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run_sims_AJmodel1.py
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run_sims_AJmodel1.py
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import subprocess
from subprocess import Popen,PIPE,call
import os
import string
from string import join
import math
import random
from random import randint
from random import uniform
from random import gauss
from random import gammavariate
from random import betavariate
from math import sqrt
import sys
from sys import argv
import datetime
import macsSwig
from bisect import bisect_left
from bisect import bisect_right
import numpy as np
import re
###summary statistics####################################
def hamming_distance(s1, s2):
#Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different
assert len(s1) == len(s2)
return sum(ch1 != ch2 for ch1, ch2 in zip(s1, s2))
def base_S_ss(seq,nbsites):
#print 'base_S_ss'
#print 'nbsites', nbsites
spec_zero=[]
for g in range(len(seq)-1):
spec_zero.append(0)
var_ss=0 #Segregating sites
#het_sum=0.0
alleles=zip(*seq)
for g in range(nbsites):
#print 'g', g
if 0<list(alleles[g]).count('1')<(list(alleles[g]).count('1')+list(alleles[g]).count('0')): ##this ignores sites that have all zeros, or all ones
var_ss=var_ss+1
#het_sum=het_sum+((float(list(alleles[g]).count('1'))/float((list(alleles[g]).count('1')+list(alleles[g]).count('0'))))**2)
spec_zero[list(alleles[g]).count('1')-1]=spec_zero[list(alleles[g]).count('1')-1]+1
if var_ss>0:
#het=1.0-((1/float(var_ss))*het_sum)
Ns=spec_zero[0]+spec_zero[-1] ##number of singletons
Nd=spec_zero[1]+spec_zero[-2] ##number of dupletons
else:
#het='NA'
Ns=0
Nd=0
##Nds=spec_zero[0] ##number of derived singletons
return [var_ss,Ns,Nd,spec_zero]
def base_h_ss(seq):
#print seq
#A set is an unordered collection with no duplicate elements
haps=list(set(seq)) #makes a list of the different haplotypes found in seq
#print haps
cnt=[]
hi=0 #hi is the mode of the haplotypes
for g in range(len(haps)):
cnt.append(seq.count(haps[g])) #count() returns the number of occurrences of substring sub (in this case, haplotypes) in seq
#print cnt[g]
if cnt[g]>hi: #you are keeping the highest occurrence of all the haplotypes, which the mode
hi=cnt[g]
#The average pairwise difference within a population can be calculated as the sum of the pairwise differences divided by the number of pairs.
pwd_dist=0 #parwise differences between haplotypes??
for g in range(len(cnt)-1): #count will have the same size as haps
m=g+1
while m<len(cnt):
pwd_dist=pwd_dist+(hamming_distance(haps[g], haps[m])*(cnt[g]*cnt[m]))
m=m+1
#p is the total sum of the pairwise differences
#p=pwd_dist*(2.0/(float(len(seq))*(float(len(seq))-1)))
#print p
#this function returns the number of different haplotypes and the mode
return [len(haps),hi]
def base_h_ss_nosing(seq): #calculates the number of different haplotypes and the mode, but ignoring singleton haplotypes
d={}
d=dict((i,seq.count(i)) for i in seq)
d2=[] #d2 has the list of the haplotypes that occur more than once in the population
for k in d.keys():
#print k #this gives you the values/haplotypes of the dictionary
#print d[k]
if d[k]!=1:
#print k
d2.append(k)
cnt=[]
hi=0 #hi is the mode of the haplotypes
for g in range(len(d2)):
cnt.append(seq.count(d2[g])) #count() returns the number of occurrences of substring sub (in this case, haplotypes) in seq
