initial add - go fmt on grpc
Change-Id: Ib0afadd2fe5571d1456a091f94f5644458f7d3f4
diff --git a/vendor/golang.org/x/net/trace/histogram.go b/vendor/golang.org/x/net/trace/histogram.go
new file mode 100644
index 0000000..9bf4286
--- /dev/null
+++ b/vendor/golang.org/x/net/trace/histogram.go
@@ -0,0 +1,365 @@
+// Copyright 2015 The Go Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package trace
+
+// This file implements histogramming for RPC statistics collection.
+
+import (
+ "bytes"
+ "fmt"
+ "html/template"
+ "log"
+ "math"
+ "sync"
+
+ "golang.org/x/net/internal/timeseries"
+)
+
+const (
+ bucketCount = 38
+)
+
+// histogram keeps counts of values in buckets that are spaced
+// out in powers of 2: 0-1, 2-3, 4-7...
+// histogram implements timeseries.Observable
+type histogram struct {
+ sum int64 // running total of measurements
+ sumOfSquares float64 // square of running total
+ buckets []int64 // bucketed values for histogram
+ value int // holds a single value as an optimization
+ valueCount int64 // number of values recorded for single value
+}
+
+// AddMeasurement records a value measurement observation to the histogram.
+func (h *histogram) addMeasurement(value int64) {
+ // TODO: assert invariant
+ h.sum += value
+ h.sumOfSquares += float64(value) * float64(value)
+
+ bucketIndex := getBucket(value)
+
+ if h.valueCount == 0 || (h.valueCount > 0 && h.value == bucketIndex) {
+ h.value = bucketIndex
+ h.valueCount++
+ } else {
+ h.allocateBuckets()
+ h.buckets[bucketIndex]++
+ }
+}
+
+func (h *histogram) allocateBuckets() {
+ if h.buckets == nil {
+ h.buckets = make([]int64, bucketCount)
+ h.buckets[h.value] = h.valueCount
+ h.value = 0
+ h.valueCount = -1
+ }
+}
+
+func log2(i int64) int {
+ n := 0
+ for ; i >= 0x100; i >>= 8 {
+ n += 8
+ }
+ for ; i > 0; i >>= 1 {
+ n += 1
+ }
+ return n
+}
+
+func getBucket(i int64) (index int) {
+ index = log2(i) - 1
+ if index < 0 {
+ index = 0
+ }
+ if index >= bucketCount {
+ index = bucketCount - 1
+ }
+ return
+}
+
+// Total returns the number of recorded observations.
+func (h *histogram) total() (total int64) {
+ if h.valueCount >= 0 {
+ total = h.valueCount
+ }
+ for _, val := range h.buckets {
+ total += int64(val)
+ }
+ return
+}
+
+// Average returns the average value of recorded observations.
+func (h *histogram) average() float64 {
+ t := h.total()
+ if t == 0 {
+ return 0
+ }
+ return float64(h.sum) / float64(t)
+}
+
+// Variance returns the variance of recorded observations.
+func (h *histogram) variance() float64 {
+ t := float64(h.total())
+ if t == 0 {
+ return 0
+ }
+ s := float64(h.sum) / t
+ return h.sumOfSquares/t - s*s
+}
+
+// StandardDeviation returns the standard deviation of recorded observations.
+func (h *histogram) standardDeviation() float64 {
+ return math.Sqrt(h.variance())
+}
+
+// PercentileBoundary estimates the value that the given fraction of recorded
+// observations are less than.
+func (h *histogram) percentileBoundary(percentile float64) int64 {
+ total := h.total()
+
+ // Corner cases (make sure result is strictly less than Total())
+ if total == 0 {
+ return 0
+ } else if total == 1 {
+ return int64(h.average())
+ }
+
+ percentOfTotal := round(float64(total) * percentile)
+ var runningTotal int64
+
+ for i := range h.buckets {
+ value := h.buckets[i]
+ runningTotal += value
+ if runningTotal == percentOfTotal {
+ // We hit an exact bucket boundary. If the next bucket has data, it is a
+ // good estimate of the value. If the bucket is empty, we interpolate the
+ // midpoint between the next bucket's boundary and the next non-zero
+ // bucket. If the remaining buckets are all empty, then we use the
+ // boundary for the next bucket as the estimate.
