fitness = PlusMinusFitness(args.selection_coef)
pop = sim.Population(size=[args.popsize] * npops,
loci=[args.nloci, 2],
lociPos=locus_position + [0, args.length],
infoFields=['ind_id', 'fitness', 'migrate_to'])
id_tagger = sim.IdTagger()
id_tagger.apply(pop)
# record recombinations
rc = RecombCollector(first_gen=pop.indInfo("ind_id"),
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),
size=[args.popsize]*npops,
loci=[args.nloci],
lociPos=locus_position,
infoFields=['ind_id','fitness','migrate_to'])
id_tagger = sim.IdTagger()
id_tagger.apply(pop)
# set up recomb collector
first_gen = pop.indInfo("ind_id")
init_ts = msprime.simulate(2*len(first_gen),
length=max(locus_position))
haploid_labels = [(k,p) for k in first_gen
for p in (0,1)]
node_ids = {x:j for x, j in zip(haploid_labels, init_ts.samples())}
rc = RecombCollector(ts=init_ts, node_ids=node_ids,
locus_position=locus_position)
if min(args.gridheight,args.gridwidth)==1:
migr_rates=migrSteppingStoneRates(
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(
id_tagger = sim.IdTagger()
id_tagger.apply(pop)
# set up recomb collector
# NB: we have to simulate an initial tree sequence
first_gen = pop.indInfo("ind_id")
init_ts = msprime.simulate(2 * len(first_gen),
Ne=args.popsize,
recombination_rate=args.recomb_rate / 2.0,
length=max(locus_position))
haploid_labels = [(k, p) for k in first_gen for p in (0, 1)]
node_ids = {x: j for x, j in zip(haploid_labels, init_ts.samples())}
rc = RecombCollector(ts=init_ts,
node_ids=node_ids,
locus_position=locus_position,
benchmark=True,
mode='binary')
pre_ops = [sim.PyOperator(lambda pop: rc.increment_time() or True)]
mating_ops = [id_tagger]
logfile.write(time.strftime('%X %x %Z') + "\n")
logfile.write("Started simulating! Generations:\n")
post_ops = [sim.PyEval(r'" %d\n" % (gen)', step=100, output=logfile)]
nselloci = int(args.sel_mut_rate / (args.mut_rate + args.sel_mut_rate))
selected_loci = random.sample(locus_position, nselloci)
neutral_loci = list(set(locus_position) - set(selected_loci))
if args.record_neutral:
pre_ops += [
sim.SNPMutator(u=args.mut_rate, v=args.mut_rate, loci=neutral_loci)
# initially polymorphic alleles
init_freqs=[[k/100,1-k/100,0,0] for k in range(1,11)]
locus_classes=[min(len(init_freqs)-1,math.floor(random.expovariate(1))) for k in range(nloci)]
init_classes=[list(filter(lambda k: locus_classes[k]==x,range(nloci))) for x in range(len(init_freqs))]
logfile.write("Locus positions:\n")
logfile.write(str(locus_position)+"\n")
logfile.write("----------\n")
logfile.flush()
init_geno=[sim.InitGenotype(freq=init_freqs[k],loci=init_classes[k]) for k in range(len(init_freqs))]
# record recombinations
rc = RecombCollector(
nsamples=nsamples, generations=generations, N=popsize*npops,
ancestor_age=ancestor_age, length=length, locus_position=locus_position)
id_tagger = sim.IdTagger()
id_tagger.apply(pop)
# record recombinations
rc = RecombCollector(
first_gen=pop.indInfo("ind_id"), ancestor_age=args.ancestor_age,
length=args.length, locus_position=locus_position)
###
# modified from http://simupop.sourceforge.net/manual_svn/build/userGuide_ch5_sec9.html
class FixedFitness:
def __init__(self,s):
self.s = s
min(len(init_freqs) - 1, math.floor(random.expovariate(1)))
for k in range(args.nselloci)
]
init_classes = [
list(filter(lambda k: locus_classes[k] == x, range(args.nselloci)))
for x in range(len(init_freqs))
]
init_geno = [
sim.InitGenotype(freq=init_freqs[k], loci=init_classes[k])
for k in range(len(init_freqs))
]
# record recombinations
rc = RecombCollector(nsamples=args.nsamples,
generations=args.generations,
N=args.popsize,
ancestor_age=args.ancestor_age,
length=args.nselloci,
locus_position=locus_position)
###
# random selection coefficients:
# modified from http://simupop.sourceforge.net/manual_svn/build/userGuide_ch5_sec9.html
class GammaDistributedFitness:
def __init__(self, alpha, beta):
# mean is alpha/beta
self.coefMap = {}
self.alpha = alpha
self.beta = beta