Calculate treatment cadence by year

html.go

@@ -123,13 +123,14 @@ type ContentSignin struct {
 }
 type ContentSignup struct{}
 type ContentSource struct {
-	Inspections      []Inspection
+	Inspections []Inspection
-	MapData          ComponentMap
+	MapData     ComponentMap
-	Source           *BreedingSourceDetail
+	Source      *BreedingSourceDetail
-	Traps            []TrapNearby
+	Traps       []TrapNearby
-	Treatments       []Treatment
+	Treatments  []Treatment
-	TreatmentCadence time.Duration
+	//TreatmentCadence TreatmentCadence
-	User             User
+	TreatmentModels []TreatmentModel
+	User            User
 }
 type Inspection struct {
 	Action     string
@@ -506,18 +507,7 @@ func htmlSource(w http.ResponseWriter, r *http.Request, user *models.User, id st
 		respondError(w, "Failed to get treatments", err, http.StatusInternalServerError)
 		return
 	}
-	treatment_times := make([]time.Time, 0)
+	treatment_models := modelTreatment(treatments)
-	for _, treatment := range treatments {
-		treatment_times = append(treatment_times, treatment.Date)
-	}
-	cadence, deltas := calculateCadenceVariance(treatment_times)
-	for i, treatment := range treatments {
-		if i >= len(deltas) {
-			break
-		}
-		treatment.CadenceDelta = deltas[i]
-		treatments[i] = treatment
-	}
 	data := ContentSource{
 		Inspections: inspections,
 		MapData: ComponentMap{
@@ -531,11 +521,11 @@ func htmlSource(w http.ResponseWriter, r *http.Request, user *models.User, id st
 			},
 			Zoom: 13,
 		},
-		Source:           s,
+		Source:          s,
-		Traps:            traps,
+		Traps:           traps,
-		Treatments:       treatments,
+		Treatments:      treatments,
-		TreatmentCadence: cadence,
+		TreatmentModels: treatment_models,
-		User:             userContent,
+		User:            userContent,
 	}
 
 	renderOrError(w, source, data)
@@ -574,14 +564,15 @@ func latLngDisplay(ll h3.LatLng) string {
 
 func makeFuncMap() template.FuncMap {
 	funcMap := template.FuncMap{
-		"bigNumber":     bigNumber,
+		"bigNumber":          bigNumber,
-		"GISStatement":  gisStatement,
+		"GISStatement":       gisStatement,
-		"latLngDisplay": latLngDisplay,
+		"latLngDisplay":      latLngDisplay,
-		"timeDelta":     timeDelta,
+		"timeAsRelativeDate": timeAsRelativeDate,
-		"timeElapsed":   timeElapsed,
+		"timeDelta":          timeDelta,
-		"timeInterval":  timeInterval,
+		"timeElapsed":        timeElapsed,
-		"timeSince":     timeSince,
+		"timeInterval":       timeInterval,
-		"uuidShort":     uuidShort,
+		"timeSince":          timeSince,
+		"uuidShort":          uuidShort,
 	}
 	return funcMap
 }
@@ -642,6 +633,10 @@ func parseFromDisk(files []string) (*template.Template, error) {
 	return templ, nil
 }
 
+func timeAsRelativeDate(d time.Time) string {
+	return d.Format("01-02")
+}
+
 // FormatTimeDuration returns a human-readable string representing a time.Duration
 // as "X units early" or "X units late"
 func timeDelta(d time.Duration) string {

templates/source.html

@@ -250,7 +250,24 @@
 			<div class="card mb-4">
 				<div class="card-body">
 					<div class="table-responsive">
-						<p>Estimated Treatment Cadence: {{.TreatmentCadence|timeInterval}}</p>
+						<table class="table table-striped">
+							<tbody>
+								<tr>
+									<th>Year</th>
+									<th>Start</th>
+									<th>End</th>
+									<th>Interval</th>
+								</tr>
+								{{ range .TreatmentModels }}
+								<tr>
+									<td>{{.Year}}</td>
+									<td>{{.SeasonStart|timeAsRelativeDate}}</td>
+									<td>{{.SeasonEnd|timeAsRelativeDate}}</td>
+									<td>{{.Interval|timeInterval}}</td>
+								</tr>
+								{{ end }}
+							</tbody>
+						</table>
 						<table class="table table-striped table-hover">
 							<thead>
 								<tr>
@@ -316,7 +333,7 @@
 					<tbody>
 						<tr>
 							<td class="info-label">Trap ID:</td>
-							<td>{{ .ID }}</td>
+							<td><a href="/trap/{{.ID}}">{{ .ID }}</a></td>
 						</tr>
 						<tr>
 							<td class="info-label">Distance</td>

time.go

@@ -1,42 +1,161 @@
 package main
 
 import (
+	"sort"
 	"time"
-
+	//"github.com/rs/zerolog/log"
-	"github.com/rs/zerolog/log"
 )
 
