analysis/app/trend.go - perf - Git at Google

 // Copyright 2017 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.

 // Loosely based on github.com/aclements/go-misc/benchplot

 package app

 import (
 	"bytes"
 	"encoding/json"
 	"fmt"
 	"html/template"
 	"io/ioutil"
 	"math"
 	"net/http"
 	"path/filepath"
 	"sort"
 	"strconv"
 	"strings"

 	"github.com/aclements/go-gg/generic/slice"
 	"github.com/aclements/go-gg/ggstat"
 	"github.com/aclements/go-gg/table"
 	"golang.org/x/net/context"
 	"golang.org/x/perf/storage"
 )

 // trend handles /trend.
 // With no query, it prints the list of recent uploads containg a "trend" key.
 // With a query, it shows a graph of the matching benchmark results.
 func (a *App) trend(w http.ResponseWriter, r *http.Request) {
 	ctx := requestContext(r)

 	if err := r.ParseForm(); err != nil {
 		http.Error(w, err.Error(), 500)
 		return
 	}

 	q := r.Form.Get("q")

 	tmpl, err := ioutil.ReadFile(filepath.Join(a.BaseDir, "template/trend.html"))
 	if err != nil {
 		http.Error(w, err.Error(), 500)
 		return
 	}

 	t, err := template.New("main").Parse(string(tmpl))
 	if err != nil {
 		http.Error(w, err.Error(), 500)
 		return
 	}

 	opt := plotOptions{
 		x:   r.Form.Get("x"),
 		raw: r.Form.Get("raw") == "1",
 	}

 	data := a.trendQuery(ctx, q, opt)

 	w.Header().Set("Content-Type", "text/html; charset=utf-8")
 	if err := t.Execute(w, data); err != nil {
 		http.Error(w, err.Error(), 500)
 		return
 	}
 }

 // trendData is the struct passed to the trend.html template.
 type trendData struct {
 	Q            string
 	Error        string
 	TrendUploads []storage.UploadInfo
 	PlotData     template.JS
 	PlotType     template.JS
 }

 // trendData computes the values for the template and returns a trendData for display.
 func (a *App) trendQuery(ctx context.Context, q string, opt plotOptions) *trendData {
 	d := &trendData{Q: q}
 	if q == "" {
 		ul := a.StorageClient.ListUploads(`trend>`, []string{"by", "upload-time", "trend"}, 16)
 		defer ul.Close()
 		for ul.Next() {
 			d.TrendUploads = append(d.TrendUploads, ul.Info())
 		}
 		if err := ul.Err(); err != nil {
 			errorf(ctx, "failed to fetch recent trend uploads: %v", err)
 		}
 		return d
 	}

 	// TODO(quentin): Chunk query based on matching upload IDs.
 	res := a.StorageClient.Query(q)
 	defer res.Close()
 	t, resultCols := queryToTable(res)
 	if err := res.Err(); err != nil {
 		errorf(ctx, "failed to read query results: %v", err)
 		d.Error = fmt.Sprintf("failed to read query results: %v", err)
 		return d
 	}
 	for _, col := range []string{"commit", "commit-time", "branch", "name"} {
 		if !hasStringColumn(t, col) {
 			d.Error = fmt.Sprintf("results missing %q label", col)
 			return d
 		}
 	}
 	if opt.x != "" && !hasStringColumn(t, opt.x) {
 		d.Error = fmt.Sprintf("results missing x label %q", opt.x)
 		return d
 	}
 	data := plot(t, resultCols, opt)

 	// TODO(quentin): Give the user control over across vs. plotting in separate graphs, instead of only showing one graph with ns/op for each benchmark.

 	if opt.raw {
 		data = table.MapTables(data, func(_ table.GroupID, t *table.Table) *table.Table {
 			// From http://tristen.ca/hcl-picker/#/hlc/9/1.13/F1796F/B3EC6C
 			colors := []string{"#F1796F", "#B3EC6C", "#F67E9D", "#6CEB98", "#E392CB", "#0AE4C6", "#B7ABEC", "#16D7E9", "#75C4F7"}
 			colorIdx := 0
 			partColors := make(map[string]string)
 			styles := make([]string, t.Len())
 			for i, part := range t.MustColumn("upload-part").([]string) {
 				if _, ok := partColors[part]; !ok {
 					partColors[part] = colors[colorIdx]
 					colorIdx++
 					if colorIdx >= len(colors) {
 						colorIdx = 0
 					}
 				}
 				styles[i] = "color: " + partColors[part]
 			}
 			return table.NewBuilder(t).Add("style", styles).Done()
 		})
 		columns := []column{
 			{Name: "commit-index"},
 			{Name: "result"},
 			{Name: "style", Role: "style"},
 			{Name: "commit", Role: "tooltip"},
 		}
 		d.PlotData = tableToJS(data.Table(data.Tables()[0]), columns)
 		d.PlotType = "ScatterChart"
 		return d
 	}