#print cnt[g]
if cnt[g]>hi: #you are keeping the highest occurrence of all the haplotypes, which the mode
hi=cnt[g]
#this function returns the number of different haplotypes and the mode
return [len(d2),hi]
def pri_sha_h_nosing(seqs1,seqs2):
d1={}
d1=dict((i,seqs1.count(i)) for i in seqs1)
d1_2=[] #d2 has the list of the haplotypes that occur more than once in the population
for k in d1.keys():
if d1[k]!=1:
d1_2.append(k)
d2={}
d2=dict((i,seqs2.count(i)) for i in seqs2)
d2_2=[] #d2 has the list of the haplotypes that occur more than once in the population
for l in d2.keys():
if d2[l]!=1:
d2_2.append(l)
priA=0
priB=0
sha=0
#this puts the two sequences together in the same array
seqs=d1_2[:]
seqs.extend(d2_2)
haps=list(set(seqs)) #makes a list of the different haplotypes found in all populations
for g in range(len(haps)):
pop1_cnt=d1_2.count(haps[g]) #count() returns the number of occurrences of substring sub (in this case, haplotypes) in seqs1, which population 1
pop2_cnt=d2_2.count(haps[g])
if pop1_cnt>0 and pop2_cnt>0:
sha=sha+1
elif pop1_cnt>0 and pop2_cnt==0:
priA=priA+1
elif pop1_cnt==0 and pop2_cnt>0:
priB=priB+1
return [sha,priA,priB]
def foldedAFS(array):
delta=0
n=len(array)
#print n
unfolded=[]
for g in range((n/2)+1):
unfolded.append(0)
#print len(unfolded)
for g in xrange((n/2)+1):
#print g
if g != (n-1-g):
delta=0
else:
delta=1
#print 'array[g]',array[g]
#print 'array[n]',array[(n-1)]
#print 'array[n-g]', array[(n-1)-g]
#print 'delta', delta
#print g
#print (n-1)-g
unfolded[g]=(array[g]+array[(n-1)-g])/(1+delta)
#print 'unfolded[g]', unfolded[g]
return unfolded
def Pi2(spec,n): #standard pi, n = sample size (in chromosomes)
theta_pi=0.0
for g in range(len(spec)):
theta_pi=theta_pi+(2.0*float(spec[g])*(g+1.0)*(n-(g+1.0)))/(n*(n-1.0))
return theta_pi
def Tajimas(p,S,n):
### pi, number of segregating sites, and number of chromosomes
if (S==0):
#return 'NA'
return 0
else:
a1=0.0
for g in range(n-1):
a1=a1+(1.0/(g+1.0))
#print a1
a2=0.0
for g in range(n-1):
a2=a2+(1.0/((g+1.0)**2))
b1=(n+1.0)/(3.0*(n-1.0))
b2=(2*((n**2.0)+n+3))/((9*n)*(n-1))
c1=b1-(1.0/a1)
#print 'c1', c1
c2=b2-((n+2.0)/(a1*n))+(a2/(a1**2.0))
e1=c1/a1
#print 'e1', e1
e2=c2/((a1**2.0)+a2)
#print 'e2', e2
TajD=(p-(S/a1))/(sqrt((e1*S)+((e2*S)*(S-1.0))))
return TajD
def FST(geno1,geno2,nbsites):
r=2.0
n1=float(len(geno1))#how many individuals
n2=float(len(geno2))
n_bar=(n1/r)+(n2/r)#average sample size
nc=((r*n_bar)-(((n1**2)/(r*n_bar))+((n2**2)/(r*n_bar))))/(r-1.0)
a_sum=0.0
abc_sum=0.0
nsit_us=0
for g in range(nbsites):
#print g
aa1=0 #how many homozygotes 0 in 1
ab1=0 #how many heterozygotes in 1
bb1=0 #how many homozygotes 1 in 1
aa2=0
ab2=0
bb2=0
for n in range(len(geno1)):
ball=geno1[n][0][g].count('1')+geno1[n][1][g].count('1')#to get the information from geno, count how many ones in one pair
if ball==0:
aa1=aa1+1
elif ball==1:
ab1=ab1+1
elif ball==2:
bb1=bb1+1
for n in range(len(geno2)):
ball=geno2[n][0][g].count('1')+geno2[n][1][g].count('1')
if ball==0:
aa2=aa2+1
elif ball==1:
ab2=ab2+1
elif ball==2:
bb2=bb2+1
if 0<aa1+aa2<(len(geno1)+len(geno2)):
#print 'if'
p1=float((bb1*2.0)+ab1)/(n1*2.0)#get frequency of the derived allele in the two populations
#print 'p1'
#print p1
p2=float((bb2*2.0)+ab2)/(n2*2.0)
#print 'p2'
#print p2
p_bar=((n1*p1)/(r*n_bar))+((n2*p2)/(r*n_bar)) #average allele frequency for that site
