run, instance)
+            chi2_array = return_sim_chi2(instance)
 
             # The total number of simulations.
             n = len(chi2_array)
@@ -178,7 +184,7 @@
         index = 0
         while 1:
             # Get the array of simulation parameters for the index.
-            param_array = return_sim_param(self.run, instance, index)
+            param_array = return_sim_param(instance, index)
 
             # Break (no more parameters).
             if param_array == None:
@@ -245,7 +251,7 @@
                 sd = None
 
             # Set the parameter error.
-            set_error(self.run, instance, index, sd)
+            set_error(instance, index, sd)
 
             # Increment the parameter index.
             index = index + 1