func analysis(ch chan Results) {
dfile, err := os.Create(fmt.Sprintf("%s_d.csv", prefix))
if err != nil {
panic(err)
}
defer dfile.Close()
dfile.WriteString(fmt.Sprintf("#size: %d\n", size))
dfile.WriteString(fmt.Sprintf("#length: %d\n", length))
dfile.WriteString(fmt.Sprintf("#fragment: %d\n", fragment))
dfile.WriteString(fmt.Sprintf("#mutation: %g\n", mutation))
dfile.WriteString(fmt.Sprintf("#transfer: %g\n", transfer))
dfile.WriteString(fmt.Sprintf("#sample: %d\n", sample))
dfile.WriteString("#ks, vd\n")
momentArr := make([][]Moments, 5)
for i := 0; i < len(momentArr); i++ {
for j := 0; j < maxl; j++ {
moments := Moments{
Mean: desc.NewMean(),
Sd: desc.NewStandardDeviationWithBiasCorrection(),
}
momentArr[i] = append(momentArr[i], moments)
}
}
for i := 0; i < repeats; i++ {
results := <-ch
dfile.WriteString(fmt.Sprintf("%g,%g\n", results.ks, results.vd))
for j := 0; j < maxl; j++ {
momentArr[0][j].Increment(results.scovs[j])
momentArr[1][j].Increment(results.rcovs[j])
momentArr[2][j].Increment(results.xyPL[j])
momentArr[3][j].Increment(results.xsysPL[j])
momentArr[4][j].Increment(results.smXYPL[j])
}
if (i+1)%(repeats/100) == 0 {
cfile, err := os.Create(fmt.Sprintf("%s_covs.csv", prefix))
if err != nil {
panic(err)
}
cfile.WriteString(fmt.Sprintf("#size: %d\n", size))
cfile.WriteString(fmt.Sprintf("#length: %d\n", length))
cfile.WriteString(fmt.Sprintf("#fragment: %d\n", fragment))
cfile.WriteString(fmt.Sprintf("#mutation: %g\n", mutation))
cfile.WriteString(fmt.Sprintf("#transfer: %g\n", transfer))
cfile.WriteString(fmt.Sprintf("#sample: %d\n", sample))
cfile.WriteString(fmt.Sprintf("#replicates: %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,
momentArr[0][j].Mean.GetResult(),
momentArr[1][j].Mean.GetResult(),
momentArr[2][j].Mean.GetResult(),
momentArr[3][j].Mean.GetResult(),
momentArr[4][j].Mean.GetResult(),
momentArr[0][j].Sd.GetResult(),
momentArr[1][j].Sd.GetResult(),
momentArr[2][j].Sd.GetResult(),
momentArr[3][j].Sd.GetResult(),
momentArr[4][j].Sd.GetResult(),
))
}
cfile.Close()
}
}
}
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())
}
func main() {
means := make([][]*desc.Mean, 5)
sds := make([][]*desc.StandardDeviation, 5)
for i := 0; i < 5; i++ {
means[i] = make([]*desc.Mean, maxl)
sds[i] = make([]*desc.StandardDeviation, maxl)
for j := 0; j < maxl; j++ {
means[i][j] = desc.NewMean()
sds[i][j] = desc.NewStandardDeviationWithBiasCorrection()
}
}
dfile, err := os.Create(prefix + "_d.csv")
if err != nil {
panic(err)
}
defer dfile.Close()
dfile.WriteString(fmt.Sprintf("#size: %d\n", size))
dfile.WriteString(fmt.Sprintf("#sample: %d\n", sample))
dfile.WriteString(fmt.Sprintf("#length: %d\n", length))
dfile.WriteString(fmt.Sprintf("#fragment: %d\n", fragment))
dfile.WriteString(fmt.Sprintf("#repeats: %d\n", repeats))
dfile.WriteString(fmt.Sprintf("#maxl: %d\n", maxl))
dfile.WriteString(fmt.Sprintf("#mutation: %g\n", mutation))
dfile.WriteString(fmt.Sprintf("#transfer: %g\n", transfer))
dfile.WriteString("#ks, vd\n")
runtime.GOMAXPROCS(runtime.NumCPU())
t0 := time.Now()