-// calculateCadenceVariance takes a slice of time.Time instances and returns
+// TreatmentModel represents the calculated model for a year's treatments
-// the average time span between consecutive instances and how much each
+type TreatmentModel struct {
-// instance deviates from its expected position in an evenly-spaced sequence
+	Year           int
-// timestamps should be in descending order (most recent time in index 0)
+	SeasonStart    time.Time
-func calculateCadenceVariance(timestamps []time.Time) (averageSpan time.Duration, deviations []time.Duration) {
+	SeasonEnd      time.Time
-	if len(timestamps) <= 1 {
+	Interval       time.Duration
-		return 0, []time.Duration{}
+	ActualDates    []time.Time
-	}
+	PredictedDates []time.Time
-
+	Errors         []time.Duration
-	// Calculate total span between first and last timestamp
+}
-	totalSpan := timestamps[0].Sub(timestamps[len(timestamps)-1])
+
-
+func modelTreatment(treatments []Treatment) []TreatmentModel {
-	// Calculate average span
+	treatment_times := make([]time.Time, 0)
-	averageSpan = totalSpan / time.Duration(len(timestamps)-1)
+	for _, treatment := range treatments {
-	if averageSpan < 0 {
+		treatment_times = append(treatment_times, treatment.Date)
-		log.Error().Int64("average", int64(averageSpan)).Msg("Negative average")
+	}
-		return 0, []time.Duration{}
+	models := calculateTreatmentModels(treatment_times)
-	}
+	/*models_by_year := make(map[int]TreatmentModel)
-
+	for _, m := range models {
-	// Calculate deviations
+		models_by_year[m.Year] = m
-	deviations = make([]time.Duration, len(timestamps))
+	}*/
-
+	offset := 0
-	// The first timestamp is our reference point (deviation = 0)
+	for _, model := range models {
-	deviations[0] = 0
+		for _, e := range model.Errors {
-
+			treatments[offset].CadenceDelta = e
-	// Calculate expected timestamps based on perfect intervals
+			offset = offset + 1
-	for i := 1; i < len(timestamps); i++ {
+		}
-		expectedTime := timestamps[0].Add(-averageSpan * time.Duration(i))
+	}
-		deviations[i] = timestamps[i].Sub(expectedTime)
+	/*
-	}
+		for i, treatment := range treatments {
-
+			model
-	log.Info().Int64("average", int64(averageSpan)).Int("number deviations", len(deviations)).Msg("Calculated cadence")
+			treatment.CadenceDelta = deltas[i]
-	return averageSpan, deviations
+			treatments[i] = treatment
+		}
+	*/
+	/*cadence, deltas := calculateCadenceVariance(treatment_times)
+	for i, treatment := range treatments {
+		if i >= len(deltas) {
+			break
+		}
+		treatment.CadenceDelta = deltas[i]
+		treatments[i] = treatment
+	}*/
+	return models
+}
+
+// calculateTreatmentModels segments treatments by year and calculates a model for each year
+func calculateTreatmentModels(treatments []time.Time) []TreatmentModel {
+	// Group treatments by year
+	yearMap := make(map[int][]time.Time)
+	for _, t := range treatments {
+		year := t.Year()
+		yearMap[year] = append(yearMap[year], t)
+	}
+
+	// Calculate a model for each year
+	var models []TreatmentModel
+	for year, dates := range yearMap {
+		// Sort dates within the year
+		sort.Slice(dates, func(i, j int) bool {
+			return dates[i].Before(dates[j])
+		})
+
+		model := calculateYearModel(year, dates)
+		models = append(models, model)
+	}
+
+	// Sort models by year
+	sort.Slice(models, func(i, j int) bool {
+		return models[i].Year < models[j].Year
+	})
+
+	return models
+}
+
+// calculateYearModel creates a model for a specific year using linear regression
+func calculateYearModel(year int, dates []time.Time) TreatmentModel {
+	n := len(dates)
+	if n < 2 {
+		// Not enough data for a model
+		return TreatmentModel{
+			Year:        year,
+			ActualDates: dates,
+		}
+	}
+
+	// Convert dates to numeric values (seconds since epoch)
+	var x []float64
+	var y []float64
+	for i, date := range dates {
+		x = append(x, float64(i))
+		y = append(y, float64(date.Unix()))
+	}
+
+	// Calculate linear regression
+	slope, intercept := linearRegression(x, y)
+
+	// Convert back to time.Time and time.Duration
+	startTime := time.Unix(int64(intercept), 0)
+	intervalSeconds := int64(slope)
+	interval := time.Duration(intervalSeconds) * time.Second
+
+	// Calculate end of season
+	endTime := time.Unix(int64(intercept+slope*float64(n-1)), 0)
+
+	// Generate predicted dates and calculate errors
+	var predictedDates []time.Time
+	var errors []time.Duration
+
+	for i := 0; i < n; i++ {
+		predicted := time.Unix(int64(intercept+slope*float64(i)), 0)
+		predictedDates = append(predictedDates, predicted)
+
+		// Calculate error
+		actualTime := dates[i]
+		error := actualTime.Sub(predicted)
+		errors = append(errors, error)
+	}
+
+	return TreatmentModel{
+		Year:           year,
+		SeasonStart:    startTime,
+		SeasonEnd:      endTime,
+		Interval:       interval,
+		ActualDates:    dates,
+		PredictedDates: predictedDates,
+		Errors:         errors,
+	}
+}
+
+// linearRegression calculates the slope and intercept of the best-fit line
+func linearRegression(x, y []float64) (float64, float64) {
+	n := float64(len(x))
+	if n < 2 {
+		return 0, 0
+	}
+
+	var sumX, sumY, sumXY, sumX2 float64
+	for i := 0; i < len(x); i++ {
+		sumX += x[i]
+		sumY += y[i]
+		sumXY += x[i] * y[i]
+		sumX2 += x[i] * x[i]
+	}
+
+	// Calculate slope
+	slope := (n*sumXY - sumX*sumY) / (n*sumX2 - sumX*sumX)
+
+	// Calculate intercept
+	intercept := (sumY - slope*sumX) / n
+
+	return slope, intercept
 }