--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/commons/core/stat/Stat.py	Fri Jan 18 04:54:14 2013 -0500
@@ -0,0 +1,209 @@
+import math
+
+class Stat(object):
+
+    def __init__(self, lValues = []):
+        self.reset()
+        if lValues != []:
+            self.fill(lValues)
+    
+    def __eq__(self, o):
+        self._lValues.sort()
+        o._lValues.sort()
+        return self._lValues == o._lValues and round(self._sum, 6) == round(o._sum, 6) \
+            and round(self._sumOfSquares, 6) == round(o._sumOfSquares, 6) and self._n == self._n \
+            and round(self._min, 6) == round(o._min, 6) and round(self._max, 6) == round(o._max, 6)
+            
+    def getValuesList(self):
+        return self._lValues
+    
+    def getSum(self):
+        return self._sum
+    
+    def getSumOfSquares(self):
+        return self._sumOfSquares
+    
+    def getValuesNumber(self):
+        return self._n
+    
+    def getMin(self):
+        return self._min
+    
+    def getMax(self):
+        return self._max
+
+    ## Reset all attributes
+    #
+    def reset(self):
+        self._lValues = []
+        self._sum = 0.0
+        self._sumOfSquares = 0.0
+        self._n = 0
+        self._max = 0.0
+        self._min = 0.0
+
+    ## Add a value to Stat instance list and update attributes
+    #
+    # @param v float value to add
+    #    
+    def add(self, v):
+        self._lValues.append( float(v) )
+        self._sum += float(v)
+        self._sumOfSquares += float(v) * float(v)
+        self._n = self._n + 1
+        if v > self._max:
+            self._max = float(v)
+        if self._n == 1:
+            self._min = float(v)
+        elif v < self._min:
+            self._min = float(v)
+         
+    ## Add a list of values to Stat instance list and update attributes
+    #
+    # @param lValues list of float list to add
+    #    
+    def fill(self, lValues):
+        for v in lValues:
+            self.add(v)
+    
+    ## Get the arithmetic mean of the Stat instance list
+    #
+    # @return float
+    #
+    def mean(self):
+        if self._n == 0:
+            return 0
+        else:
+            return self._sum / float(self._n)
+    
+    ## Get the variance of the sample
+    # @note we consider a sample, not a population. So for calculation, we use n-1
+    #
+    # @return float
+    #
+    def var(self):
+        if self._n < 2 or self.mean() == 0.0:
+            return 0
+        else:
+            variance = self._sumOfSquares/float(self._n - 1) - self._n/float(self._n - 1) * self.mean()*self.mean()
+            if round(variance, 10) == 0:
+                variance = 0
+            return variance
+
+    ## Get the standard deviation of the sample
+    #
+    # @return float
+    #
+    def sd(self):
+        return math.sqrt( self.var() )
+
+    ## Get the coefficient of variation of the sample
+    #
+    # @return float
+    #
+    def cv(self):
+        if self._n < 2 or self.mean() == 0.0:
+            return 0
+        else:
+            return self.sd() / self.mean()
+
+    ## Get the median of the sample
+    #
+    # @return number or "NA" (Not available)
+    #
+    def median( self ):
+        if len(self._lValues) == 0:
+            return "NA"
+        if len(self._lValues) == 1:
+            return self._lValues[0]
+        self._lValues.sort()
+        m = int( math.ceil( len(self._lValues) / 2.0 ) )
+        if len(self._lValues) % 2:
+            return self._lValues[m-1]
+        else:
+            return ( self._lValues[m-1] + self._lValues[m] ) / 2.0
+        
+    ## Get the kurtosis (measure of whether the data are peaked or flat relative to a normal distribution, 'coef d'aplatissement ' in french)).
+    #  k = 0 -> completely flat
+    #  k = 3 -> same as normal distribution
+    #  k >> 3 -> peak
+    #
+    # @return float 
+    #
+    def kurtosis(self):
+        numerator = 0
+        for i in self._lValues:
+            numerator += math.pow( i - self.mean(), 4 )
+        return numerator / float(self._n - 1) * self.sd() 
+
+    ## Prepare a string with calculations on your values
+    #
+    # @return string 
+    #
+    def string(self):
+        msg = ""
+        msg += "n=%d" % ( self._n )
+        msg += " mean=%5.3f" % ( self.mean() )
+        msg += " var=%5.3f" % ( self.var() )
+        msg += " sd=%5.3f" % ( self.sd() )
+        msg += " min=%5.3f" % ( self.getMin() )
+        median = self.median()
+        if median == "NA":
+            msg += " med=%s" % (median)
+        else:
+            msg += " med=%5.3f" % (median)
+        msg += " max=%5.3f" % ( self.getMax() )
+        return msg
+    
+    ## Print descriptive statistics
+    #
+    def view(self):
+        print self.string()
+
+    ## Return sorted list of values, ascending (default) or descending
+    #
+    # @return list
+    #
+    def sort( self, isReverse = False ):
+        self._lValues.sort(reverse = isReverse)
+        return self._lValues
+    
+    ## Give the quantile corresponding to the chosen percentage
+    #
+    # @return number 
+    #
+    def quantile( self, percentage ):
+        if self._n == 0:
+            return 0
+        elif percentage == 1:
+            return self.getMax()
+        else:
+            return self.sort()[int(self._n * percentage)]
+        
+    ## Prepare a string with quantile values
+    #
+    # @return string
+    #    
+    def stringQuantiles( self ):
+        return "n=%d min=%5.3f Q1=%5.3f median=%5.3f Q3=%5.3f max=%5.3f" % \
+               (self._n, self.quantile(0), self.quantile(0.25), self.quantile(0.5), self.quantile(0.75), self.quantile(1))
+
+    ## Print quantiles string
+    #
+    def viewQuantiles( self ):
+        print self.stringQuantiles()
+        
+    ## Compute N50 
+    # @return number
+    def N50(self ):
+        lSorted = self.sort(True)
+        midlValues = self.getSum() / 2
+        cumul = 0
+        index = 0
+        while cumul < midlValues:
+            cumul =  cumul + lSorted[index]
+            index += 1
+        if (index == 0):
+            return lSorted[index]
+        else :
+            return lSorted[index - 1]
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