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main_function_AJmodel3_chr1.py
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import random
from subprocess import Popen
import numpy as np
import os
from bitarray import bitarray
import itertools
from alleles_generator.macs_swig_alleles import AllelesMacsSwig
from ascertainment.pseudo_array import pseudo_array_bits
from simulation import def_params_M3_inst, run_sim_M3_inst
from summary_statistics import afs_stats_bitarray
# from memory_profiler import profile
#@profile()
def main(arguments):
#####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 = arguments[1] # must be a number
print 'JOB', job
####Get parameter values from priors
seed_option = int(arguments[4])
print seed_option
if seed_option > int(0):
random.seed(seed_option)
if arguments[5] == 'prior':
param_model = def_params_M3_inst.param_sim_asc_rand()
if arguments[5] == 'min':
param_model = def_params_M3_inst.param_sim_asc_min()
if arguments[5] == 'max':
param_model = def_params_M3_inst.param_sim_asc_max()
###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
nWA = 38
nEA = 38
nJ = 28
nM = 28 # 114
print 'naf_CGI ' + str(naf_CGI)
print 'neu_CGI ' + str(neu_CGI)
print 'nas_CGI ' + str(nas_CGI)
print 'nWA ' + str(nWA)
print 'nEA ' + str(nEA)
print 'nJ ' + str(nJ)
print 'nM ' + str(nM)
total_CGI = naf_CGI + neu_CGI + nas_CGI + nWA + nEA + nJ + nM
print 'total samples ' + str(total_CGI)
###Discovery panel
asc_nb_af = para_out[0]
asc_nb_eu = para_out[1]
asc_nb_as = para_out[2]
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
chr_number=1
path = arguments[7]
#### Check if necessary directories exist.
sim_data_dir = str(path)+'/sim_data_AJ_M3'
germline_out_dir=str(path)+'/germline_out_AJ_M3'
sim_values_dir=str(path)+'/sim_values_AJ_M3'
results_sims_dir=str(path)+'/results_sims_AJ_M3'
try:
os.makedirs(sim_data_dir)
except OSError:
if not os.path.isdir(sim_data_dir):
raise
try:
os.makedirs(germline_out_dir)
except OSError:
if not os.path.isdir(germline_out_dir):
raise
try:
os.makedirs(sim_values_dir)
except OSError:
if not os.path.isdir(sim_values_dir):
raise
try:
os.makedirs(results_sims_dir)
except OSError:
if not os.path.isdir(results_sims_dir):
raise
##############START SIMULATIONS
##############
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 = arguments[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)
###define simulation size
size = arguments[3]
if size == "full":
length = lengths[chr_number - 1]
else:
length = size
##flag to check if the simulation work (generate the number of file
flag_sim = False
rep = 1
while flag_sim == False:
#####Run simulations
print 'running simulation'
