1 files changed, 0 insertions, 393 deletions
diff --git a/vendor/github.com/olivere/elastic/search_aggs_pipeline_mov_avg.go b/vendor/github.com/olivere/elastic/search_aggs_pipeline_mov_avg.go
deleted file mode 100644
index 821d73842..000000000
--- a/vendor/github.com/olivere/elastic/search_aggs_pipeline_mov_avg.go
+++ /dev/null
@@ -1,393 +0,0 @@
-// Copyright 2012-present Oliver Eilhard. All rights reserved.
-// Use of this source code is governed by a MIT-license.
-// See http://olivere.mit-license.org/license.txt for details.
-
-package elastic
-
-// MovAvgAggregation operates on a series of data. It will slide a window
-// across the data and emit the average value of that window.
-//
-// For more details, see
-// https://www.elastic.co/guide/en/elasticsearch/reference/6.0/search-aggregations-pipeline-movavg-aggregation.html
-type MovAvgAggregation struct {
-	format    string
-	gapPolicy string
-	model     MovAvgModel
-	window    *int
-	predict   *int
-	minimize  *bool
-
-	subAggregations map[string]Aggregation
-	meta            map[string]interface{}
-	bucketsPaths    []string
-}
-
-// NewMovAvgAggregation creates and initializes a new MovAvgAggregation.
-func NewMovAvgAggregation() *MovAvgAggregation {
-	return &MovAvgAggregation{
-		subAggregations: make(map[string]Aggregation),
-		bucketsPaths:    make([]string, 0),
-	}
-}
-
-func (a *MovAvgAggregation) Format(format string) *MovAvgAggregation {
-	a.format = format
-	return a
-}
-
-// GapPolicy defines what should be done when a gap in the series is discovered.
-// Valid values include "insert_zeros" or "skip". Default is "insert_zeros".
-func (a *MovAvgAggregation) GapPolicy(gapPolicy string) *MovAvgAggregation {
-	a.gapPolicy = gapPolicy
-	return a
-}
-
-// GapInsertZeros inserts zeros for gaps in the series.
-func (a *MovAvgAggregation) GapInsertZeros() *MovAvgAggregation {
-	a.gapPolicy = "insert_zeros"
-	return a
-}
-
-// GapSkip skips gaps in the series.
-func (a *MovAvgAggregation) GapSkip() *MovAvgAggregation {
-	a.gapPolicy = "skip"
-	return a
-}
-
-// Model is used to define what type of moving average you want to use
-// in the series.
-func (a *MovAvgAggregation) Model(model MovAvgModel) *MovAvgAggregation {
-	a.model = model
-	return a
-}
-
-// Window sets the window size for the moving average. This window will
-// "slide" across the series, and the values inside that window will
-// be used to calculate the moving avg value.
-func (a *MovAvgAggregation) Window(window int) *MovAvgAggregation {
-	a.window = &window
-	return a
-}
-
-// Predict sets the number of predictions that should be returned.
-// Each prediction will be spaced at the intervals in the histogram.
-// E.g. a predict of 2 will return two new buckets at the end of the
-// histogram with the predicted values.
-func (a *MovAvgAggregation) Predict(numPredictions int) *MovAvgAggregation {
-	a.predict = &numPredictions
-	return a
-}
-
-// Minimize determines if the model should be fit to the data using a
-// cost minimizing algorithm.
-func (a *MovAvgAggregation) Minimize(minimize bool) *MovAvgAggregation {
-	a.minimize = &minimize
-	return a
-}
-
-// SubAggregation adds a sub-aggregation to this aggregation.
-func (a *MovAvgAggregation) SubAggregation(name string, subAggregation Aggregation) *MovAvgAggregation {
-	a.subAggregations[name] = subAggregation
-	return a
-}
-
-// Meta sets the meta data to be included in the aggregation response.
-func (a *MovAvgAggregation) Meta(metaData map[string]interface{}) *MovAvgAggregation {
-	a.meta = metaData
-	return a
-}
-
-// BucketsPath sets the paths to the buckets to use for this pipeline aggregator.
-func (a *MovAvgAggregation) BucketsPath(bucketsPaths ...string) *MovAvgAggregation {
-	a.bucketsPaths = append(a.bucketsPaths, bucketsPaths...)
