//Tss vs Duration
func tvd(user types.UserSettings, filter Filter) ([]Tvd, string) {
user_id := user.Id
tvd_data_points := make([]Tvd_data_point, 0)
tvd_data := make([]Tvd, 0)
var user_data types.Metrics
var end_summary_json []byte
var activity_start time.Time
var tvdLegend string
cluster := gocql.NewCluster(config.DbHost)
cluster.Keyspace = "joulepersecond"
cluster.Consistency = gocql.Quorum
session, _ := cluster.CreateSession()
defer session.Close()
//get all of the user's data (at least all for now) TODO limit these queries by date if poss. Done!
timeNow := time.Now()
timeThen := timeNow.AddDate(0, 0, -filter.Historylen)
iter := session.Query(`SELECT activity_start, end_summary_json FROM joulepersecond.user_activity WHERE user_id = ? AND activity_start > ? ORDER BY activity_start ASC`, user_id, timeThen).Iter()
for iter.Scan(&activity_start, &end_summary_json) {
var tvd_data_point Tvd_data_point
json.Unmarshal(end_summary_json, &user_data)
tvd_data_point.Date = user_data.StartTime
tvd_data_point.Dur = user_data.Dur
if user_data.Utss > 0 {
tvd_data_point.Tss = user_data.Utss
} else if user_data.Tss > 0 {
tvd_data_point.Tss = user_data.Tss
} else if user_data.Etss > 0 {
tvd_data_point.Tss = user_data.Etss
} else {
tvd_data_point.Tss = 0
}
tvd_data_points = append(tvd_data_points, tvd_data_point)
}
//we now have all the data... Now sort it
sumTss := 0
var sumDur time.Duration
var lastActivity time.Time
//loope through each retrieved activity
for i := 1; i < len(tvd_data_points); i++ {
//set last activity on first iteration only
if i == 1 {
lastActivity = tvd_data_points[i].Date
}
//if we want to show data in monthly format
if filter.Historylen > 366 { //show over 365 days as monthly
thisDate := tvd_data_points[i].Date
prevDate := lastActivity
//if we're still in the current month sum these values
if thisDate.Month() != prevDate.Month() || i == len(tvd_data_points)-1 {
var summedMonthlyTvd Tvd
summedMonthlyTvd.TotalTss = sumTss
summedMonthlyTvd.TotalDur = utility.Round(sumDur.Hours(), .5, 2)
var month time.Month
var year string
if thisDate.Month() != prevDate.Month() {
month = prevDate.Month()
year = strconv.Itoa(prevDate.Year())
} else {
month = thisDate.Month()
year = strconv.Itoa(thisDate.Year())
}
monthStr := month.String()
summedMonthlyTvd.TimeLabel = monthStr[0:3] + " '" + year[2:4]
sumTss = 0
sumDur = 0
tvd_data = append(tvd_data, summedMonthlyTvd)
//reset the last activity date for the next loop
lastActivity = thisDate
}
sumTss += tvd_data_points[i].Tss
sumDur += tvd_data_points[i].Dur
tvdLegend = "By Month"
} else {
thisDate := tvd_data_points[i].Date //we use this to compare the activity being scanned with the last to see if it is in the same week
prevDate := lastActivity //this is the last activity that we scanned that was the first of the new week, last week. Confusing init? we have to get the value now, and change if the weeks are not equal (new week)
prevIterDate := tvd_data_points[i-1].Date
//get week number for this and last activity
_, thisDateWeek := thisDate.ISOWeek()
prevDateYear, prevDateWeek := prevDate.ISOWeek()
if thisDateWeek != prevDateWeek || i == len(tvd_data_points)-1 {
var summedWeeklyTvd Tvd
summedWeeklyTvd.TotalTss = sumTss
summedWeeklyTvd.TotalDur = utility.Round(sumDur.Hours(), .5, 2)
monthS := prevDate.Month()
var monthF time.Month
var dayF string
if thisDateWeek != prevDateWeek {
monthF = prevIterDate.Month()
dayF = strconv.Itoa(prevIterDate.Day())
} else {
monthF = thisDate.Month()
dayF = strconv.Itoa(thisDate.Day())
}
dayS := strconv.Itoa(prevDate.Day())
monthStrS := monthS.String()
monthAbrS := monthStrS[0:3]
monthStrF := monthF.String()
monthAbrF := monthStrF[0:3]
//format labels according to number to displau
if filter.Historylen < 120 {
summedWeeklyTvd.TimeLabel = dayS + " " + monthAbrS + " - " + dayF + " " + monthAbrF
} else {
summedWeeklyTvd.TimeLabel = dayS + " " + monthAbrS
}
sumTss = 0
sumDur = 0
tvd_data = append(tvd_data, summedWeeklyTvd)
//reset the last activity date for the next loop
lastActivity = thisDate
}
//sum the values
sumTss += tvd_data_points[i].Tss
sumDur += tvd_data_points[i].Dur
tvdLegend = "By week number: Series ending wk" + strconv.Itoa(prevDateWeek) + ", " + strconv.Itoa(prevDateYear)
}
}
return tvd_data, tvdLegend
}
func processActivity(activityId string, user types.UserSettings) {
//define one second
second := time.Second
//data is now of type []map[string]interface {}
data := getResults(activityId)
//vars to hold a single data row (instances of our struct types)
var row SampleRow
var lapSummary types.Metrics
var endSummary types.Metrics
//slices to hold mulitple rows
rows := make([]SampleRow, 0)
//and lap summary data
lapSummaries := make([]types.Metrics, 0)
//cp data
cpRows := make([]CpRow, 0)
//init vars
var activityStart time.Time //first lap start time
var activity Samples //aggregated activity samples
var lap Samples //aggregated lap samples
var laptime time.Time //last interation's lap time marker
var sampletime time.Time //last iteration's sample time
var sampleDistance time.Duration //time duration between this and last iteration's sample time
//var timeSubtract time.Duration //total time to subtract from sample time to give continuous line when using continuous axes
var pedalcount int //temporary var storing number of samples with cadence value of 0
var ElapsedTime time.Duration
hasPower := true
hasHeart := true
hasCadence := true
powerSeries := make([]int, 0) //power time series data
heartSeries := make([]int, 0) //heart rate time series data
cadenceSeries := make([]int, 0) //cadence time series data
//see http://golang.org/pkg/time/#example_Parse
const layout = "15:04:05"
for _, val := range data {
//val is now of type map[string]interface {}
//e.g. val["tp_heartrate"] is type interface{}, but these values need to be asserted to their correct type for use in calculations etc.
