--- a/python/indicators.py	Fri Dec 21 18:33:36 2012 -0500
+++ b/python/indicators.py	Sat Jan 26 19:02:25 2013 -0500
@@ -38,11 +38,22 @@
         return len(self.values) == 0
 
     def __getitem__(self, i):
+        'Returns ith value'
+        sortedKeys = sorted(self.values.keys())
+        if 0<=i<len(sortedKeys):
+            return self.values[sortedKeys[i]]
+        else:
+            return None
+
+    def valueAtInstant(self, i):
         if i in self.values.keys():
             return self.values[i]
         else:
             return None
 
+    def __len__(self):
+        return len(self.values)
+
     def __iter__(self):
         self.iterInstantNum = 0 # index in the interval or keys of the dict
         return self
@@ -53,7 +64,7 @@
             raise StopIteration
         else:
             self.iterInstantNum += 1
-            return self.values[self.values.keys()[self.iterInstantNum-1]]
+            return self.__getitem__(self.iterInstantNum-1)
 
     def getTimeInterval(self):
         return self.timeInterval
@@ -83,6 +94,20 @@
         if self.maxValue:
             ylim(ymax = self.maxValue)
 
+    @staticmethod
+    def computeDLCSS(indicator1, indicator2, threshold, delta = float('inf'), method= 'min'):
+	''' compute the distance between two indicators using LCSS
+	two common methods are used: min or mean of the indicators length'''
+        # l1= TemporalIndicator1.valueSorted
+	# 	l2= TemporalIndicator2.valueSorted
+	# 	if method = 'min':
+	# 		DLCSS= 1- (LCSS(l1,l2, threshold, delta, distance))/min(len(l1),len(l2)))
+	# 	if method = 'mean':
+	# 		average= len(l1)+len(l2))/2
+	# 		DLCSS= 1- ((LCSS(l1,l2, threshold, delta, distance))/average)
+	# 	return DLCSS
+        return 0
+
 class SeverityIndicator(TemporalIndicator):
     '''Class for severity indicators 
     field mostSevereIsMax is True 
@@ -93,7 +118,7 @@
         self.mostSevereIsMax = mostSevereIsMax
         self.ignoredValue = ignoredValue
 
-    def getMostSevereValue(self, minNInstants=1):
+    def getMostSevereValue(self, minNInstants=1): # TODO use scoreatpercentile
         from matplotlib.mlab import find
         from numpy.core.multiarray import array
         from numpy.core.fromnumeric import mean