sim = run_sim_M3_inst.run_sim(parameters, length, chr_number, total, total_naf, total_nas, total_neu, nJ, nM, nEA, nWA, seed_option)
print 'finished simulation'
##number of segregating sites
nbss = sim.getNumSites()
print 'number sites in simulation', nbss
print 'number of chromosomes', total
##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) * (float(length))) # you have to give the number of the snp
pos.append(position)
###Get data from the simulations
seq_macsswig = AllelesMacsSwig(nbss, sim, total)
seqAF_bits = seq_macsswig.make_bitarray_seq(0, total_naf)
seqEu_bits = seq_macsswig.make_bitarray_seq(total_naf, total_naf + total_neu)
seqAs_bits = seq_macsswig.make_bitarray_seq(total_naf + total_neu, total_naf + total_neu + total_nas)
seqJ_bits = seq_macsswig.make_bitarray_seq(total_naf + total_neu + total_nas, total_naf + total_neu + total_nas + nJ)
seqM_bits = seq_macsswig.make_bitarray_seq(total_naf + total_neu + total_nas + nJ, total_naf + total_neu + total_nas + nJ + nM)
seqEA_bits = seq_macsswig.make_bitarray_seq(total_naf + total_neu + total_nas + nJ + nM, total_naf + total_neu + total_nas + nJ + nM + nEA)
seqWA_bits = seq_macsswig.make_bitarray_seq(total_naf + total_neu + total_nas + nJ + nM + nEA, total_naf + total_neu + total_nas + nJ + nM + nEA + nWA)
del sim
####CGI data
seqAfCGI_bits = bitarray()
for first_index in xrange(0, len(seqAF_bits), total_naf):
seqAfCGI_bits.extend(seqAF_bits[first_index:first_index + naf_CGI])
seqEuCGI_bits = bitarray()
for first_index in xrange(0, len(seqEu_bits), total_neu):
seqEuCGI_bits.extend(seqEu_bits[first_index:first_index + neu_CGI])
seqAsCGI_bits = bitarray()
for first_index in xrange(0, len(seqAs_bits), total_nas):
seqAsCGI_bits.extend(seqAs_bits[first_index:first_index + nas_CGI])
####Discovery subset. Put all the samples together to calculate the daf and select SNPs (matching distance as the array)
asc_panel_bits = bitarray()
for site in xrange(0, nbss):
asc_panel_bits.extend(seqAF_bits[site*total_naf+naf_CGI:site*total_naf+total_naf])
asc_panel_bits.extend(seqEu_bits[site * total_neu + neu_CGI:site * total_neu + total_neu])
asc_panel_bits.extend(seqAs_bits[site * total_nas + nas_CGI:site * total_nas + total_nas])
print 'number of chromosomes in asc_panel:', asc_panel_bits.length()/nbss
####Get pseudo array sites
print 'Make pseudo array'
pos_asc, nbss_asc, index_avail_sites, avail_sites = pseudo_array_bits(asc_panel_bits, daf, pos, snps)
nb_avail_sites = len(avail_sites)
if (nb_avail_sites >= len(snps)):
flag_sim = True
else:
flag_sim = False
rep = rep + 1
#######################
#####Calculate summary statistics from the regions for the CGI data
if nbss > 0: ###no segregating sites in the simulations which is not possible
res = []
