sim.PyOperator(env_update, at=atgen),
#Set environmental value (env infoField) for each individual in the population
#Takes place at each generation after the first fission
sim.PyOperator(env_set, begin=atgen[1]),
#Selection occures at selected loci according to env information field
sim.PySelector(fit_env, loci=locisel, begin=atgen[1]),
],
#Mating at random (pangamy)
matingScheme=sim.RandomMating(
#Fixed population size (fixed at 'popsize')
subPopSize=demo,
#Recombination
ops=[sim.Recombinator(rates=0.002)]),
postOps=[
#Mutation rate 10e-6
sim.SNPMutator(u=0.000001, v=0.000001)
],
#Evolve for a number 'numgen' of generations
gen=numgen)
#Getting population informations (number of subpopulations, population size)
sim.stat(pop, popSize=True)
subsize = pop.dvars().subPopSize
numpop = len(subsize)
#Setting environmental value for all individuals in each subpopulation
for i in range(numpop):
pop.setIndInfo(vec_env[i], 'env', subPop=i)
#Sampling 20 individuals at random in each population
sample = drawRandomSample(pop, sizes=[20] * numpop)
#Adding population name to the field of individuals
sample.addInfoFields('pop_name')
vecname = []
# 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),
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),
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(size=[2000], loci=[1, 1], lociNames=['A', 'B'],
infoFields='fitness')
pop.evolve(
initOps=sim.InitSex(),
preOps=[
sim.SNPMutator(u=0.001),
sim.MaSelector(loci='A', fitness=[1, 0.99, 0.98]),
],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=['A', 'B'], step=100),
sim.PyEval(r"'%.3f\t%.3f\n' % (alleleFreq[0][1], alleleFreq[1][1])",
step=100),
],
gen=500
)
import simuPOP as sim
from math import log
pop = sim.Population(size=10000, loci=1)
simu = sim.Simulator(pop, rep=3)
simu.evolve(
initOps=[
sim.InitSex(),
# Because of the use of parameter reps, these operators are
# applied to different populations.
sim.InitGenotype(freq=(0.2, 0.8), reps=0),
sim.InitGenotype(freq=(0.5, 0.5), reps=1),
sim.InitGenotype(freq=(0.8, 0.2), reps=2)
],
preOps=sim.SNPMutator(u=0.01, v=0.001),
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0, step=100),
sim.PyEval('gen', reps=0, step=100),
sim.PyEval(r"'\t%.3f' % alleleFreq[0][0]", step=100),
sim.PyOutput('\n', reps=-1, step=100)
],
gen=500)
def simuGWAS(pop,
mutaRate=1.8e-8,
recIntensity=1e-8,
migrRate=0.0001,
expandGen=500,
expandSize=[10000],
DPL=[],
curFreq=[],
fitness=[1, 1, 1],
scale=1,
logger=None):
# handling scaling...
mutaRate *= scale
recIntensity *= scale
migrRate *= scale
expandGen = int(expandGen / scale)
fitness = [1 + (x - 1) * scale for x in fitness]
pop.dvars().scale = scale
# Demographic function
demoFunc = linearExpansion(pop.subPopSizes(), expandSize, expandGen)
# define a trajectory function
trajFunc = None
introOps = []
if len(DPL) > 0:
stat(pop, alleleFreq=DPL, vars='alleleFreq_sp')
currentFreq = []
for sp in range(pop.numSubPop()):
for loc in pop.lociByNames(DPL):
currentFreq.append(pop.dvars(sp).alleleFreq[loc][1])
# if there is no existing mutants at DPL
if sum(currentFreq) == 0.:
endFreq = [(x - min(0.01, x / 5.), x + min(0.01, x / 5.,
(1 - x) / 5.))
for x in curFreq]
traj = simulateForwardTrajectory(N=demoFunc,
beginGen=0,
endGen=expandGen,
beginFreq=currentFreq,
endFreq=endFreq,
nLoci=len(DPL),
fitness=fitness,
maxAttempts=1000,
logger=logger)
introOps = []
else:
traj = simulateBackwardTrajectory(N=demoFunc,
endGen=expandGen,
endFreq=curFreq,
nLoci=len(DPL),
fitness=fitness,
minMutAge=1,
maxMutAge=expandGen,
logger=logger)
introOps = traj.mutators(loci=DPL)
if traj is None:
raise SystemError(
'Failed to generated trajectory after 1000 attempts.')