row.Heartrate = val["tp_heartrate"].(int)
row.Power = val["tp_watts"].(int)
row.Cadence = val["tp_cadence"].(int)
row.Lapnumber = val["lap_number"].(int)
row.Lapstart = val["lap_start"].(time.Time)
//set the activity start time to that of the first lap
if activityStart.IsZero() {
activityStart = row.Lapstart
}
row.Timestamp = val["tp_timestamp"].(time.Time) //remember 'e.g. timestamp'.sub('e.g. lapstart') returns type time.Duration
//subtract last sample time from this sample time to give a remainder duration
sampleDistance = row.Timestamp.Sub(sampletime)
//function summing metrics, repeated for each 'missing data point'
sum := func() {
//sum some of the metrics for averaging
activity.Power += row.Power
activity.Hr += row.Heartrate
activity.Cad += row.Cadence
//don't add to the average cadence val when freewheeling
if activity.Cad == 0 {
activity.Freewheelcount++
}
activity.Samplecount++
//and for each lap too...
lap.Power += row.Power
lap.Hr += row.Heartrate
lap.Cad += row.Cadence
//don't add to the average cadence val when freewheeling
if row.Cadence == 0 {
lap.Freewheelcount++
activity.Freewheelcount++
}
lap.Samplecount++
}
setZero := func() {
//sum some of the metrics for averaging
row.Power = 0
row.Heartrate = 0
row.Cadence = 0
lap.Freewheelcount++
activity.Freewheelcount++
activity.Samplecount++
lap.Samplecount++
}
//if time period is set and is greater than n seconds
if ((sampleDistance / second) > user.Stopgap) && !(sampletime.IsZero()) {
//check for error
//fmt.Printf("Seconds: %v \n", sampleDistance)
} else if ((sampleDistance / second) < user.Stopgap) && !(sampletime.IsZero()) {
/**
* Add samples / and missing samples if required by user (up until user defined stop duration)
**/
//define one second duration
second := time.Second
//get number of seconds between samples (all based on 1hz sample frequency)
seconds := int(sampleDistance / second)
for i := 0; i < seconds; i++ {
//only add samples up until the user defined number of seconds
if i < int(user.Stopgap) {
//fmt.Printf("adding second %d\n", i)
//add the missing samples
if user.Autofill == "autofill" {
sum()
} else if user.Autofill == "setzero" {
//only zero for more than 1 second else we'd never have any data!
if seconds > 1 {
setZero()
} else {
sum()
}
}
//add a second for each second past, but not if autofill set to 'remove'
if (user.Autofill == "autofill" || user.Autofill == "setzero") || (user.Autofill == "remove" && seconds == 1) {
ElapsedTime += second
row.NewTimestamp[0] = int(ElapsedTime.Hours())
row.NewTimestamp[1] = int(ElapsedTime.Minutes()) % 60
row.NewTimestamp[2] = int(ElapsedTime.Seconds()) % 60
if user.Autofill == "remove" && seconds == 1 {
sum()
}
//add this row's data to the slice
rows = append(rows, row)
//add the power value to the power time series
powerSeries = append(powerSeries, row.Power)
heartSeries = append(heartSeries, row.Heartrate)
cadenceSeries = append(cadenceSeries, row.Cadence)
}
}
}
}
sampletime = row.Timestamp //this might need to move position
//if a new lap
if row.Lapstart != laptime && lap.Samplecount > 0 && !(laptime.IsZero()) {
//calculate lap totals
lapSummary.Avpower = lap.Power / lap.Samplecount
lapSummary.Avheart = lap.Hr / lap.Samplecount
lapSummary.Dur = time.Duration(lap.Samplecount) * time.Second
pedalcount = (lap.Samplecount - lap.Freewheelcount)
if pedalcount > 0 {
lapSummary.Avcad = lap.Cad / (lap.Samplecount - lap.Freewheelcount)
} else {
lapSummary.Avcad = 0
}
//append the summary lap data
lapSummaries = append(lapSummaries, lapSummary)
//reset lap
laptime = row.Lapstart
lap.Samplecount = 0
lap.Freewheelcount = 0
lap.Power = 0
lap.Hr = 0
lap.Cad = 0
}
if laptime.IsZero() {
laptime = row.Lapstart
}
}
//get the number of samples (these are already processed and are at one second intervals)
seriesLen := len(rows) //***would be good to save this data in cassandra***
activityDuration := (time.Duration(seriesLen) * time.Second).Hours() //and this
/***
* Critical power
***/
var cpms types.CPMs
var cpRow CpRow
var maxCpVal int
var maxCpHrVal int
var maxCpCadVal int
var sumCpVal int
var sumHrVal int
var sumCadVal int
const accuracyVal = 2.25 //controls the accuracy of the output (l is more accurate [more sample points])
var isPreset bool
//make a set of preset timecodes/snapshot times - accuracyVal should not be changed once in production!