Af_res = []
Af_res.extend(afs_stats_bitarray.base_S_ss(seqAfCGI_bits, naf_CGI))
pi_AfCGI = afs_stats_bitarray.Pi2(Af_res[3], naf_CGI)
Af_res.append(afs_stats_bitarray.Tajimas(pi_AfCGI, Af_res[0], naf_CGI))
del (Af_res[3])
res.extend(Af_res)
head = 'SegS_Af_CGI\tSing_Af_CGI\tDupl_Af_CGI\tTajD_Af_CGI\t'
Eu_res = []
Eu_res.extend(afs_stats_bitarray.base_S_ss(seqEuCGI_bits, neu_CGI))
pi_EuCGI = afs_stats_bitarray.Pi2(Eu_res[3], neu_CGI)
Eu_res.append(afs_stats_bitarray.Tajimas(pi_EuCGI, Eu_res[0], neu_CGI))
del (Eu_res[3])
res.extend(Eu_res)
head = head + 'SegS_Eu_CGI\tSing_Eu_CGI\tDupl_Eu_CGI\tTajD_Eu_CGI\t'
As_res = []
As_res.extend(afs_stats_bitarray.base_S_ss(seqAsCGI_bits, nas_CGI))
pi_AsCGI = afs_stats_bitarray.Pi2(As_res[3], nas_CGI)
As_res.append(afs_stats_bitarray.Tajimas(pi_AsCGI, As_res[0], nas_CGI))
del (As_res[3])
res.extend(As_res)
head = head + 'SegS_As_CGI\tSing_As_CGI\tDupl_As_CGI\tTajD_As_CGI\t'
##fst between populations
res.append(afs_stats_bitarray.FST2(seqAfCGI_bits, pi_AfCGI, naf_CGI, seqEuCGI_bits, pi_EuCGI, neu_CGI))
res.append(afs_stats_bitarray.FST2(seqAfCGI_bits, pi_AfCGI, naf_CGI, seqAsCGI_bits, pi_AsCGI, nas_CGI))
res.append(afs_stats_bitarray.FST2(seqEuCGI_bits, pi_EuCGI, neu_CGI, seqAsCGI_bits, pi_AsCGI, nas_CGI))
head = head + 'FST_AfEu_CGI\tFST_AfAs_CGI\tFST_EuAs_CGI\t'
seqAf_asc_bits = bitarray()
seqEu_asc_bits = bitarray()
seqAs_asc_bits = bitarray()
seqJ_asc_bits = bitarray()
seqM_asc_bits = bitarray()
seqEA_asc_bits = bitarray()
seqWA_asc_bits = bitarray()
if (nbss_asc == len(index_avail_sites)):
for x in xrange(0, nbss_asc):
seqAf_asc_bits.extend(seqAfCGI_bits[pos_asc[x] * naf_CGI:pos_asc[x] * naf_CGI + naf_CGI])
seqEu_asc_bits.extend(seqEuCGI_bits[pos_asc[x] * neu_CGI:pos_asc[x] * neu_CGI + neu_CGI])
seqAs_asc_bits.extend(seqAsCGI_bits[pos_asc[x] * nas_CGI:pos_asc[x] * nas_CGI + nas_CGI])
seqJ_asc_bits.extend(seqJ_bits[pos_asc[x] * nJ:pos_asc[x] * nJ + nJ])
seqM_asc_bits.extend(seqM_bits[pos_asc[x] * nM:pos_asc[x] * nM + nM])
seqEA_asc_bits.extend(seqEA_bits[pos_asc[x] * nEA:pos_asc[x] * nEA + nEA])
seqWA_asc_bits.extend(seqWA_bits[pos_asc[x] * nWA:pos_asc[x] * nWA + nWA])
elif (len(index_avail_sites) > nbss_asc):
for x in xrange(0, len(pos_asc)):
seqAf_asc_bits.extend(seqAfCGI_bits[index_avail_sites[pos_asc[x]] * naf_CGI:index_avail_sites[pos_asc[
x]] * naf_CGI + naf_CGI])
seqEu_asc_bits.extend(seqEuCGI_bits[index_avail_sites[pos_asc[x]] * neu_CGI:index_avail_sites[pos_asc[
x]] * neu_CGI + neu_CGI])
seqAs_asc_bits.extend(seqAsCGI_bits[index_avail_sites[pos_asc[x]] * nas_CGI:index_avail_sites[pos_asc[
x]] * nas_CGI + nas_CGI])
seqJ_asc_bits.extend(seqJ_bits[index_avail_sites[pos_asc[x]] * nJ:index_avail_sites[pos_asc[x]] * nJ + nJ])
