func bench(requests, concurrency int, image string) {
start := time.Now()
timings := make([]float64, requests)
completeCh := make(chan time.Duration)
current := 0
go func() {
for timing := range completeCh {
timings = append(timings, timing.Seconds())
current++
percent := float64(current) / float64(requests) * 100
fmt.Printf("[%3.f%%] %d/%d containers started\n", percent, current, requests)
}
}()
session(requests, concurrency, image, completeCh)
close(completeCh)
total := time.Since(start)
p50th, _ := stats.Median(timings)
p90th, _ := stats.Percentile(timings, 90)
p99th, _ := stats.Percentile(timings, 99)
fmt.Println("")
fmt.Printf("Time taken for tests: %s\n", total.String())
fmt.Printf("Time per container: %vms [50th] | %vms [90th] | %vms [99th]\n", int(p50th*1000), int(p90th*1000), int(p99th*1000))
}
// Finalize calculation of the risk using available datapoints
func riskFinalize(op opContext, rs *slib.RRAServiceRisk) error {
var (
rvals []float64
err error
)
for _, x := range rs.Scenarios {
// If the scenario had no data, don't include it in the
// final scoring
if x.NoData {
continue
}
rvals = append(rvals, x.Score)
}
// Note the highest business impact value that was determined from
// the RRA. This can be used as an indication of the business impact
// for the service.
rs.Risk.Impact = rs.UsedRRAAttrib.Impact
rs.Risk.ImpactLabel, err = slib.ImpactLabelFromValue(rs.Risk.Impact)
if err != nil {
return err
}
if len(rvals) == 0 {
// This can occur if we have no metric data, including no valid
// information in the RRA
logf("error in risk calculation: %q has no valid scenarios", rs.RRA.Name)
rs.Risk.Median = 0.0
rs.Risk.Average = 0.0
rs.Risk.WorstCase = 0.0
rs.Risk.MedianLabel = "unknown"
rs.Risk.AverageLabel = "unknown"
rs.Risk.WorstCaseLabel = "unknown"
rs.Risk.DataClass, err = slib.DataValueFromLabel(rs.RRA.DefData)
return nil
}
rs.Risk.Median, err = stats.Median(rvals)
if err != nil {
return err
}
rs.Risk.MedianLabel = slib.NormalLabelFromValue(rs.Risk.Median)
rs.Risk.Average, err = stats.Mean(rvals)
if err != nil {
return err
}
rs.Risk.AverageLabel = slib.NormalLabelFromValue(rs.Risk.Average)
rs.Risk.WorstCase, err = stats.Max(rvals)
if err != nil {
return err
}
rs.Risk.WorstCaseLabel = slib.NormalLabelFromValue(rs.Risk.WorstCase)
rs.Risk.DataClass, err = slib.DataValueFromLabel(rs.RRA.DefData)
if err != nil {
return err
}
return nil
}
func main() {
d := stats.LoadRawData([]interface{}{1.1, "2", 3.0, 4, "5"})
a, _ := stats.Min(d)
fmt.Println(a) // 1.1
a, _ = stats.Max(d)
fmt.Println(a) // 5
a, _ = stats.Sum([]float64{1.1, 2.2, 3.3})
fmt.Println(a) // 6.6
a, _ = stats.Mean([]float64{1, 2, 3, 4, 5})
fmt.Println(a) // 3
a, _ = stats.Median([]float64{1, 2, 3, 4, 5, 6, 7})
fmt.Println(a) // 4
m, _ := stats.Mode([]float64{5, 5, 3, 3, 4, 2, 1})
fmt.Println(m) // [5 3]
a, _ = stats.PopulationVariance([]float64{1, 2, 3, 4, 5})
fmt.Println(a) // 2
a, _ = stats.SampleVariance([]float64{1, 2, 3, 4, 5})
fmt.Println(a) // 2.5
a, _ = stats.MedianAbsoluteDeviationPopulation([]float64{1, 2, 3})
fmt.Println(a) // 1
a, _ = stats.StandardDeviationPopulation([]float64{1, 2, 3})
