Got sick of dealing with errors

- just return 0.0 for empty OnlineStats

ostat.go

@@ -1,7 +1,6 @@
 package ostat
 
 import (
-	"errors"
 	"math"
 )
 
@@ -46,21 +45,20 @@ func (os *OnlineStat) Push(v float64) {
 	os.m2 = os.m2 + delta*(v-os.mean)
 }
 
-func (os *OnlineStat) Mean() (float64, error) {
+func (os *OnlineStat) Mean() float64 {
 	if os.n == 0 {
-		return 0.0, errors.New("no data")
+		return 0.0
 	}
-	return os.mean, nil
+	return os.mean
 }
 
-func (os *OnlineStat) Variance() (float64, error) {
+func (os *OnlineStat) Variance() float64 {
 	if os.n == 0 {
-		return 0.0, errors.New("no data")
+		return 0.0
 	}
-	return os.m2 / float64(os.n-os.typ), nil
+	return os.m2 / float64(os.n-os.typ)
 }
 
-func (os *OnlineStat) StdDev() (float64, error) {
-	variance, err := os.Variance()
-	return math.Sqrt(variance), err
+func (os *OnlineStat) StdDev() float64 {
+	return math.Sqrt(os.Variance())
 }

ostat_test.go

@@ -1,6 +1,7 @@
 package ostat
 
 import (
+	"log"
 	"math"
 	"testing"
 )
@@ -93,43 +94,47 @@ func TestInsert(t *testing.T) {
 			t.Errorf("incorrectly calculated max: %f != %f", ps.Max, test.max)
 		}
 
-		pmean, _ := ps.Mean()
-		smean, _ := ss.Mean()
-		if pmean != smean {
+		if ps.Mean() != ss.Mean() {
 			t.Errorf("Means don't match")
 		}
-		if m, _ := ps.Mean(); math.Abs(m-test.mean) > tolerance {
-			t.Errorf("incorrect mean: %f != %f", m, test.mean)
+		if math.Abs(ps.Mean()-test.mean) > tolerance {
+			t.Errorf("incorrect mean: %f != %f", ps.Mean(), test.mean)
 		}
 
-		if variance, _ := ps.Variance(); math.Abs(variance-test.pvariance) > tolerance {
-			t.Errorf("incorrect variance: %f != %f", variance, test.pvariance)
+		if math.Abs(ps.Variance()-test.pvariance) > tolerance {
+			t.Errorf("incorrect variance: %f != %f", ps.Variance(), test.pvariance)
 		}
-		if stdev, _ := ps.StdDev(); math.Abs(stdev-test.pstdev) > tolerance {
-			t.Errorf("incorrect stdev: %f != %f", stdev, test.pstdev)
+		if math.Abs(ps.StdDev()-test.pstdev) > tolerance {
+			t.Errorf("incorrect stdev: %f != %f", ps.StdDev(), test.pstdev)
 		}
 
-		if variance, _ := ss.Variance(); math.Abs(variance-test.svariance) > tolerance {
-			t.Errorf("incorrect variance: %f != %f", variance, test.svariance)
+		if math.Abs(ss.Variance()-test.svariance) > tolerance {
+			t.Errorf("incorrect variance: %f != %f", ss.Variance(), test.svariance)
 		}
-		if stdev, _ := ss.StdDev(); math.Abs(stdev-test.sstdev) > tolerance {
-			t.Errorf("incorrect stdev: %f != %f", stdev, test.sstdev)
+		if math.Abs(ss.StdDev()-test.sstdev) > tolerance {
+			t.Errorf("incorrect stdev: %f != %f", ss.StdDev(), test.sstdev)
 		}
 	}
 }
 
 func TestEmpty(t *testing.T) {
 	ps := NewSampleStat()
-	_, err := ps.Mean()
-	if err == nil {
+	if ps.Mean() != 0.0 {
 		t.Errorf("failure to notify the running stat was empty")
 	}
-	_, err = ps.Variance()
-	if err == nil {
+	if ps.Variance() != 0.0 {
 		t.Errorf("failure to notify the running stat was empty")
 	}
-	_, err = ps.StdDev()
-	if err == nil {
+	if ps.StdDev() != 0.0 {
 		t.Errorf("failure to notify the running stat was empty")
 	}
 }
+
+func TestReconstitutedStat(t *testing.T) {
+	os := OnlineStat{
+		n:    3,
+		mean: 8,
+		m2:   21 * 3,
+	}
+	log.Printf("%+v: mean: %f, stddev: %f", os, os.Mean(), os.StdDev())
+}