 	// Pivot all of the benchmarks into columns of a single table.
 	ar := &aggResults{
 		Across: "name",
 		Values: []string{"filtered normalized mean result", "normalized mean result", "normalized median result", "normalized min result", "normalized max result"},
 	}
 	data = ggstat.Agg("commit", "branch", "commit-index")(ar.agg).F(data)

 	tables := data.Tables()
 	infof(ctx, "tables: %v", tables)
 	columns := []column{{Name: "commit-index"}}
 	for _, prefix := range ar.Prefixes {
 		if len(ar.Prefixes) == 1 {
 			columns = append(columns,
 				column{Name: prefix + "/normalized mean result"},
 				column{Name: prefix + "/normalized min result", Role: "interval"},
 				column{Name: prefix + "/normalized max result", Role: "interval"},
 				column{Name: prefix + "/normalized median result"},
 			)
 		}
 		columns = append(columns,
 			column{Name: prefix + "/filtered normalized mean result"},
 		)
 	}
 	d.PlotData = tableToJS(data.Table(tables[0]), columns)
 	d.PlotType = "LineChart"
 	return d
 }

 // queryToTable converts the result of a Query into a Table for later processing.
 // Each label is placed in a column named after the key.
 // Each metric is placed in a separate result column named after the unit.
 func queryToTable(q *storage.Query) (t *table.Table, resultCols []string) {
 	var names []string
 	labels := make(map[string][]string)
 	results := make(map[string][]float64)
 	i := 0
 	for q.Next() {
 		res := q.Result()
 		// TODO(quentin): Handle multiple results with the same name but different NameLabels.
 		names = append(names, res.NameLabels["name"])
 		for k := range res.Labels {
 			if labels[k] == nil {
 				labels[k] = make([]string, i)
 			}
 		}
 		for k := range labels {
 			labels[k] = append(labels[k], res.Labels[k])
 		}
 		f := strings.Fields(res.Content)
 		metrics := make(map[string]float64)
 		for j := 2; j+2 <= len(f); j += 2 {
 			val, err := strconv.ParseFloat(f[j], 64)
 			if err != nil {
 				continue
 			}
 			unit := f[j+1]
 			if results[unit] == nil {
 				results[unit] = make([]float64, i)
 			}
 			metrics[unit] = val
 		}
 		for k := range results {
 			results[k] = append(results[k], metrics[k])
 		}
 		i++
 	}

 	tab := new(table.Builder).Add("name", names)

 	for k, v := range labels {
 		tab.Add(k, v)
 	}
 	for k, v := range results {
 		tab.Add(k, v)
 		resultCols = append(resultCols, k)
 	}

 	sort.Strings(resultCols)

 	return tab.Done(), resultCols
 }

 type plotOptions struct {
 	// x names the column to use for the X axis.
 	// If unspecified, "commit" is used.
 	x string
 	// raw will return the raw points without any averaging/smoothing.
 	// The only result column will be "result".
 	raw bool
 	// correlate will use the string column "upload-part" as an indication that results came from the same machine. Commits present in multiple parts will be used to correlate results.
 	correlate bool
 }

 // plot takes raw benchmark data in t and produces a Grouping object containing filtered, normalized metric results for a graph.
 // t must contain the string columns "commit", "commit-time", "branch". resultCols specifies the names of float64 columns containing metric results.
 // The returned grouping has columns "commit", "commit-time", "commit-index", "branch", "metric", "normalized min result", "normalized max result", "normalized mean result", "filtered normalized mean result".
 // This is roughly the algorithm from github.com/aclements/go-misc/benchplot
 func plot(t table.Grouping, resultCols []string, opt plotOptions) table.Grouping {
 	nrows := len(table.GroupBy(t, "name").Tables())

 	// Turn ordered commit-time into a "commit-index" column.
 	if opt.x == "" {
 		opt.x = "commit"
 	}
 	// TODO(quentin): One SortBy call should do this, but
 	// sometimes it seems to sort by the second column instead of
 	// the first. Do them in separate steps until SortBy is fixed.
 	t = table.SortBy(t, opt.x)
 	t = table.SortBy(t, "commit-time")
 	t = colIndex{col: opt.x}.F(t)

 	// Unpivot all of the metrics into one column.
 	t = table.Unpivot(t, "metric", "result", resultCols...)