seqM_asc_bits.extend(seqM_bits[index_avail_sites[pos_asc[x]] * nM:index_avail_sites[pos_asc[x]] * nM + nM])
seqEA_asc_bits.extend(seqEA_bits[index_avail_sites[pos_asc[x]] * nEA:index_avail_sites[pos_asc[x]] * nEA + nEA])
seqWA_asc_bits.extend(seqWA_bits[index_avail_sites[pos_asc[x]] * nWA:index_avail_sites[pos_asc[x]] * nWA + nWA])
##Make ped file
print 'Make ped and map files'
filenameped = str(sim_data_dir) + '/macs_asc_' + str(job) + '_chr' + str(chr_number) + '.ped'
fileped = open(filenameped, 'w')
for indiv in xrange(0, neu_CGI, 2):
fileped.write('E ' + str(indiv / 2 + 1) + '_E 0 0 1 -9 ')
for bit in itertools.chain.from_iterable(
[seqEu_asc_bits[i:i + 2] for i in xrange(indiv, seqEu_asc_bits.length(), neu_CGI)]):
if bit:
fileped.write('2 ')
else:
fileped.write('1 ')
fileped.write('\n')
for indiv in xrange(0, nJ, 2):
fileped.write('J ' + str(indiv / 2 + 1) + '_J 0 0 1 -9 ')
for bit in itertools.chain.from_iterable(
[seqJ_asc_bits[i:i + 2] for i in xrange(indiv, seqJ_asc_bits.length(), nJ)]):
if bit:
fileped.write('2 ')
else:
fileped.write('1 ')
fileped.write('\n')
for indiv in xrange(0, nM, 2):
fileped.write('M ' + str(indiv / 2 + 1) + '_M 0 0 1 -9 ')
for bit in itertools.chain.from_iterable(
[seqM_asc_bits[i:i + 2] for i in xrange(indiv, seqM_asc_bits.length(), nM)]):
if bit:
fileped.write('2 ')
else:
fileped.write('1 ')
fileped.write('\n')
for indiv in xrange(0, nEA, 2):
fileped.write('EA ' + str(indiv / 2 + 1) + '_EA 0 0 1 -9 ')
for bit in itertools.chain.from_iterable(
[seqEA_asc_bits[i:i + 2] for i in xrange(indiv, seqEA_asc_bits.length(), nEA)]):
if bit:
fileped.write('2 ')
else:
fileped.write('1 ')
fileped.write('\n')
for indiv in xrange(0, nWA, 2):
fileped.write('WA ' + str(indiv / 2 + 1) + '_WA 0 0 1 -9 ')
for bit in itertools.chain.from_iterable(
[seqWA_asc_bits[i:i + 2] for i in xrange(indiv, seqWA_asc_bits.length(), nWA)]):
if bit:
fileped.write('2 ')
else:
fileped.write('1 ')
fileped.write('\n')
fileped.close()
##Make map file
filenamemap = str(sim_data_dir) + '/macs_asc_' + str(job) + '_chr' + str(chr_number) + '.map'
filemap = open(filenamemap, 'a')
map = ''
for g in range(0, len(pos_asc)):
map = str(chr_number) + ' ' + 'chr' + str(chr_number) + '_' + str(pos_asc[g]) + ' ' + str(
int(avail_sites[pos_asc[g]] - 1)) + ' ' + str(int(avail_sites[pos_asc[g]])) + '\n'
filemap.write(map)
filemap.close()
########Use Germline to find IBD on pseduo array ped and map files
run_germline = int(arguments[6])
filenameout = str(germline_out_dir) + '/macs_asc_' + str(job) + '_chr' + str(chr_number)
print 'run germline? '+str(run_germline)
if (run_germline == 0):
print 'Running Germline on ' + str(filenameped) + ' ' + str(filenamemap)