fmt.Println(a) // 0.816496580927726
a, _ = stats.StandardDeviationSample([]float64{1, 2, 3})
fmt.Println(a) // 1
a, _ = stats.Percentile([]float64{1, 2, 3, 4, 5}, 75)
fmt.Println(a) // 4
a, _ = stats.PercentileNearestRank([]float64{35, 20, 15, 40, 50}, 75)
fmt.Println(a) // 40
c := []stats.Coordinate{
{1, 2.3},
{2, 3.3},
{3, 3.7},
{4, 4.3},
{5, 5.3},
}
r, _ := stats.LinearRegression(c)
fmt.Println(r) // [{1 2.3800000000000026} {2 3.0800000000000014} {3 3.7800000000000002} {4 4.479999999999999} {5 5.179999999999998}]
r, _ = stats.ExponentialRegression(c)
fmt.Println(r) // [{1 2.5150181024736638} {2 3.032084111136781} {3 3.6554544271334493} {4 4.406984298281804} {5 5.313022222665875}]
r, _ = stats.LogarithmicRegression(c)
fmt.Println(r) // [{1 2.1520822363811702} {2 3.3305559222492214} {3 4.019918836568674} {4 4.509029608117273} {5 4.888413396683663}]
s, _ := stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 3, false)
fmt.Println(s) // [0.2,0.4,0.3]
s, _ = stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 10, true)
fmt.Println(s) // [0.2,0.2,0.4,0.1,0.2,0.4,0.3,0.2,0.2,0.1]
q, _ := stats.Quartile([]float64{7, 15, 36, 39, 40, 41})
fmt.Println(q) // {15 37.5 40}
iqr, _ := stats.InterQuartileRange([]float64{102, 104, 105, 107, 108, 109, 110, 112, 115, 116, 118})
fmt.Println(iqr) // 10
mh, _ := stats.Midhinge([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
fmt.Println(mh) // 7.5
tr, _ := stats.Trimean([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
fmt.Println(tr) // 7.25
o, _ := stats.QuartileOutliers([]float64{-1000, 1, 3, 4, 4, 6, 6, 6, 6, 7, 8, 15, 18, 100})
fmt.Printf("%+v\n", o) // {Mild:[15 18] Extreme:[-1000 100]}
gm, _ := stats.GeometricMean([]float64{10, 51.2, 8})
fmt.Println(gm) // 15.999999999999991
hm, _ := stats.HarmonicMean([]float64{1, 2, 3, 4, 5})
fmt.Println(hm) // 2.18978102189781
a, _ = stats.Round(2.18978102189781, 3)
fmt.Println(a) // 2.189
}
func main() {
flag.Parse()
n := *concurrency
m := *total / n
fmt.Printf("concurrency: %d\nrequests per client: %d\n\n", n, m)
args := prepareArgs()
b, _ := proto.Marshal(args)
fmt.Printf("message size: %d bytes\n\n", len(b))
var wg sync.WaitGroup
wg.Add(n * m)
var trans uint64
var transOK uint64
d := make([][]int64, n, n)
//it contains warmup time but we can ignore it
totalT := time.Now().UnixNano()
for i := 0; i < n; i++ {
dt := make([]int64, 0, m)
d = append(d, dt)
go func(i int) {
conn, err := grpc.Dial(*host, grpc.WithInsecure())
if err != nil {
log.Fatalf("did not connect: %v", err)
}
c := NewHelloClient(conn)
//warmup
for j := 0; j < 5; j++ {
c.Say(context.Background(), args)
}
for j := 0; j < m; j++ {
t := time.Now().UnixNano()
reply, err := c.Say(context.Background(), args)
t = time.Now().UnixNano() - t
d[i] = append(d[i], t)
if err == nil && *(reply.Field1) == "OK" {
atomic.AddUint64(&transOK, 1)
}
atomic.AddUint64(&trans, 1)
wg.Done()
}
conn.Close()
}(i)
}
wg.Wait()
totalT = time.Now().UnixNano() - totalT
totalT = totalT / 1000000
fmt.Printf("took %d ms for %d requests", totalT, n*m)
totalD := make([]int64, 0, n*m)
for _, k := range d {
totalD = append(totalD, k...)