 	// TODO(quentin): Let user choose which metric(s) to keep.
 	t = table.FilterEq(t, "metric", "ns/op")

 	if opt.raw {
 		return t
 	}

 	// Average each result at each commit (but keep columns names
 	// the same to keep things easier to read).
 	t = ggstat.Agg("commit", "name", "metric", "branch", "commit-index")(ggstat.AggMean("result"), ggstat.AggQuantile("median", .5, "result"), ggstat.AggMin("result"), ggstat.AggMax("result")).F(t)
 	y := "mean result"

 	// Normalize to earliest commit on master. It's important to
 	// do this before the geomean if there are commits missing.
 	// Unfortunately, that also means we have to *temporarily*
 	// group by name and metric, since the geomean needs to be
 	// done on a different grouping.
 	t = table.GroupBy(t, "name", "metric")
 	t = ggstat.Normalize{X: "branch", By: firstMasterIndex, Cols: []string{"mean result", "median result", "max result", "min result"}, DenomCols: []string{"mean result", "mean result", "mean result", "mean result"}}.F(t)
 	y = "normalized " + y
 	for _, col := range []string{"mean result", "median result", "max result", "min result"} {
 		t = table.Remove(t, col)
 	}
 	t = table.Ungroup(table.Ungroup(t))

 	// Compute geomean for each metric at each commit if there's
 	// more than one benchmark.
 	if len(table.GroupBy(t, "name").Tables()) > 1 {
 		gt := removeNaNs(t, y)
 		gt = ggstat.Agg("commit", "metric", "branch", "commit-index")(ggstat.AggGeoMean(y, "normalized median result"), ggstat.AggMin("normalized min result"), ggstat.AggMax("normalized max result")).F(gt)
 		gt = table.MapTables(gt, func(_ table.GroupID, t *table.Table) *table.Table {
 			return table.NewBuilder(t).AddConst("name", " geomean").Done()
 		})
 		gt = table.Rename(gt, "geomean "+y, y)
 		gt = table.Rename(gt, "geomean normalized median result", "normalized median result")
 		gt = table.Rename(gt, "min normalized min result", "normalized min result")
 		gt = table.Rename(gt, "max normalized max result", "normalized max result")
 		t = table.Concat(t, gt)
 		nrows++
 	}

 	// Filter the data to reduce noise.
 	t = table.GroupBy(t, "name", "metric")
 	t = kza{y, 15, 3}.F(t)
 	y = "filtered " + y
 	t = table.Ungroup(table.Ungroup(t))

 	return t
 }

 // hasStringColumn returns whether t has a []string column called col.
 func hasStringColumn(t table.Grouping, col string) bool {
 	c := t.Table(t.Tables()[0]).Column(col)
 	if c == nil {
 		return false
 	}
 	_, ok := c.([]string)
 	return ok
 }

 // aggResults pivots the table, taking the columns in Values and making a new column for each distinct value in Across.
 // aggResults("in", []string{"value1", "value2"} will reshape a table like
 //   in		value1	value2
 //   one	1	2
 //   two	3	4
 // and will turn in into a table like
 //   one/value1	one/value2	two/value1	two/value2
 //   1		2		3		4
 // across columns must be []string, and value columns must be []float64.
 type aggResults struct {
 	// Across is the name of the column whose values are the column prefix.
 	Across string
 	// Values is the name of the columns to split.
 	Values []string
 	// Prefixes is filled in after calling agg with the name of each prefix that was found.
 	Prefixes []string
 }

