rc = RecombCollector(first_gen=pop.indInfo("ind_id"),
ancestor_age=args.ancestor_age,
length=args.length,
locus_position=locus_position)
migr_mat = [[0, args.m, 0], [args.m, 0, args.m], [0, args.m, 0]]
pop.evolve(initOps=[
sim.InitSex(),
] + init_geno,
preOps=[
sim.PyOperator(lambda pop: rc.increment_time() or True),
sim.Migrator(rate=migr_mat, mode=sim.BY_PROBABILITY),
sim.SNPMutator(u=args.sel_mut_rate, v=args.sel_mut_rate),
sim.PyMlSelector(fitness.AB,
subPops=fitness.AB_subpops,
output=">>" + selloci_file),
sim.PyMlSelector(fitness.Ab,
subPops=fitness.Ab_subpops,
output=">>" + selloci_file),
sim.PyMlSelector(fitness.ab,
subPops=fitness.ab_subpops,
output=">>" + selloci_file),
],
matingScheme=sim.RandomMating(ops=[
id_tagger,
sim.Recombinator(intensity=args.recomb_rate,
output=rc.collect_recombs,
infoFields="ind_id"),
]),
postOps=[
ancestor_age=args.ancestor_age,
length=2 * args.length,
locus_position=locus_position +
[args.length, 2 * args.length])
migr_mat = [[0, args.m, 0], [args.m, 0, args.M], [0, args.M, 0]]
pop.evolve(initOps=[
sim.InitSex(),
] + init_geno,
preOps=[
sim.PyOperator(lambda pop: rc.increment_time() or True),
sim.Migrator(rate=migr_mat, mode=sim.BY_PROBABILITY),
sim.SNPMutator(u=args.sel_mut_rate, v=args.sel_mut_rate),
sim.PyMlSelector(fitness.left,
subPops=fitness.left_subpops,
output=">>" + selloci_file),
sim.PyMlSelector(fitness.right,
subPops=fitness.right_subpops,
output=">>" + selloci_file),
],
matingScheme=sim.RandomMating(ops=[
id_tagger,
sim.Recombinator(intensity=args.recomb_rate,
output=rc.collect_recombs,
infoFields="ind_id"),
]),
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL, step=50),
sim.PyEval(
r"'Gen: %2d #seg sites: %d\n' % (gen, numOfSegSites)",
args.migr, n=max(args.gridwidth,args.gridheight), circular=False)
else:
migr_rates=migr2DSteppingStoneRates(
args.migr, m=args.gridwidth, n=args.gridheight, diagonal=False, circular=False)
pop.evolve(
initOps=[
sim.InitSex(),
]+init_geno,
preOps=[
sim.PyOperator(lambda pop: rc.increment_time() or True),
sim.Migrator(
rate=migr_rates,
mode=sim.BY_PROBABILITY),
sim.SNPMutator(u=args.sel_mut_rate, v=args.sel_mut_rate),
sim.PyMlSelector(GammaDistributedFitness(args.gamma_alpha, args.gamma_beta),
output=">>"+selloci_file),
],
matingScheme=sim.RandomMating(
ops=[
id_tagger,
sim.Recombinator(intensity=args.recomb_rate,
output=rc.collect_recombs,
infoFields="ind_id"),
] ),
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL, step=50),
sim.PyEval(r"'Gen: %2d #seg sites: %d\n' % (gen, numOfSegSites)", step=50),
sim.PyOperator(lambda pop: rc.simplify(pop.indInfo("ind_id")) or True,
step=args.simplify_interval),
],
gen = args.generations
loci=[nloci],
lociPos=locus_position,
infoFields=['ind_id','fitness','migrate_to'])
pop.evolve(
initOps=[
sim.InitSex(),
sim.IdTagger(),
]+init_geno,
preOps=[
sim.Migrator(
rate=migr2DSteppingStoneRates(
migr, m=width, n=width, diagonal=False, circular=False),
mode=sim.BY_PROBABILITY),
sim.AcgtMutator(rate=[mut_rate], model='JC69'),
sim.PyMlSelector(GammaDistributedFitness(alpha, beta),
output='>>'+selloci_file),
],
matingScheme=sim.RandomMating(
ops=[
sim.IdTagger(),
sim.Recombinator(intensity=recomb_rate,
output=outfile,
infoFields="ind_id"),
] ),
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL, step=50),
sim.PyEval(r"'Gen: %2d #seg sites: %d\n' % (gen, numOfSegSites)", step=50)
],
gen = generations
)
rate=migr_rates,
mode=sim.BY_PROBABILITY,
begin=0,
end=args.pre_generations),
sim.Migrator(
rate=barrier_rates,
mode=sim.BY_PROBABILITY,
begin=1 + args.pre_generations,
end=args.split_generations + args.pre_generations),
sim.Migrator(
rate=migr_rates,
mode=sim.BY_PROBABILITY,