for c := 0; c < repeats; c++ {
w := coals.NewWFPopulation(size, sample, length, mutation, transfer, fragment)
w.Seed(c)
w.Backtrace()
seqs := w.Fortrace()
diffmatrix := [][]int{}
for i := 0; i < sample; i++ {
for j := i + 1; j < sample; j++ {
diff := []int{}
for k := 0; k < length; k++ {
if seqs[i][k] != seqs[j][k] {
diff = append(diff, k)
}
}
diffmatrix = append(diffmatrix, diff)
}
}
cmatrix := covs.NewCMatrix(len(diffmatrix), length, diffmatrix)
ks, vd := cmatrix.D()
dfile.WriteString(fmt.Sprintf("%g,%g\n", ks, vd))
scovs, rcovs, xyPL, xsysPL, smXYPL := cmatrix.CovCircle(maxl)
for l := 0; l < maxl; l++ {
means[0][l].Increment(scovs[l])
means[1][l].Increment(rcovs[l])
means[2][l].Increment(xyPL[l])
means[3][l].Increment(xsysPL[l])
means[4][l].Increment(smXYPL[l])
sds[0][l].Increment(scovs[l])
sds[1][l].Increment(rcovs[l])
sds[2][l].Increment(xyPL[l])
sds[3][l].Increment(xsysPL[l])
sds[4][l].Increment(smXYPL[l])
}
if (c+1)%(repeats/100) == 0 {
t1 := time.Now()
fmt.Printf("%d%%,%v\n", (c+1)/(repeats/100), t1.Sub(t0))
covfile, err := os.Create(prefix + "_covs.csv")
if err != nil {
fmt.Println(err)
}
covfile.WriteString(fmt.Sprintf("#size: %d\n", size))
covfile.WriteString(fmt.Sprintf("#sample: %d\n", sample))
covfile.WriteString(fmt.Sprintf("#length: %d\n", length))
covfile.WriteString(fmt.Sprintf("#fragment: %d\n", fragment))
covfile.WriteString(fmt.Sprintf("#repeats: %d\n", repeats))
covfile.WriteString(fmt.Sprintf("#maxl: %d\n", maxl))
covfile.WriteString(fmt.Sprintf("#mutation: %g\n", mutation))
covfile.WriteString(fmt.Sprintf("#transfer: %g\n", transfer))
covfile.WriteString("#dist, scov, rcov, xy, xsys, smxy, scov_sd, rcov_sd, xy_sd, xsys_sd, smxy_sd\n")
for i := 0; i < maxl; i++ {
covfile.WriteString(fmt.Sprintf("%d,%g,%g,%g,%g,%g,%g,%g,%g,%g,%g\n",
i,
means[0][i].GetResult(),
means[1][i].GetResult(),
means[2][i].GetResult(),
means[3][i].GetResult(),
means[4][i].GetResult(),
sds[0][i].GetResult()/math.Sqrt(float64(repeats)),
sds[1][i].GetResult()/math.Sqrt(float64(repeats)),
sds[2][i].GetResult()/math.Sqrt(float64(repeats)),
sds[3][i].GetResult()/math.Sqrt(float64(repeats)),
sds[4][i].GetResult()/math.Sqrt(float64(repeats)),
))
}
covfile.Close()
}
}
covfile, err := os.Create(prefix + "_covs.csv")
if err != nil {
fmt.Println(err)
}
defer covfile.Close()
covfile.WriteString(fmt.Sprintf("#size: %d\n", size))
covfile.WriteString(fmt.Sprintf("#sample: %d\n", sample))
covfile.WriteString(fmt.Sprintf("#length: %d\n", length))
covfile.WriteString(fmt.Sprintf("#fragment: %d\n", fragment))
covfile.WriteString(fmt.Sprintf("#repeats: %d\n", repeats))
covfile.WriteString(fmt.Sprintf("#maxl: %d\n", maxl))
covfile.WriteString(fmt.Sprintf("#mutation: %g\n", mutation))
covfile.WriteString(fmt.Sprintf("#transfer: %g\n", transfer))
covfile.WriteString("#dist, scov, rcov, xy, xsys, smxy, scov_sd, rcov_sd, xy_sd, xsys_sd, smxy_sd\n")
for i := 0; i < maxl; i++ {
covfile.WriteString(fmt.Sprintf("%d,%g,%g,%g,%g,%g,%g,%g,%g,%g,%g\n",
i,
means[0][i].GetResult(),
means[1][i].GetResult(),
means[2][i].GetResult(),