presets := make([]int, 0)
for i := 0; i < seriesLen; i++ {
preset := int(math.Pow(float64(i), accuracyVal))
if preset <= seriesLen {
presets = append(presets, preset)
}
}
//set initial val
logVal := math.Log(float64(seriesLen)) //TODO remove this - not required/used?
//this loop determines the length of the rolling sampling period to average (from the length of the activity to 1 second)
for i := seriesLen; i > 0; i-- { //3600, 3599, 3598...=
//check it this is one of our snapshots
for _, presetTime := range presets {
if i == presetTime {
isPreset = true
break
}
isPreset = false
}
//don't calculate ALL samples
if isPreset || i == 1 || i == 2 || i == 3 || i == 4 || i == 5 || i == 10 || i == 20 || i == 30 || i == 60 || i == 5*60 || i == 20*60 || i == 30*60 || i == 60*60 || i == 120*60 || i == 240*60 || i == 360*60 || i == 480*60 || i == 600*60 {
//reset max for each duration calculated
maxCpVal = 0
//this loop determines the point at which to start searching
for j := 0; j <= (seriesLen - i); j++ { ///j=0; j<1; j++ ... j=1; j<2; j++ etc
//rolling power slice is from start pos to smapling period
rollingPowerSlice := powerSeries[j : j+i] // eg [1:2], [2:3], [3:4] etc...
rollingHeartSlice := heartSeries[j : j+i]
rollingCadenceSlice := cadenceSeries[j : j+i]
//reset sum of slice vals
sumCpVal = 0
sumHrVal = 0
sumCadVal = 0
for _, val := range rollingPowerSlice {
//sum the sliding slice values
sumCpVal += val
}
for _, val := range rollingHeartSlice {
//sum the sliding slice values
sumHrVal += val
}
for _, val := range rollingCadenceSlice {
//sum the sliding slice values
sumCadVal += val
}
if (sumCpVal / i) > maxCpVal {
maxCpVal = (sumCpVal / i)
maxCpHrVal = (sumHrVal / i) //calucate the averate heart rate that accompanies the high critical power
maxCpCadVal = (sumCadVal / i)
}
}
//preset duration vals
switch i {
case 5:
cpms.FiveSecondCP = maxCpVal
cpms.FiveSecondCPHR = maxCpHrVal
cpms.FiveSecondCPCAD = maxCpCadVal
break
case 20:
cpms.TwentySecondCP = maxCpVal
cpms.TwentySecondCPHR = maxCpHrVal
cpms.TwentySecondCPCAD = maxCpCadVal
break
case 60:
cpms.SixtySecondCP = maxCpVal
cpms.SixtySecondCPHR = maxCpHrVal
cpms.SixtySecondCPCAD = maxCpCadVal
break
case 300:
cpms.FiveMinuteCP = maxCpVal
cpms.FiveMinuteCPHR = maxCpHrVal
cpms.FiveMinuteCPCAD = maxCpCadVal
break
case 1200:
cpms.TwentyMinuteCP = maxCpVal
cpms.TwentyMinuteCPHR = maxCpHrVal
cpms.TwentyMinuteCPCAD = maxCpCadVal
break
case 3600:
cpms.SixtyMinuteCP = maxCpVal
cpms.SixtyMinuteCPHR = maxCpHrVal
cpms.SixtyMinuteCPCAD = maxCpCadVal
break
}
//should be able to do a plot from this info
//fmt.Printf("Max Mean power for %d seconds is %d Watts. Log of i: %v\n", i, maxCpVal, math.Log(float64(i)))
//plotinfo
ElapsedTime = time.Duration(i) * time.Second //convert iterator (seconds) to Duration type
cpRow.CpTime[0] = int(ElapsedTime.Hours())
cpRow.CpTime[1] = int(ElapsedTime.Minutes()) % 60
cpRow.CpTime[2] = int(ElapsedTime.Seconds()) % 60
cpRow.CpVal = maxCpVal
cpRow.CpAhr = maxCpHrVal
cpRow.CpAcad = maxCpCadVal
cpRows = append(cpRows, cpRow)
logVal -= accuracyVal
}
}
//post loop calculations
if activity.Samplecount > 0 {
//calculate lap totals
lapSummary.Avpower = lap.Power / lap.Samplecount
lapSummary.Avheart = lap.Hr / lap.Samplecount
lapSummary.Dur = time.Duration(lap.Samplecount) * time.Second
pedalcount = (lap.Samplecount - lap.Freewheelcount)
if pedalcount > 0 {
lapSummary.Avcad = lap.Cad / (lap.Samplecount - lap.Freewheelcount)
} else {
lapSummary.Avcad = 0
}
//append the summary lap data
lapSummaries = append(lapSummaries, lapSummary)
//calculate totals
endSummary.Avpower = activity.Power / activity.Samplecount
endSummary.Avheart = activity.Hr / activity.Samplecount
pedalcount = (activity.Samplecount - activity.Freewheelcount)
if pedalcount > 0 {
endSummary.Avcad = activity.Cad / (activity.Samplecount - activity.Freewheelcount)
} else {
endSummary.Avcad = 0
}
}
/***
* normalised power
***/
var fourthPower float64