#print 'p_bar'
#print p_bar
s_sq=(n1*((p1-p_bar)**2.0))/((r-1.0)*n_bar)+(n2*((p2-p_bar)**2.0))/((r-1.0)*n_bar)
#print 's_sq'
#print s_sq
h1=float(ab1)/n1 #frequency of the heterozygotes in population 1
#print 'h1'
#print h1
h2=float(ab2)/n2
##print 'h2'
#print h2
h_bar=((n1*h1)/(r*n_bar))+((n2*h2)/(r*n_bar))
#print 'h_bar'
#print h_bar
a=(n_bar/nc)*((s_sq)-(1.0/(n_bar-1.0))*((p_bar*(1.0-p_bar))-(((r-1.0)/r)*s_sq)-(h_bar/4.0)))
#print 'a'
#print a
b=(n_bar/(n_bar-1))*(((p_bar)*(1.0-p_bar))-(((r-1.0)/r)*s_sq)-((((2.0*n_bar)-1)/(4*n_bar))*h_bar))
#print 'b'
#print b
c=(1.0/2.0)*h_bar
#print 'c'
#print c
a_sum=a_sum+a
abc_sum=abc_sum+(a+b+c)
#print 'abc_sum'
#print abc_sum
nsit_us=nsit_us+1
else:
nsit_us=nsit_us
if abc_sum==0.0:
theta='NA'
else:
theta=a_sum/abc_sum
return theta
def Pi(seq1,nseq1):
k1=0
for i in xrange(0,nseq1):
for j in xrange(i+1,nseq1):
k1=k1+hamming_distance(seq1[i],seq1[j])
p1=(2/(float(nseq1)*(float(nseq1)-1)))*k1
return p1
def FST2(seq1,pi1,nseq1,seq2,pi2,nseq2): ###FST based on pi within populations and between populations
###number of chromosomes
k3=0
##Pi within populations
pw=(pi1+pi2)/2
#print 'pw', pw
for i in xrange(len(seq1)):
for j in xrange(len(seq2)):
k3=k3+hamming_distance(seq1[i],seq2[j])
pb=k3/(float(nseq1)*float(nseq2))
#print 'pb', pb
if (pb==0):
#return 'NA'
return '0'
else:
fst=float(1-(pw/pb))
return fst
def pri_sha_h(seqs1,seqs2):
#how many haplotypes are shared and how many are private to the populations
priA=0
priB=0
sha=0
#this puts the two sequences together in the same array
seqs=seqs1[:]
seqs.extend(seqs2)
haps=list(set(seqs)) #makes a list of the different haplotypes found in all populations
for g in range(len(haps)):
pop1_cnt=seqs1.count(haps[g]) #count() returns the number of occurrences of substring sub (in this case, haplotypes) in seqs1, which population 1
pop2_cnt=seqs2.count(haps[g])
#print pop1_cnt
#print pop2_cnt
if pop1_cnt>0 and pop2_cnt>0:
sha=sha+1
elif pop1_cnt>0 and pop2_cnt==0:
priA=priA+1
elif pop1_cnt==0 and pop2_cnt>0:
priB=priB+1
return [sha,priA,priB]
#find simulated snps closest to snps from chip in pop A with maf>0.05 in n indivs
def find(a, x, alleles, na_samp, daf, sim):
if x <= a[0]: #x is smaller than first value in a
k=0
while float(alleles[k][0:na_samp].count('1')/float(na_samp))<daf or float(alleles[k][0:na_samp].count('1')/float(na_samp))>1-daf:
k=k+1
return k
if x >= a[len(a)-1]: #x is larger than last value in a
j=len(a)-1
while float(alleles[j][0:na_samp].count('1')/float(na_samp))<daf or float(alleles[j][0:na_samp].count('1')/float(na_samp))>1-daf:
j=j-1
return j
#Find leftmost item greater than or equal to x
else:
i = bisect_left(a, x)
k=i
j=i
#walk up simulated sites
while k<int(len(a)-1) and float(alleles[k][0:na_samp].count('1')/float(na_samp))<daf or float(alleles[k][0:na_samp].count('1')/float(na_samp))>1-daf:
k=k+1
#walk down simulated sites
while int(j)>0 and float(alleles[j][0:na_samp].count('1')/float(na_samp))<daf or float(alleles[j][0:na_samp].count('1')/float(na_samp))>1-daf:
j=j-1
if float(alleles[j][0:na_samp].count('1')/float(na_samp))<daf or float(alleles[j][0:na_samp].count('1')/float(na_samp))>1-daf:
if float(alleles[k][0:na_samp].count('1')/float(na_samp))<daf or float(alleles[k][0:na_samp].count('1')/float(na_samp))>1-daf: #no simulated sites meet daf cutoff
print 'poopjk!'
