Mercurial > hg > nsaunier > traffic-intelligence

comparison python/pavement.py @ 447:7ef40014236c

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updated weather indicator calculations
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 04 Feb 2014 15:00:39 -0500
parents a65a14c90834
children 0b15fdeb903e
comparison
equal deleted inserted replaced
446:a65a14c90834 447:7ef40014236c
117 else: 117 else:
118 somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs, neigeMTQ_sup_seuil = '','','','','','','','','','' 118 somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs, neigeMTQ_sup_seuil = '','','','','','','','','',''
119 119
120 return (somme_eau, somme_neige, neigeMTQ_sup_seuil, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs) 120 return (somme_eau, somme_neige, neigeMTQ_sup_seuil, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs)
121 121
122 def ecWeatherIndicators(data, startDate, endDate, snowThreshold): 122 def ecWeatherIndicators(data, startDate, endDate, snowThreshold, minProportionMeasures = 0.):
123 '''Computes the indicators from Environment Canada files 123 '''Computes the indicators from Environment Canada files
124 (loaded as a recarray using csv2rec in data), 124 (loaded as a recarray using csv2rec in data),
125 between start and end dates (datetime.datetime objects)''' 125 between start and end dates (datetime.datetime objects)
126
127 minProportionMeasures is proportion of measures necessary to consider the indicators'''
128 from matplotlib.mlab import find
126 nbre_jours_T_negatif,nbre_jours_gel_degel,pluie_tot,neige_tot,ecart_type_T = 0,0,0,0,0 129 nbre_jours_T_negatif,nbre_jours_gel_degel,pluie_tot,neige_tot,ecart_type_T = 0,0,0,0,0
127 compteur,nbre_jours_gel_consecutifs=0,0 130 compteur,nbre_jours_gel_consecutifs=0,0
128 tmoys = [] 131 tmoys = []
129 seuils_T = [20,15,10,5] 132 seuils_T = [20,15,10,5]
130 deltas_T = [0,0,0,0] 133 deltas_T = [0,0,0,0]
131 for i in range(int((endDate - startDate).days)+1): 134 startIndex = find(data['date'] == startDate)
135 nDays = (endDate - startDate).days+1
136 for i in range(startIndex, startIndex+int(nDays)):
132 if data['tmax'][i] != '' and data['tmax'][i] != None: 137 if data['tmax'][i] != '' and data['tmax'][i] != None:
133 tmax = float(data['tmax'][i].replace(',','.')) 138 tmax = float(data['tmax'][i].replace(',','.'))
134 else: 139 else:
135 tmax = None 140 tmax = None
136 if data['tmin'][i] != '' and data['tmin'][i] != None: 141 if data['tmin'][i] != '' and data['tmin'][i] != None:
157 nbre_jours_gel_consecutifs = compteur 162 nbre_jours_gel_consecutifs = compteur
158 compteur = 0 163 compteur = 0
159 else: 164 else:
160 compteur = 0 165 compteur = 0
161 nbre_jours_gel_consecutifs = max(nbre_jours_gel_consecutifs,compteur) 166 nbre_jours_gel_consecutifs = max(nbre_jours_gel_consecutifs,compteur)
162 ecart_type_T = np.std(tmoys) 167 if float(len(tmoys))/nDays >= minProportionMeasures:
163 if neige_tot < snowThreshold: 168 ecart_type_T = np.std(tmoys)
164 neigeEC_sup_seuil = 0 169 if neige_tot < snowThreshold:
165 else: 170 neigeEC_sup_seuil = 0
166 neigeEC_sup_seuil = 1 171 else:
167 172 neigeEC_sup_seuil = 1
168 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) 173 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)
169 174 else:
175 return [None]*2+[[None]*len(seuils_T)]+[None]*5
170 176
171 class RTSS: 177 class RTSS:
172 'class for data related to a RTSS, including agregating pavement marking measurements' 178 'class for data related to a RTSS, including agregating pavement marking measurements'
173 179
174 def __init__(self, id): 180 def __init__(self, id):
189 195
190 def getSite(self): 196 def getSite(self):
191 return int(self.id[:2]) 197 return int(self.id[:2])
192 198
193 def getTestAttributes(self): 199 def getTestAttributes(self):
