methods moved in generic plugin

dream/plugins/BatchesTabularExit.py

@@ -104,20 +104,4 @@ class BatchesTabularExit(plugin.OutputPreparationPlugin):
                                                                             "%.2f" % avgCycleTimeCI['lb'],
                                                                             "%.2f" % avgCycleTimeCI['ub']]
                                                                            ) 
-        return data
-    
-    def getConfidenceInterval(self, value_list, confidenceLevel):
-        from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals
-        from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures
-        BSM=BasicStatisticalMeasures()
-        lb, ub = Intervals().ConfidIntervals(value_list, confidenceLevel)
-        return {'lb': lb,
-                'ub': ub,
-                'avg': BSM.mean(value_list) 
-            }
-        
-    def getAverage(self, value_list):
-        return sum(value_list) / float(len(value_list))
-                                       
-    def getStDev(self, value_list):
-        return numpy.std(value_list)
\ No newline at end of file
+        return data
\ No newline at end of file

dream/plugins/plugin.py

 from copy import deepcopy
 import json
+import numpy
 
 from zope.dottedname.resolve import resolve
 
@@ -32,6 +33,25 @@ class Plugin(object):
             successors.append(edge['destination'])
     return successors
 
+  # calcualted the confidence inteval for a list and a confidence level
+  def getConfidenceInterval(self, value_list, confidenceLevel):
+    from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals
+    from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures
+    BSM=BasicStatisticalMeasures()
+    lb, ub = Intervals().ConfidIntervals(value_list, confidenceLevel)
+    return {'lb': lb,
+            'ub': ub,
+            'avg': BSM.mean(value_list) 
+        }
+    
+  # return the average of a list    
+  def getAverage(self, value_list):
+    return sum(value_list) / float(len(value_list))
+  
+  # return the standard deviation of a list                                      
+  def getStDev(self, value_list):
+    return numpy.std(value_list)
+
   # returns name of a node given its id
   def getNameFromId(self, data, node_id):
       return data['graph']['node'][node_id]['name']