else: #all simulated sites to the left of i do not meet daf cutoff
return k
elif float(alleles[k][0:na_samp].count('1')/float(na_samp))<daf or float(alleles[k][0:na_samp].count('1')/float(na_samp))>1-daf: #all simulated sites to the right of i do not meet daf cutoff
return j
elif int(a[k]-x)<int(x-a[j]): #the position greater than x is closer
return k
else: #the position less than x is closer
return j
##This function will receive the array with available sites (sites that passed the frequency cut-off)
def find2(a, x):
#print a
if x <= a[0]: #x is smaller than first value in a
#print a[0]
return 0
elif x >= a[len(a)-1]: #x is larger than last value in a
#print a[len(a)-1]
return len(a)-1
#Find leftmost item greater than or equal to x
else:
#print 'entro al ese, bisect'
i = bisect_right(a, x)
#print 'i', i
#print a[i]
j = i-1
#print 'j', j
#print a[j]
d1=abs(a[i]-x)
d2=abs(a[j]-x)
if d2<d1:
#print 'return j'
return j
elif d1<d2:
#print 'return i'
return i
elif d1==d2:
return i
#print a[i-1]
#return i-1
def add_snps(avail_sites, nb_avail_sites, pos_asc, nbss_asc, nb_array_snps):
first_index=pos_asc[0]
last_index=pos_asc[-1]
if(nb_avail_sites>nbss_asc): ###this should happen all the time
if (last_index<nb_avail_sites-1):
try:
avail_sites[last_index+1]
except:
print "well, it WASN'T defined after all!"
try:
avail_sites[first_index-1]
except:
print "well, it WASN'T defined after all!"
else:
for i in xrange(len(pos_asc)):
pos_asc[i]=pos_asc[i]-1
else:
print "sure, it was defined"
pos_asc.append(last_index+1)
elif (last_index==nb_avail_sites-1):
if(first_index-1)>=0:
pos_asc.insert(0,first_index-1)
return pos_asc
def param_sim_asc(): ##get parameter values from the priors
#print "choosing param values"
para_out=[]
parameters={}
###Discovery panel
low=2
high=20
asc_nb_af=randint(low,high)
asc_nb_eu=randint(low,high)
asc_nb_as=randint(low,high)
para_out.extend([asc_nb_af])
para_out.extend([asc_nb_eu])
para_out.extend([asc_nb_as])
daf=random.uniform(0.05,0.10)
para_out.extend([daf])
####Demographic model
#population size in Africa
NAF=float(round(10**random.uniform(3.7,5.0)))
para_out.extend([math.log10(NAF)])
parameters['NAF']=NAF
#print NAF
#Ancestral population size, before population growth in AF
#choose ancestral Ne based on being some value smaller than NAF between 1 or 0.1 x.
#population growth
Nrat_High=0.0 #Allow only growth for now
Nrat_Low=-1.0
NANC=float(round((10**random.uniform(Nrat_Low,Nrat_High))*NAF))
para_out.extend([math.log10(NANC)])
parameters['NANC']=NANC
NCEU=float(round(10**random.uniform(3.0,5.0)))
para_out.extend([math.log10(NCEU)])
parameters['NCEU']=NCEU
NCHB=float(round(10**random.uniform(3.0,5.0)))
para_out.extend([math.log10(NCHB)])
parameters['NCHB']=NCHB
#Ancestral population size of Eurasians
#NEU_AS=float(randint(1500,5000))
#para_out.extend([NEU_AS])
#parameters['NEU_AS']=NEU_AS
#Population size of AJ
NA=float(round(10**random.uniform(4.0,6.7)))
para_out.extend([math.log10(NA)])
parameters['NA']=NA
#Population size of Jews
NJ=float(round(10**random.uniform(3.0,6.0)))
para_out.extend([math.log10(NJ)])
parameters['NJ']=NJ
#Population size of Middle Easterns
NM=float(round(10**random.uniform(3.0,6.0)))
para_out.extend([math.log10(NM)])
parameters['NM']=NM
#Growth rate in AJ
rA=float(round(10**random.uniform(0,1))) #should it be -1?
para_out.extend([math.log10(rA)])
parameters['rA']=rA
#Growth rate in Jews and Middle East
rMJ=float(round(10**random.uniform(0,1))) #should it be -1?
para_out.extend([math.log10(rMJ)])
parameters['rMJ']=rMJ
#migration rate from Europe to AJ
m_High=1
m_Low=0
m=float(randint(m_Low,m_High))
para_out.extend([m])
parameters['m']=m
#Time of the instantaneous growth in Africa, before the split between Africans and non Africans
Tgrowth_Low=1
Tgrowth_High=4100
Tgrowth_Af=float(randint(Tgrowth_Low,Tgrowth_High))
parameters['Tgrowth_Af']=Tgrowth_Af
para_out.extend([Tgrowth_Af])
#Time of split between YRI and CEU/CHB
Taf_High=4100 #102,500 years using 25 years per generation
Taf_Low=1600 #40,000 years using 25 years per generation.
