// calcInChunck does calculation in a chunck of SNPs.
func (cmd *cmdCt) calcInChunck(snpChan chan *pileup.SNP) *calc.Calculator {
// Create job channel.
// Each job is a array of SNPs, which we will calculate
// correlations of the first SNP with the rest ones.
jobChan := make(chan []*pileup.SNP)
go func() {
defer close(jobChan)
arr := []*pileup.SNP{}
for s := range snpChan {
arr = append(arr, s)
lag := s.Pos - arr[0].Pos
if lag < 0 {
cmd.panic("SNPs are not in order.")
}
if lag >= cmd.maxl {
jobChan <- arr
arr = arr[1:]
}
}
jobChan <- arr
}()
// Make ncpu workers.
// For each worker, do the calculation,
// and push the result into a channel.
ncpu := runtime.GOMAXPROCS(0)
c := make(chan *calc.Calculator)
for i := 0; i < ncpu; i++ {
go func() {
covs := calc.New(cmd.maxl)
maxN := 10000
xArr := make([]float64, maxN)
yArr := make([]float64, maxN)
for arr := range jobChan {
cmd.calcSNPArr(arr, covs, xArr, yArr)
}
c <- covs
}()
}
// Wait for all the worker,
// and collect their results.
var covs *calc.Calculator
for i := 0; i < ncpu; i++ {
cc := <-c
if i == 0 {
covs = cc
} else {
covs.Append(cc)
}
}
return covs
}