-	return a
-}
-
-func (a *MovAvgAggregation) Source() (interface{}, error) {
-	source := make(map[string]interface{})
-	params := make(map[string]interface{})
-	source["moving_avg"] = params
-
-	if a.format != "" {
-		params["format"] = a.format
-	}
-	if a.gapPolicy != "" {
-		params["gap_policy"] = a.gapPolicy
-	}
-	if a.model != nil {
-		params["model"] = a.model.Name()
-		settings := a.model.Settings()
-		if len(settings) > 0 {
-			params["settings"] = settings
-		}
-	}
-	if a.window != nil {
-		params["window"] = *a.window
-	}
-	if a.predict != nil {
-		params["predict"] = *a.predict
-	}
-	if a.minimize != nil {
-		params["minimize"] = *a.minimize
-	}
-
-	// Add buckets paths
-	switch len(a.bucketsPaths) {
-	case 0:
-	case 1:
-		params["buckets_path"] = a.bucketsPaths[0]
-	default:
-		params["buckets_path"] = a.bucketsPaths
-	}
-
-	// AggregationBuilder (SubAggregations)
-	if len(a.subAggregations) > 0 {
-		aggsMap := make(map[string]interface{})
-		source["aggregations"] = aggsMap
-		for name, aggregate := range a.subAggregations {
-			src, err := aggregate.Source()
-			if err != nil {
-				return nil, err
-			}
-			aggsMap[name] = src
-		}
-	}
-
-	// Add Meta data if available
-	if len(a.meta) > 0 {
-		source["meta"] = a.meta
-	}
-
-	return source, nil
-}
-
-// -- Models for moving averages --
-// See https://www.elastic.co/guide/en/elasticsearch/reference/6.0/search-aggregations-pipeline-movavg-aggregation.html#_models
-
-// MovAvgModel specifies the model to use with the MovAvgAggregation.
-type MovAvgModel interface {
-	Name() string
-	Settings() map[string]interface{}
-}
-
-// -- EWMA --
-
-// EWMAMovAvgModel calculates an exponentially weighted moving average.
-//
-// For more details, see
-// https://www.elastic.co/guide/en/elasticsearch/reference/6.0/search-aggregations-pipeline-movavg-aggregation.html#_ewma_exponentially_weighted
-type EWMAMovAvgModel struct {
-	alpha *float64
-}
-
-// NewEWMAMovAvgModel creates and initializes a new EWMAMovAvgModel.
-func NewEWMAMovAvgModel() *EWMAMovAvgModel {
-	return &EWMAMovAvgModel{}
-}
-
-// Alpha controls the smoothing of the data. Alpha = 1 retains no memory
-// of past values (e.g. a random walk), while alpha = 0 retains infinite
-// memory of past values (e.g. the series mean). Useful values are somewhere
-// in between. Defaults to 0.5.
-func (m *EWMAMovAvgModel) Alpha(alpha float64) *EWMAMovAvgModel {
-	m.alpha = &alpha
-	return m
-}
-
-// Name of the model.
-func (m *EWMAMovAvgModel) Name() string {
-	return "ewma"
-}
-
-// Settings of the model.
-func (m *EWMAMovAvgModel) Settings() map[string]interface{} {
-	settings := make(map[string]interface{})
-	if m.alpha != nil {
-		settings["alpha"] = *m.alpha
-	}
-	return settings
-}
-
-// -- Holt linear --
-
-// HoltLinearMovAvgModel calculates a doubly exponential weighted moving average.
-//
-// For more details, see
-// https://www.elastic.co/guide/en/elasticsearch/reference/6.0/search-aggregations-pipeline-movavg-aggregation.html#_holt_linear
-type HoltLinearMovAvgModel struct {
-	alpha *float64
-	beta  *float64
-}
-
-// NewHoltLinearMovAvgModel creates and initializes a new HoltLinearMovAvgModel.
-func NewHoltLinearMovAvgModel() *HoltLinearMovAvgModel {
-	return &HoltLinearMovAvgModel{}
-}
-
-// Alpha controls the smoothing of the data. Alpha = 1 retains no memory
-// of past values (e.g. a random walk), while alpha = 0 retains infinite
-// memory of past values (e.g. the series mean). Useful values are somewhere
-// in between. Defaults to 0.5.
-func (m *HoltLinearMovAvgModel) Alpha(alpha float64) *HoltLinearMovAvgModel {
-	m.alpha = &alpha
-	return m
-}
-
-// Beta is equivalent to Alpha but controls the smoothing of the trend
-// instead of the data.
-func (m *HoltLinearMovAvgModel) Beta(beta float64) *HoltLinearMovAvgModel {
-	m.beta = &beta
-	return m
-}
-
-// Name of the model.
-func (m *HoltLinearMovAvgModel) Name() string {
-	return "holt"
-}
-
-// Settings of the model.
-func (m *HoltLinearMovAvgModel) Settings() map[string]interface{} {
-	settings := make(map[string]interface{})
-	if m.alpha != nil {
-		settings["alpha"] = *m.alpha
-	}
-	if m.beta != nil {
-		settings["beta"] = *m.beta
-	}
-	return settings
-}
-
-// -- Holt Winters --
-
-// HoltWintersMovAvgModel calculates a triple exponential weighted moving average.