 // agg implements ggstat.Aggregator and allows using a with ggstat.Agg.
 func (a *aggResults) agg(input table.Grouping, output *table.Builder) {
 	var prefixes []string
 	rows := len(input.Tables())
 	columns := make(map[string][]float64)
 	for i, gid := range input.Tables() {
 		var vs [][]float64
 		for _, col := range a.Values {
 			vs = append(vs, input.Table(gid).MustColumn(col).([]float64))
 		}
 		as := input.Table(gid).MustColumn(a.Across).([]string)
 		for j, prefix := range as {
 			for k, col := range a.Values {
 				key := prefix + "/" + col
 				if columns[key] == nil {
 					if k == 0 {
 						// First time we've seen this prefix, track it.
 						prefixes = append(prefixes, prefix)
 					}
 					columns[key] = make([]float64, rows)
 					for i := range columns[key] {
 						columns[key][i] = math.NaN()
 					}
 				}
 				columns[key][i] = vs[k][j]
 			}
 		}
 	}
 	sort.Strings(prefixes)
 	a.Prefixes = prefixes
 	for _, prefix := range prefixes {
 		for _, col := range a.Values {
 			key := prefix + "/" + col
 			output.Add(key, columns[key])
 		}
 	}
 }

 // firstMasterIndex returns the index of the first commit on master.
 // This is used to find the value to normalize against.
 func firstMasterIndex(bs []string) int {
 	return slice.Index(bs, "master")
 }

 // colIndex is a gg.Stat that adds a column called "commit-index" sequentially counting unique values of the column "commit".
 type colIndex struct {
 	// col specifies the string column to assign indices to. If unspecified, "commit" will be used.
 	col string
 }

 func (ci colIndex) F(g table.Grouping) table.Grouping {
 	if ci.col == "" {
 		ci.col = "commit"
 	}
 	return table.MapTables(g, func(_ table.GroupID, t *table.Table) *table.Table {
 		idxs := make([]int, t.Len())
 		last, idx := "", -1
 		for i, hash := range t.MustColumn(ci.col).([]string) {
 			if hash != last {
 				idx++
 				last = hash
 			}
 			idxs[i] = idx
 		}
 		t = table.NewBuilder(t).Add("commit-index", idxs).Done()

 		return t
 	})
 }

 // removeNaNs returns a new Grouping with rows containg NaN in col removed.
 func removeNaNs(g table.Grouping, col string) table.Grouping {
 	return table.Filter(g, func(result float64) bool {
 		return !math.IsNaN(result)
 	}, col)
 }

 // kza implements adaptive Kolmogorov-Zurbenko filtering on the data in X.
 type kza struct {
 	X    string
 	M, K int
 }

 func (k kza) F(g table.Grouping) table.Grouping {
 	return table.MapTables(g, func(_ table.GroupID, t *table.Table) *table.Table {
 		var xs []float64
 		slice.Convert(&xs, t.MustColumn(k.X))
 		nxs := AdaptiveKolmogorovZurbenko(xs, k.M, k.K)
 		return table.NewBuilder(t).Add("filtered "+k.X, nxs).Done()
 	})
 }

 // column represents a column in a google.visualization.DataTable
 type column struct {
 	Name string `json:"id"`
 	Role string `json:"role,omitempty"`
 	// These fields are filled in by tableToJS if unspecified.
 	Type  string `json:"type"`
 	Label string `json:"label"`
 }

 // tableToJS converts a Table to a javascript literal which can be passed to "new google.visualization.DataTable".
 func tableToJS(t *table.Table, columns []column) template.JS {
 	var out bytes.Buffer
 	fmt.Fprint(&out, "{cols: [")
 	var slices []table.Slice
 	for i, c := range columns {
 		if i > 0 {
 			fmt.Fprint(&out, ",\n")
 		}
 		col := t.Column(c.Name)
 		slices = append(slices, col)
 		if c.Type == "" {
 			switch col.(type) {
 			case []string:
 				c.Type = "string"
 			case []int, []float64:
 				c.Type = "number"
 			default:
 				// Matches the hardcoded string below.
 				c.Type = "string"
 			}
 		}
 		if c.Label == "" {
 			c.Label = c.Name
 		}
 		data, err := json.Marshal(c)
 		if err != nil {
 			panic(err)
 		}
 		out.Write(data)
 	}
 	fmt.Fprint(&out, "],\nrows: [")
 	for i := 0; i < t.Len(); i++ {
 		if i > 0 {
 			fmt.Fprint(&out, ",\n")
 		}
 		fmt.Fprint(&out, "{c:[")
 		for j := range columns {
 			if j > 0 {
 				fmt.Fprint(&out, ", ")
 			}
 			fmt.Fprint(&out, "{v: ")
 			var value []byte
 			var err error
 			switch column := slices[j].(type) {
 			case []string:
 				value, err = json.Marshal(column[i])
 			case []int:
 				value, err = json.Marshal(column[i])
 			case []float64:
 				value, err = json.Marshal(column[i])
 			default:
 				value = []byte(`"unknown column type"`)
 			}
 			if err != nil {
 				panic(err)
 			}
 			out.Write(value)
 			fmt.Fprint(&out, "}")
 		}
 		fmt.Fprint(&out, "]}")
 	}
 	fmt.Fprint(&out, "]}")
 	return template.JS(out.String())
 }
	// Copyright 2017 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.