###Germline seems to be outputting in the wrong unit - so I am putting the min at 3000000 so that it is 3Mb, but should be the default.
print 'bash ./bin/phasing_pipeline/gline.sh ./bin/germline-1-5-1/germline ' + str(filenameped) + ' ' + str(filenamemap) + ' ' + str(filenameout) + ' "-bits 10 -min_m 3000000"'
germline = Popen.wait(Popen('bash ./bin/phasing_pipeline/gline.sh ./bin/germline-1-5-1/germline ' + str(filenameped) + ' ' + str(filenamemap) + ' ' + str(filenameout) + ' "-bits 10 -min_m 3000000"', shell=True))
print 'finished running germline'
########Get IBD stats from Germline output
if os.path.isfile(str(filenameout) + '.match'):
print 'reading Germline IBD output'
filegermline = open(str(filenameout) + '.match', 'r')
IBDlengths_eAeA = []
IBDlengths_wAwA = []
IBDlengths_JJ = []
IBDlengths_MM = []
IBDlengths_EE = []
IBDlengths_eAwA = []
IBDlengths_eAE = []
IBDlengths_wAE = []
IBDlengths_eAJ = []
IBDlengths_wAJ = []
IBDlengths_eAM = []
IBDlengths_wAM = []
IBDlengths_JM = []
IBDlengths_JE = []
IBDlengths_ME = []
for line in filegermline:
pop1 = line.split()[0]
pop2 = line.split()[2]
segment = float(line.split()[10]) / 1000000
pair = str(pop1) + '_' + str(pop2)
if pair == 'EA_EA':
IBDlengths_eAeA.append(segment)
if pair == 'WA_WA':
IBDlengths_wAwA.append(segment)
if pair == 'J_J':
IBDlengths_JJ.append(segment)
if pair == 'M_M':
IBDlengths_MM.append(segment)
if pair == 'E_E':
IBDlengths_EE.append(segment)
if pair == 'EA_WA' or pair == 'WA_EA':
IBDlengths_eAwA.append(segment)
if pair == 'EA_E' or pair == 'E_EA':
IBDlengths_eAE.append(segment)
if pair == 'WA_E' or pair == 'E_WA':
IBDlengths_wAE.append(segment)
if pair == 'EA_J' or pair == 'J_EA':
IBDlengths_eAJ.append(segment)
if pair == 'WA_J' or pair == 'J_WA':
IBDlengths_wAJ.append(segment)
if pair == 'EA_M' or pair == 'M_EA':
IBDlengths_eAM.append(segment)
if pair == 'WA_M' or pair == 'M_WA':
IBDlengths_wAM.append(segment)
if pair == 'J_M' or pair == 'M_J':
IBDlengths_JM.append(segment)
if pair == 'J_E' or pair == 'E_J':
IBDlengths_JE.append(segment)
if pair == 'M_E' or pair == 'E_M':
IBDlengths_ME.append(segment)
filegermline.close()
print 'calculating summary stats'
IBDlengths_mean = []
IBDlengths_median = []
IBDlengths_num = []
IBDlengths_var = []
IBDlengths_mean30 = []
IBDlengths_median30 = []
IBDlengths_num30 = []
IBDlengths_var30 = []
pairs = [IBDlengths_eAeA, IBDlengths_wAwA, IBDlengths_JJ, IBDlengths_MM, IBDlengths_EE, IBDlengths_eAwA,
IBDlengths_eAE, IBDlengths_wAE, IBDlengths_eAJ, IBDlengths_wAJ, IBDlengths_eAM, IBDlengths_wAM,
IBDlengths_JM, IBDlengths_JE, IBDlengths_ME]
for p in pairs:
IBDlengths_num.append(len(p))
if len(p) < 1:
p.append(0)
IBDlengths_mean.append(np.mean(p))
IBDlengths_median.append(np.median(p))