var thirtySecondSum int
var thirtySecondAv float64
for i := 30; i < seriesLen; i++ {
//reset total
thirtySecondSum = 0
//get thirty second rolling slice
rollingPowerSlice := powerSeries[i-30 : i]
for _, val := range rollingPowerSlice {
//sum the sliding slice values
thirtySecondSum += val
}
thirtySecondAv = float64(thirtySecondSum / 30)
//multply by the power of 4
fourthPower += math.Pow(thirtySecondAv, 4)
}
//normalised power = 4th root of total of 30 second averages divided my number of averages taken (total - 30 to allow for start offset and slice length)
normalisedPower := int(math.Pow(fourthPower/float64(seriesLen-30), 0.25)) //4th root is power 1/4 (0.25)
endSummary.Np = normalisedPower
/***
* Intensity factor
***/
intensity := float64(normalisedPower) / float64(user.Ftp)
endSummary.If = utility.Round(intensity, .5, 2) * 100 //times by 100 and use as a percentage to avoid troubles
/***
* Intensity factor from heart rate (as a percentage)
***/
maxHr := float64(user.Thr) * 1.06
endSummary.IfHr = int((float64(endSummary.Avheart) / maxHr) * 100)
/***
* TSS
***/
endSummary.Tss = int((float64(seriesLen) * float64(normalisedPower) * intensity) / (float64(user.Ftp) * 3600) * 100)
/***
* Estimated TSS
***/
var etssSum int
var inc int
for _, val := range heartSeries {
if val == 0 {
inc = 0 //no hr data
} else if float64(val) < 0.81*float64(user.Thr) {
inc = 55 //zone 1
} else if float64(val) > 0.81*float64(user.Thr) && float64(val) <= 0.89*float64(user.Thr) {
inc = 60 //zone 2
} else if float64(val) > 0.89*float64(user.Thr) && float64(val) <= 0.93*float64(user.Thr) {
inc = 69 //zone 3
} else if float64(val) > 0.93*float64(user.Thr) && float64(val) <= 0.99*float64(user.Thr) {
inc = 87 //zone 4
} else if float64(val) > 0.99*float64(user.Thr) && float64(val) <= 1.02*float64(user.Thr) {
inc = 100 //zone 5a
} else if float64(val) > 1.02*float64(user.Thr) && float64(val) <= 1.06*float64(user.Thr) {
inc = 118 //zone 5b
} else if float64(val) > 1.06*float64(user.Thr) {
inc = 140 //zone 5c
}
etssSum += inc
}
if len(heartSeries) > 0 {
endSummary.Etss = int(float64(etssSum/len(heartSeries)) * activityDuration)
}
//set page var stuff
if endSummary.Avpower == 0 {
hasPower = false
}
if endSummary.Avheart == 0 {
hasHeart = false
}
if endSummary.Avcad == 0 {
hasCadence = false
}
/***
* Energy
***/
if endSummary.Avpower > 0 {
endSummary.WorkDone = int(float64(endSummary.Avpower)*float64(seriesLen)) / 1000 //KJ
endSummary.EnergyUsedKj = int(float64(endSummary.WorkDone) * 4.444444444) //KJoules 0.4444' is 1/22.5 (22.5% efficiency)
endSummary.EnergyUsedKc = int(float64(endSummary.EnergyUsedKj) / 4.186) //KCals 4.186 convert KJoules -> KCals
} else if endSummary.Avheart > 0 && endSummary.Avpower == 0 {
//if not power present, but vo2 Max is, use that instead (http://www.shapesense.com/fitness-exercise/calculators/heart-rate-based-calorie-burn-calculator.aspx)
if user.Vo2 > 0 && user.Gender == "male" {
endSummary.EnergyUsedKc = int(((-95.7735 + (0.634 * float64(endSummary.Avheart)) + (0.404 * float64(user.Vo2)) + (0.394 * float64(user.Weight)) + (0.271 * float64(user.Age))) / 4.184) * 60.00 * (float64(seriesLen) / 3600.00))
endSummary.EnergyUsedKj = int(float64(endSummary.EnergyUsedKc) * 4.186)
endSummary.WorkDone = int(float64(endSummary.EnergyUsedKj) * 0.225)
} else if user.Vo2 > 0 && user.Gender == "female" {
endSummary.EnergyUsedKc = int(((-59.3954 + (0.45 * float64(endSummary.Avheart)) + (0.380 * float64(user.Vo2)) + (0.103 * float64(user.Weight)) + (0.274 * float64(user.Age))) / 4.184) * 60.00 * (float64(seriesLen) / 3600.00))
endSummary.EnergyUsedKj = int(float64(endSummary.EnergyUsedKc) * 4.186)
endSummary.WorkDone = int(float64(endSummary.EnergyUsedKj) * 0.225)
}
//if user.Vo2 Max not known, but we have hr data
if user.Vo2 == 0 && user.Gender == "male" {
endSummary.EnergyUsedKc = int(((-55.0969 + (0.6309 * float64(endSummary.Avheart)) + (0.1988 * float64(user.Weight)) + (0.2017 * float64(user.Age))) / 4.184) * 60 * (float64(seriesLen) / 3600.00))