begin=1 + args.split_generations + args.pre_generations,
end=args.split_generations + args.pre_generations + args.post_generations),
sim.SNPMutator(u=args.sel_mut_rate, v=args.sel_mut_rate),
sim.PyMlSelector(fitness_fun,
output=">>"+selloci_file),
],
matingScheme=sim.RandomMating(
ops=[
id_tagger,
sim.Recombinator(intensity=args.recomb_rate,
output=rc.collect_recombs,
infoFields="ind_id"),
] ),
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL, step=50),
sim.PyEval(r"'Gen: %2d #seg sites: %d\n' % (gen, numOfSegSites)", step=50),
sim.PyOperator(lambda pop: rc.simplify(pop.indInfo("ind_id")) or True,
step=args.simplify_interval),
],
gen = args.split_generations + args.pre_generations + args.post_generations
pop = sim.Population(
size=args.popsize,
loci=[args.nloci],
lociPos=locus_position,
infoFields=['ind_id','fitness','migrate_to'])
pop.evolve(
initOps=[
sim.InitSex(),
sim.IdTagger(),
]+init_geno,
preOps=[
sim.SNPMutator(u=args.sel_mut_rate, v=args.sel_mut_rate),
sim.PyMlSelector(FixedFitness(args.selection_coef),
output=">>"+args.selloci_file),
],
matingScheme=sim.RandomMating(
ops=[
sim.IdTagger(),
sim.Recombinator(intensity=args.recomb_rate,
output=">>" + args.treefile,
infoFields="ind_id"),
] ),
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL, step=10),
sim.PyEval(r"'Gen: %2d #seg sites: %d\n' % (gen, numOfSegSites)", step=50)
],
gen = args.generations
)
]
post_ops += [
sim.PyOperator(lambda pop: rc.simplify(pop.indInfo("ind_id")) or True,
step=args.simplify_interval)
]
pop.evolve(
initOps=[
sim.InitSex(),
] + init_geno,
preOps=pre_ops + [
sim.SNPMutator(u=args.sel_mut_rate, v=args.sel_mut_rate),
# loci=selected_loci),
# so that selector returns, for f_i fitness values, \prod_i f_i
sim.PyMlSelector(GammaDistributedFitness(shape=args.gamma_shape,
scale=args.gamma_scale,
popsize=args.popsize),
mode=sim.MULTIPLICATIVE,
loci=selected_loci),
],
matingScheme=sim.RandomMating(ops=mating_ops),
postOps=post_ops,
gen=args.generations)
tsim = time.process_time() - before_time
logfile.write("Done simulating!\n")
logfile.write(time.strftime('%X %x %Z') + "\n")
logfile.write("----------\n")
logfile.flush()
logfile.write("Collecting samples:\n")
s = self.coefMap[loc]
else:
s = random.gammavariate(self.alpha, self.beta)
self.coefMap[loc] = s
# print(str(loc)+":"+str(alleles)+"\n")
# needn't return fitness for alleles=(0,0) as simupop knows that's 1
if 0 in alleles:
return 1. - s
else:
return 1. - 2. * s
simu.evolve(initOps=[sim.InitSex(), meioser] + init_geno,
preOps=[
sim.AcgtMutator(rate=[0.0001], model='JC69'),
sim.PyMlSelector(GammaDistributedFitness(alpha, beta),
output='>>sel_loci.txt'),
sim.PyOperator(func=step_gen, param=(1, )),
],
matingScheme=reproduction,
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL, step=50),
sim.PyEval(
r"'Gen: %2d #seg sites: %d\n' % (gen, numOfSegSites)",
step=50)
],
gen=generations)
pop = simu.population(0)
pop_ids = [ind.info('ind_id') for ind in pop.individuals()]
# because s is assigned for each locus, we need to make sure the
# same s is used for fitness of genotypes 01 (1-s) and 11 (1-2s)
# at each locus
if loc in self.coefMap:
s = self.coefMap[loc]
else:
s = random.gammavariate(self.alpha, self.beta)
self.coefMap[loc] = s
#
if 0 in alleles:
return 1. - s
else:
return 1. - 2. * s
pop.evolve(initOps=sim.InitSex(),
preOps=[
sim.AcgtMutator(rate=[0.00001], model='JC69'),
sim.PyMlSelector(GammaDistributedFitness(0.23, 0.185),
output='>>sel.txt'),
],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL, step=50),
sim.PyEval(
r"'Gen: %2d #seg sites: %d\n' % (gen, numOfSegSites)",
step=50)
],
gen=201)
print(''.join(open('sel.txt').readlines()[:5]))
