Fix for resampler

This commit is contained in:
Aditya Ujeniya 2024-08-25 16:13:43 +02:00
parent d34e0d9348
commit 036eba68e1
3 changed files with 30 additions and 10 deletions

View File

@ -528,14 +528,14 @@ func (ccms *CCMetricStore) LoadStats(
ctx context.Context,
) (map[string]map[string]schema.MetricStatistics, error) {
metricConfigs := archive.GetCluster(job.Cluster).MetricConfig
resolution := 9000
// metricConfigs := archive.GetCluster(job.Cluster).MetricConfig
// resolution := 9000
for _, mc := range metricConfigs {
resolution = min(resolution, mc.Timestep)
}
// for _, mc := range metricConfigs {
// resolution = min(resolution, mc.Timestep)
// }
queries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, resolution) // #166 Add scope shere for analysis view accelerator normalization?
queries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0) // #166 Add scope shere for analysis view accelerator normalization?
if err != nil {
log.Warn("Error while building query")
return nil, err

View File

@ -90,6 +90,7 @@ func LargestTriangleThreeBucket(data []schema.Float, old_frequency int, new_freq
maxArea := -1.0
var maxAreaPoint int
flag_ := 0
for ; currBucketStart < currBucketEnd; currBucketStart++ {
area := calculateTriangleArea(schema.Float(pointX), pointY, avgPointX, avgPointY, schema.Float(currBucketStart), data[currBucketStart])
@ -97,9 +98,18 @@ func LargestTriangleThreeBucket(data []schema.Float, old_frequency int, new_freq
maxArea = area
maxAreaPoint = currBucketStart
}
if math.IsNaN(float64(avgPointY)) {
flag_ = 1
}
}
if flag_ == 1 {
new_data = append(new_data, schema.NaN) // Pick this point from the bucket
} else {
new_data = append(new_data, data[maxAreaPoint]) // Pick this point from the bucket
}
prevMaxAreaPoint = maxAreaPoint // This MaxArea point is the next's prevMAxAreaPoint
//move to the next window

View File

@ -12,14 +12,24 @@ func calculateTriangleArea(paX, paY, pbX, pbY, pcX, pcY schema.Float) float64 {
}
func calculateAverageDataPoint(points []schema.Float, xStart int64) (avgX schema.Float, avgY schema.Float) {
flag := 0
for _, point := range points {
avgX += schema.Float(xStart)
avgY += point
xStart++
if math.IsNaN(float64(point)) {
flag = 1
}
}
l := schema.Float(len(points))
avgX /= l
avgY /= l
if flag == 1 {
return avgX, schema.NaN
} else {
return avgX, avgY
}
}