func performancePlotHandlerV2(w http.ResponseWriter, r *http.Request) {
/* Extract fields in GET request */
line := r.URL.Query().Get("line")
bound := r.URL.Query().Get("bound")
origin := r.URL.Query().Get("origin")
destination := r.URL.Query().Get("destination")
departureTime := parseISOTime(r.URL.Query().Get("departureTime"))
/* Error message string that functions should append to */
errorMessage := ""
/* Some additional info for nice display*/
originName := selectdb.SelectStop(origin)
destinationName := selectdb.SelectStop(destination)
/* Find out which vehicle produced the data and list its journey history */
vehicleId, vehicleHistory, actualJourneyTime, totalPredictions, totalStddevs := lookupVehicleHistory2(line, bound, origin, destination, departureTime, &errorMessage)
/* Write HTML output */
writePerformancePlotDetails2(w, PerformancePlotDetails2{
ErrorMessage: errorMessage,
Line: line,
Bound: bound,
Origin: origin,
DepartureTime: departureTime.Format(timeFormatString),
OriginName: originName,
DestinationName: destinationName,
VehicleId: vehicleId,
VehicleHistory: vehicleHistory,
PredictionMethods: performanceeval.Methods(),
ActualTime: actualJourneyTime,
Predictions: totalPredictions,
Stddevs: totalStddevs,
})
}
/* Grabs all stored historic and real time data, produce time plots and
also percentages of consistently delayed or early */
func DelayConsistencyPlot(startTime, endTime time.Time, wr io.Writer, templatePath string) {
charts := make([]DelayConsistencyChart, 0)
availableRoutes := selectdb.SelectAvailableRoutesTflPredictions()
for _, route := range availableRoutes {
// Extract fields from routes
line := route["line"]
bound := route["bound"]
fromStop := route["fromStop"]
toStop := route["toStop"]
fromStopName := selectdb.SelectStop(fromStop)
toStopName := selectdb.SelectStop(toStop)
actualJourneyTimes := performanceeval.FetchActualJourneyTimes(line, bound, fromStop, toStop, startTime, endTime)
// Extract the actual departure times for generating predictions later on (discarding those that are)
actualDepartureTimes := make([]time.Time, len(actualJourneyTimes))
actualJourneyTimeDurations := make([]time.Duration, len(actualJourneyTimes))
i := 0
for departureTime, duration := range actualJourneyTimes {
actualDepartureTimes[i] = departureTime
actualJourneyTimeDurations[i] = duration
i++
}
// Fetch Tfl predictions
// tflPredictions := performanceeval.FetchTflJourneyTimes(line, bound, fromStop, toStop, actualDepartureTimes)
/* Cache for real time estimates which can be reused for different prediction methods
map (fromStop => (toStop => (departureTime => "estimate"/"stddev" => int in seconds)))) */
realTimeEstimatesCache := make(map[string]map[string]map[time.Time]map[string]int64)
// Generate predictions using our methods (best historic and real time only)
predictions := make(map[performanceeval.Method]map[time.Time]time.Duration)
predictionsAdjacentStops := make(map[performanceeval.Method]map[time.Time]map[string]map[string]time.Duration)
methods := []performanceeval.Method{
performanceeval.DefaultHistoric(),
performanceeval.DefaultRealTime(),
}
// Highest # consistency possible - number of pairs of adjacent stops in route
maxScore := 0
for _, method := range methods {
predictions[method], predictionsAdjacentStops[method], maxScore = predictJourneyTimesWithDetails(line, bound, fromStop, toStop, actualDepartureTimes, method, realTimeEstimatesCache)
}
// Convert the above into PerformanceTestSeries2
actualJourneyTimesSeries := PerformanceTestSeries2{
SeriesName: "Actual Time",
X: actualDepartureTimes,
Y: actualJourneyTimeDurations,
Count: len(actualJourneyTimeDurations),
}
sort.Sort(actualJourneyTimesSeries)
/*tflPredictionsSeries := mapToPerformanceTestSeries2("Tfl Expected Time", tflPredictions, actualJourneyTimes)
sort.Sort(tflPredictionsSeries)*/