trajFunc = traj.func()
if pop.numSubPop() > 1:
pop.addInfoFields('migrate_to')
pop.dvars().scale = scale
pop.evolve(
initOps=sim.InitSex(),
preOps=[
sim.SNPMutator(u=mutaRate, v=mutaRate),
sim.IfElse(
pop.numSubPop() > 1,
sim.Migrator(
rate=migrSteppingStoneRates(migrRate, pop.numSubPop()))),
] + introOps,
matingScheme=sim.ControlledRandomMating(
loci=DPL,
alleles=[1] * len(DPL),
freqFunc=trajFunc,
ops=sim.Recombinator(intensity=recIntensity),
subPopSize=demoFunc),
postOps=[
sim.Stat(popSize=True, structure=range(pop.totNumLoci())),
sim.PyEval(
r'"After %3d generations, size=%s\n" % ((gen + 1 )* scale, subPopSize)'
),
sim.IfElse(pop.numSubPop() > 1,
sim.PyEval(r"'F_st = %.3f\n' % F_st", step=10),
step=10),
],
gen=expandGen)
return pop
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)
# initially, population is monogenic
init_geno = [sim.InitGenotype(freq=1.0)]
pop.evolve(
initOps=[
sim.InitSex(),
] + init_geno,
preOps=[
sim.PyOperator(lambda pop: rc.increment_time() or True),
sim.SNPMutator(u=args.neut_mut_rate, v=0, loci=neutral_loci),
sim.SNPMutator(u=args.sel_mut_rate, v=0, loci=selected_loci),
sim.PyMlSelector(GammaDistributedFitness(args.gamma_alpha,
args.gamma_beta),
loci=selected_loci,
output=">>" + selloci_file),
],
matingScheme=sim.RandomMating(ops=[
id_tagger,
sim.Recombinator(rates=args.recomb_rate,
output=rc.collect_recombs,
infoFields="ind_id"),
]),
postOps=[
sim.Stat(numOfSegSites=sim.ALL_AVAIL,
step=REPORTING_STEP,
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)
]
mating_ops += [
sim.Recombinator(rates=args.recomb_rate, infoFields="ind_id")
]
elif not args.record_neutral:
mating_ops += [
sim.Recombinator(rates=args.recomb_rate,
output=sim.WithMode(rc.collect_recombs, 'b'),
infoFields="ind_id")
]
post_ops += [
sim.PyOperator(lambda pop: rc.simplify(pop.indInfo("ind_id")) or True,
step=args.simplify_interval)
]
pop.evolve(
else:
return 1. - 2. * s
pop = sim.Population(size=args.popsize,
loci=[args.nselloci],
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=0, loci=range(1, args.nselloci)),
sim.PyMlSelector(GammaDistributedFitness(args.gamma_alpha,
args.gamma_beta),
loci=range(1, args.nselloci),
output=">>" + selloci_file),
],
matingScheme=sim.RandomMating(ops=[
sim.IdTagger(),
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)
def MutationSelection(N=1000,
generations=10000,
X_loci=100,
A_loci=0,
AgingModel='two_phases',
seed=2001,
reps=1,
InitMutFreq=0.001,
aging_a1=0.003,
aging_a2=0.05,
aging_b=-0.019,
aging_k=0.1911,
MutRate=0.001,
StatsStep=100,
OutPopPrefix='z1',
PrintFreqs=False,
debug=False):
'''Creates and evolves a population to reach mutation-selection balance.'''