Taf=float(randint(Taf_Low,Taf_High))
para_out.extend([Taf])
parameters['Taf']=Taf
#Time of split between Europe and Middle East
TEM_High=1200
TEM_Low=400
TEM=float(randint(TEM_Low,TEM_High))
para_out.extend([TEM])
parameters['TEM']=TEM
#Time of split between CEU and CHB
Teu_as_High=int(Taf)-1
Teu_as_Low=int(TEM)+1
Teu_as=float(randint(Teu_as_Low,Teu_as_High))
para_out.extend([Teu_as])
parameters['Teu_as']=Teu_as
#Time of split between Jews and AJ
TA_High=36
TA_Low=20
TA=float(randint(TA_Low,TA_High))
para_out.extend([TA])
parameters['TA']=TA
#Time of split between Jews and Middle East
TMJ_High=int(TEM)-1
TMJ_Low=int(TA)+1
TMJ=float(randint(TMJ_Low,TMJ_High))
para_out.extend([TMJ])
parameters['TMJ']=TMJ
#Time of migration
Tm_High=int(TA)-1
Tm_Low=16
Tm=float(randint(Tm_Low,Tm_High))
para_out.extend([Tm])
parameters['Tm']=Tm
##choose model/topology
print "choosing case"
#################
if(parameters['Tgrowth_Af'] > parameters['Taf']):
case=1
if(parameters['Tgrowth_Af'] == parameters['Taf']):
Tgrowth_Af+=0.00001
parameters['Tgrowth_Af']=Tgrowth_Af
case=1
################
if(parameters['Taf'] > parameters['Tgrowth_Af'] > parameters['Teu_as']):
case=2
if(parameters['Taf'] > parameters['Tgrowth_Af'] == parameters['Teu_as']):
Tgrowth_Af+=0.00001
parameters['Tgrowth_Af']=Tgrowth_Af
case=2
################
if(parameters['Taf'] > parameters['Teu_as'] > parameters['Tgrowth_Af'] > parameters['TEM']):
case=3
if(parameters['Taf'] > parameters['Teu_as'] > parameters['Tgrowth_Af'] == parameters['TEM']):
Tgrowth_Af+=0.00001
parameters['Tgrowth_Af']=Tgrowth_Af
case=3
################
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['Tgrowth_Af'] > parameters['TMJ']):
case=4
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['Tgrowth_Af'] == parameters['TMJ']):
Tgrowth_Af+=0.00001
parameters['Tgrowth_Af']=Tgrowth_Af
case=4
################
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['TMJ'] > parameters['Tgrowth_Af'] > parameters['TA']):
case=5
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['TMJ'] > parameters['Tgrowth_Af'] == parameters['TA']):
Tgrowth_Af+=0.00001
parameters['Tgrowth_Af']=Tgrowth_Af
case=5
################
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['TMJ'] > parameters['TA'] > parameters['Tgrowth_Af'] > parameters['Tm']):
case=6
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['TMJ'] > parameters['TA'] > parameters['Tgrowth_Af'] == parameters['Tm']):
Tgrowth_Af+=0.00001
parameters['Tgrowth_Af']=Tgrowth_Af
case=6
################
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['TMJ'] > parameters['TA'] > parameters['Tm'] > parameters['Tgrowth_Af']):
case=7
if(parameters['Taf'] > parameters['Teu_as'] > parameters['TEM'] > parameters['TMJ'] > parameters['TA'] > parameters['Tm'] == parameters['Tgrowth_Af']):
Tgrowth_Af+=-0.00001
parameters['Tgrowth_Af']=Tgrowth_Af
case=7
################
#################
#print 'params:', parameters
#print 'case:',case
return [parameters, para_out, case, daf]
def run_sim(parameters,case,length,chr_number):
mu=2.5e-8
rho=1e-8
NAF=float(parameters['NAF'])
NANC=float(parameters['NANC'])
NCEU=float(parameters['NCEU'])
NCHB=float(parameters['NCHB'])
#NEU_AS=float(parameters['NEU_AS'])
NA=float(parameters['NA'])
NJ=float(parameters['NJ'])
NM=float(parameters['NM'])
rA=float(parameters['rA'])
rMJ=float(parameters['rMJ'])
m=float(parameters['m'])
Tgrowth_Af=float(parameters['Tgrowth_Af'])
Taf=float(parameters['Taf'])
TEM=float(parameters['TEM'])