performanceTestSeries2 := []PerformanceTestSeries2{
actualJourneyTimesSeries,
// tflPredictionsSeries,
}
// Add all prediction series into performance test series 2
for method, predictedJouneyTimes := range predictions {
predictionSeries := mapToPerformanceTestSeries2(method.String(), predictedJouneyTimes, actualJourneyTimes)
sort.Sort(predictionSeries)
performanceTestSeries2 = append(performanceTestSeries2, predictionSeries)
}
// Form delay consistency (percentages) series
delayConsistencySeries := getDelayConsistencySeries(predictionsAdjacentStops[performanceeval.DefaultHistoric()],
predictionsAdjacentStops[performanceeval.DefaultRealTime()], maxScore)
charts = append(charts, DelayConsistencyChart{
Line: line,
Bound: bound,
Origin: fromStop,
Destination: toStop,
OriginName: fromStopName,
DestinationName: toStopName,
MultipleSeries: performanceTestSeries2,
MultiplePercentageSeries: []DelayConsistencySeries{delayConsistencySeries},
ServerDomain: credentials.GetGoServerDomain(),
})
logger.GetLogger().Info("Done for line: %v, bound: %v, fromStop: %v, toStop: %v", line, bound, fromStop, toStop)
}
summary := DelayConsistencyPlotSummary{
Methods: performanceeval.Methods(),
Charts: charts,
}
writeDelayConsistencyChart(wr, summary, templatePath)
}
/* Find out which vehicle produced the data and list its journey history, together with
the total actual journey time, total predictions and total standard deviations */
func lookupVehicleHistory2(line, bound, origin, destination string, departureTime time.Time, errorMessage *string) (string, []VehicleRecord2, time.Duration, map[performanceeval.Method]time.Duration, map[performanceeval.Method]time.Duration) {
vehicleId, arrivalTime, err := selectdb.SelectVehicleAndArrivalTimeFromJourneyHistory(line, bound, origin, destination, departureTime)
if err != nil {
logger.GetLogger().Panic(err)
}
startTime := departureTime
endTime := arrivalTime
vehicleHistory := selectdb.SelectVehicleHistory(vehicleId, startTime, endTime, timeFormatString)
rawArrivalTimes := selectdb.SelectRawArrivalTimes(vehicleId, startTime, endTime, timeFormatString)
intermediateStopsOrdered := selectdb.SelectIntermediateStops(line, bound, origin, destination)
intermediateStops := make(map[string]map[string]bool)
for i := 0; i < len(intermediateStopsOrdered)-1; i++ {
intermediateStops[intermediateStopsOrdered[i]] = map[string]bool{
intermediateStopsOrdered[i+1]: true,
}
}
/* Temporarily discarded */
// predictionsUsedRealTime := make(map[string]map[string]map[time.Time]map[string]int64)
totalPredictions := make(map[performanceeval.Method]time.Duration)
totalStddevs := make(map[performanceeval.Method]time.Duration)
idvPredictions := make(map[performanceeval.Method]map[string]map[string]time.Duration)
idvStddevs := make(map[performanceeval.Method]map[string]map[string]time.Duration)
idvExpectedArrivalTimesShifting := make(map[performanceeval.Method]map[string]map[string]time.Time)
idvMixedTypes := make(map[performanceeval.Method]map[string]map[string]string)
for _, method := range performanceeval.Methods() {
totalPrediction, totalStddev, idvPredictionsMap, idvStddevsMap, edtShifting, mixedTypes := lookupPredictionsUsed(line, bound, intermediateStops, intermediateStopsOrdered, departureTime,
method, nil, errorMessage)
totalPredictions[method] = totalPrediction
totalStddevs[method] = totalStddev
idvPredictions[method] = idvPredictionsMap
idvStddevs[method] = idvStddevsMap
if method.IsShiftingBased() {
idvExpectedArrivalTimesShifting[method] = edtShifting
}
if method.IsMixedBased() {
idvMixedTypes[method] = mixedTypes
}
}
actualJourneyTime := performanceeval.FetchActualJourneyTime(line, bound, origin, destination, departureTime)
/* Flag for use later, in case bus switches direction invalidate and prevent travel time details from further generated */
firstTimeFlag := true
