feat(alerting): add timeserie aggregation functions

pkg/models/timeseries.go

@@ -1,8 +1,47 @@
 package models
 
+import "math"
+
 type TimeSeries struct {
 	Name   string       `json:"name"`
 	Points [][2]float64 `json:"points"`
+
+	Avg  float64
+	Sum  float64
+	Min  float64
+	Max  float64
+	Mean float64
 }
 
 type TimeSeriesSlice []*TimeSeries
+
+func NewTimeSeries(name string, points [][2]float64) *TimeSeries {
+	ts := &TimeSeries{
+		Name:   name,
+		Points: points,
+	}
+
+	ts.Min = points[0][0]
+	ts.Max = points[0][0]
+
+	for _, v := range points {
+		value := v[0]
+
+		if value > ts.Max {
+			ts.Max = value
+		}
+
+		if value < ts.Min {
+			ts.Min = value
+		}
+
+		ts.Sum += value
+	}
+
+	ts.Avg = ts.Sum / float64(len(points))
+	midPosition := int64(math.Floor(float64(len(points)) / float64(2)))
+
+	ts.Mean = points[midPosition][0]
+
+	return ts
+}

pkg/models/timeseries_test.go

@@ -0,0 +1,36 @@
+package models
+
+import (
+	. "github.com/smartystreets/goconvey/convey"
+	"testing"
+)
+
+func TestTimeSeries(t *testing.T) {
+	Convey("timeseries aggregation tests", t, func() {
+		ts := NewTimeSeries("test", [][2]float64{
+			{1, 0},
+			{2, 0},
+			{3, 0},
+		})
+
+		Convey("sum", func() {
+			So(ts.Sum, ShouldEqual, 6)
+		})
+
+		Convey("avg", func() {
+			So(ts.Avg, ShouldEqual, 2)
+		})
+
+		Convey("min", func() {
+			So(ts.Min, ShouldEqual, 1)
+		})
+
+		Convey("max", func() {
+			So(ts.Max, ShouldEqual, 3)
+		})
+
+		Convey("mean", func() {
+			So(ts.Mean, ShouldEqual, 2)
+		})
+	})
+}