func Analyze(times []float64) {
sorted := sort.Float64Array(times)
sorted.Sort()
tot := 0.0
for _, v := range times {
tot += v
}
n := float64(len(times))
avg := tot / n
variance := 0.0
for _, v := range times {
variance += (v - avg) * (v - avg)
}
variance /= n
stddev := math.Sqrt(variance)
median := sorted[len(times)/2]
perc90 := sorted[int(n*0.9)]
perc10 := sorted[int(n*0.1)]
fmt.Printf(
"%d samples\n"+
"avg %.2f ms 2sigma %.2f "+
"median %.2fms\n"+
"10%%tile %.2fms, 90%%tile %.2fms\n",
len(times), avg, 2*stddev, median, perc10, perc90)
}
// Compute minimum, p percentil, median, average, 100-p percentil and maximum of values in data.
func SixvalFloat64(data []float64, p int) (min, lq, med, avg, uq, max float64) {
n := len(data)
// Special cases 0 and 1
if n == 0 {
return
}
if n == 1 {
min = data[0]
lq = data[0]
med = data[0]
avg = data[0]
uq = data[0]
max = data[0]
return
}
// First pass (min, max, coarse average)
var sum float64
min, max = math.MaxFloat64, -math.MaxFloat64
for _, v := range data {
if v < min {
min = v
}
if v > max {
max = v
}
sum += v
}
avg = sum / float64(n)
// Second pass: Correct average
var corr float64
for _, v := range data {
corr += v - avg
}
avg += corr / float64(n)
// Median
sort.Sort(sort.Float64Array(data))
if n%2 == 1 {
med = data[(n-1)/2]
} else {
med = (data[n/2] + data[n/2-1]) / 2
}
// Percentiles
if p < 0 {
p = 0
}
if p > 100 {
p = 100
}
lq = percentilFloat64(data, p)
uq = percentilFloat64(data, 100-p)
return
}