# HG changeset patch
# User Nicolas Saunier <nicolas.saunier@polymtl.ca>
# Date 1531369336 14400
# Node ID a2e20aba0740098c37d2803764503a36247a810e
# Parent  671426ce0f3e22ae90fc7ff1d735d134dd4f6c6c
bug correctiong

diff -r 671426ce0f3e -r a2e20aba0740 scripts/process.py
--- a/scripts/process.py	Wed Jul 11 17:31:26 2018 -0400
+++ b/scripts/process.py	Thu Jul 12 00:22:16 2018 -0400
@@ -214,7 +214,7 @@
     # aggregation per site
     data = [] # list of observation per site-user with time
     headers = ['site', 'date', 'time', 'user_type']
-    aggFunctions, tmpheaders = utils.aggregationMethods(arg.aggMethods, args.aggCentiles)
+    aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles)
     headers.extend(tmpheaders)
     for vs in videoSequences:
         d = vs.startTime.date()
@@ -276,7 +276,7 @@
         plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
         plt.close()
 
-if args.analyze == 'event': # aggregate event data by 15 min interval (arg.intervalDuration), count events with thresholds
+if args.analyze == 'event': # aggregate event data by 15 min interval (args.intervalDuration), count events with thresholds
     data = pd.read_csv(args.eventFilename, parse_dates = [2])
     #data = pd.read_csv('./speeds.csv', converters = {'time': lambda s: datetime.datetime.strptime(s, "%H:%M:%S").time()}, nrows = 5000)
     # create time for end of each 15 min, then group by, using the agg method for each data column