func simulateSome(b, e int, ch chan Result) {
for i := b; i < e; i++ {
sp := fwd.NewSeqPop(size, lens, mutation, transfer, frag)
sp.SetExpTime(exptime)
sp.Seed(i)
for j := 0; j < gens; j++ {
sp.Evolve()
}
seqs := sp.GetGenomes()
diffmatrix := [][]int{}
for j := 0; j < samp; j++ {
a := rand.Intn(size)
b := rand.Intn(size)
for a == b {
b = rand.Intn(size)
}
diff := []int{}
for k := 0; k < lens; k++ {
if seqs[a][k] != seqs[b][k] {
diff = append(diff, k)
}
}
diffmatrix = append(diffmatrix, diff)
}
cmatrix := covs.NewCMatrix(samp, lens, diffmatrix)
ks, vd := cmatrix.D()
scovs, rcovs, xyPL, xsysPL, smXYPL := cmatrix.CovCircle(maxl)
result := Result{
ks: ks,
vd: vd,
scovs: scovs,
rcovs: rcovs,
xyPL: xyPL,
xsysPL: xsysPL,
smXYPL: smXYPL,
}
ch <- result
}
}
func main() {
t0 := time.Now()
size := 1000
length := 10000
mutation := 0.001
transfer := 0.001
fragment := 1000
pop := fwd.NewSeqPop(size, length, mutation, transfer, fragment)
n := 1000
for i := 0; i < n; i++ {
pop.Evolve()
}
t1 := time.Now()
t := t1.Sub(t0)
fmt.Println(t)
}
func main() {
// create d file
dfile, err := os.Create(fmt.Sprintf("%s_d.csv", prefix))
if err != nil {
log.Panic(err)
}
defer dfile.Close()
// create population for simulation
sp := fwd.NewSeqPop(size, lens, mutation, transfer, frag)
// do eqvGens generations for reaching equilibrium
for i := 0; i < eqvGens; i++ {
sp.Evolve()
}
// create moments
moments := make([][]Moment, 5)
for i := 0; i < len(moments); i++ {
for j := 0; j < maxl; j++ {
m := Moment{
Mean: desc.NewMean(),
Sd: desc.NewStandardDeviationWithBiasCorrection(),
}
moments[i] = append(moments[i], m)
}
}
// do sample generations
for i := 0; i < gens; i++ {
sp.Evolve()
seqs := sp.GetGenomes()
diffmatrix := [][]int{}
for j := 0; j < samp; j++ {
a := rand.Intn(size)
b := rand.Intn(size)
for a == b {
b = rand.Intn(size)
}
diff := []int{}
for k := 0; k < lens; k++ {
if seqs[a][k] != seqs[b][k] {
diff = append(diff, k)
}
}
diffmatrix = append(diffmatrix, diff)
}
cmatrix := covs.NewCMatrix(samp, lens, diffmatrix)
ks, vd := cmatrix.D()
dfile.WriteString(fmt.Sprintf("%g,%g\n", ks, vd))
scovs, rcovs, xyPL, xsysPL, smXYPL := cmatrix.CovCircle(maxl)
for j := 0; j < maxl; j++ {
moments[0][j].Increment(scovs[j])
moments[1][j].Increment(rcovs[j])
moments[2][j].Increment(xyPL[j])
moments[3][j].Increment(xsysPL[j])
moments[4][j].Increment(smXYPL[j])
}
if (i+1)%(gens/100) == 0 {
cfile, err := os.Create(fmt.Sprintf("%s_covs.csv", prefix))
if err != nil {
log.Panic(err)
}
cfile.WriteString(fmt.Sprintf("#size: %d\n", size))
cfile.WriteString(fmt.Sprintf("#length: %d\n", lens))
cfile.WriteString(fmt.Sprintf("#fragment: %d\n", frag))
cfile.WriteString(fmt.Sprintf("#mutation: %g\n", mutation))
cfile.WriteString(fmt.Sprintf("#transfer: %g\n", transfer))
cfile.WriteString(fmt.Sprintf("#generations: %d\n", i+1))
cfile.WriteString("dist, scov, rcov, xy, xsys, smxy_sd, scov_sd, rcov_sd, xy_sd, xsys_sd, smxy_sd\n")
for j := 0; j < maxl; j++ {
cfile.WriteString(fmt.Sprintf("%d,%g,%g,%g,%g,%g,%g,%g,%g,%g,%g\n",
j,
moments[0][j].Mean.GetResult(),
moments[1][j].Mean.GetResult(),
moments[2][j].Mean.GetResult(),
moments[3][j].Mean.GetResult(),
moments[4][j].Mean.GetResult(),
moments[0][j].Sd.GetResult(),
moments[1][j].Sd.GetResult(),
moments[2][j].Sd.GetResult(),
moments[3][j].Sd.GetResult(),
moments[4][j].Sd.GetResult(),
))
}
cfile.Close()
log.Printf("Finish %%%d\n", (i+1)/(gens/100))
}
}
// save the population for further use
pfile, err := os.Create(fmt.Sprintf("%s.json", prefix))
if err != nil {
log.Panic(err)
}
defer pfile.Close()
pfile.Write(sp.Json())
}