Teu_as=float(parameters['Teu_as'])
TA=float(parameters['TA'])
TMJ=float(parameters['TMJ'])
Tm=float(parameters['Tm'])
macs_theta=float(mu*4*NANC)
macs_rho=float(rho*4*NANC)
scaled_NAF=float(NAF/NANC)
scaled_NANC=float(NANC/NANC)
scaled_NCEU=float(NCEU/NANC)
scaled_NCHB=float(NCHB/NANC)
#scaled_NEU_AS=float(NEU_AS/NANC)
scaled_NA=float(NA/NANC)
scaled_NJ=float(NJ/NANC)
scaled_NM=float(NM/NANC)
scaled_m=float(4*m*NANC)
scaled_Tgrowth_Af=float(Tgrowth_Af/(4*NANC))
scaled_Taf=float(Taf/(4*NANC))
scaled_TEM=float(TEM/(4*NANC))
scaled_Teu_as=float(Teu_as/(4*NANC))
scaled_TA=float(TA/(4*NANC))
scaled_TMJ=float(TMJ/(4*NANC))
scaled_Tm=float(Tm/(4*NANC))
#############
if case==1:
macs_args = ['macs',str(total),str(length),'-t',str(macs_theta),'-r',str(macs_rho),'-h','1e5','-R','genetic_map_b37/genetic_map_GRCh37_chr'+str(chr_number)+'.txt.macshs','-I','6',str(total_naf),str(total_nas),str(total_neu),str(nJ),str(nM),str(nA),'-n','1',str(scaled_NAF),'-n','2',str(scaled_NCHB),'-n','3',str(scaled_NCEU),'-n','4',str(scaled_NJ),'-n','5',str(scaled_NM),'-n','6',str(scaled_NA),'-eg','0','6',str(rA),'-eg','0.000001','4',str(rMJ),'-eg','0.000002','5',str(rMJ),'-em',str(scaled_Tm),'6','3',str(scaled_m),'-em',str(scaled_Tm+0.000001),'6','3','0','-ej',str(scaled_TA),'6','4','-ej',str(scaled_TMJ),'5','4','-ej',str(scaled_TEM),'4','3','-ej',str(scaled_Teu_as),'3','2','-ej',str(scaled_Taf),'2','1','-en',str(scaled_Tgrowth_Af),'1',str(scaled_NANC)]
if case==2:
macs_args = ['macs',str(total),str(length),'-t',str(macs_theta),'-r',str(macs_rho),'-h','1e5','-R','genetic_map_b37/genetic_map_GRCh37_chr'+str(chr_number)+'.txt.macshs','-I','6',str(total_naf),str(total_nas),str(total_neu),str(nJ),str(nM),str(nA),'-n','1',str(scaled_NAF),'-n','2',str(scaled_NCHB),'-n','3',str(scaled_NCEU),'-n','4',str(scaled_NJ),'-n','5',str(scaled_NM),'-n','6',str(scaled_NA),'-eg','0','6',str(rA),'-eg','0.000001','4',str(rMJ),'-eg','0.000002','5',str(rMJ),'-em',str(scaled_Tm),'6','3',str(scaled_m),'-em',str(scaled_Tm+0.000001),'6','3','0','-ej',str(scaled_TA),'6','4','-ej',str(scaled_TMJ),'5','4','-ej',str(scaled_TEM),'4','3','-ej',str(scaled_Teu_as),'3','2','-en',str(scaled_Tgrowth_Af),'1',str(scaled_NANC),'-ej',str(scaled_Taf),'2','1']
if case==3:
macs_args = ['macs',str(total),str(length),'-t',str(macs_theta),'-r',str(macs_rho),'-h','1e5','-R','genetic_map_b37/genetic_map_GRCh37_chr'+str(chr_number)+'.txt.macshs','-I','6',str(total_naf),str(total_nas),str(total_neu),str(nJ),str(nM),str(nA),'-n','1',str(scaled_NAF),'-n','2',str(scaled_NCHB),'-n','3',str(scaled_NCEU),'-n','4',str(scaled_NJ),'-n','5',str(scaled_NM),'-n','6',str(scaled_NA),'-eg','0','6',str(rA),'-eg','0.000001','4',str(rMJ),'-eg','0.000002','5',str(rMJ),'-em',str(scaled_Tm),'6','3',str(scaled_m),'-em',str(scaled_Tm+0.000001),'6','3','0','-ej',str(scaled_TA),'6','4','-ej',str(scaled_TMJ),'5','4','-ej',str(scaled_TEM),'4','3','-en',str(scaled_Tgrowth_Af),'1',str(scaled_NANC),'-ej',str(scaled_Teu_as),'3','2','-ej',str(scaled_Taf),'2','1']
if case==4:
macs_args = ['macs',str(total),str(length),'-t',str(macs_theta),'-r',str(macs_rho),'-h','1e5','-R','genetic_map_b37/genetic_map_GRCh37_chr'+str(chr_number)+'.txt.macshs','-I','6',str(total_naf),str(total_nas),str(total_neu),str(nJ),str(nM),str(nA),'-n','1',str(scaled_NAF),'-n','2',str(scaled_NCHB),'-n','3',str(scaled_NCEU),'-n','4',str(scaled_NJ),'-n','5',str(scaled_NM),'-n','6',str(scaled_NA),'-eg','0','6',str(rA),'-eg','0.000001','4',str(rMJ),'-eg','0.000002','5',str(rMJ),'-em',str(scaled_Tm),'6','3',str(scaled_m),'-em',str(scaled_Tm+0.000001),'6','3','0','-ej',str(scaled_TA),'6','4','-ej',str(scaled_TMJ),'5','4','-en',str(scaled_Tgrowth_Af),'1',str(scaled_NANC),'-ej',str(scaled_TEM),'4','3','-ej',str(scaled_Teu_as),'3','2','-ej',str(scaled_Taf),'2','1']