IBDlengths_var.append(np.var(p))
#### Get IBD greater than 30 Mb
IBDlengths30 = []
for l in p:
if l > 30:
IBDlengths30.append(l)
IBDlengths_num30.append(len(IBDlengths30))
if len(IBDlengths30) == 0:
IBDlengths30.append(0)
IBDlengths_mean30.append(np.mean(IBDlengths30))
IBDlengths_median30.append(np.median(IBDlengths30))
IBDlengths_var30.append(np.var(IBDlengths30))
res.extend(IBDlengths_mean)
head = head + 'IBD_mean_eAeA\tIBD_mean_wAwA\tIBD_mean_JJ\tIBD_mean_MM\tIBD_mean_EE\tIBD_mean_eAwA\tIBD_mean_eAE\tIBD_mean_wAE\tIBD_mean_eAJ\tIBD_mean_wAJ\tIBD_mean_eAM\tIBD_mean_wAM\tIBD_mean_JM\tIBD_mean_JE\tIBD_mean_ME\t'
res.extend(IBDlengths_median)
head = head + 'IBD_median_eAeA\tIBD_median_wAwA\tIBD_median_JJ\tIBD_median_MM\tIBD_median_EE\tIBD_median_eAwA\tIBD_median_eAE\tIBD_median_wAE\tIBD_median_eAJ\tIBD_median_wAJ\tIBD_median_eAM\tIBD_median_wAM\tIBD_median_JM\tIBD_median_JE\tIBD_median_ME\t'
res.extend(IBDlengths_num)
head = head + 'IBD_num_eAeA\tIBD_num_wAwA\tIBD_num_JJ\tIBD_num_MM\tIBD_num_EE\tIBD_num_eAwA\tIBD_num_eAE\tIBD_num_wAE\tIBD_num_eAJ\tIBD_num_wAJ\tIBD_num_eAM\tIBD_num_wAM\tIBD_num_JM\tIBD_num_JE\tIBD_num_ME\t'
res.extend(IBDlengths_var)
head = head + 'IBD_var_eAeA\tIBD_var_wAwA\tIBD_var_JJ\tIBD_var_MM\tIBD_var_EE\tIBD_var_eAwA\tIBD_var_eAE\tIBD_var_wAE\tIBD_var_eAJ\tIBD_var_wAJ\tIBD_var_eAM\tIBD_var_wAM\tIBD_var_JM\tIBD_var_JE\tIBD_var_ME\t'
res.extend(IBDlengths_mean30)
head = head + 'IBD30_mean_eAeA\tIBD30_mean_wAwA\tIBD30_mean_JJ\tIBD30_mean_MM\tIBD30_mean_EE\tIBD30_mean_eAwA\tIBD30_mean_eAE\tIBD30_mean_wAE\tIBD30_mean_eAJ\tIBD30_mean_wAJ\tIBD30_mean_eAM\tIBD30_mean_wAM\tIBD30_mean_JM\tIBD30_mean_JE\tIBD30_mean_ME\t'
res.extend(IBDlengths_median30)
head = head + 'IBD30_median_eAeA\tIBD30_median_wAwA\tIBD30_median_JJ\tIBD30_median_MM\tIBD30_median_EE\tIBD30_median_eAwA\tIBD30_median_eAE\tIBD30_median_wAE\tIBD30_median_eAJ\tIBD30_median_wAJ\tIBD30_median_eAM\tIBD30_median_wAM\tIBD30_median_JM\tIBD30_median_JE\tIBD30_median_ME\t'
res.extend(IBDlengths_num30)
head = head + 'IBD30_num_eAeA\tIBD30_num_wAwA\tIBD30_num_JJ\tIBD30_num_MM\tIBD30_num_EE\tIBD30_num_eAwA\tIBD30_num_eAE\tIBD30_num_wAE\tIBD30_num_eAJ\tIBD30_num_wAJ\tIBD30_num_eAM\tIBD30_num_wAM\tIBD30_num_JM\tIBD30_num_JE\tIBD30_num_ME\t'
res.extend(IBDlengths_var30)
head = head + 'IBD30_var_eAeA\tIBD30_var_wAwA\tIBD30_var_JJ\tIBD30_var_MM\tIBD30_var_EE\tIBD30_var_eAwA\tIBD30_var_eAE\tIBD30_var_wAE\tIBD30_var_eAJ\tIBD30_var_wAJ\tIBD30_var_eAM\tIBD30_var_wAM\tIBD30_var_JM\tIBD30_var_JE\tIBD30_var_ME\t'
#########calculate summary stats from the ascertained SNPs
if nbss_asc > 0:
Af_asc = []
ss_Af_asc = afs_stats_bitarray.base_S_ss(seqAf_asc_bits, naf_CGI)
if (ss_Af_asc[0] == 0):
for i in xrange(5):