means[3][i].GetResult(),
means[4][i].GetResult(),
sds[0][i].GetResult()/math.Sqrt(float64(repeats)),
sds[1][i].GetResult()/math.Sqrt(float64(repeats)),
sds[2][i].GetResult()/math.Sqrt(float64(repeats)),
sds[3][i].GetResult()/math.Sqrt(float64(repeats)),
sds[4][i].GetResult()/math.Sqrt(float64(repeats)),
))
}
}
func main() {
ncpu := runtime.NumCPU()
runtime.GOMAXPROCS(ncpu)
ch := make(chan Result, ncpu)
for i := 0; i < ncpu; i++ {
b := i * reps / ncpu
e := (i + 1) * reps / ncpu
go simulateSome(b, e, ch)
}
dfile, err := os.Create(fmt.Sprintf("%s_d.csv", prefix))
if err != nil {
log.Panic(err)
}
defer dfile.Close()
dfile.WriteString(fmt.Sprintf("#size: %d\n", size))
dfile.WriteString(fmt.Sprintf("#length: %d\n", lens))
dfile.WriteString(fmt.Sprintf("#fragment: %d\n", frag))
dfile.WriteString(fmt.Sprintf("#mutation: %g\n", mutation))
dfile.WriteString(fmt.Sprintf("#transfer: %g\n", transfer))
dfile.WriteString(fmt.Sprintf("#generations: %d\n", gens))
dfile.WriteString(fmt.Sprintf("#sample: %d\n", samp))
dfile.WriteString("#ks, vd\n")
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)
}
}
for i := 0; i < reps; i++ {
result := <-ch
dfile.WriteString(fmt.Sprintf("%g, %g\n", result.ks, result.vd))
for j := 0; j < maxl; j++ {
moments[0][j].Increment(result.scovs[j])
moments[1][j].Increment(result.rcovs[j])
moments[2][j].Increment(result.xyPL[j])
moments[3][j].Increment(result.xsysPL[j])
moments[4][j].Increment(result.smXYPL[j])
}
if (i+1)%(reps/100) == 0 {
err = dfile.Sync()
if err != nil {
log.Panic(err)
}
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", gens))
cfile.WriteString(fmt.Sprintf("#replicates: %d\n", i+1))
cfile.WriteString(fmt.Sprintf("#sample: %d\n", samp))
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)/(reps/100))
}
}
}
func main() {
momentArr := make([][]Moments, 5)
for i := 0; i < num; i++ {
filename := fmt.Sprintf("%s/covs_%d.csv", dir, i)
f, err := os.Open(filename)
if err != nil {
log.Panic(err)
}
reader := csv.NewReader(f)
reader.Comment = '#'
records, err := reader.ReadAll()
if err != nil {
log.Panic(err)
}
for j, rec := range records {
if j >= len(momentArr[0]) {
for k := 0; k < len(momentArr); k++ {
m := Moments{
Mean: desc.NewMean(),
Sd: desc.NewStandardDeviationWithBiasCorrection(),
}
momentArr[k] = append(momentArr[k], m)
}
}
for k := 0; k < len(momentArr); k++ {
v, err := strconv.ParseFloat(rec[k+1], 64)
if err != nil {
log.Panic(err)
}
momentArr[k][j].Increment(v)
}
}
f.Close()
}
filename := fmt.Sprintf("%s_covs.csv", path.Base(dir))
f, err := os.Create(filename)
if err != nil {
log.Panic(err)
}
defer f.Close()
for i := 0; i < len(momentArr[0]); i++ {
f.WriteString(fmt.Sprintf("%d,%g,%g,%g,%g,%g,%g,%g,%g,%g,%g\n",
i,
momentArr[0][i].Mean.GetResult(),
momentArr[1][i].Mean.GetResult(),
momentArr[2][i].Mean.GetResult(),
momentArr[3][i].Mean.GetResult(),
momentArr[4][i].Mean.GetResult(),
momentArr[0][i].Sd.GetResult(),
momentArr[1][i].Sd.GetResult(),
momentArr[2][i].Sd.GetResult(),
momentArr[3][i].Sd.GetResult(),
momentArr[4][i].Sd.GetResult(),
))
}
}