if case==5:
macs_args = ['macs',str(total),str(length),'-t',str(macs_theta),'-r',str(macs_rho),'-h','1e5','-R','genetic_map_b37/genetic_map_GRCh37_chr'+str(chr_number)+'.txt.macshs','-I','6',str(total_naf),str(total_nas),str(total_neu),str(nJ),str(nM),str(nA),'-n','1',str(scaled_NAF),'-n','2',str(scaled_NCHB),'-n','3',str(scaled_NCEU),'-n','4',str(scaled_NJ),'-n','5',str(scaled_NM),'-n','6',str(scaled_NA),'-eg','0','6',str(rA),'-eg','0.000001','4',str(rMJ),'-eg','0.000002','5',str(rMJ),'-em',str(scaled_Tm),'6','3',str(scaled_m),'-em',str(scaled_Tm+0.000001),'6','3','0','-ej',str(scaled_TA),'6','4','-en',str(scaled_Tgrowth_Af),'1',str(scaled_NANC),'-ej',str(scaled_TMJ),'5','4','-ej',str(scaled_TEM),'4','3','-ej',str(scaled_Teu_as),'3','2','-ej',str(scaled_Taf),'2','1']
if case==6:
macs_args = ['macs',str(total),str(length),'-t',str(macs_theta),'-r',str(macs_rho),'-h','1e5','-R','genetic_map_b37/genetic_map_GRCh37_chr'+str(chr_number)+'.txt.macshs','-I','6',str(total_naf),str(total_nas),str(total_neu),str(nJ),str(nM),str(nA),'-n','1',str(scaled_NAF),'-n','2',str(scaled_NCHB),'-n','3',str(scaled_NCEU),'-n','4',str(scaled_NJ),'-n','5',str(scaled_NM),'-n','6',str(scaled_NA),'-eg','0','6',str(rA),'-eg','0.000001','4',str(rMJ),'-eg','0.000002','5',str(rMJ),'-em',str(scaled_Tm),'6','3',str(scaled_m),'-em',str(scaled_Tm+0.000001),'6','3','0','-en',str(scaled_Tgrowth_Af),'1',str(scaled_NANC),'-ej',str(scaled_TA),'6','4','-ej',str(scaled_TMJ),'5','4','-ej',str(scaled_TEM),'4','3','-ej',str(scaled_Teu_as),'3','2','-ej',str(scaled_Taf),'2','1']
if case==7:
macs_args = ['macs',str(total),str(length),'-t',str(macs_theta),'-r',str(macs_rho),'-h','1e5','-R','genetic_map_b37/genetic_map_GRCh37_chr'+str(chr_number)+'.txt.macshs','-I','6',str(total_naf),str(total_nas),str(total_neu),str(nJ),str(nM),str(nA),'-n','1',str(scaled_NAF),'-n','2',str(scaled_NCHB),'-n','3',str(scaled_NCEU),'-n','4',str(scaled_NJ),'-n','5',str(scaled_NM),'-n','6',str(scaled_NA),'-eg','0','6',str(rA),'-eg','0.000001','4',str(rMJ),'-eg','0.000002','5',str(rMJ),'-en',str(scaled_Tgrowth_Af),'1',str(scaled_NANC),'-em',str(scaled_Tm),'6','3',str(scaled_m),'-em',str(scaled_Tm+0.000001),'6','3','0','-ej',str(scaled_TA),'6','4','-ej',str(scaled_TMJ),'5','4','-ej',str(scaled_TEM),'4','3','-ej',str(scaled_Teu_as),'3','2','-ej',str(scaled_Taf),'2','1']
##########
#print macs_args
sim=macsSwig.swigMain(len(macs_args),macs_args)
return sim
#####set up simulations#####################################
############################################################
##Length of chromosomes
lengths = [249163442, 243078003, 197813415, 191015739, 180695227, 170959304, 159091448, 146137372, 141069069, 135430928, 134747460, 133630123, 96085774, 87668527, 82491127, 90079543, 81032226, 78003657, 58843222, 62887650, 37234222, 35178458]
job=int(argv[1]) #must be a number
print 'JOB', job
####Get parameter values from priors
param_model=param_sim_asc()
###parameters is a dictionary with the parameter values
parameters=param_model[0]
###case is an integer that indicates which topology/model is going to be simulated
para_out=param_model[1]
case=param_model[2]
daf=param_model[3]