	// Loosely based on github.com/aclements/go-misc/benchplot

	package app

	import (
	"bytes"
	"encoding/json"
	"fmt"
	"html/template"
	"io/ioutil"
	"math"
	"net/http"
	"path/filepath"
	"sort"
	"strconv"
	"strings"

	"github.com/aclements/go-gg/generic/slice"
	"github.com/aclements/go-gg/ggstat"
	"github.com/aclements/go-gg/table"
	"golang.org/x/net/context"
	"golang.org/x/perf/storage"
	)

	// trend handles /trend.
	// With no query, it prints the list of recent uploads containg a "trend" key.
	// With a query, it shows a graph of the matching benchmark results.
	func (a App) trend(w http.ResponseWriter, r http.Request) {
	ctx := requestContext(r)

	if err := r.ParseForm(); err != nil {
	http.Error(w, err.Error(), 500)
	return
	}

	q := r.Form.Get("q")

	tmpl, err := ioutil.ReadFile(filepath.Join(a.BaseDir, "template/trend.html"))
	if err != nil {
	http.Error(w, err.Error(), 500)
	return
	}

	t, err := template.New("main").Parse(string(tmpl))
	if err != nil {
	http.Error(w, err.Error(), 500)
	return
	}

	opt := plotOptions{
	x: r.Form.Get("x"),
	raw: r.Form.Get("raw") == "1",
	}

	data := a.trendQuery(ctx, q, opt)

	w.Header().Set("Content-Type", "text/html; charset=utf-8")
	if err := t.Execute(w, data); err != nil {
	http.Error(w, err.Error(), 500)
	return
	}
	}

	// trendData is the struct passed to the trend.html template.
	type trendData struct {
	Q string
	Error string
	TrendUploads []storage.UploadInfo
	PlotData template.JS
	PlotType template.JS
	}

	// trendData computes the values for the template and returns a trendData for display.
	func (a App) trendQuery(ctx context.Context, q string, opt plotOptions) trendData {
	d := &trendData{Q: q}
	if q == "" {
	ul := a.StorageClient.ListUploads(`trend>`, []string{"by", "upload-time", "trend"}, 16)
	defer ul.Close()
	for ul.Next() {
	d.TrendUploads = append(d.TrendUploads, ul.Info())
	}
	if err := ul.Err(); err != nil {
	errorf(ctx, "failed to fetch recent trend uploads: %v", err)
	}
	return d
	}

	// TODO(quentin): Chunk query based on matching upload IDs.
	res := a.StorageClient.Query(q)
	defer res.Close()
	t, resultCols := queryToTable(res)
	if err := res.Err(); err != nil {
	errorf(ctx, "failed to read query results: %v", err)
	d.Error = fmt.Sprintf("failed to read query results: %v", err)
	return d
	}
	for _, col := range []string{"commit", "commit-time", "branch", "name"} {
	if !hasStringColumn(t, col) {
	d.Error = fmt.Sprintf("results missing %q label", col)
	return d
	}
	}
	if opt.x != "" && !hasStringColumn(t, opt.x) {
	d.Error = fmt.Sprintf("results missing x label %q", opt.x)
	return d
	}
	data := plot(t, resultCols, opt)

	// TODO(quentin): Give the user control over across vs. plotting in separate graphs, instead of only showing one graph with ns/op for each benchmark.

