Moved to google.golang.org/genproto/googleapis/api/annotations

Fixes #52

vendor/github.com/go-kit/kit/metrics/teststat/populate.go

@@ -0,0 +1,72 @@
+package teststat
+
+import (
+	"math"
+	"math/rand"
+
+	"github.com/go-kit/kit/metrics"
+)
+
+// PopulateNormalHistogram makes a series of normal random observations into the
+// histogram. The number of observations is determined by Count. The randomness
+// is determined by Mean, Stdev, and the seed parameter.
+//
+// This is a low-level function, exported only for metrics that don't perform
+// dynamic quantile computation, like a Prometheus Histogram (c.f. Summary). In
+// most cases, you don't need to use this function, and can use TestHistogram
+// instead.
+func PopulateNormalHistogram(h metrics.Histogram, seed int) {
+	r := rand.New(rand.NewSource(int64(seed)))
+	for i := 0; i < Count; i++ {
+		sample := r.NormFloat64()*float64(Stdev) + float64(Mean)
+		if sample < 0 {
+			sample = 0
+		}
+		h.Observe(sample)
+	}
+}
+
+func normalQuantiles() (p50, p90, p95, p99 float64) {
+	return nvq(50), nvq(90), nvq(95), nvq(99)
+}
+
+func nvq(quantile int) float64 {
+	// https://en.wikipedia.org/wiki/Normal_distribution#Quantile_function
+	return float64(Mean) + float64(Stdev)*math.Sqrt2*erfinv(2*(float64(quantile)/100)-1)
+}
+
+func erfinv(y float64) float64 {
+	// https://stackoverflow.com/questions/5971830/need-code-for-inverse-error-function
+	if y < -1.0 || y > 1.0 {
+		panic("invalid input")
+	}
+
+	var (
+		a = [4]float64{0.886226899, -1.645349621, 0.914624893, -0.140543331}
+		b = [4]float64{-2.118377725, 1.442710462, -0.329097515, 0.012229801}
+		c = [4]float64{-1.970840454, -1.624906493, 3.429567803, 1.641345311}
+		d = [2]float64{3.543889200, 1.637067800}
+	)
+
+	const y0 = 0.7
+	var x, z float64
+
+	if math.Abs(y) == 1.0 {
+		x = -y * math.Log(0.0)
+	} else if y < -y0 {
+		z = math.Sqrt(-math.Log((1.0 + y) / 2.0))
+		x = -(((c[3]*z+c[2])*z+c[1])*z + c[0]) / ((d[1]*z+d[0])*z + 1.0)
+	} else {
+		if y < y0 {
+			z = y * y
+			x = y * (((a[3]*z+a[2])*z+a[1])*z + a[0]) / ((((b[3]*z+b[3])*z+b[1])*z+b[0])*z + 1.0)
+		} else {
+			z = math.Sqrt(-math.Log((1.0 - y) / 2.0))
+			x = (((c[3]*z+c[2])*z+c[1])*z + c[0]) / ((d[1]*z+d[0])*z + 1.0)
+		}
+		x = x - (math.Erf(x)-y)/(2.0/math.SqrtPi*math.Exp(-x*x))
+		x = x - (math.Erf(x)-y)/(2.0/math.SqrtPi*math.Exp(-x*x))
+	}
+
+	return x
+}