Af_asc.append(0)
pi_Af_asc = 0
else:
Af_asc.extend(afs_stats_bitarray.base_S_ss(seqAf_asc_bits, naf_CGI))
pi_Af_asc = afs_stats_bitarray.Pi2(Af_asc[3], naf_CGI)
Af_asc.append(pi_Af_asc)
Af_asc.append(afs_stats_bitarray.Tajimas(pi_Af_asc, Af_asc[0], naf_CGI))
del (Af_asc[3])
res.extend(Af_asc)
head = head + 'SegS_Af_ASC\tSing_Af_ASC\tDupl_Af_ASC\tPi_Af_ASC\tTajD_Af_ASC\t'
Eu_asc = []
ss_Eu_asc = afs_stats_bitarray.base_S_ss(seqEu_asc_bits, neu_CGI)
if (ss_Eu_asc[0] == 0):
for i in xrange(5):
Eu_asc.append(0)
pi_Eu_asc = 0
else:
Eu_asc.extend(afs_stats_bitarray.base_S_ss(seqEu_asc_bits, neu_CGI))
pi_Eu_asc = afs_stats_bitarray.Pi2(Eu_asc[3], neu_CGI)
Eu_asc.append(pi_Eu_asc)
Eu_asc.append(afs_stats_bitarray.Tajimas(pi_Eu_asc, Eu_asc[0], neu_CGI))
del (Eu_asc[3])
res.extend(Eu_asc)
head = head + 'SegS_Eu_ASC\tSing_Eu_ASC\tDupl_Eu_ASC\tPi_Eu_ASC\tTajD_Eu_ASC\t'
As_asc = []
ss_As_asc = afs_stats_bitarray.base_S_ss(seqAs_asc_bits, nas_CGI)
if (ss_As_asc[0] == 0):
for i in xrange(5):
As_asc.append(0)
pi_As_asc = 0
else:
As_asc.extend(afs_stats_bitarray.base_S_ss(seqAs_asc_bits, nas_CGI))
pi_As_asc = afs_stats_bitarray.Pi2(As_asc[3], nas_CGI)
As_asc.append(pi_As_asc)
As_asc.append(afs_stats_bitarray.Tajimas(pi_As_asc, As_asc[0], nas_CGI))
del (As_asc[3])
res.extend(As_asc)
head = head + 'SegS_As_ASC\tSing_As_ASC\tDupl_As_ASC\tPi_As_ASC\tTajD_As_ASC\t'
J_asc = []
ss_J_asc = afs_stats_bitarray.base_S_ss(seqJ_asc_bits, nJ)
if (ss_J_asc[0] == 0):
for i in xrange(5):
J_asc.append(0)
pi_J_asc = 0
else:
J_asc.extend(afs_stats_bitarray.base_S_ss(seqJ_asc_bits, nJ))
pi_J_asc = afs_stats_bitarray.Pi2(J_asc[3], nJ)
J_asc.append(pi_J_asc)
J_asc.append(afs_stats_bitarray.Tajimas(pi_J_asc, J_asc[0], nJ))
del (J_asc[3])
res.extend(J_asc)
head = head + 'SegS_J_ASC\tSing_J_ASC\tDupl_J_ASC\tPi_J_ASC\tTajD_J_ASC\t'
M_asc = []
ss_M_asc = afs_stats_bitarray.base_S_ss(seqM_asc_bits, nM)
if (ss_M_asc[0] == 0):
for i in xrange(5):
M_asc.append(0)
pi_M_asc = 0
else:
M_asc.extend(afs_stats_bitarray.base_S_ss(seqM_asc_bits, nM))
pi_M_asc = afs_stats_bitarray.Pi2(M_asc[3], nM)
M_asc.append(pi_M_asc)
M_asc.append(afs_stats_bitarray.Tajimas(pi_M_asc, M_asc[0], nM))
del (M_asc[3])
res.extend(M_asc)
head = head + 'SegS_M_ASC\tSing_M_ASC\tDupl_M_ASC\tPi_M_ASC\tTajD_M_ASC\t'
EA_asc = []
ss_EA_asc = afs_stats_bitarray.base_S_ss(seqEA_asc_bits, nEA)
if (ss_EA_asc[0] == 0):
for i in xrange(5):
EA_asc.append(0)
pi_EA_asc = 0
else:
EA_asc.extend(afs_stats_bitarray.base_S_ss(seqEA_asc_bits, nEA))
pi_EA_asc = afs_stats_bitarray.Pi2(EA_asc[3], nEA)
EA_asc.append(pi_EA_asc)
EA_asc.append(afs_stats_bitarray.Tajimas(pi_EA_asc, EA_asc[0], nEA))
del (EA_asc[3])
res.extend(EA_asc)