}
totalD2 := make([]float64, 0, n*m)
for _, k := range totalD {
totalD2 = append(totalD2, float64(k))
}
mean, _ := stats.Mean(totalD2)
median, _ := stats.Median(totalD2)
max, _ := stats.Max(totalD2)
min, _ := stats.Min(totalD2)
fmt.Printf("sent requests : %d\n", n*m)
fmt.Printf("received requests : %d\n", atomic.LoadUint64(&trans))
fmt.Printf("received requests_OK : %d\n", atomic.LoadUint64(&transOK))
fmt.Printf("throughput (TPS) : %d\n", int64(n*m)*1000/totalT)
fmt.Printf("mean: %.f ns, median: %.f ns, max: %.f ns, min: %.f ns\n", mean, median, max, min)
fmt.Printf("mean: %d ms, median: %d ms, max: %d ms, min: %d ms\n", int64(mean/1000000), int64(median/1000000), int64(max/1000000), int64(min/1000000))
}
func main() {
flag.Parse()
n := *concurrency
m := *total / n
fmt.Printf("concurrency: %d\nrequests per client: %d\n\n", n, m)
serviceMethodName := "Hello.Say"
args := prepareArgs()
b := make([]byte, 1024*1024)
i, _ := args.MarshalTo(b)
fmt.Printf("message size: %d bytes\n\n", i)
var wg sync.WaitGroup
wg.Add(n * m)
var trans uint64
var transOK uint64
d := make([][]int64, n, n)
//it contains warmup time but we can ignore it
totalT := time.Now().UnixNano()
for i := 0; i < n; i++ {
dt := make([]int64, 0, m)
d = append(d, dt)
go func(i int) {
s := &rpcx.DirectClientSelector{Network: "tcp", Address: *host}
client := rpcx.NewClient(s)
client.ClientCodecFunc = codec.NewProtobufClientCodec
var reply BenchmarkMessage
//warmup
for j := 0; j < 5; j++ {
client.Call(serviceMethodName, args, &reply)
}
for j := 0; j < m; j++ {
t := time.Now().UnixNano()
err := client.Call(serviceMethodName, args, &reply)
t = time.Now().UnixNano() - t
d[i] = append(d[i], t)
if err == nil && reply.Field1 == "OK" {
atomic.AddUint64(&transOK, 1)
}
atomic.AddUint64(&trans, 1)
wg.Done()
}
client.Close()
}(i)
}
wg.Wait()
totalT = time.Now().UnixNano() - totalT
totalT = totalT / 1000000
fmt.Printf("took %d ms for %d requests", totalT, n*m)
totalD := make([]int64, 0, n*m)
for _, k := range d {
totalD = append(totalD, k...)
}
totalD2 := make([]float64, 0, n*m)
for _, k := range totalD {
totalD2 = append(totalD2, float64(k))
}
mean, _ := stats.Mean(totalD2)
median, _ := stats.Median(totalD2)
max, _ := stats.Max(totalD2)
min, _ := stats.Min(totalD2)
fmt.Printf("sent requests : %d\n", n*m)
fmt.Printf("received requests : %d\n", atomic.LoadUint64(&trans))
fmt.Printf("received requests_OK : %d\n", atomic.LoadUint64(&transOK))
fmt.Printf("throughput (TPS) : %d\n", int64(n*m)*1000/totalT)
fmt.Printf("mean: %.f ns, median: %.f ns, max: %.f ns, min: %.f ns\n", mean, median, max, min)
fmt.Printf("mean: %d ms, median: %d ms, max: %d ms, min: %d ms\n", int64(mean/1000000), int64(median/1000000), int64(max/1000000), int64(min/1000000))
}
func main() {
t := time.Now()
fmt.Println(t.Format(time.RFC3339))
rand.Seed(1)
// Read in data
readData()
// Set one level with all row criteria,
// this is used to start the set creation
levelOne = fullOneLevel()
//levels = fullTwoLevel()
outputRowCriteria(levels)
// experiment variables
rand_numSets = 1000
rand_maxSetMembers = 5
maxExperiments = 1
var expMin []float64
var expMax []float64
scoreCutoff = -0.89
rowThreshhold = 2
zScore = 2.58
