Mercurial Hosting > traffic-intelligence
changeset 1084:1a7e0b2c858b
remove debugging
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Tue, 24 Jul 2018 01:24:42 -0400 |
| parents | 5b597b021aed |
| children | 7853106677b7 |
| files | scripts/process.py |
| diffstat | 1 files changed, 2 insertions(+), 2 deletions(-) [+] |
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--- a/scripts/process.py Mon Jul 23 20:17:27 2018 -0400 +++ b/scripts/process.py Tue Jul 24 01:24:42 2018 -0400 @@ -336,7 +336,7 @@ data.to_csv(args.eventFilename, index = False) if args.analyze == 'event-speed': # aggregate event data by 15 min interval (args.intervalDuration), count events with thresholds - data = pd.read_csv(args.eventFilename, parse_dates = [2], nrows = 10000) + 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 headers = ['site', 'date', 'intervalend15', 'duration', 'count'] @@ -373,7 +373,7 @@ pd.DataFrame(outputData, columns = headers).to_csv(utils.removeExtension(args.eventFilename)+'-aggregated.csv', index = False) elif args.analyze == 'event-interaction': # aggregate event data by 15 min interval (args.intervalDuration), count events with thresholds - data = pd.read_csv(args.eventFilename, parse_dates = [2], nrows = 20000) + data = pd.read_csv(args.eventFilename, parse_dates = [2]) headers = ['site', 'date', 'intervalend15', 'duration', 'count'] aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles) dataColumns = list(data.columns[3:])