endSummary.EnergyUsedKj = int(float64(endSummary.EnergyUsedKc) * 4.186)
endSummary.WorkDone = int(float64(endSummary.EnergyUsedKj) * 0.225)
} else if user.Vo2 == 0 && user.Gender == "female" {
endSummary.EnergyUsedKc = int(((-20.4022 + (0.4472 * float64(endSummary.Avheart)) + (0.1263 * float64(user.Weight)) + (0.074 * float64(user.Age))) / 4.184) * 60 * (float64(seriesLen) / 3600.00))
endSummary.EnergyUsedKj = int(float64(endSummary.EnergyUsedKc) * 4.186)
endSummary.WorkDone = int(float64(endSummary.EnergyUsedKj) * 0.225)
}
}
const longForm = "Monday Jan 2, 2006 at 3:04pm"
const shortForm = "2006, 0, 2"
var title = activityStart.Format(longForm)
endSummary.Dur = time.Duration(seriesLen) * time.Second
endSummary.StartTime = activityStart
//**** Marshal JSON and save to Cassandra ********//
row_json, err := json.Marshal(rows) //raw processed data
power_json, err := json.Marshal(powerSeries) //for chart
heart_json, err := json.Marshal(heartSeries) //for chart
cadence_json, err := json.Marshal(cadenceSeries) //for chart
cp_row_json, err := json.Marshal(cpRows) //rows for chart
cp_data_json, err := json.Marshal(cpms) //critical power metrics
lap_summaries_json, err := json.Marshal(lapSummaries)
end_summary_json, err := json.Marshal(endSummary)
if err != nil {
fmt.Println("error:", err)
}
saveProcessed(user, activityId, title, row_json, power_json, heart_json, cadence_json, cp_row_json, cp_data_json, lap_summaries_json, end_summary_json, hasPower, hasHeart, hasCadence, user.Ftp, user.Thr, activityStart)
}
//heart/Power by zone
func hpbz(user types.UserSettings, filter Filter) ([]Hbz, []Pbz) {
user_id := user.Id
var user_data types.Metrics
var end_summary_json []byte
var heart_json []byte
var power_json []byte
var cur_ftp int
var cur_thr int
var power_series []int
var heart_series []int
var has_power, has_heart bool
var activity_id string
var activity_start time.Time
hbz_data := make([]Hbz, 0)
pbz_data := make([]Pbz, 0)
var temp_row Hpbz
temp_rows := make([]Hpbz, 0)
cluster := gocql.NewCluster(config.DbHost)
cluster.Keyspace = "joulepersecond"
cluster.Consistency = gocql.Quorum
session, _ := cluster.CreateSession()
defer session.Close()
var sH1, sH2, sH3, sH4, sH5a, sH5b, sH5c, sP1, sP2, sP3, sP4, sP5, sP6 int
timeNow := time.Now()
timeThen := timeNow.AddDate(0, 0, -filter.Historylen)
//get all of the user's data (at least all for now) TODO limit these queries by date if poss.
iter := session.Query(`SELECT activity_start, activity_id FROM joulepersecond.user_activity WHERE user_id = ? AND activity_start > ? ORDER BY activity_start ASC`, user_id, timeThen).Iter()
for iter.Scan(&activity_start, &activity_id) {
iter := session.Query(`SELECT power_json, heart_json, end_summary_json, has_power, has_heart, cur_ftp, cur_thr FROM joulepersecond.proc_activity WHERE activity_id = ? `, activity_id).Iter()
for iter.Scan(&power_json, &heart_json, &end_summary_json, &has_power, &has_heart, &cur_ftp, &cur_thr) {
json.Unmarshal(end_summary_json, &user_data)
json.Unmarshal(power_json, &power_series)
json.Unmarshal(heart_json, &heart_series)
temp_row.StartTime = activity_start
//TODO next: Split all time series data in to zones and add it to temp_row/(s) for further date processing
if has_power {
temp_row.Samples = len(power_series)
temp_row.Has_power = true
}
if has_heart {
temp_row.Samples = len(heart_series)
temp_row.Has_heart = true
}
if !has_heart && !has_power {
break
}
//clear the values
temp_row.CountPZ1 = 0
temp_row.CountPZ2 = 0
temp_row.CountPZ3 = 0
temp_row.CountPZ4 = 0
temp_row.CountPZ5 = 0
temp_row.CountPZ6 = 0
temp_row.CountHZ1 = 0
temp_row.CountHZ2 = 0
temp_row.CountHZ3 = 0
temp_row.CountHZ4 = 0
temp_row.CountHZ5a = 0
temp_row.CountHZ5b = 0
temp_row.CountHZ5c = 0
if has_power {
var sum int
var average float64
for i := user.SampleSize; i < temp_row.Samples; i++ {
//reset total
sum = 0
//get thirty second rolling slice
