mirror of
https://github.com/ClusterCockpit/cc-backend
synced 2024-12-26 13:29:05 +01:00
124 lines
3.6 KiB
Go
124 lines
3.6 KiB
Go
package resampler
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import (
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"errors"
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"fmt"
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"math"
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"github.com/ClusterCockpit/cc-backend/pkg/schema"
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)
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func SimpleResampler(data []schema.Float, old_frequency int64, new_frequency int64) ([]schema.Float, int64, error) {
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if old_frequency == 0 || new_frequency == 0 || new_frequency <= old_frequency {
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return data, old_frequency, nil
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}
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if new_frequency%old_frequency != 0 {
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return nil, 0, errors.New("new sampling frequency should be multiple of the old frequency")
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}
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var step int = int(new_frequency / old_frequency)
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var new_data_length = len(data) / step
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if new_data_length == 0 || len(data) < 100 || new_data_length >= len(data) {
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return data, old_frequency, nil
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}
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new_data := make([]schema.Float, new_data_length)
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for i := 0; i < new_data_length; i++ {
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new_data[i] = data[i*step]
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}
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return new_data, new_frequency, nil
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}
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// Inspired by one of the algorithms from https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf
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// Adapted from https://github.com/haoel/downsampling/blob/master/core/lttb.go
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func LargestTriangleThreeBucket(data []schema.Float, old_frequency int, new_frequency int) ([]schema.Float, int, error) {
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if old_frequency == 0 || new_frequency == 0 || new_frequency <= old_frequency {
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return data, old_frequency, nil
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}
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if new_frequency%old_frequency != 0 {
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return nil, 0, errors.New(fmt.Sprintf("new sampling frequency : %d should be multiple of the old frequency : %d", new_frequency, old_frequency))
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}
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var step int = int(new_frequency / old_frequency)
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var new_data_length = len(data) / step
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if new_data_length == 0 || len(data) < 100 || new_data_length >= len(data) {
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return data, old_frequency, nil
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}
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new_data := make([]schema.Float, 0, new_data_length)
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// Bucket size. Leave room for start and end data points
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bucketSize := float64(len(data)-2) / float64(new_data_length-2)
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new_data = append(new_data, data[0]) // Always add the first point
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// We have 3 pointers represent for
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// > bucketLow - the current bucket's beginning location
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// > bucketMiddle - the current bucket's ending location,
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// also the beginning location of next bucket
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// > bucketHight - the next bucket's ending location.
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bucketLow := 1
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bucketMiddle := int(math.Floor(bucketSize)) + 1
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var prevMaxAreaPoint int
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for i := 0; i < new_data_length-2; i++ {
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bucketHigh := int(math.Floor(float64(i+2)*bucketSize)) + 1
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if bucketHigh >= len(data)-1 {
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bucketHigh = len(data) - 2
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}
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// Calculate point average for next bucket (containing c)
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avgPointX, avgPointY := calculateAverageDataPoint(data[bucketMiddle:bucketHigh+1], int64(bucketMiddle))
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// Get the range for current bucket
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currBucketStart := bucketLow
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currBucketEnd := bucketMiddle
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// Point a
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pointX := prevMaxAreaPoint
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pointY := data[prevMaxAreaPoint]
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maxArea := -1.0
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var maxAreaPoint int
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flag_ := 0
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for ; currBucketStart < currBucketEnd; currBucketStart++ {
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area := calculateTriangleArea(schema.Float(pointX), pointY, avgPointX, avgPointY, schema.Float(currBucketStart), data[currBucketStart])
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if area > maxArea {
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maxArea = area
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maxAreaPoint = currBucketStart
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}
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if math.IsNaN(float64(avgPointY)) {
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flag_ = 1
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}
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}
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if flag_ == 1 {
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new_data = append(new_data, schema.NaN) // Pick this point from the bucket
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} else {
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new_data = append(new_data, data[maxAreaPoint]) // Pick this point from the bucket
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}
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prevMaxAreaPoint = maxAreaPoint // This MaxArea point is the next's prevMAxAreaPoint
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//move to the next window
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bucketLow = bucketMiddle
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bucketMiddle = bucketHigh
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}
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new_data = append(new_data, data[len(data)-1]) // Always add last
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return new_data, new_frequency, nil
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}
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