for experiment := 1; experiment <= maxExperiments; experiment++ {
// experiment variables, changes per experiment
rand_numSets += 0
rand_maxSetMembers += 0
scoreCutoff += -0.00
zScore += 0.0
// Setup experiment variables
var scores []scoreResult
var minScore float64 = -100
var maxScore float64 = 0
levels = fullFourLevel() //randLevels()
fmt.Printf("sets count: %d, max set members: %d, level 1 count: %d, rowThreshhold: %d, scoreCutoff: %f, zScore: %f\n", len(levels), rand_maxSetMembers+2, len(levelOne), rowThreshhold, scoreCutoff, zScore)
for dataSetId := 1; dataSetId <= datasets; dataSetId++ {
s := levelEval(dataSetId)
sort.Sort(scoreResults(s))
// s contains a list of scores for one dataset, sorted
// this is were we can get some info on that data
//outputScoreList(s)
if len(s) > 0 {
//var sEval = evaluateScores(s)
// pick the top score
var sEval = s[0]
scores = append(scores, sEval)
fmt.Printf("%d, %f \n", sEval.dataSetId, sEval.score)
if minScore < sEval.score {
minScore = sEval.score
}
if maxScore > sEval.score {
maxScore = sEval.score
}
}
// For all score in this set write out the median and standard deviation
var set []float64
for _, scoreItem := range s {
if scoreItem.score < 0.0 {
set = append(set, scoreItem.score)
}
}
var median, _ = stats.Median(set)
var sd, _ = stats.StandardDeviation(set)
var min, _ = stats.Min(set)
var max, _ = stats.Max(set)
fmt.Printf("dataset: %d, median: %f, sd: %f, min: %f, max: %f, len: %d\n", dataSetId, median, sd, min, max, len(set))
}
expMin = append(expMin, minScore)
expMax = append(expMax, maxScore)
//scoreCutoff = (minScore * (percentRofMin / 100.0)) + minScore
//fmt.Printf(" scoreCutoff: %f \n", scoreCutoff)
outputScores(scores)
// Write output file
outputResults(scores)
// Compare to training truth data
// compareTrainingDataWithResults()
}
t = time.Now()
fmt.Println(t.Format(time.RFC3339))
// Output min max scores per experiment
for _, each := range expMin {
fmt.Printf("min: %f, ", each)
}
fmt.Println()
for _, each := range expMax {
fmt.Printf("max: %f, ", each)
}
}
func (c *cmdReport2) getFeatures(geneSnpChan chan SNPArr) {
w, err := os.Create(c.prefix + ".detectable.gene.csv")
if err != nil {
log.Fatalln(err)
}
defer w.Close()
w.WriteString("patric_id,genome,figfam,sample,pi,depth\n")
fn := func(txn *lmdb.Txn) error {
dbi, err := txn.OpenDBI("feature", 0)
if err != nil {
return err
}
for gs := range geneSnpChan {
if len(gs.Arr) < 100 {
continue
}
k := gs.Key
v, err := txn.Get(dbi, k)
if err != nil {
return err
}
f := Feature{}
if err := msgpack.Unmarshal(v, &f); err != nil {
return err
}
seqLen := f.End - f.Start + 1
// calculate median of depth
depthArr := []float64{}
piArr := []float64{}
for _, snp := range gs.Arr {
pos := snp.Position - f.Start
if f.IsComplementaryStrand() {
pos = seqLen - 1 - pos
}
if (pos+1)%3 == 0 {
depthArr = append(depthArr, float64(len(snp.Bases)))
piArr = append(piArr, snp.Pi())
}
}
depthMedian, _ := stats.Median(depthArr)
sort.Float64s(piArr)
piMean, _ := stats.Mean(piArr[10 : len(piArr)-10])
w.WriteString(fmt.Sprintf("%s,%s,%s,%s,%g,%g\n",
f.PatricID,
f.TaxID,
f.FigfamID,
c.prefix,
piMean,
depthMedian))
}
return nil
}
err = c.featureDB.View(fn)
if err != nil {
log.Panicln(err)
}
}