rollingPowerSlice := power_series[i-user.SampleSize : i]
for _, val := range rollingPowerSlice {
//sum the sliding slice values
sum += val
}
average = float64(sum / user.SampleSize)
if average < 0.55*float64(cur_ftp) {
temp_row.CountPZ1++
} else if average > 0.55*float64(cur_ftp) && average <= 0.74*float64(cur_ftp) {
temp_row.CountPZ2++
} else if average > 0.74*float64(cur_ftp) && average <= 0.89*float64(cur_ftp) {
temp_row.CountPZ3++
} else if average > 0.89*float64(cur_ftp) && average <= 1.04*float64(cur_ftp) {
temp_row.CountPZ4++
} else if average > 1.04*float64(cur_ftp) && average <= 1.2*float64(cur_ftp) {
temp_row.CountPZ5++
} else if average > 1.2*float64(cur_ftp) {
temp_row.CountPZ6++
}
}
}
//loop through each sample and post the value into the correct pidgeon hole
for i := 0; i < temp_row.Samples; i++ {
if has_heart {
if float64(heart_series[i]) < 0.81*float64(cur_thr) {
temp_row.CountHZ1++
} else if float64(heart_series[i]) > 0.81*float64(cur_thr) && float64(heart_series[i]) <= 0.89*float64(cur_thr) {
temp_row.CountHZ2++
} else if float64(heart_series[i]) > 0.89*float64(cur_thr) && float64(heart_series[i]) <= 0.93*float64(cur_thr) {
temp_row.CountHZ3++
} else if float64(heart_series[i]) > 0.93*float64(cur_thr) && float64(heart_series[i]) <= 0.99*float64(cur_thr) {
temp_row.CountHZ4++
} else if float64(heart_series[i]) > 0.99*float64(cur_thr) && float64(heart_series[i]) <= 1.02*float64(cur_thr) {
temp_row.CountHZ5a++
} else if float64(heart_series[i]) > 1.02*float64(cur_thr) && float64(heart_series[i]) <= 1.06*float64(cur_thr) {
temp_row.CountHZ5b++
} else if float64(heart_series[i]) > 1.06*float64(cur_thr) {
temp_row.CountHZ5c++
}
}
}
temp_rows = append(temp_rows, temp_row)
}
}
clearVals := func() {
sH1 = 0
sH2 = 0
sH3 = 0
sH4 = 0
sH5a = 0
sH5b = 0
sH5c = 0
sP1 = 0
sP2 = 0
sP3 = 0
sP4 = 0
sP5 = 0
sP6 = 0
}
//so now for each activity we have the sum of each of the zones (value for each second * number seconds) and the number of seconds to divide by later once summed by date
//loope through each retrieved activity
var lastActivity time.Time
var numResult int
for i := 1; i < len(temp_rows); i++ {
if i == 1 {
lastActivity = temp_rows[i].StartTime
}
//if we want to show data in monthly format
if filter.Historylen > 366 { //show over 365 days as monthly
//set last activity on first iteration only
thisDate := temp_rows[i].StartTime
prevDate := lastActivity
//if we're still in the current month sum these values
if thisDate.Month() != prevDate.Month() || i == len(temp_rows)-1 {
var summedMonthlyHbz Hbz
var summedMonthlyPbz Pbz
summedMonthlyHbz.AvZ1 = utility.Round((float64(sH1) / 3600.0), .5, 2)
summedMonthlyHbz.AvZ2 = utility.Round((float64(sH2) / 3600.0), .5, 2)
summedMonthlyHbz.AvZ3 = utility.Round((float64(sH3) / 3600.0), .5, 2)
summedMonthlyHbz.AvZ4 = utility.Round((float64(sH4) / 3600.0), .5, 2)
summedMonthlyHbz.AvZ5a = utility.Round((float64(sH5a) / 3600.0), .5, 2)
summedMonthlyHbz.AvZ5b = utility.Round((float64(sH5b) / 3600.0), .5, 2)
summedMonthlyHbz.AvZ5c = utility.Round((float64(sH5c) / 3600.0), .5, 2)
summedMonthlyPbz.AvZ1 = utility.Round((float64(sP1) / 3600.0), .5, 2)
summedMonthlyPbz.AvZ2 = utility.Round((float64(sP2) / 3600.0), .5, 2)
summedMonthlyPbz.AvZ3 = utility.Round((float64(sP3) / 3600.0), .5, 2)
summedMonthlyPbz.AvZ4 = utility.Round((float64(sP4) / 3600.0), .5, 2)
summedMonthlyPbz.AvZ5 = utility.Round((float64(sP5) / 3600.0), .5, 2)
summedMonthlyPbz.AvZ6 = utility.Round((float64(sP6) / 3600.0), .5, 2)
var month time.Month
var year string
if thisDate.Month() != prevDate.Month() {
month = prevDate.Month()
year = strconv.Itoa(prevDate.Year())
} else {
month = thisDate.Month()
year = strconv.Itoa(thisDate.Year())
}
monthStr := month.String()
summedMonthlyHbz.TimeLabel = monthStr[0:3] + " '" + year[2:4]
summedMonthlyPbz.TimeLabel = monthStr[0:3] + " '" + year[2:4]
clearVals()
hbz_data = append(hbz_data, summedMonthlyHbz)
