[Feature] Major update: Track measurements that may be delayed

Among other major feature additions, this version of the client tracks
any measurements that may be long delayed and considers their presence
or absence as part of a stability measurement.

This version of the client also more closely tracks the spec. In
particular, it performs a sinle-sided trimmed mean rather than a
double-sided trimmed mean.

Signed-off-by: Will Hawkins <[email protected]>

1 files changed, 74 insertions, 0 deletions

diff --git a/series/statistics.go b/series/statistics.go
new file mode 100644
index 0000000..2742aa7
--- /dev/null
+++ b/series/statistics.go
@@ -0,0 +1,74 @@
+package series
+
+import (
+	"github.com/network-quality/goresponsiveness/utilities"
+	"golang.org/x/exp/constraints"
+)
+
+func SeriesStandardDeviation[Data utilities.Number, Bucket constraints.Ordered](s WindowSeries[Data, Bucket]) (bool, float64) {
+	complete := s.Complete()
+
+	inputValues := s.GetValues()
+
+	actualValues := utilities.Filter(inputValues, func(d utilities.Optional[Data]) bool {
+		return utilities.IsSome[Data](d)
+	})
+	values := utilities.Fmap(actualValues, func(d utilities.Optional[Data]) Data { return utilities.GetSome[Data](d) })
+
+	return complete, utilities.CalculateStandardDeviation[Data](values)
+}
+
+func Percentile[Data utilities.Number, Bucket constraints.Ordered](s WindowSeries[Data, Bucket], p int) (bool, Data) {
+	complete := s.Complete()
+
+	inputValues := s.GetValues()
+
+	actualValues := utilities.Filter(inputValues, func(d utilities.Optional[Data]) bool {
+		return utilities.IsSome[Data](d)
+	})
+	values := utilities.Fmap(actualValues, func(d utilities.Optional[Data]) Data { return utilities.GetSome[Data](d) })
+
+	return complete, utilities.CalculatePercentile(values, p)
+}
+
+func AllSequentialIncreasesLessThan[Data utilities.Number, Bucket constraints.Ordered](s WindowSeries[Data, Bucket], limit float64,
+) (bool, bool, float64) {
+	complete := s.Complete()
+
+	inputValues := s.GetValues()
+
+	actualValues := utilities.Filter(utilities.Reverse(inputValues), func(d utilities.Optional[Data]) bool {
+		return utilities.IsSome[Data](d)
+	})
+	values := utilities.Fmap(actualValues, func(d utilities.Optional[Data]) Data { return utilities.GetSome[Data](d) })
+
+	result, actualLimit := utilities.AllSequentialIncreasesLessThan(values, limit)
+	return complete, result, actualLimit
+}
+
+func CalculateAverage[Data utilities.Number, Bucket constraints.Ordered](s WindowSeries[Data, Bucket]) (bool, float64) {
+	complete := s.Complete()
+
+	inputValues := s.GetValues()
+
+	actualValues := utilities.Filter(inputValues, func(d utilities.Optional[Data]) bool {
+		return utilities.IsSome[Data](d)
+	})
+	values := utilities.Fmap(actualValues, func(d utilities.Optional[Data]) Data { return utilities.GetSome[Data](d) })
+
+	return complete, utilities.CalculateAverage(values)
+}
+
+func TrimmedMean[Data utilities.Number, Bucket constraints.Ordered](s WindowSeries[Data, Bucket], trim int) (bool, float64, []Data) {
+	complete := s.Complete()
+
+	inputValues := s.GetValues()
+
+	actualValues := utilities.Filter(inputValues, func(d utilities.Optional[Data]) bool {
+		return utilities.IsSome[Data](d)
+	})
+	values := utilities.Fmap(actualValues, func(d utilities.Optional[Data]) Data { return utilities.GetSome[Data](d) })
+
+	trimmedMean, trimmedElements := utilities.TrimmedMean(values, trim)
+	return complete, trimmedMean, trimmedElements
+}