func evolve(fname string, size, length int, mutation, transfer float64, fragment, samplesize, numofgen int, ch chan bool) {
t0 := time.Now()
file, err := os.Create(fname)
if err != nil {
panic(err)
}
defer file.Close()
pop := fwd.NewSeqPop(size, length, mutation, transfer, fragment)
for i := 0; i < numofgen; i++ {
pop.Evolve()
// get #{samplesize} samples
rSeq := rand.Perm(pop.Size) // get a permutation sequence
samples := make([]fwd.Sequence, samplesize)
for j := 0; j < samplesize; j++ {
samples[j] = pop.Genomes[rSeq[j]]
}
// calculate the distance matrix for different lengthes
dmatrix := [][]int{}
for j := 0; j < samplesize; j++ {
for k := j + 1; k < samplesize; k++ {
a := samples[j]
b := samples[k]
ds := []int{}
for l := 0; l < len(a); l++ {
if a[l] != b[l] {
ds = append(ds, l)
}
}
dmatrix = append(dmatrix, ds)
}
}
// create cmatrix
cmatrix := covs.NewCMatrix(samplesize, pop.Length, dmatrix)
// calculate ks and vard
ks, vd := cmatrix.D()
// write to file
file.WriteString(fmt.Sprintf("%d,%g,%g\n", pop.NumOfGen, ks, vd))
// printing the process
if pop.NumOfGen%1000 == 0 {
t2 := time.Now()
fmt.Printf("Number of Generation with mutation = %f: %d, time used = %v\n", pop.Mutation, pop.NumOfGen, t2.Sub(t0))
}
}
ch <- true
}
func main() {
t0 := time.Now() // start time
log.Printf("Start up at: %v\n", t0)
// population paramters
length := 1000 // genome length = 100000
mutation := 0.0001 // mutation rate = 1e-4
transfer := 0.0 // transfer rate = 0
fragment := 0 // transferred length = 0
// simulation parameters
samplesize := 100
numofgen := 100000
sizearray := []int{100, 1000, 10000, 100000}
for _, size := range sizearray {
file, err := os.Create(fmt.Sprintf("D_size_%d.csv", size))
if err != nil {
panic(err)
}
pop := fwd.NewSeqPop(size, length, mutation, transfer, fragment)
for i := 0; i < numofgen; i++ {
pop.Evolve()
// get #{samplesize} samples
rSeq := rand.Perm(pop.Size) // get a permutation sequence
samples := make([]fwd.Sequence, samplesize)
for j := 0; j < samplesize; j++ {
samples[j] = pop.Genomes[rSeq[j]]
}
// calculate the distance matrix for different lengthes
dmatrix := [][]int{}
for j := 0; j < samplesize; j++ {
for k := j + 1; k < samplesize; k++ {
a := samples[j]
b := samples[k]
ds := []int{}
for l := 0; l < len(a); l++ {
if a[l] != b[l] {
ds = append(ds, l)
}
}
dmatrix = append(dmatrix, ds)
}
}
// create cmatrix
cmatrix := covs.NewCMatrix(samplesize, pop.Length, dmatrix)
// calculate ks and vard
ks, vd := cmatrix.D()
// write to file
file.WriteString(fmt.Sprintf("%d,%g,%g\n", pop.NumOfGen, ks, vd))
// printing the process
if pop.NumOfGen%1000 == 0 {
t2 := time.Now()
fmt.Printf("Number of Generation with size = %d: %d, time used = %v\n", pop.Size, pop.NumOfGen, t2.Sub(t0))
}
}
file.Close()
}
t1 := time.Now()
log.Printf("End at: %v\n", t1)
log.Printf("Duration: %v\n", t1.Sub(t0))
}
func main() {
t0 := time.Now() // start time
log.Printf("Start up at: %v\n", t0)
// population paramters
size := 1000 // population size = 1000
length := 100000 // genome length = 100000
mutation := 0.0001 // mutation rate = 1e-4
transfer := 0.0 // transfer rate = 0
fragment := 0 // transferred length = 0
// construct a population
pop := fwd.NewSeqPop(size, length, mutation, transfer, fragment)
// simulation parameters
samplesize := 100
numofgen := 100000
// create files
// file for length 100
file1, err := os.Create("D_length_100.csv")
if err != nil {
panic(err)
}
defer file1.Close()
// file for length 1000
file2, err := os.Create("D_length_1000.csv")
if err != nil {
panic(err)
}
defer file2.Close()
// file for length 10000
file3, err := os.Create("D_length_10000.csv")
if err != nil {
panic(err)
}
defer file3.Close()
// file for length 100000
file4, err := os.Create("D_length_100000.csv")
if err != nil {
panic(err)
}
defer file4.Close()
// do the simulation
for i := 0; i < numofgen; i++ {
pop.Evolve()
// get #{samplesize} samples
rSeq := rand.Perm(pop.Size) // get a permutation sequence
samples := make([]fwd.Sequence, samplesize)
for j := 0; j < samplesize; j++ {
samples[j] = pop.Genomes[rSeq[j]]
}
// calculate the distance matrix for different lengthes
dmatrix1 := [][]int{} // for length 100
dmatrix2 := [][]int{} // for length 1000
dmatrix3 := [][]int{} // for length 10000
dmatrix4 := [][]int{} // for length 100000
for j := 0; j < samplesize; j++ {
for k := j + 1; k < samplesize; k++ {
a := samples[j]
b := samples[k]
ds1 := []int{} // for length 100
ds2 := []int{} // for length 1000
ds3 := []int{} // for length 10000
ds4 := []int{} // for length 100000
for l := 0; l < len(a); l++ {
if a[l] != b[l] {
if l < 100 { // for length 100
ds1 = append(ds1, l)
}
if l < 1000 { // for length 1000
ds2 = append(ds2, l)
}
if l < 10000 { // for length 10000
ds3 = append(ds3, l)
}
ds4 = append(ds4, l)
}
}
dmatrix1 = append(dmatrix1, ds1)
dmatrix2 = append(dmatrix2, ds2)
dmatrix3 = append(dmatrix3, ds3)
dmatrix4 = append(dmatrix4, ds4)
}
}
// create cmatrix
cmatrix1 := covs.NewCMatrix(samplesize, 100, dmatrix1)
cmatrix2 := covs.NewCMatrix(samplesize, 1000, dmatrix2)
cmatrix3 := covs.NewCMatrix(samplesize, 10000, dmatrix3)
cmatrix4 := covs.NewCMatrix(samplesize, 100000, dmatrix4)
// calculate ks and vard
ks1, vd1 := cmatrix1.D()
ks2, vd2 := cmatrix2.D()
ks3, vd3 := cmatrix3.D()
ks4, vd4 := cmatrix4.D()
// write to file
file1.WriteString(fmt.Sprintf("%d,%g,%g\n", pop.NumOfGen, ks1, vd1))
file2.WriteString(fmt.Sprintf("%d,%g,%g\n", pop.NumOfGen, ks2, vd2))
file3.WriteString(fmt.Sprintf("%d,%g,%g\n", pop.NumOfGen, ks3, vd3))
file4.WriteString(fmt.Sprintf("%d,%g,%g\n", pop.NumOfGen, ks4, vd4))
// printing the process
if pop.NumOfGen%1000 == 0 {
fmt.Printf("Number of Generation: %d", pop.NumOfGen)
}
}
t1 := time.Now()
log.Printf("End at: %v\n", t1)
log.Printf("Duration: %v\n", t1.Sub(t0))
}