194 return [self.paintingType, self.color] 200 return [self.paintingType, self.color, self.paintingDate.year]
195 201
196 def plot(self, measure, options = 'o', dayRatio = 1., **kwargs): 202 def plot(self, measure, options = 'o', dayRatio = 1., **kwargs):
197 from matplotlib.pyplot import plot 203 from matplotlib.pyplot import plot
198 plot(self.data['jours']/float(dayRatio), 204 plot(self.data['jours']/float(dayRatio),
199 self.data[measure], options, **kwargs) 205 self.data[measure], options, **kwargs)
200 206
201 def getMarkingMeasures(self, dataLabel): 207 def getMarkingMeasures(self, dataLabel):
202 from numpy import isnan 208 nonZeroIndices = ~np.isnan(self.data[dataLabel])
203 nonZeroIndices = ~isnan(self.data[dataLabel])
204 return self.data[nonZeroIndices]['jours'], self.data[nonZeroIndices][dataLabel] 209 return self.data[nonZeroIndices]['jours'], self.data[nonZeroIndices][dataLabel]
205 210
206 def plotMarkingMeasures(self, measure, options = 'o', dayRatio = 1., **kwargs): 211 def plotMarkingMeasures(self, measure, options = 'o', dayRatio = 1., **kwargs):
207 for i in range(1,7): 212 for i in range(1,7):
208 self.plot('{}_{}'.format(measure, i), options, dayRatio, **kwargs) 213 self.plot('{}_{}'.format(measure, i), options, dayRatio, **kwargs)
209 214
210 def computeMarkingMeasureVariations(self, dataLabel, lanePositions, weatherData, snowThreshold): 215 def computeMarkingMeasureVariations(self, dataLabel, lanePositions, weatherData, snowThreshold, minProportionMeasures = 0.):
211 '''Computes for each successive measurement 216 '''Computes for each successive measurement
212 lanePositions = None 217 lanePositions = None
213 measure variation, initial measure, time duration, weather indicators 218 measure variation, initial measure, time duration, weather indicators
214 219
215 TODO if measurements per lane, add a variable for lane position (position1 to 6) 220 TODO if measurements per lane, add a variable for lane position (position1 to 6)
216 lanePositions = list of integers (range(1,7)) 221 lanePositions = list of integers (range(1,7))
217 measure variation, initial measure, time duration, lane position1, weather indicators 222 measure variation, initial measure, time duration, lane position1, weather indicators
218 measure variation, initial measure, time duration, lane position2, weather indicators 223 measure variation, initial measure, time duration, lane position2, weather indicators
219 ...''' 224 ...'''
220 from numpy import isnan
221 variationData = [] 225 variationData = []
222 if lanePositions == None: 226 if lanePositions == None:
223 nonZeroIndices = ~isnan(self.data[dataLabel]) 227 nonZeroIndices = ~np.isnan(self.data[dataLabel])
224 days = self.data[nonZeroIndices]['jours'] 228 days = self.data[nonZeroIndices]['jours']
225 dates = self.data[nonZeroIndices]['date_mesure'] 229 dates = self.data[nonZeroIndices]['date_mesure']
226 measures = self.data[nonZeroIndices][dataLabel] 230 measures = self.data[nonZeroIndices][dataLabel]
227 for i in range(1, len(dates)): 231 for i in range(1, len(dates)):
228 nDaysTNegative, nDaysThawFreeze, deltaTemp, nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp = ecWeatherIndicators(weatherData, dates[i-1], dates[i], snowThreshold) 232 nDaysTNegative, nDaysThawFreeze, deltaTemp, nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp = ecWeatherIndicators(weatherData, dates[i-1], dates[i], snowThreshold, minProportionMeasures)
229 variationData.append([measures[i-1]-measures[i], measures[i-1], days[i]-days[i-1], nDaysTNegative, nDaysThawFreeze] + deltaTemp + [nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp]) 233 variationData.append([measures[i-1]-measures[i], measures[i-1], days[i]-days[i-1], days[i-1], nDaysTNegative, nDaysThawFreeze] + deltaTemp + [nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp])
230 return variationData 234 return variationData