	if opt.raw {
	data = table.MapTables(data, func(_ table.GroupID, t table.Table) table.Table {
	// From http://tristen.ca/hcl-picker/#/hlc/9/1.13/F1796F/B3EC6C
	colors := []string{"#F1796F", "#B3EC6C", "#F67E9D", "#6CEB98", "#E392CB", "#0AE4C6", "#B7ABEC", "#16D7E9", "#75C4F7"}
	colorIdx := 0
	partColors := make(map[string]string)
	styles := make([]string, t.Len())
	for i, part := range t.MustColumn("upload-part").([]string) {
	if _, ok := partColors[part]; !ok {
	partColors[part] = colors[colorIdx]
	colorIdx++
	if colorIdx >= len(colors) {
	colorIdx = 0
	}
	}
	styles[i] = "color: " + partColors[part]
	}
	return table.NewBuilder(t).Add("style", styles).Done()
	})
	columns := []column{
	{Name: "commit-index"},
	{Name: "result"},
	{Name: "style", Role: "style"},
	{Name: "commit", Role: "tooltip"},
	}
	d.PlotData = tableToJS(data.Table(data.Tables()[0]), columns)
	d.PlotType = "ScatterChart"
	return d
	}

	// Pivot all of the benchmarks into columns of a single table.
	ar := &aggResults{
	Across: "name",
	Values: []string{"filtered normalized mean result", "normalized mean result", "normalized median result", "normalized min result", "normalized max result"},
	}
	data = ggstat.Agg("commit", "branch", "commit-index")(ar.agg).F(data)

	tables := data.Tables()
	infof(ctx, "tables: %v", tables)
	columns := []column{{Name: "commit-index"}}
	for _, prefix := range ar.Prefixes {
	if len(ar.Prefixes) == 1 {
	columns = append(columns,
	column{Name: prefix + "/normalized mean result"},
	column{Name: prefix + "/normalized min result", Role: "interval"},
	column{Name: prefix + "/normalized max result", Role: "interval"},
	column{Name: prefix + "/normalized median result"},
	)
	}
	columns = append(columns,
	column{Name: prefix + "/filtered normalized mean result"},
	)
	}
	d.PlotData = tableToJS(data.Table(tables[0]), columns)
	d.PlotType = "LineChart"
	return d
	}

	// queryToTable converts the result of a Query into a Table for later processing.
	// Each label is placed in a column named after the key.
	// Each metric is placed in a separate result column named after the unit.
	func queryToTable(q storage.Query) (t table.Table, resultCols []string) {
	var names []string
	labels := make(map[string][]string)
	results := make(map[string][]float64)
	i := 0
	for q.Next() {
	res := q.Result()
	// TODO(quentin): Handle multiple results with the same name but different NameLabels.
	names = append(names, res.NameLabels["name"])
	for k := range res.Labels {
	if labels[k] == nil {
	labels[k] = make([]string, i)
	}
	}
	for k := range labels {
	labels[k] = append(labels[k], res.Labels[k])
	}
	f := strings.Fields(res.Content)
	metrics := make(map[string]float64)
	for j := 2; j+2 <= len(f); j += 2 {
	val, err := strconv.ParseFloat(f[j], 64)
	if err != nil {
	continue
	}
	unit := f[j+1]
	if results[unit] == nil {
	results[unit] = make([]float64, i)
	}
	metrics[unit] = val
	}
	for k := range results {
	results[k] = append(results[k], metrics[k])
	}
	i++
	}

	tab := new(table.Builder).Add("name", names)

	for k, v := range labels {
	tab.Add(k, v)
	}
	for k, v := range results {
	tab.Add(k, v)
	resultCols = append(resultCols, k)
	}

	sort.Strings(resultCols)

	return tab.Done(), resultCols
	}

	type plotOptions struct {
	// x names the column to use for the X axis.
	// If unspecified, "commit" is used.
	x string
	// raw will return the raw points without any averaging/smoothing.
	// The only result column will be "result".
	raw bool
	// correlate will use the string column "upload-part" as an indication that results came from the same machine. Commits present in multiple parts will be used to correlate results.
	correlate bool
	}

	// plot takes raw benchmark data in t and produces a Grouping object containing filtered, normalized metric results for a graph.
	// t must contain the string columns "commit", "commit-time", "branch". resultCols specifies the names of float64 columns containing metric results.
	// The returned grouping has columns "commit", "commit-time", "commit-index", "branch", "metric", "normalized min result", "normalized max result", "normalized mean result", "filtered normalized mean result".
	// This is roughly the algorithm from github.com/aclements/go-misc/benchplot
	func plot(t table.Grouping, resultCols []string, opt plotOptions) table.Grouping {
	nrows := len(table.GroupBy(t, "name").Tables())

	// Turn ordered commit-time into a "commit-index" column.
	if opt.x == "" {
	opt.x = "commit"
	}
	// TODO(quentin): One SortBy call should do this, but
	// sometimes it seems to sort by the second column instead of
	// the first. Do them in separate steps until SortBy is fixed.
	t = table.SortBy(t, opt.x)
	t = table.SortBy(t, "commit-time")
	t = colIndex{col: opt.x}.F(t)

	// Unpivot all of the metrics into one column.
	t = table.Unpivot(t, "metric", "result", resultCols...)

