--- a/python/pavement.py	Fri Jan 31 00:10:31 2014 -0500
+++ b/python/pavement.py	Tue Feb 04 02:13:27 2014 -0500
@@ -5,38 +5,6 @@
 
 __metaclass__ = type
 
-class RTSS:
-    'class for data related to a RTSS, including agregating pavement marking measurements'
-
-    def __init__(self, id):
-        self.id = id
-
-class MarkingTest:
-    '''class for a test site for a given product'''
-
-    def __init__(self, siteId, paintingDate, paintingType, color, data):
-        self.siteId = siteId
-        self.paintingDate = paintingDate
-        self.paintingType = paintingType
-        self.color = color
-        self.data = data
-        self.nMeasures = len(data)
-
-    def plot(self, measure, options = 'o', dayRatio = 1., **kwargs):
-        from matplotlib.pyplot import plot
-        plot(self.data['jours']/float(dayRatio), 
-             self.data[measure], options, **kwargs)
-
-    def getMarkingMeasures(self, dataLabel):
-        from numpy import isnan
-        nonZeroIndices = ~isnan(self.data[dataLabel])
-        return self.data[nonZeroIndices]['jours'], self.data[nonZeroIndices][dataLabel]
-
-    def plotMarkingMeasures(self, measure, options = 'o', dayRatio = 1., **kwargs):
-        for i in range(1,7):
-            self.plot('{}_{}'.format(measure, i), options, dayRatio, **kwargs)
-
-
 def occ_max(a):
     if a != []:
         s = set(a)
@@ -198,3 +166,65 @@
         neigeEC_sup_seuil = 1
 
     return (nbre_jours_T_negatif,nbre_jours_gel_degel, deltas_T, nbre_jours_gel_consecutifs, pluie_tot, neige_tot, neigeEC_sup_seuil, ecart_type_T)
+
+
+class RTSS:
+    'class for data related to a RTSS, including agregating pavement marking measurements'
+
+    def __init__(self, id):
+        self.id = id
+
+class MarkingTest:
+    '''class for a test site for a given product
+
+    including the series of measurements over the years'''
+
+    def __init__(self, id, paintingDate, paintingType, color, data):
+        self.id = id
+        self.paintingDate = paintingDate
+        self.paintingType = paintingType
+        self.color = color
+        self.data = data
+        self.nMeasures = len(data)
+
+    def getSite(self):
+        return int(self.id[:2])
+
+    def getTestAttributes(self):
+        return [self.paintingType, self.color]
+
+    def plot(self, measure, options = 'o', dayRatio = 1., **kwargs):
+        from matplotlib.pyplot import plot
+        plot(self.data['jours']/float(dayRatio), 
+             self.data[measure], options, **kwargs)
+
+    def getMarkingMeasures(self, dataLabel):
+        from numpy import isnan
+        nonZeroIndices = ~isnan(self.data[dataLabel])
+        return self.data[nonZeroIndices]['jours'], self.data[nonZeroIndices][dataLabel]
+
+    def plotMarkingMeasures(self, measure, options = 'o', dayRatio = 1., **kwargs):
+        for i in range(1,7):
+            self.plot('{}_{}'.format(measure, i), options, dayRatio, **kwargs)
+
+    def computeMarkingMeasureVariations(self, dataLabel, lanePositions, weatherData, snowThreshold):
+        '''Computes for each successive measurement
+        lanePositions = None
+        measure variation, initial measure, time duration, weather indicators
+        
+        TODO if measurements per lane, add a variable for lane position (position1 to 6)
+        lanePositions = list of integers (range(1,7))
+        measure variation, initial measure, time duration, lane position1, weather indicators
+        measure variation, initial measure, time duration, lane position2, weather indicators
+        ...'''
+        from numpy import isnan
+        variationData = []
+        if lanePositions == None:
+            nonZeroIndices = ~isnan(self.data[dataLabel])
+            days = self.data[nonZeroIndices]['jours']
+            dates = self.data[nonZeroIndices]['date_mesure']
+            measures = self.data[nonZeroIndices][dataLabel]
+            for i in range(1, len(dates)):
+                nDaysTNegative, nDaysThawFreeze, deltaTemp, nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp = ecWeatherIndicators(weatherData, dates[i-1], dates[i], snowThreshold)
+                variationData.append([measures[i-1]-measures[i], measures[i-1], days[i]-days[i-1], nDaysTNegative, nDaysThawFreeze] + deltaTemp + [nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp])
+        return variationData