+ j := uint8(i + 1)
+ min := bucketBoundary(j)
+ if runningTotal < total {
+ for h.buckets[j] == 0 {
+ j++
+ }
+ }
+ max := bucketBoundary(j)
+ return min + round(float64(max-min)/2)
+ } else if runningTotal > percentOfTotal {
+ // The value is in this bucket. Interpolate the value.
+ delta := runningTotal - percentOfTotal
+ percentBucket := float64(value-delta) / float64(value)
+ bucketMin := bucketBoundary(uint8(i))
+ nextBucketMin := bucketBoundary(uint8(i + 1))
+ bucketSize := nextBucketMin - bucketMin
+ return bucketMin + round(percentBucket*float64(bucketSize))
+ }
+ }
+ return bucketBoundary(bucketCount - 1)
+}
+
+// Median returns the estimated median of the observed values.
+func (h *histogram) median() int64 {
+ return h.percentileBoundary(0.5)
+}
+
+// Add adds other to h.
+func (h *histogram) Add(other timeseries.Observable) {
+ o := other.(*histogram)
+ if o.valueCount == 0 {
+ // Other histogram is empty
+ } else if h.valueCount >= 0 && o.valueCount > 0 && h.value == o.value {
+ // Both have a single bucketed value, aggregate them
+ h.valueCount += o.valueCount
+ } else {
+ // Two different values necessitate buckets in this histogram
+ h.allocateBuckets()
+ if o.valueCount >= 0 {
+ h.buckets[o.value] += o.valueCount
+ } else {
+ for i := range h.buckets {
+ h.buckets[i] += o.buckets[i]
+ }
+ }
+ }
+ h.sumOfSquares += o.sumOfSquares
+ h.sum += o.sum
+}
+
+// Clear resets the histogram to an empty state, removing all observed values.
+func (h *histogram) Clear() {
+ h.buckets = nil
+ h.value = 0
+ h.valueCount = 0
+ h.sum = 0
+ h.sumOfSquares = 0
+}
+
+// CopyFrom copies from other, which must be a *histogram, into h.
+func (h *histogram) CopyFrom(other timeseries.Observable) {
+ o := other.(*histogram)
+ if o.valueCount == -1 {
+ h.allocateBuckets()
+ copy(h.buckets, o.buckets)
+ }
+ h.sum = o.sum
+ h.sumOfSquares = o.sumOfSquares
+ h.value = o.value
+ h.valueCount = o.valueCount
+}
+
+// Multiply scales the histogram by the specified ratio.
+func (h *histogram) Multiply(ratio float64) {
+ if h.valueCount == -1 {
+ for i := range h.buckets {
+ h.buckets[i] = int64(float64(h.buckets[i]) * ratio)
+ }
+ } else {
+ h.valueCount = int64(float64(h.valueCount) * ratio)
+ }
+ h.sum = int64(float64(h.sum) * ratio)
+ h.sumOfSquares = h.sumOfSquares * ratio
+}
+
+// New creates a new histogram.
+func (h *histogram) New() timeseries.Observable {
+ r := new(histogram)
+ r.Clear()
+ return r
+}
+
+func (h *histogram) String() string {
+ return fmt.Sprintf("%d, %f, %d, %d, %v",
+ h.sum, h.sumOfSquares, h.value, h.valueCount, h.buckets)
+}
+
+// round returns the closest int64 to the argument
+func round(in float64) int64 {
+ return int64(math.Floor(in + 0.5))
+}
+
+// bucketBoundary returns the first value in the bucket.
+func bucketBoundary(bucket uint8) int64 {
+ if bucket == 0 {
+ return 0
+ }
+ return 1 << bucket
+}
+
+// bucketData holds data about a specific bucket for use in distTmpl.