pbz_data = append(pbz_data, summedMonthlyPbz)
//reset the last activity date for the next loop
lastActivity = thisDate
}
} else {
thisDate := temp_rows[i].StartTime
prevDate := lastActivity
prevIterDate := temp_rows[i-1].StartTime
_, thisDateWeek := thisDate.ISOWeek() //adding day keeps i in the correct week
_, prevDateWeek := prevDate.ISOWeek() // "
if thisDateWeek != prevDateWeek || i == len(temp_rows)-1 { //if new week or last activity
var summedWeeklyHbz Hbz
var summedWeeklyPbz Pbz
numResult++
summedWeeklyHbz.AvZ1 = utility.Round((float64(sH1) / 3600.0), .5, 2)
summedWeeklyHbz.AvZ2 = utility.Round((float64(sH2) / 3600.0), .5, 2)
summedWeeklyHbz.AvZ3 = utility.Round((float64(sH3) / 3600.0), .5, 2)
summedWeeklyHbz.AvZ4 = utility.Round((float64(sH4) / 3600.0), .5, 2)
summedWeeklyHbz.AvZ5a = utility.Round((float64(sH5a) / 3600.0), .5, 2)
summedWeeklyHbz.AvZ5b = utility.Round((float64(sH5b) / 3600.0), .5, 2)
summedWeeklyHbz.AvZ5c = utility.Round((float64(sH5c) / 3600.0), .5, 2)
summedWeeklyPbz.AvZ1 = utility.Round((float64(sP1) / 3600.0), .5, 2)
summedWeeklyPbz.AvZ2 = utility.Round((float64(sP2) / 3600.0), .5, 2)
summedWeeklyPbz.AvZ3 = utility.Round((float64(sP3) / 3600.0), .5, 2)
summedWeeklyPbz.AvZ4 = utility.Round((float64(sP4) / 3600.0), .5, 2)
summedWeeklyPbz.AvZ5 = utility.Round((float64(sP5) / 3600.0), .5, 2)
summedWeeklyPbz.AvZ6 = utility.Round((float64(sP6) / 3600.0), .5, 2)
monthS := prevDate.Month()
dayS := strconv.Itoa(prevDate.Day())
var dayF string
var monthF time.Month
if thisDateWeek != prevDateWeek {
monthF = prevIterDate.Month()
dayF = strconv.Itoa(prevIterDate.Day())
} else {
monthF = thisDate.Month()
dayF = strconv.Itoa(thisDate.Day())
}
monthStrS := monthS.String()
monthAbrS := monthStrS[0:3]
monthStrF := monthF.String()
monthAbrF := monthStrF[0:3]
//format labels according to number to displau
if filter.Historylen < 120 {
summedWeeklyHbz.TimeLabel = dayS + " " + monthAbrS + " - " + dayF + " " + monthAbrF
summedWeeklyPbz.TimeLabel = dayS + " " + monthAbrS + " - " + dayF + " " + monthAbrF
} else {
summedWeeklyHbz.TimeLabel = dayS + " " + monthAbrS
summedWeeklyPbz.TimeLabel = dayS + " " + monthAbrS
}
clearVals()
hbz_data = append(hbz_data, summedWeeklyHbz)
pbz_data = append(pbz_data, summedWeeklyPbz)
//reset the last activity date for the next loop
lastActivity = thisDate
}
}
sP1 += temp_rows[i].CountPZ1
sP2 += temp_rows[i].CountPZ2
sP3 += temp_rows[i].CountPZ3
sP4 += temp_rows[i].CountPZ4
sP5 += temp_rows[i].CountPZ5
sP6 += temp_rows[i].CountPZ6
sH1 += temp_rows[i].CountHZ1
sH2 += temp_rows[i].CountHZ2
sH3 += temp_rows[i].CountHZ3
sH4 += temp_rows[i].CountHZ4
sH5a += temp_rows[i].CountHZ5a
sH5b += temp_rows[i].CountHZ5b
sH5c += temp_rows[i].CountHZ5c
}
return hbz_data, pbz_data
}
//get the week's activities
func dashboard(user types.UserSettings) (types.Tvd, types.Zones, types.ZoneLabels) {
user_id := user.Id
tvd_data_points := make([]types.Tvd_data_point, 0)
tvd_data := make([]types.Tvd, 0)
var user_data types.Metrics
var activity_id string
var end_summary_json []byte
var activity_start time.Time
var heart_json []byte
var power_json []byte
var cur_ftp int
var cur_thr int
var power_series []int
var heart_series []int
var has_power, has_heart bool
var zoneData types.Zones
cluster := gocql.NewCluster(config.DbHost)
cluster.Keyspace = "joulepersecond"
cluster.Consistency = gocql.Quorum
session, _ := cluster.CreateSession()
defer session.Close()
//get all of the user's data (at least all for now) TODO limit these queries by date if poss. Done!
timeNow := time.Now()
//we can use TimeOffset to test from other dates
timeNow = timeNow.AddDate(0, 0, user.TimeOffset)
//timeTruncated is a time at the beginning of the day
timeTruncated := timeNow.Truncate(time.Hour * 24)
//we will use timeThen to refer to the beginning of the current week
var timeThen time.Time
dayOfWeek := int(timeTruncated.Weekday())
if int(timeTruncated.Weekday()) != 0 { //if not equal to Sunday...