	// TODO(quentin): Let user choose which metric(s) to keep.
	t = table.FilterEq(t, "metric", "ns/op")

	if opt.raw {
	return t
	}

	// Average each result at each commit (but keep columns names
	// the same to keep things easier to read).
	t = ggstat.Agg("commit", "name", "metric", "branch", "commit-index")(ggstat.AggMean("result"), ggstat.AggQuantile("median", .5, "result"), ggstat.AggMin("result"), ggstat.AggMax("result")).F(t)
	y := "mean result"

	// Normalize to earliest commit on master. It's important to
	// do this before the geomean if there are commits missing.
	// Unfortunately, that also means we have to temporarily
	// group by name and metric, since the geomean needs to be
	// done on a different grouping.
	t = table.GroupBy(t, "name", "metric")
	t = ggstat.Normalize{X: "branch", By: firstMasterIndex, Cols: []string{"mean result", "median result", "max result", "min result"}, DenomCols: []string{"mean result", "mean result", "mean result", "mean result"}}.F(t)
	y = "normalized " + y
	for _, col := range []string{"mean result", "median result", "max result", "min result"} {
	t = table.Remove(t, col)
	}
	t = table.Ungroup(table.Ungroup(t))

	// Compute geomean for each metric at each commit if there's
	// more than one benchmark.
	if len(table.GroupBy(t, "name").Tables()) > 1 {
	gt := removeNaNs(t, y)
	gt = ggstat.Agg("commit", "metric", "branch", "commit-index")(ggstat.AggGeoMean(y, "normalized median result"), ggstat.AggMin("normalized min result"), ggstat.AggMax("normalized max result")).F(gt)
	gt = table.MapTables(gt, func(_ table.GroupID, t table.Table) table.Table {
	return table.NewBuilder(t).AddConst("name", " geomean").Done()
	})
	gt = table.Rename(gt, "geomean "+y, y)
	gt = table.Rename(gt, "geomean normalized median result", "normalized median result")
	gt = table.Rename(gt, "min normalized min result", "normalized min result")
	gt = table.Rename(gt, "max normalized max result", "normalized max result")
	t = table.Concat(t, gt)
	nrows++
	}

	// Filter the data to reduce noise.
	t = table.GroupBy(t, "name", "metric")
	t = kza{y, 15, 3}.F(t)
	y = "filtered " + y
	t = table.Ungroup(table.Ungroup(t))

	return t
	}

	// hasStringColumn returns whether t has a []string column called col.
	func hasStringColumn(t table.Grouping, col string) bool {
	c := t.Table(t.Tables()[0]).Column(col)
	if c == nil {
	return false
	}
	_, ok := c.([]string)
	return ok
	}

	// aggResults pivots the table, taking the columns in Values and making a new column for each distinct value in Across.
	// aggResults("in", []string{"value1", "value2"} will reshape a table like
	// in value1 value2
	// one 1 2
	// two 3 4
	// and will turn in into a table like
	// one/value1 one/value2 two/value1 two/value2
	// 1 2 3 4
	// across columns must be []string, and value columns must be []float64.
	type aggResults struct {
	// Across is the name of the column whose values are the column prefix.
	Across string
	// Values is the name of the columns to split.
	Values []string
	// Prefixes is filled in after calling agg with the name of each prefix that was found.
	Prefixes []string
	}

	// agg implements ggstat.Aggregator and allows using a with ggstat.Agg.
	func (a aggResults) agg(input table.Grouping, output table.Builder) {
	var prefixes []string
	rows := len(input.Tables())
	columns := make(map[string][]float64)
	for i, gid := range input.Tables() {
	var vs [][]float64
	for _, col := range a.Values {
	vs = append(vs, input.Table(gid).MustColumn(col).([]float64))
	}
	as := input.Table(gid).MustColumn(a.Across).([]string)
	for j, prefix := range as {
	for k, col := range a.Values {
	key := prefix + "/" + col
	if columns[key] == nil {
	if k == 0 {
	// First time we've seen this prefix, track it.
	prefixes = append(prefixes, prefix)
	}
	columns[key] = make([]float64, rows)
	for i := range columns[key] {
	columns[key][i] = math.NaN()
	}
	}
	columns[key][i] = vs[k][j]
	}
	}
	}
	sort.Strings(prefixes)
	a.Prefixes = prefixes
	for _, prefix := range prefixes {
	for _, col := range a.Values {
	key := prefix + "/" + col
	output.Add(key, columns[key])
	}
	}
	}