head = head + 'SegS_EA_ASC\tSing_EA_ASC\tDupl_EA_ASC\tPi_EA_ASC\tTajD_EA_ASC\t'
WA_asc = []
ss_WA_asc = afs_stats_bitarray.base_S_ss(seqWA_asc_bits, nWA)
if (ss_WA_asc[0] == 0):
for i in xrange(5):
WA_asc.append(0)
pi_WA_asc = 0
else:
WA_asc.extend(afs_stats_bitarray.base_S_ss(seqWA_asc_bits, nWA))
pi_WA_asc = afs_stats_bitarray.Pi2(WA_asc[3], nWA)
WA_asc.append(pi_WA_asc)
WA_asc.append(afs_stats_bitarray.Tajimas(pi_WA_asc, WA_asc[0], nWA))
del (WA_asc[3])
res.extend(WA_asc)
head = head + 'SegS_WA_ASC\tSing_WA_ASC\tDupl_WA_ASC\tPi_WA_ASC\tTajD_WA_ASC\t'
res.append(afs_stats_bitarray.FST2(seqAf_asc_bits, pi_Af_asc, naf_CGI, seqEu_asc_bits, pi_Eu_asc, neu_CGI))
res.append(afs_stats_bitarray.FST2(seqAf_asc_bits, pi_Af_asc, naf_CGI, seqAs_asc_bits, pi_As_asc, nas_CGI))
res.append(afs_stats_bitarray.FST2(seqEu_asc_bits, pi_Eu_asc, neu_CGI, seqAs_asc_bits, pi_As_asc, nas_CGI))
head = head + 'FST_AfEu_ASC\tFST_AfAs_ASC_m\tFST_EuAs_ASC\t'
res.append(afs_stats_bitarray.FST2(seqEA_asc_bits, pi_EA_asc, nEA, seqWA_asc_bits, pi_WA_asc, nWA))
res.append(afs_stats_bitarray.FST2(seqEA_asc_bits, pi_EA_asc, nEA, seqEu_asc_bits, pi_Eu_asc, neu_CGI))
res.append(afs_stats_bitarray.FST2(seqEA_asc_bits, pi_EA_asc, nEA, seqJ_asc_bits, pi_J_asc, nJ))
res.append(afs_stats_bitarray.FST2(seqEA_asc_bits, pi_EA_asc, nEA, seqM_asc_bits, pi_M_asc, nM))
res.append(afs_stats_bitarray.FST2(seqM_asc_bits, pi_M_asc, nM, seqJ_asc_bits, pi_J_asc, nJ))
res.append(afs_stats_bitarray.FST2(seqWA_asc_bits, pi_WA_asc, nWA, seqEu_asc_bits, pi_Eu_asc, neu_CGI))
res.append(afs_stats_bitarray.FST2(seqWA_asc_bits, pi_WA_asc, nWA, seqJ_asc_bits, pi_J_asc, nJ))
res.append(afs_stats_bitarray.FST2(seqWA_asc_bits, pi_WA_asc, nWA, seqM_asc_bits, pi_M_asc, nM))
head = head + 'FST_eAwA_ASC\tFST_eAEu_ASC\tFST_eAJ_ASC\tFST_eAM_ASC\tFST_MJ_ASC\tFST_wAEu_ASC\tFST_wAJ_ASC\tFST_wAM_ASC\n'
print 'finished calculating ss'
################
#####write parameter values to file
param_file=str(sim_values_dir)+'/sim_'+str(job)+'_values.txt'
fileoutparam=open(param_file,'w')
##write parameter values
head_param = 'Asc_NAF\tAsc_NEU\tAsc_NCHB\tdaf\tLog10_NAF\tLog10_NANC\tLog10_NCEU\tLog10_NCHB\tLog10_NWA\tLog10_NEA\tLog10_NAg\tLog10_NJ\tLog10_NM\tmE\tmW\tTgrowth_Af\tTAF\tTEM\tTeu_as\tTA\tTMJ\tTAEW\tTmE\tTmW\tTAg\n'
fileoutparam.write(head_param)
for z in range(len(para_out)):
if z==(len(para_out)-1):
fileoutparam.write("%s\n" % para_out[z])
else:
fileoutparam.write("%s\t" % para_out[z])
fileoutparam.close()
filesummary=str(results_sims_dir)+'/ms_output_'+str(job)+'.summary'
filesumm=open(filesummary,'w')
filesumm.write(head)
out=''
for g in range(len(res)):
out=out+str(res[g])+'\t'
out=out[:-1]+'\n'
filesumm.write(out)
filesumm.close()
return [res,para_out]