result := make([]VehicleRecord2, len(vehicleHistory))
for i, v := range vehicleHistory {
result[i] = VehicleRecord2{
Line: v["line"],
Bound: v["bound"],
StopSeq: v["stopSeq"],
StopName: v["stopName"],
ArrivalTime: v["time"],
RawArrivalTime: rawArrivalTimes[v["naptanId"]],
NaptanId: v["naptanId"],
Details: generateTravelTimeDetails(line, bound, intermediateStops, departureTime, vehicleHistory, i,
idvPredictions, idvStddevs, idvExpectedArrivalTimesShifting, idvMixedTypes, &firstTimeFlag),
}
}
if !firstTimeFlag {
*errorMessage += "Bus terminated early\n"
}
/*predictions := []time.Duration{
actualJourneyTime,
predictionThreeDays,
predictionWeekly,
predictionWeeklyDiscardInterpolation,
predictionWeeklySameRouteOnly,
predictionWeeklyShifting,
predictionWeeklyShiftingDiscardInterpolation,
predictionWeeklyShiftingSameRouteOnly,
predictionRealTimeMedian,
predictionRealTimeLastTwo,
totalStddevThreeDays,
totalStddevWeekly,
totalStddevWeeklyDiscardInterpolation,
totalStddevWeeklySameRouteOnly,
totalStddevWeeklyShifting,
totalStddevWeeklyShiftingDiscardInterpolation,
totalStddevWeeklyShiftingSameRouteOnly,
totalStddevRealTimeMedian,
totalStddevRealTimeLastTwo,
}*/
return vehicleId, result, actualJourneyTime, totalPredictions, totalStddevs
}
func generateTravelTimeDetails(line, bound string, intermediateStops map[string]map[string]bool, departureTime time.Time,
vehicleHistory []map[string]string, idx int,
idvPredictions map[performanceeval.Method]map[string]map[string]time.Duration,
idvStddevs map[performanceeval.Method]map[string]map[string]time.Duration,
expectedArrivalTimesShifting map[performanceeval.Method]map[string]map[string]time.Time,
mixedTypesAtEachStop map[performanceeval.Method]map[string]map[string]string,
firstTimeFlag *bool) TravelTimeDetails {
/* Cannot generate travel time details if it is the first row (since no previous stop) */
if idx == 0 {
return TravelTimeDetails{
TimeTaken: time.Duration(0),
Predictions: nil,
Stddevs: nil,
ExpectedArrivalTimesShifting: nil,
MixedTypes: nil,
}
}
fromTime := parseTime(vehicleHistory[idx-1]["time"])
toTime := parseTime(vehicleHistory[idx]["time"])
timeTaken := toTime.Sub(fromTime)
checkline := vehicleHistory[idx]["line"]
checkBound := vehicleHistory[idx]["bound"]
/* If changed direction, invalidate flag and return nil, so that no further travel time details are generated */
if line != checkline || bound != checkBound || !*firstTimeFlag {
*firstTimeFlag = false
return TravelTimeDetails{
TimeTaken: timeTaken,
Predictions: nil,
Stddevs: nil,
ExpectedArrivalTimesShifting: nil,
MixedTypes: nil,
}
}
fromStop := vehicleHistory[idx-1]["naptanId"]
toStop := vehicleHistory[idx]["naptanId"]
fromStopSeq, _ := strconv.Atoi(vehicleHistory[idx-1]["stopSeq"])
toStopSeq, _ := strconv.Atoi(vehicleHistory[idx]["stopSeq"])
if fromStopSeq+1 != toStopSeq {
return TravelTimeDetails{
TimeTaken: timeTaken,
Predictions: nil,
Stddevs: nil,
ExpectedArrivalTimesShifting: nil,
MixedTypes: nil,
}
}
predictions := make(map[performanceeval.Method]time.Duration)
stddevs := make(map[performanceeval.Method]time.Duration)
edtsShifting := make(map[performanceeval.Method]time.Time)
mixedTypes := make(map[performanceeval.Method]string)
for _, method := range performanceeval.Methods() {
predictions[method] = idvPredictions[method][fromStop][toStop]
stddevs[method] = idvStddevs[method][fromStop][toStop]
if method.IsShiftingBased() {
edtsShifting[method] = expectedArrivalTimesShifting[method][fromStop][toStop]
}
if method.IsMixedBased() {
mixedTypes[method] = mixedTypesAtEachStop[method][fromStop][toStop]
}
}
return TravelTimeDetails{
TimeTaken: timeTaken,
Predictions: predictions,
Stddevs: stddevs,
ExpectedArrivalTimesShifting: edtsShifting,
MixedTypes: mixedTypes,
}
}
return fmt.Sprintf("%v (s.d. %v)", predictions[method].String(), stddevs[method].String())
},