	// firstMasterIndex returns the index of the first commit on master.
	// This is used to find the value to normalize against.
	func firstMasterIndex(bs []string) int {
	return slice.Index(bs, "master")
	}

	// colIndex is a gg.Stat that adds a column called "commit-index" sequentially counting unique values of the column "commit".
	type colIndex struct {
	// col specifies the string column to assign indices to. If unspecified, "commit" will be used.
	col string
	}

	func (ci colIndex) F(g table.Grouping) table.Grouping {
	if ci.col == "" {
	ci.col = "commit"
	}
	return table.MapTables(g, func(_ table.GroupID, t table.Table) table.Table {
	idxs := make([]int, t.Len())
	last, idx := "", -1
	for i, hash := range t.MustColumn(ci.col).([]string) {
	if hash != last {
	idx++
	last = hash
	}
	idxs[i] = idx
	}
	t = table.NewBuilder(t).Add("commit-index", idxs).Done()

	return t
	})
	}

	// removeNaNs returns a new Grouping with rows containg NaN in col removed.
	func removeNaNs(g table.Grouping, col string) table.Grouping {
	return table.Filter(g, func(result float64) bool {
	return !math.IsNaN(result)
	}, col)
	}

	// kza implements adaptive Kolmogorov-Zurbenko filtering on the data in X.
	type kza struct {
	X string
	M, K int
	}

	func (k kza) F(g table.Grouping) table.Grouping {
	return table.MapTables(g, func(_ table.GroupID, t table.Table) table.Table {
	var xs []float64
	slice.Convert(&xs, t.MustColumn(k.X))
	nxs := AdaptiveKolmogorovZurbenko(xs, k.M, k.K)
	return table.NewBuilder(t).Add("filtered "+k.X, nxs).Done()
	})
	}

	// column represents a column in a google.visualization.DataTable
	type column struct {
	Name string `json:"id"`
	Role string `json:"role,omitempty"`
	// These fields are filled in by tableToJS if unspecified.
	Type string `json:"type"`
	Label string `json:"label"`
	}

	// tableToJS converts a Table to a javascript literal which can be passed to "new google.visualization.DataTable".
	func tableToJS(t *table.Table, columns []column) template.JS {
	var out bytes.Buffer
	fmt.Fprint(&out, "{cols: [")
	var slices []table.Slice
	for i, c := range columns {
	if i > 0 {
	fmt.Fprint(&out, ",\n")
	}
	col := t.Column(c.Name)
	slices = append(slices, col)
	if c.Type == "" {
	switch col.(type) {
	case []string:
	c.Type = "string"
	case []int, []float64:
	c.Type = "number"
	default:
	// Matches the hardcoded string below.
	c.Type = "string"
	}
	}
	if c.Label == "" {
	c.Label = c.Name
	}
	data, err := json.Marshal(c)
	if err != nil {
	panic(err)
	}
	out.Write(data)
	}
	fmt.Fprint(&out, "],\nrows: [")
	for i := 0; i < t.Len(); i++ {
	if i > 0 {
	fmt.Fprint(&out, ",\n")
	}
	fmt.Fprint(&out, "{c:[")
	for j := range columns {
	if j > 0 {
	fmt.Fprint(&out, ", ")
	}
	fmt.Fprint(&out, "{v: ")
	var value []byte
	var err error
	switch column := slices[j].(type) {
	case []string:
	value, err = json.Marshal(column[i])
	case []int:
	value, err = json.Marshal(column[i])
	case []float64:
	value, err = json.Marshal(column[i])
	default:
	value = []byte(`"unknown column type"`)
	}
	if err != nil {
	panic(err)
	}
	out.Write(value)
	fmt.Fprint(&out, "}")
	}
	fmt.Fprint(&out, "]}")
	}
	fmt.Fprint(&out, "]}")
	return template.JS(out.String())
	}