timeThen = timeTruncated.AddDate(0, 0, -(dayOfWeek - 1)) //fetch records for the week so far (second -1 to start from Monday)
} else {
timeThen = timeTruncated.AddDate(0, 0, -6) //if today is Sunday, query back to Monday
}
iter := session.Query(`SELECT activity_id, activity_start, end_summary_json FROM joulepersecond.user_activity WHERE user_id = ? AND activity_start <=? AND activity_start >= ? `, user_id, timeNow, timeThen).Iter()
for iter.Scan(&activity_id, &activity_start, &end_summary_json) {
var tvd_data_point types.Tvd_data_point
json.Unmarshal(end_summary_json, &user_data)
tvd_data_point.Date = user_data.StartTime
tvd_data_point.Dur = user_data.Dur
if user_data.Utss > 0 {
tvd_data_point.Tss = user_data.Utss
} else if user_data.Tss > 0 {
tvd_data_point.Tss = user_data.Tss
} else if user_data.Etss > 0 {
tvd_data_point.Tss = user_data.Etss
} else {
tvd_data_point.Tss = 0
}
tvd_data_points = append(tvd_data_points, tvd_data_point)
//for each activity, get the exended data
iter := session.Query(`SELECT power_json, heart_json, end_summary_json, has_power, has_heart, cur_ftp, cur_thr FROM joulepersecond.proc_activity WHERE activity_id = ? `, activity_id).Iter()
for iter.Scan(&power_json, &heart_json, &end_summary_json, &has_power, &has_heart, &cur_ftp, &cur_thr) {
json.Unmarshal(end_summary_json, &user_data)
json.Unmarshal(power_json, &power_series)
json.Unmarshal(heart_json, &heart_series)
var samples int
if has_power {
samples = len(power_series)
has_power = true
}
if has_heart {
samples = len(heart_series)
has_heart = true
}
if !has_heart && !has_power {
break
}
if has_power {
zoneData.HasPower = true
var sum int
var average float64
for i := user.SampleSize; i < samples; i++ {
//reset total
sum = 0
//get thirty second rolling slice
rollingPowerSlice := power_series[i-user.SampleSize : i]
for _, val := range rollingPowerSlice {
//sum the sliding slice values
sum += val
}
average = float64(sum / user.SampleSize)
if average < 0.55*float64(cur_ftp) {
zoneData.Z1++
} else if average > 0.55*float64(cur_ftp) && average <= 0.74*float64(cur_ftp) {
zoneData.Z2++
} else if average > 0.74*float64(cur_ftp) && average <= 0.89*float64(cur_ftp) {
zoneData.Z3++
} else if average > 0.89*float64(cur_ftp) && average <= 1.04*float64(cur_ftp) {
zoneData.Z4++
} else if average > 1.04*float64(cur_ftp) && average <= 1.2*float64(cur_ftp) {
zoneData.Z5++
} else if average > 1.2*float64(cur_ftp) {
zoneData.Z6++
}
}
}
//loop through each sample and post the value into the correct pidgeon hole
if has_heart {
zoneData.HasHeart = true
for i := 0; i < samples; i++ {
if float64(heart_series[i]) < 0.81*float64(cur_thr) {
zoneData.HR1++
} else if float64(heart_series[i]) > 0.81*float64(cur_thr) && float64(heart_series[i]) <= 0.89*float64(cur_thr) {
zoneData.HR2++
} else if float64(heart_series[i]) > 0.89*float64(cur_thr) && float64(heart_series[i]) <= 0.93*float64(cur_thr) {
zoneData.HR3++
} else if float64(heart_series[i]) > 0.93*float64(cur_thr) && float64(heart_series[i]) <= 0.99*float64(cur_thr) {
zoneData.HR4++
} else if float64(heart_series[i]) > 0.99*float64(cur_thr) && float64(heart_series[i]) <= 1.02*float64(cur_thr) {
zoneData.HR5a++
} else if float64(heart_series[i]) > 1.02*float64(cur_thr) && float64(heart_series[i]) <= 1.06*float64(cur_thr) {
zoneData.HR5b++
} else if float64(heart_series[i]) > 1.06*float64(cur_thr) {
zoneData.HR5c++
}
}
}
}
}
//we now have all the data... Now sort it
sumTss := 0
var sumDur time.Duration
var summedWeeklyTvd types.Tvd
//loop through each retrieved activity
for i := 0; i < len(tvd_data_points); i++ {
tvd_data = append(tvd_data, summedWeeklyTvd)
//sum the values
sumTss += tvd_data_points[i].Tss
sumDur += tvd_data_points[i].Dur
}
summedWeeklyTvd.TotalTss = sumTss
summedWeeklyTvd.TotalDur = utility.Round(sumDur.Hours(), .5, 2)
var zoneLabels types.ZoneLabels
zoneLabels.PowerZ1 = int(0.55 * float64(user.Ftp))
zoneLabels.PowerZ2 = int(0.74 * float64(user.Ftp))
zoneLabels.PowerZ3 = int(0.89 * float64(user.Ftp))
zoneLabels.PowerZ4 = int(1.04 * float64(user.Ftp))
zoneLabels.PowerZ5 = int(1.2 * float64(user.Ftp))
zoneLabels.HeartZ1 = int(0.81 * float64(user.Thr))
zoneLabels.HeartZ2 = int(0.89 * float64(user.Thr))
zoneLabels.HeartZ3 = int(0.93 * float64(user.Thr))
zoneLabels.HeartZ4 = int(0.99 * float64(user.Thr))
zoneLabels.HeartZ5a = int(1.02 * float64(user.Thr))
zoneLabels.HeartZ5b = int(1.06 * float64(user.Thr))
//get the power and heartrate zone data
return summedWeeklyTvd, zoneData, zoneLabels
}