"formatErrorMessage": func(errorMessage string) string {
return strings.Replace(errorMessage, "\n", "<br>", -1)
},
"formatEAT": func(method performanceeval.Method, expectedArrivalTimes map[performanceeval.Method]time.Time) string {
expectedArrivalTime, exists := expectedArrivalTimes[method]
if exists {
return formatTime(expectedArrivalTime)
} else {
return ""
}
},
"getTargetColumnArray": func() string {
startColumn := 5
columns := make([]string, len(performanceeval.Methods()))
for i := 0; i < len(performanceeval.Methods()); i++ {
columns[i] = fmt.Sprintf("%v", startColumn+i)
}
return "[" + strings.Join(columns, ",") + "]"
},
}
)
type PerformancePlotDetails2 struct {
ErrorMessage string
Line string
Bound string
Origin string
DepartureTime string
OriginName string
/* Grabs all stored data to produce time plots */
func PerformancePlot2(startTime, endTime time.Time, wr io.Writer, templatePath string) {
charts := make([]PerformanceChart2, 0)
availableRoutes := selectdb.SelectAvailableRoutesTflPredictions()
for _, route := range availableRoutes {
// Extract fields from routes
line := route["line"]
bound := route["bound"]
fromStop := route["fromStop"]
toStop := route["toStop"]
fromStopName := selectdb.SelectStop(fromStop)
toStopName := selectdb.SelectStop(toStop)
actualJourneyTimes := performanceeval.FetchActualJourneyTimes(line, bound, fromStop, toStop, startTime, endTime)
// Extract the actual departure times for generating predictions later on (discarding those that are)
actualDepartureTimes := make([]time.Time, len(actualJourneyTimes))
actualJourneyTimeDurations := make([]time.Duration, len(actualJourneyTimes))
i := 0
for departureTime, duration := range actualJourneyTimes {
actualDepartureTimes[i] = departureTime
actualJourneyTimeDurations[i] = duration
i++
}
// Fetch Tfl predictions
tflPredictions := performanceeval.FetchTflJourneyTimes(line, bound, fromStop, toStop, actualDepartureTimes)
/* Cache for real time estimates which can be reused for different prediction methods
map (fromStop => (toStop => (departureTime => "estimate"/"stddev" => int in seconds)))) */
realTimeEstimatesCache := make(map[string]map[string]map[time.Time]map[string]int64)
// Generate predictions using our methods
predictions := make(map[performanceeval.Method]map[time.Time]time.Duration)
for _, method := range performanceeval.Methods() {
predictions[method] = performanceeval.PredictJourneyTimes(line, bound, fromStop, toStop, actualDepartureTimes, method, realTimeEstimatesCache)
}
// Convert the above into PerformanceTestSeries2
actualJourneyTimesSeries := PerformanceTestSeries2{
SeriesName: "Actual Time",
X: actualDepartureTimes,
Y: actualJourneyTimeDurations,
Count: len(actualJourneyTimeDurations),
}
sort.Sort(actualJourneyTimesSeries)
tflPredictionsSeries := mapToPerformanceTestSeries2("Tfl Expected Time", tflPredictions, actualJourneyTimes)
sort.Sort(tflPredictionsSeries)
performanceTestSeries2 := []PerformanceTestSeries2{
actualJourneyTimesSeries,
tflPredictionsSeries,
}
// Add all prediction series into performance test series 2
for method, predictedJouneyTimes := range predictions {
predictionSeries := mapToPerformanceTestSeries2(method.String(), predictedJouneyTimes, actualJourneyTimes)
sort.Sort(predictionSeries)
performanceTestSeries2 = append(performanceTestSeries2, predictionSeries)
}
charts = append(charts, PerformanceChart2{
Line: line,
Bound: bound,
Origin: fromStop,
Destination: toStop,
OriginName: fromStopName,
DestinationName: toStopName,
MultipleSeries: performanceTestSeries2,
ServerDomain: credentials.GetGoServerDomain(),
})
logger.GetLogger().Info("Done for line: %v, bound: %v, fromStop: %v, toStop: %v", line, bound, fromStop, toStop)
}
summary := PerformancePlotSummary2{
Methods: performanceeval.Methods(),
Charts: charts,
}
writePerformanceChart2(wr, summary, templatePath)
}