+type bucketData struct {
+ Lower, Upper int64
+ N int64
+ Pct, CumulativePct float64
+ GraphWidth int
+}
+
+// data holds data about a Distribution for use in distTmpl.
+type data struct {
+ Buckets []*bucketData
+ Count, Median int64
+ Mean, StandardDeviation float64
+}
+
+// maxHTMLBarWidth is the maximum width of the HTML bar for visualizing buckets.
+const maxHTMLBarWidth = 350.0
+
+// newData returns data representing h for use in distTmpl.
+func (h *histogram) newData() *data {
+ // Force the allocation of buckets to simplify the rendering implementation
+ h.allocateBuckets()
+ // We scale the bars on the right so that the largest bar is
+ // maxHTMLBarWidth pixels in width.
+ maxBucket := int64(0)
+ for _, n := range h.buckets {
+ if n > maxBucket {
+ maxBucket = n
+ }
+ }
+ total := h.total()
+ barsizeMult := maxHTMLBarWidth / float64(maxBucket)
+ var pctMult float64
+ if total == 0 {
+ pctMult = 1.0
+ } else {
+ pctMult = 100.0 / float64(total)
+ }
+
+ buckets := make([]*bucketData, len(h.buckets))
+ runningTotal := int64(0)
+ for i, n := range h.buckets {
+ if n == 0 {
+ continue
+ }
+ runningTotal += n
+ var upperBound int64
+ if i < bucketCount-1 {
+ upperBound = bucketBoundary(uint8(i + 1))
+ } else {
+ upperBound = math.MaxInt64
+ }
+ buckets[i] = &bucketData{
+ Lower: bucketBoundary(uint8(i)),
+ Upper: upperBound,
+ N: n,
+ Pct: float64(n) * pctMult,
+ CumulativePct: float64(runningTotal) * pctMult,
+ GraphWidth: int(float64(n) * barsizeMult),
+ }
+ }
+ return &data{
+ Buckets: buckets,
+ Count: total,
+ Median: h.median(),
+ Mean: h.average(),
+ StandardDeviation: h.standardDeviation(),
+ }
+}
+
+func (h *histogram) html() template.HTML {
+ buf := new(bytes.Buffer)
+ if err := distTmpl().Execute(buf, h.newData()); err != nil {
+ buf.Reset()
+ log.Printf("net/trace: couldn't execute template: %v", err)
+ }
+ return template.HTML(buf.String())
+}
+
+var distTmplCache *template.Template
+var distTmplOnce sync.Once
+
+func distTmpl() *template.Template {
+ distTmplOnce.Do(func() {
+ // Input: data
+ distTmplCache = template.Must(template.New("distTmpl").Parse(`
+<table>
+<tr>
+ <td style="padding:0.25em">Count: {{.Count}}</td>
+ <td style="padding:0.25em">Mean: {{printf "%.0f" .Mean}}</td>
+ <td style="padding:0.25em">StdDev: {{printf "%.0f" .StandardDeviation}}</td>
+ <td style="padding:0.25em">Median: {{.Median}}</td>
+</tr>
+</table>
+<hr>
+<table>
+{{range $b := .Buckets}}
+{{if $b}}
+ <tr>
+ <td style="padding:0 0 0 0.25em">[</td>
+ <td style="text-align:right;padding:0 0.25em">{{.Lower}},</td>
+ <td style="text-align:right;padding:0 0.25em">{{.Upper}})</td>
+ <td style="text-align:right;padding:0 0.25em">{{.N}}</td>
+ <td style="text-align:right;padding:0 0.25em">{{printf "%#.3f" .Pct}}%</td>
+ <td style="text-align:right;padding:0 0.25em">{{printf "%#.3f" .CumulativePct}}%</td>
+ <td><div style="background-color: blue; height: 1em; width: {{.GraphWidth}};"></div></td>
+ </tr>
+{{end}}
+{{end}}
+</table>
+`))
+ })
+ return distTmplCache
+}