####Samples to be simulated
naf_CGI=18
neu_CGI=18
nas_CGI=8
nA=76#528
nJ=28
nM=114
total_CGI=naf_CGI+neu_CGI+nas_CGI+nA+nJ+nM
print total_CGI
###Discovery panel
asc_nb_af=para_out[0]
asc_nb_eu=para_out[1]
asc_nb_as=para_out[2]
print 'asc_nb_af', asc_nb_af
print 'asc_nb_eu', asc_nb_eu
print 'asc_nb_as', asc_nb_as
total_naf=naf_CGI+asc_nb_af
total_neu=neu_CGI+asc_nb_eu
total_nas=nas_CGI+asc_nb_as
###Total number of chromosomes
total_asc=asc_nb_af+asc_nb_eu+asc_nb_as
total=total_CGI+total_asc
def main():
###########
#######Summary statistics
chr=1
#####change the number of summary stats
ss_cnt=137 ##how many summary statistics
nbseq=chr #number of regions
###this is going to store all the results
results=np.zeros((ss_cnt,nbseq),dtype='float')
##############START SIMULATIONS
##############
cont=0
reg_use=0
####
for chr_number in range(1,chr+1):
print 'running chr'+str(chr_number)
length=lengths[chr_number-1]
res=[]
#flag to check if the nb of asc SNPs is the same as the nb of Array SNPs
flag_nb_asc_snps=0
####Get the positions of the SNPs that are on the chip
snp_file=argv[2] #SNP file
fileSNP=open(snp_file,'r')
#print "read SNP file"
SNP=[]
for line in fileSNP:
SNP.append(line)
fileSNP.close()
###get sites from snp array
#print "get sites from snp array"
snps=[]
for line_snp in SNP:
columns=line_snp.split('\t')
snps.append(int(columns[2]))
print 'nb Array snps', len(snps)
nb_array_snps=len(snps)
#print snps
##flag to check if the simulation work (generate the number of file
flag_sim=False
rep=1
while flag_sim==False:
#print 'rep', rep
#####Run simulations
print 'running simulation'
sim=run_sim(parameters,case,length,chr_number)
##number of segregating sites
nbss=sim.getNumSites()
#print 'number sites in simulation', nbss
##get position of the simulated sites and scale it to the "real" position in the SNP chip
pos=[]
for i in xrange(nbss):
position=round(sim.getPosition(i)*(length))#you have to give the number of the snp
pos.append(position)
##alleles will have all the information of all the simulated sites for all the pops
#sites in each row
alleles=[]
for x in xrange(0,nbss):
loc=[]
for m in xrange(0,total):
loc.append(sim.getSite(x,m))
alleles.append(loc)
print 'total number of sites:',len(alleles) #number of elements in alleles
print 'total number of chromosomes:',len(alleles[0])
del sim
#########get data from the simulations
Talleles=zip(*alleles)
###Get data from the simulations
seqAf=Talleles[0:total_naf]
seqEu=Talleles[total_naf:total_naf+total_neu]
seqAs=Talleles[total_naf+total_neu:total_naf+total_neu+total_nas]
####CGI data
seqAfCGI=seqAf[:naf_CGI]
seqEuCGI=seqEu[:neu_CGI]
seqAsCGI=seqAs[:nas_CGI]
####Discovery subset
seqAf_ds=seqAf[naf_CGI:total_naf]
seqEu_ds=seqEu[neu_CGI:total_neu]
seqAs_ds=seqAs[nas_CGI:total_nas]
#####put all the samples together to calculate the daf and select SNPs (matching distance as the array)
asc_panel=[]
asc_panel.extend(seqAf_ds)
asc_panel.extend(seqEu_ds)
asc_panel.extend(seqAs_ds)
#print asc_panel
Tasc_panel=zip(*asc_panel)
print 'number of sites in Tasc_panel:',len(Tasc_panel)
print 'number of chromosomes in Tasc_panel:',len(Tasc_panel[0])
#print Tasc_panel
#######Array with the available sites given the frequency cut off
##array with the frequency of all the simulated snps
sites_freq=[]
##array with the available sites, that pass the frequency cut-off
avail_sites=[] ##this one has the positions of the snps
index_avail_sites=[] ##this one has the indexes of the snps