if debug:
sim.turnOnDebug('DBG_ALL')
else:
sim.turnOffDebug('DBG_ALL')
sim.setRNG('mt19937', seed)
pop = sim.Population(N,
loci=[X_loci, A_loci],
ploidy=2,
chromTypes=[sim.CHROMOSOME_X, sim.AUTOSOME],
infoFields=[
'age', 'a', 'b', 'smurf', 'ind_id', 'father_id',
'mother_id', 'luck', 't0', 'fitness'
])
pop.setVirtualSplitter(
sim.CombinedSplitter(
splitters=[
sim.ProductSplitter(splitters=[
sim.InfoSplitter(field='age', cutoff=9),
sim.InfoSplitter(field='smurf', values=[0, 1])
]),
sim.SexSplitter(),
sim.InfoSplitter(field='age', values=0)
],
vspMap=[(0), (2), (1, 3), (4), (5), (6)],
names=['larvae', 'adults', 'smurfs', 'males', 'females', 'zero']))
pop.dvars().k = aging_k
pop.dvars().N = N
pop.dvars().seed = seed
pop.dvars().X_loci = X_loci
pop.dvars().A_loci = A_loci
pop.dvars().AgingModel = AgingModel
exec("import random\nrandom.seed(seed)", pop.vars(), pop.vars())
exec("import math", pop.vars(), pop.vars())
simu = sim.Simulator(pop, rep=reps)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[1 - InitMutFreq, InitMutFreq]),
sim.InitInfo([0], infoFields='age'),
sim.InitInfo([aging_a1], infoFields='a'),
sim.InitInfo([aging_b], infoFields='b'),
sim.InitInfo(lambda: random.random(), infoFields='luck'),
sim.InfoExec('t0 = -ind.b / ind.a', exposeInd='ind'),
sim.InfoExec(
'smurf = 1.0 if AgingModel == "two_phases" and (ind.smurf == 1 or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0',
exposeInd='ind'),
sim.IdTagger(),
sim.PyExec('XFreqChange={}'),
sim.PyExec('AFreqChange={}')
],
preOps=[
sim.InfoExec('luck = random.random()'),
sim.InfoExec(
'smurf = 1.0 if AgingModel == "two_phases" and (ind.smurf == 1 or (ind.age > ind.t0 and ind.luck < 1.0 - math.exp(-ind.a * ind.age + ind.a * ind.t0 - ind.a / 2.0))) else 0.0',
exposeInd='ind'),
sim.DiscardIf(natural_death(AgingModel)),
sim.InfoExec('age += 1'),
sim.PySelector(func=fitness_func1)
],
matingScheme=sim.HeteroMating([
sim.CloneMating(subPops=[(0, 0), (0, 1), (0, 2)], weight=-1),
sim.RandomMating(ops=[
sim.IdTagger(),
sim.PedigreeTagger(),
sim.InfoExec('smurf = 0.0'),
sexSpecificRecombinator(
rates=[0.75 / X_loci for x in range(X_loci)] +
[2.07 / A_loci for x in range(A_loci)],
maleRates=0.0),
sim.PyQuanTrait(loci=sim.ALL_AVAIL,
func=TweakAdditiveRecessive(
aging_a1, aging_a2, aging_b, X_loci),
infoFields=['a', 'b'])
],
weight=1,
subPops=[(0, 1)],
numOffspring=1)
],
subPopSize=demo),
postOps=[
sim.SNPMutator(u=MutRate, subPops=[(0, 5)]),
sim.Stat(alleleFreq=sim.ALL_AVAIL, step=StatsStep),
sim.IfElse(
'X_loci > 0',
ifOps=[
sim.PyExec(
'XFreqChange[gen] = [alleleFreq[x][1] for x in range(X_loci)]'
)
],
elseOps=[sim.PyExec('XFreqChange[gen] = []')],
step=StatsStep),
sim.IfElse(
'A_loci > 0',
ifOps=[
sim.PyExec(
'AFreqChange[gen] = [alleleFreq[a][1] for a in range(X_loci, pop.totNumLoci())]',
exposePop='pop')
],
elseOps=[sim.PyExec('AFreqChange[gen] = []')],
step=StatsStep),
sim.IfElse(
PrintFreqs,
ifOps=[
sim.PyEval(
r"str(rep) + '\t' + str(gen) + '\t' + '\t'.join(map('{0:.4f}'.format, XFreqChange[gen])) + '\t\t' + '\t'.join(map('{0:.4f}'.format, AFreqChange[gen])) + '\n'"
)
],
step=StatsStep),
sim.TerminateIf(
'sum([alleleFreq[x][0] * alleleFreq[x][1] for x in range(X_loci + A_loci)]) == 0'
)
],
gen=generations)
i = 0
for pop in simu.populations():
pop.save('{}_{}.pop'.format(OutPopPrefix, i))
i += 1
