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