/
correlate.go
128 lines (107 loc) · 2.89 KB
/
correlate.go
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// Copyright 2019 Radiation Detection and Imaging (RDI), LLC
// Use of this source code is governed by the BSD 3-clause
// license that can be found in the LICENSE file.
package data
import (
"github.com/rditech/rdi-live/model/rdi/currentmode"
"github.com/proio-org/go-proio"
)
func CorrelateCmEvent(event *proio.Event) {
for _, entryId := range event.TaggedEntries("Mapped") {
frame, ok := event.GetEntry(entryId).(*currentmode.Frame)
if !ok {
continue
}
nSamples := len(frame.Sample)
if nSamples == 0 {
continue
}
nAxes := len(frame.Sample[0].Axis)
sum := make([]float64, nAxes)
prodSum := make([][]float64, nAxes)
cov := make([][]float64, nAxes)
for i := 0; i < nAxes; i++ {
prodSum[i] = make([]float64, nAxes)
cov[i] = make([]float64, nAxes)
}
for i := 0; i < nSamples; i++ {
sample := frame.Sample[i]
for j := 0; j < nAxes; j++ {
axisJSum := float64(sample.Axis[j].Sum)
sum[j] += axisJSum
for k := j; k < nAxes; k++ {
axisKSum := float64(sample.Axis[k].Sum)
prodSum[j][k] += axisJSum * axisKSum
}
}
}
for i := 0; i < nAxes; i++ {
for j := i; j < nAxes; j++ {
cov[i][j] = prodSum[i][j] - sum[i]*sum[j]/float64(nSamples)
}
}
corr := float64(1.0)
for i := 0; i < nAxes; i++ {
for j := i + 1; j < nAxes; j++ {
corr *= cov[i][j] * cov[i][j] / (cov[i][i] * cov[j][j])
}
}
frame.Correlation = float32(corr)
}
}
type Correlator struct {
NFrames int
Default float32
}
func (c *Correlator) CorrelateCmEvent(input <-chan *proio.Event, output chan<- *proio.Event) {
i := 0
for event := range input {
for _, entryId := range event.TaggedEntries("Mapped") {
i++
frame, ok := event.GetEntry(entryId).(*currentmode.Frame)
if !ok {
continue
}
if c.NFrames == 0 || i <= c.NFrames || (c.NFrames < 0 && i > -c.NFrames) {
nSamples := len(frame.Sample)
if nSamples == 0 {
continue
}
nAxes := len(frame.Sample[0].Axis)
sum := make([]float64, nAxes)
prodSum := make([][]float64, nAxes)
cov := make([][]float64, nAxes)
for i := 0; i < nAxes; i++ {
prodSum[i] = make([]float64, nAxes)
cov[i] = make([]float64, nAxes)
}
for i := 0; i < nSamples; i++ {
sample := frame.Sample[i]
for j := 0; j < nAxes; j++ {
axisJSum := float64(sample.Axis[j].Sum)
sum[j] += axisJSum
for k := j; k < nAxes; k++ {
axisKSum := float64(sample.Axis[k].Sum)
prodSum[j][k] += axisJSum * axisKSum
}
}
}
for i := 0; i < nAxes; i++ {
for j := i; j < nAxes; j++ {
cov[i][j] = prodSum[i][j] - sum[i]*sum[j]/float64(nSamples)
}
}
corr := float64(1.0)
for i := 0; i < nAxes; i++ {
for j := i + 1; j < nAxes; j++ {
corr *= cov[i][j] * cov[i][j] / (cov[i][i] * cov[j][j])
}
}
frame.Correlation = float32(corr)
continue
}
frame.Correlation = c.Default
}
output <- event
}
}