--- a/python/cvutils.py	Fri May 23 17:33:11 2014 -0400
+++ b/python/cvutils.py	Wed May 28 17:46:38 2014 -0400
@@ -5,7 +5,7 @@
     import cv2
     opencvAvailable = True
 except ImportError:
-    print('OpenCV library could not be loaded (video replay functions will not be available)')
+    print('OpenCV library could not be loaded (video replay functions will not be available)') # TODO change to logging module
     opencvAvailable = False
 try:
     import skimage

--- a/python/ml.py	Fri May 23 17:33:11 2014 -0400
+++ b/python/ml.py	Wed May 28 17:46:38 2014 -0400
@@ -24,8 +24,8 @@
     def train(self, samples, responses):
         self.model.train(samples, responses, params = self.params)
 
-    def predict(self, sample):
-        return np.float32(self.model.predict(s))
+    def predict(self, samples):
+        return np.float32([self.model.predict(s) for s in samples])
 
 
 class Centroid:

--- a/python/moving.py	Fri May 23 17:33:11 2014 -0400
+++ b/python/moving.py	Wed May 28 17:46:38 2014 -0400
@@ -805,11 +805,15 @@
         at constant speed'''
         return predictPositionNoLimit(nTimeSteps, self.getPositionAtInstant(instant), self.getVelocityAtInstant(instant), externalAcceleration)
 
-    def classifyUserTypeSpeed(self, threshold, statisticsFunc = median):
+    def classifyUserTypeSpeed(self, threshold, statisticsFunc = median, ignoreNInstantsAtEnds = 0):
         '''Classifies slow and fast road users
         slow: non-motorized -> pedestrians
         fast: motorized -> cars'''
-        if statisticsFunc(self.velocities.norm()) >= threshold:
+        if ignoreNInstantsAtEnds > 0:
+            speeds = self.velocities.norm()[ignoreNInstantsAtEnds:-ignoreNInstantsAtEnds]
+        else:
+            speeds = self.velocities.norm()
+        if statisticsFunc(speeds) >= threshold:
             self.setUserType(userType2Num['car'])
         else:
             self.setUserType(userType2Num['pedestrian'])

--- a/python/pavement.py	Fri May 23 17:33:11 2014 -0400
+++ b/python/pavement.py	Wed May 28 17:46:38 2014 -0400
@@ -15,7 +15,7 @@
 
 durabilities = {1: 98, #96 to 100
                 2: 85, #75 to 96
-                3: 72, #50 to 75
+                3: 62, #50 to 75
                 4: 32, #15 to 50
                 5: 7 #0 to 15
                 }
@@ -106,12 +106,12 @@
     '''Computes several winter maintenance indicators
     data = entretien_hivernal = pylab.csv2rec('C:\Users\Alexandre\Documents\Cours\Poly\Projet\mesures_entretien_hivernal\mesures_deneigement.txt', delimiter = ',')'''
     import datetime
-    somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, compteur_somme_abrasif = 0,0,0,0,0,0,0,0,0
+    somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, compteur_premiere_neige, compteur_somme_abrasif = 0,0,0,0,0,0,0,0,0
 
     if circuitReference in data['ref_circuit']:
         for i in range(len(data)):
             if data['ref_circuit'][i] == circuitReference and (data['date'][i] + datetime.timedelta(days = 6)) <= endDate and (data['date'][i] + datetime.timedelta(days = 6)) > startDate:
-                premiere_neige += data['premiere_neige'][i]
+                compteur_premiere_neige += float(data['premiere_neige'][i])
                 somme_neige += float(data['neige'][i])
                 somme_eau += float(data['eau'][i])
                 somme_abrasif += float(data['abrasif'][i])
@@ -119,7 +119,11 @@
                 somme_lc += float(data['lc'][i])
                 somme_lrg += float(data['lrg'][i])
                 somme_lrd += float(data['lrd'][i])
-                compteur_somme_abrasif += float(data['autre_abrasif_binaire'][i])			
+                compteur_somme_abrasif += float(data['autre_abrasif_binaire'][i])
+        if compteur_premiere_neige >= 1:
+            premiere_neige = 1
+        else:
+            premiere_neige = 0
         if compteur_somme_abrasif >= 1:
             autres_abrasifs = 1
         else:
@@ -133,7 +137,7 @@
 
     return (somme_eau, somme_neige, neigeMTQ_sup_seuil, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs)
 
-def ecWeatherIndicators(data, startDate, endDate, snowThreshold, weatherDatatype, minProportionMeasures = 0.):
+def weatherIndicators(data, startDate, endDate, snowThreshold, weatherDatatype, minProportionMeasures = 0.):
     '''Computes the indicators from Environment Canada files
     (loaded as a recarray using csv2rec in data),
     between start and end dates (datetime.datetime objects)
@@ -147,11 +151,12 @@
     seuils_T = [20,15,10,5]
     deltas_T = [0,0,0,0]
     startIndex = find(data['date'] == startDate)
-    nDays = (endDate - startDate).days+1
-    if startIndex.size != 0:
-        for i in range(startIndex, startIndex+int(nDays)):
+    nDays = int((endDate - startDate).days)+1
+    if len(startIndex) > 0 and startIndex+nDays <= len(data):
+        startIndex = startIndex[0]
+        for i in range(startIndex, startIndex+nDays):
             if not np.isnan(data['tmax'][i]):
-                tmax = data['tmax'][i]            
+                tmax = data['tmax'][i]
             else:
                 tmax = None
             if not np.isnan(data['tmin'][i]):
@@ -197,6 +202,7 @@
         return [None]*2+[[None]*len(seuils_T)]+[None]*5
 
 def mtqWeatherIndicators(data, startDate, endDate,tmax,tmin,tmoy):
+    print("Deprecated, use weatherIndicators")
     from matplotlib.mlab import find
     nbre_jours_T_negatif,nbre_jours_gel_degel,ecart_type_T = 0,0,0
     compteur,nbre_jours_gel_consecutifs=0,0
@@ -296,7 +302,7 @@
             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, weatherDataType, minProportionMeasures)
+                nDaysTNegative, nDaysThawFreeze, deltaTemp, nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp = weatherIndicators(weatherData, dates[i-1], dates[i], snowThreshold, weatherDataType, minProportionMeasures)
                 if dates[i-1].year+1 == dates[i].year:
                     winter = 1
                     if days[i-1]<365:

--- a/python/utils.py	Fri May 23 17:33:11 2014 -0400
+++ b/python/utils.py	Wed May 28 17:46:38 2014 -0400
@@ -32,7 +32,12 @@
     return (k*stdev/tolerance)**2
 
 def confidenceInterval(mean, stdev, nSamples, percentConfidence, trueStd = True, printLatex = False):
-    'if trueStd, use normal distribution, otherwise, Student'
+    '''if trueStd, use normal distribution, otherwise, Student
+
+    Use otherwise t.interval or norm.interval
+    ex: norm.interval(0.95, loc = 0., scale = 2.3/sqrt(11))
+    t.interval(0.95, 10, loc=1.2, scale = 2.3/sqrt(nSamples))
+    loc is mean, scale is sigma/sqrt(n) (for Student, 10 is df)'''
     from math import sqrt
     from scipy.stats.distributions import norm, t
     if trueStd: