dbcbac206dd35af5592963dce4655de94dc7e7e4,bnpy/learnalg/ElapsedTimeLogger.py,,writeToLogOnLapCompleted,#Any#Any#,37

Before Change

        for subeventName in sorted(CumulativeTimesDict[eventName]):
            curlapstr += " cur_%s %.2f" % (
                subeventName, CurrentLapTimesDict[eventName][subeventName])
            totalstr += " ttl_%s %.2f" % (
                subeventName, CumulativeTimesDict[eventName][subeventName])
            agg_curlap += CurrentLapTimesDict[eventName][subeventName]
            agg_total += CumulativeTimesDict[eventName][subeventName]
            CurrentLapTimesDict[eventName][subeventName] = 0.0
        agg_curlapstr = " cur %.2f" % (agg_curlap)

After Change

        agg_curlap = 0.0
        agg_total = 0.0

        df_dict = OrderedDict()
        df_dict["lap"] = lapFrac
        for subeventName in sorted(CumulativeTimesDict[eventName]):
            df_dict["curlap_%s" % subeventName] = (
                CurrentLapTimesDict[eventName][subeventName])
            df_dict["alllaps_%s" % subeventName] = (
                CumulativeTimesDict[eventName][subeventName])

            agg_curlap += CurrentLapTimesDict[eventName][subeventName]
            agg_total += CumulativeTimesDict[eventName][subeventName]
            // Reset counter
            CurrentLapTimesDict[eventName][subeventName] = 0.0

        df_dict["curlap_total"] = agg_curlap
        df_dict["alllaps_total"] = agg_total
        df = pd.DataFrame([df_dict])
        opts = dict(index=False, float_format="%.3f")
        if np.allclose(lapFrac, 1.0):
            msg = df.to_csv(header=True, **opts)
        else:
            msg = df.to_csv(header=False, **opts)
        LogDict[eventName].log(level, msg)

Instances