Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/process.py @ 1062:a2e20aba0740

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bug correctiong
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 12 Jul 2018 00:22:16 -0400
parents 671426ce0f3e
children 3c37d8d20e97
comparison
equal deleted inserted replaced
1061:671426ce0f3e 1062:a2e20aba0740
212 if args.analyze == 'object': 212 if args.analyze == 'object':
213 # user speeds, accelerations 213 # user speeds, accelerations
214 # aggregation per site 214 # aggregation per site
215 data = [] # list of observation per site-user with time 215 data = [] # list of observation per site-user with time
216 headers = ['site', 'date', 'time', 'user_type'] 216 headers = ['site', 'date', 'time', 'user_type']
217 aggFunctions, tmpheaders = utils.aggregationMethods(arg.aggMethods, args.aggCentiles) 217 aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles)
218 headers.extend(tmpheaders) 218 headers.extend(tmpheaders)
219 for vs in videoSequences: 219 for vs in videoSequences:
220 d = vs.startTime.date() 220 d = vs.startTime.date()
221 t1 = vs.startTime.time() 221 t1 = vs.startTime.time()
222 minUserDuration = args.minUserDuration*vs.cameraView.cameraType.frameRate 222 minUserDuration = args.minUserDuration*vs.cameraView.cameraType.frameRate
274 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators]) 274 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
275 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')') 275 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')')
276 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150) 276 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
277 plt.close() 277 plt.close()
278 278
279 if args.analyze == 'event': # aggregate event data by 15 min interval (arg.intervalDuration), count events with thresholds 279 if args.analyze == 'event': # aggregate event data by 15 min interval (args.intervalDuration), count events with thresholds
280 data = pd.read_csv(args.eventFilename, parse_dates = [2]) 280 data = pd.read_csv(args.eventFilename, parse_dates = [2])
281 #data = pd.read_csv('./speeds.csv', converters = {'time': lambda s: datetime.datetime.strptime(s, "%H:%M:%S").time()}, nrows = 5000) 281 #data = pd.read_csv('./speeds.csv', converters = {'time': lambda s: datetime.datetime.strptime(s, "%H:%M:%S").time()}, nrows = 5000)
282 # create time for end of each 15 min, then group by, using the agg method for each data column 282 # create time for end of each 15 min, then group by, using the agg method for each data column
283 headers = ['site', 'date', 'intervalend15', 'duration', 'count'] 283 headers = ['site', 'date', 'intervalend15', 'duration', 'count']
284 aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles) 284 aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles)