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How to use Histogram method of diff Package

Best Got code snippet using diff.Histogram

metric_diff.go

Source:metric_diff.go

...98	case pdata.MetricDataTypeSum:99		diffs = diff(diffs, expected.Sum().IsMonotonic(), actual.Sum().IsMonotonic(), "Sum IsMonotonic")100		diffs = diff(diffs, expected.Sum().AggregationTemporality(), actual.Sum().AggregationTemporality(), "Sum AggregationTemporality")101		diffs = diffNumberPts(diffs, expected.Sum().DataPoints(), actual.Sum().DataPoints())102	case pdata.MetricDataTypeHistogram:103		diffs = diff(diffs, expected.Histogram().AggregationTemporality(), actual.Histogram().AggregationTemporality(), "Histogram AggregationTemporality")104		diffs = diffHistogramPts(diffs, expected.Histogram().DataPoints(), actual.Histogram().DataPoints())105	}106	return diffs107}108func diffMetricDescriptor(109	diffs []*MetricDiff,110	expected pdata.Metric,111	actual pdata.Metric,112) ([]*MetricDiff, bool) {113	diffs = diff(diffs, expected.Name(), actual.Name(), "Metric Name")114	diffs = diff(diffs, expected.Description(), actual.Description(), "Metric Description")115	diffs = diff(diffs, expected.Unit(), actual.Unit(), "Metric Unit")116	return diffValues(diffs, expected.DataType(), actual.DataType(), "Metric Type")117}118func diffNumberPts(119	diffs []*MetricDiff,120	expected pdata.NumberDataPointSlice,121	actual pdata.NumberDataPointSlice,122) []*MetricDiff {123	var mismatch bool124	diffs, mismatch = diffValues(diffs, expected.Len(), actual.Len(), "NumberDataPointSlice len")125	if mismatch {126		return diffs127	}128	for i := 0; i < expected.Len(); i++ {129		diffs, mismatch = diffValues(diffs, expected.At(i).ValueType(), actual.At(i).ValueType(), "NumberDataPoint Value Type")130		if mismatch {131			return diffs132		}133		switch expected.At(i).ValueType() {134		case pdata.MetricValueTypeInt:135			diffs = diff(diffs, expected.At(i).IntVal(), actual.At(i).IntVal(), "NumberDataPoint Value")136		case pdata.MetricValueTypeDouble:137			diffs = diff(diffs, expected.At(i).DoubleVal(), actual.At(i).DoubleVal(), "NumberDataPoint Value")138		}139		diffExemplars(diffs, expected.At(i).Exemplars(), actual.At(i).Exemplars())140	}141	return diffs142}143func diffHistogramPts(144	diffs []*MetricDiff,145	expected pdata.HistogramDataPointSlice,146	actual pdata.HistogramDataPointSlice,147) []*MetricDiff {148	var mismatch bool149	diffs, mismatch = diffValues(diffs, expected.Len(), actual.Len(), "HistogramDataPointSlice len")150	if mismatch {151		return diffs152	}153	for i := 0; i < expected.Len(); i++ {154		diffs = diffDoubleHistogramPt(diffs, expected.At(i), actual.At(i))155	}156	return diffs157}158func diffDoubleHistogramPt(159	diffs []*MetricDiff,160	expected pdata.HistogramDataPoint,161	actual pdata.HistogramDataPoint,162) []*MetricDiff {163	diffs = diff(diffs, expected.Count(), actual.Count(), "HistogramDataPoint Count")164	diffs = diff(diffs, expected.Sum(), actual.Sum(), "HistogramDataPoint Sum")165	diffs = diff(diffs, expected.BucketCounts(), actual.BucketCounts(), "HistogramDataPoint BucketCounts")166	diffs = diff(diffs, expected.ExplicitBounds(), actual.ExplicitBounds(), "HistogramDataPoint ExplicitBounds")167	// todo LabelsMap()168	return diffExemplars(diffs, expected.Exemplars(), actual.Exemplars())169}170func diffExemplars(171	diffs []*MetricDiff,172	expected pdata.ExemplarSlice,173	actual pdata.ExemplarSlice,174) []*MetricDiff {175	var mismatch bool176	diffs, mismatch = diffValues(diffs, expected.Len(), actual.Len(), "ExemplarSlice len")177	if mismatch {178		return diffs179	}180	for i := 0; i < expected.Len(); i++ {...

histogram.go

Source:histogram.go

...11	"chromiumos/tast/local/chrome/metrics"12	"chromiumos/tast/testing"13)14// WasHWAccelUsed returns whether HW acceleration is used for certain action.15// initHistogram is the histogram obtained before the action.16// successValue is the bucket value of HW acceleration success case.17// Note that it returns true even if more than one successful count is observed.18// It is because in some cases, we occassionally observed more than one19// successful count (crbug.com/985068). To prevent it from reporting false20// negative result, we relax the condition from successful count == 1 to >= 1,21// regardless it may introuce some false positive result.22// TODO(crbug.com/985068#c5): follow up the improvement plan.23func WasHWAccelUsed(ctx context.Context, tconn *chrome.TestConn, initHistogram *metrics.Histogram, histogramName string, successValue int64) (bool, error) {24	// There are three valid cases.25	// 1. No histogram is updated. This is the case if HW Acceleration is disabled due to Chrome flag, ex. --disable-accelerated-video-decode.26	// 2. Histogram is updated with 15. This is the case if Chrome tries to initailize HW Acceleration but it fails because the codec is not supported on DUT.27	// 3. Histogram is updated with 0. This is the case if Chrome sucessfully initializes HW Acceleration.28	// err is not nil here if HW Acceleration is disabled and then Chrome doesn't try HW Acceleration initialization at all.29	// For the case 1, we pass a short time context to WaitForHistogramUpdate to avoid the whole test context (ctx) from reaching deadline.30	histogramDiff, err := metrics.WaitForHistogramUpdate(ctx, tconn, histogramName, initHistogram, 15*time.Second)31	if err != nil {32		// This is the first case; no histogram is updated.33		return false, nil34	}35	testing.ContextLogf(ctx, "Got update to %s histogram: %s", histogramName, histogramDiff)36	if len(histogramDiff.Buckets) > 1 {37		return false, errors.Wrapf(err, "unexpected histogram update: %v", histogramDiff)38	}39	diff := histogramDiff.Buckets[0]40	hwAccelUsed := diff.Min == successValue && diff.Max == successValue+1 && diff.Count >= 141	if !hwAccelUsed {42		testing.ContextLogf(ctx, "Histogram update: %s, if HW accel were used, it should be [[%d, %d) 1]", histogramDiff, successValue, successValue+1)43	} else if diff.Count > 1 {44		testing.ContextLog(ctx, "Note that more than one successful count is observed: ", diff.Count)45	}46	return hwAccelUsed, nil47}

Histogram

Using AI Code Generation

1import (2func main() {3	rand.Seed(time.Now().UnixNano())4	canvas := svg.New(os.Stdout)5	canvas.Start(w, h)6	canvas.Rect(0, 0, w, h, "fill:white")7	canvas.Gstyle("fill:none;stroke:blue;stroke-width:2")8	canvas.Line(0, h/2, w, h/2)9	canvas.Line(w/2, 0, w/2, h)10	canvas.Gend()11	canvas.Gstyle("fill:none;stroke:red;stroke-width:2")12	for i = 0; i < 100000; i++ {13		x = x + rand.NormFloat64()14		y = y + rand.NormFloat64()15		canvas.Line(w/2+x, h/2+y, w/2+x+1, h/2+y+1)16	}17	canvas.Gend()18	canvas.End()19}20import (21func main() {22	rand.Seed(time.Now().UnixNano())23	fmt.Printf("<rect x='0' y='0' width='%d' height='%d' fill='white'/>", w, h)24	fmt.Printf("<g style='fill:none;stroke:blue;stroke-width:2'>")25	fmt.Printf("<line x1='0' y1='%d' x2='%d' y2='%d'/>", h/2, w, h/2)26	fmt.Printf("<line x1='%d' y1='0' x2='%d' y2='%d'/>", w/2, w/2, h)27	fmt.Printf("</g>")28	fmt.Printf("<g style='fill:none;stroke:red;stroke-width:2'>

Histogram

Using AI Code Generation

1import (2func main() {3	rand.Seed(time.Now().UnixNano())4	canvas = svg.New(os.Stdout)5	canvas.Start(600, 400)6	canvas.Rect(0, 0, 600, 400, "fill:white")7	canvas.Gstyle("font-family:Verdana;font-size:12px")8	canvas.Text(10, 20, "Histogram", "fill:black")9	canvas.Gend()10	canvas.Gstyle("fill:steelblue;stroke-width:3;stroke:rgb(0,0,0)")11	h := make([]int, 10)12	for i := 0; i < 1000; i++ {13		h[rand.Intn(10)]++14	}15	canvas.Histogram(50, 50, 500, 300, h)16	canvas.Gend()17	canvas.End()18}19import (20func main() {21	rand.Seed(time.Now().UnixNano())22	canvas = svg.New(os.Stdout)23	canvas.Start(600, 400)24	canvas.Rect(0, 0, 600, 400, "fill:white")25	canvas.Gstyle("font-family:Verdana;font-size:12px")26	canvas.Text(10, 20, "Histogram", "fill:black")27	canvas.Gend()28	canvas.Gstyle("fill:steelblue;stroke-width:3;stroke:rgb(0,0,0)")29	h := make([]int, 10)30	for i := 0; i < 1000; i++ {31		h[rand.Intn(10)]++32	}33	canvas.Histogram(50, 50, 500, 300, h)34	canvas.Gend()35	canvas.End()36}37import (38func main() {39	rand.Seed(time.Now().UnixNano())40	canvas = svg.New(os.Stdout)41	canvas.Start(600

Histogram

Using AI Code Generation

1import (2func main() {3	if len(os.Args) == 2 {4		if width, err = strconv.Atoi(os.Args[1]); err != nil {5			fmt.Println("Invalid width")6			os.Exit(1)7		}8	} else {9	}10	canvas := svg.New(os.Stdout)11	canvas.Start(width, height)12	canvas.Rect(0, 0, width, height, "fill:rgb(255,255,255)")13	canvas.Gstyle("fill:rgb(255,0,0);stroke-width:3;stroke:rgb(0,0,0)")14	canvas.Translate(0, height)15	canvas.Line(0, 0, width, 0)16	canvas.Line(0, 0, 0, -height)17	canvas.Gend()18	canvas.Gstyle("fill:rgb(0,0,0);stroke-width:3;stroke:rgb(0,0,0)")19	canvas.Translate(0, 0)20	d := new(diff)21	rand.Seed(42)22	for i := 0; i < 1000; i++ {23		a[i] = rand.Intn(1000)24	}25	d.Histogram(a[:], 100, width-20, height-20, width-10, 10, canvas)26	canvas.Gend()27	canvas.End()28}29import (30func main() {31	if len(os.Args) == 2 {32		if width, err = strconv.Atoi(os.Args[1]); err != nil {33			fmt.Println("Invalid width")34			os.Exit(1)35		}36	} else {37	}38	canvas := svg.New(os.Stdout)

Histogram

Using AI Code Generation

1import (2func main() {3    fmt.Println("Enter the number of elements in the array")4    fmt.Scanln(&a)5    arr := make([]int, a)6    fmt.Println("Enter the elements in the array")7    for i = 0; i < a; i++ {8        fmt.Scanln(&arr[i])9    }10    fmt.Println("Enter the number of classes")11    fmt.Scanln(&b)12    arr2 := make([]int, b)13    fmt.Println("Enter the elements in the class")14    for i = 0; i < b; i++ {15        fmt.Scanln(&arr2[i])16    }17    fmt.Println("Enter the upper and lower limit")18    fmt.Scanln(&c, &d)19    e = (c - d) / b20    fmt.Println("The class interval is", e)21    f = int(math.Ceil(float64(c) / float64(e)))22    g = int(math.Floor(float64(d) / float64(e)))23    fmt.Println("The upper and lower limit of the class is", f, g)24    arr3 := make([]int, b)25    for i = 0; i < a; i++ {26        if arr[i] <= f && arr[i] >= g {27        } else {28        }29    }30    fmt.Println("The frequency distribution is")31    for i = 0; i < b; i++ {32        fmt.Println(g, "to", f, "is", arr3[i])33    }34    for i = 0; i < b; i++ {35    }36    fmt.Println("The total number of elements is", h)37    for j = 0; j < b; j++ {38    }39    fmt.Println("The mean is", i/h)40}

Histogram

Using AI Code Generation

1import (2type diff struct {3}4func newDiff() *diff {5    return &diff{}6}7func (d *diff) Add(x float64) {8    d.data = append(d.data, x)9}10func (d *diff) Mean() float64 {11    if len(d.data) == 0 {12        return math.NaN()13    }14    for _, x := range d.data {15    }16    return sum / float64(len(d.data))17}18func (d *diff) Variance() float64 {19    if len(d.data) == 0 {20        return math.NaN()21    }22    mean := d.Mean()23    for _, x := range d.data {24        sum += (x - mean) * (x - mean)25    }26    return sum / float64(len(d.data))27}28func (d *diff) StdDev() float64 {29    return math.Sqrt(d.Variance())30}31func (d *diff) Histogram() map[float64]int {32    if len(d.data) == 0 {33    }34    sort.Float64s(d.data)35    min = math.Inf(1)36    max = math.Inf(-1)37    for _, x := range d.data {38        if x < min {39        }40        if x > max {41        }42    }43    binSize := (max - min) / numBins44    bins := make(map[float64]int)45    for _, x := range d.data {46        bin := min + binSize*math.Floor((x-min)/binSize)47    }48}49func main() {50    rand.Seed(time.Now().UnixNano())51    d := newDiff()52    for i := 0; i < 1000; i++ {53        d.Add(rand.NormFloat64())54    }55    fmt.Printf("mean: %f56", d.Mean())57    fmt.Printf("stddev: %f58", d.StdDev())59    fmt.Printf("histogram: %v60", d.Histogram())61}

Histogram

Using AI Code Generation

1import (2func main() {3	h := NewHistogram()4	h.Add(1)5	h.Add(1)6	h.Add(1)7	h.Add(1)8	h.Add(2)9	h.Add(2)10	h.Add(2)11	h.Add(3)12	h.Add(3)13	h.Add(4)14	h.Add(4)15	h.Add(4)16	h.Add(4)17	h.Add(5)18	h.Add(5)19	h.Add(5)20	h.Add(5)21	h.Add(5)22	h.Add(6)

Histogram

Using AI Code Generation

1import (2func diffImage(img1 gocv.Mat, img2 gocv.Mat) gocv.Mat {3	diff := gocv.NewMat()4	gocv.AbsDiff(img1, img2, &diff)5}6func histogram(img gocv.Mat) gocv.Mat {7	hist := gocv.NewMat()8	gocv.CalcHist([]gocv.Mat{img}, []int{0}, gocv.NewMat(), &hist, []int{256}, []float32{0, 256}, false)9}10func similarity(img1 gocv.Mat, img2 gocv.Mat) float64 {11	diff := diffImage(img1, img2)12	hist := histogram(diff)13	for i := 0; i < hist.Rows(); i++ {14		sum += hist.GetFloatAt(i, 0)15	}16	sim := (1 - (sum / float64(diff.Rows()*diff.Cols()))) * 10017}18func main() {19	img1, err := gocv.IMRead("images/1.jpg", gocv.IMReadGrayScale)20	if err != nil {21		log.Fatalf("error reading image from file: %v", err)22	}23	defer img1.Close()24	img2, err := gocv.IMRead("images/2

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