-//
-// For more details, see
-// https://www.elastic.co/guide/en/elasticsearch/reference/6.0/search-aggregations-pipeline-movavg-aggregation.html#_holt_winters
-type HoltWintersMovAvgModel struct {
-	alpha           *float64
-	beta            *float64
-	gamma           *float64
-	period          *int
-	seasonalityType string
-	pad             *bool
-}
-
-// NewHoltWintersMovAvgModel creates and initializes a new HoltWintersMovAvgModel.
-func NewHoltWintersMovAvgModel() *HoltWintersMovAvgModel {
-	return &HoltWintersMovAvgModel{}
-}
-
-// Alpha controls the smoothing of the data. Alpha = 1 retains no memory
-// of past values (e.g. a random walk), while alpha = 0 retains infinite
-// memory of past values (e.g. the series mean). Useful values are somewhere
-// in between. Defaults to 0.5.
-func (m *HoltWintersMovAvgModel) Alpha(alpha float64) *HoltWintersMovAvgModel {
-	m.alpha = &alpha
-	return m
-}
-
-// Beta is equivalent to Alpha but controls the smoothing of the trend
-// instead of the data.
-func (m *HoltWintersMovAvgModel) Beta(beta float64) *HoltWintersMovAvgModel {
-	m.beta = &beta
-	return m
-}
-
-func (m *HoltWintersMovAvgModel) Gamma(gamma float64) *HoltWintersMovAvgModel {
-	m.gamma = &gamma
-	return m
-}
-
-func (m *HoltWintersMovAvgModel) Period(period int) *HoltWintersMovAvgModel {
-	m.period = &period
-	return m
-}
-
-func (m *HoltWintersMovAvgModel) SeasonalityType(typ string) *HoltWintersMovAvgModel {
-	m.seasonalityType = typ
-	return m
-}
-
-func (m *HoltWintersMovAvgModel) Pad(pad bool) *HoltWintersMovAvgModel {
-	m.pad = &pad
-	return m
-}
-
-// Name of the model.
-func (m *HoltWintersMovAvgModel) Name() string {
-	return "holt_winters"
-}
-
-// Settings of the model.
-func (m *HoltWintersMovAvgModel) Settings() map[string]interface{} {
-	settings := make(map[string]interface{})
-	if m.alpha != nil {
-		settings["alpha"] = *m.alpha
-	}
-	if m.beta != nil {
-		settings["beta"] = *m.beta
-	}
-	if m.gamma != nil {
-		settings["gamma"] = *m.gamma
-	}
-	if m.period != nil {
-		settings["period"] = *m.period
-	}
-	if m.pad != nil {
-		settings["pad"] = *m.pad
-	}
-	if m.seasonalityType != "" {
-		settings["type"] = m.seasonalityType
-	}
-	return settings
-}
-
-// -- Linear --
-
-// LinearMovAvgModel calculates a linearly weighted moving average, such
-// that older values are linearly less important. "Time" is determined
-// by position in collection.
-//
-// For more details, see
-// https://www.elastic.co/guide/en/elasticsearch/reference/6.0/search-aggregations-pipeline-movavg-aggregation.html#_linear
-type LinearMovAvgModel struct {
-}
-
-// NewLinearMovAvgModel creates and initializes a new LinearMovAvgModel.
-func NewLinearMovAvgModel() *LinearMovAvgModel {
-	return &LinearMovAvgModel{}
-}
-
-// Name of the model.
-func (m *LinearMovAvgModel) Name() string {
-	return "linear"
-}
-
-// Settings of the model.
-func (m *LinearMovAvgModel) Settings() map[string]interface{} {
-	return nil
-}
-
-// -- Simple --
-
-// SimpleMovAvgModel calculates a simple unweighted (arithmetic) moving average.
-//
-// For more details, see
-// https://www.elastic.co/guide/en/elasticsearch/reference/6.0/search-aggregations-pipeline-movavg-aggregation.html#_simple
-type SimpleMovAvgModel struct {
-}
-
-// NewSimpleMovAvgModel creates and initializes a new SimpleMovAvgModel.
-func NewSimpleMovAvgModel() *SimpleMovAvgModel {
-	return &SimpleMovAvgModel{}
-}
-
-// Name of the model.
-func (m *SimpleMovAvgModel) Name() string {
-	return "simple"
-}
-
-// Settings of the model.
-func (m *SimpleMovAvgModel) Settings() map[string]interface{} {
-	return nil
-}