# 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
simu = sim.Simulator(sim.Population(50, loci=[10], ploidy=1),
rep=3)
simu.evolve(gen = 5)
simu.dvars(0).gen
simu.evolve(
initOps=[sim.InitGenotype(freq=[0.5, 0.5])],
matingScheme=sim.RandomSelection(),
postOps=[
sim.Stat(alleleFreq=5),
sim.IfElse('alleleNum[5][0] == 0',
sim.PyEval(r"'Allele 0 is lost in rep %d at gen %d\n' % (rep, gen)")),
sim.IfElse('alleleNum[5][0] == 50',
sim.PyEval(r"'Allele 0 is fixed in rep %d at gen %d\n' % (rep, gen)")),
sim.TerminateIf('len(alleleNum[5]) == 1'),
],
)
[simu.dvars(x).gen for x in range(3)]
# 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
import time
pop = sim.Population(1000, loci=10)
pop.dvars().init_time = time.time()
pop.dvars().last_time = time.time()
exec('import time', pop.vars(), pop.vars())
pop.evolve(
initOps=sim.InitSex(),
matingScheme=sim.RandomMating(),
postOps=[
sim.IfElse('time.time() - last_time > 5', [
sim.PyEval(r'"Gen: %d\n" % gen'),
sim.PyExec('last_time = time.time()')
]),
sim.TerminateIf('time.time() - init_time > 20')
]
)
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# 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
simu = sim.Simulator(sim.Population(100, loci=1), rep=5)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5])
],
matingScheme = sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.TerminateIf('len(alleleFreq[0]) == 1')
]
)
import simuOpt
simuOpt.setOptions(quiet=True, alleleType='long')
import simuPOP as sim
pop = sim.Population(size=1000, loci=[1])
simu = sim.Simulator(pop, 5)
simu.evolve(
initOps=[
sim.InitSex(),
sim.PyExec('introGen=[]')
],
preOps=[
sim.Stat(alleleFreq=0),
sim.IfElse('alleleFreq[0][1] == 0', ifOps=[
sim.PointMutator(loci=0, allele=1, inds=0),
sim.PyExec('introGen.append(gen)')
]),
sim.TerminateIf('alleleFreq[0][1] >= 0.05')
],
matingScheme=sim.RandomMating()
)
# number of attempts
print([len(x.dvars().introGen) for x in simu.populations()])
# age of mutant
print([x.dvars().gen - x.dvars().introGen[-1] for x in simu.populations()])
sim.PyExec("Surviving = {'larvae': [], 'adults': [], 'smurfs': []}")
],
preOps = [
sim.InfoExec("luck = random.random()"),
sim.InfoExec("smurf = 1 if ((ind.smurf == 1) or (model == 'two_phases' and 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", exposeInd='ind'),
sim.DiscardIf(aging_model(args.model)),
sim.InfoExec("age += 1")
],
matingScheme = sim.CloneMating(subPops = sim.ALL_AVAIL, subPopSize = demo),
postOps = [
sim.Stat(popSize=True, subPops=[(0,0), (0,1), (0,2)]),
sim.PyExec("Surviving['larvae'].append(subPopSize[0])"),
sim.PyExec("Surviving['adults'].append(subPopSize[1])"),
sim.PyExec("Surviving['smurfs'].append(subPopSize[2])"),
# sim.PyEval(r'"{:d}\t{:d}\t{:d}\t{:d}\n".format(gen, subPopSize[0], subPopSize[1], subPopSize[2])', step=1),
sim.TerminateIf('popSize == 0')
],
gen=args.G
)
args.output.write("#Gen\t" + "\t".join(['Larvae\tAdults\tSmurfs' for a in range(args.replicates)]) + "\n")
for day in range(args.G):
line = "{:3d}".format(day + 1)
for rep in range(args.replicates):
for kind in ['larvae', 'adults', 'smurfs']:
try:
line += "\t{:.4f}".format(simu.vars(rep)['Surviving'][kind][day] / args.N)
except IndexError:
line += "\t0.0000"
args.output.write(line + "\n")
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
import simuPOP.demography as demo
model = demo.MultiStageModel([
demo.InstantChangeModel(
N0=1000,
ops=[
sim.Stat(alleleFreq=sim.ALL_AVAIL, numOfSegSites=sim.ALL_AVAIL),
# terminate if the average allele frequency of segregating sites
# are more than 0.1
sim.TerminateIf(
'sum([x[1] for x in alleleFreq.values() if '
'x[1] != 0])/(1 if numOfSegSites==0 else numOfSegSites) > 0.1')
]),
demo.ExponentialGrowthModel(N0=[0.5, 0.5], r=0.01, NT=[2000, 5000])
])
pop = sim.Population(size=model.init_size, loci=100)
pop.evolve(
initOps=sim.InitSex(),
preOps=sim.SNPMutator(u=0.001, v=0.001),
matingScheme=sim.RandomMating(subPopSize=model),
postOps=[
sim.Stat(alleleFreq=sim.ALL_AVAIL,
numOfSegSites=sim.ALL_AVAIL,
popSize=True,
step=50),
N = 100
p = 0.4
pop = sim.Population(2 * N, ploidy=1, loci=1)
# Use a simulator to simulate 500 populations simultaneously.
simu = sim.Simulator(pop, rep=500)
gens = simu.evolve(
initOps=sim.InitGenotype(prop=[p, 1 - p]),
# A RandomSelection mating scheme choose parents randomly regardless
# of sex and copy parental genotype to offspring directly.
matingScheme=sim.RandomSelection(),
postOps=[
# calculate allele frequency at locus 0
sim.Stat(alleleFreq=0),
# and terminate the evolution of a population if it has no
# allele 0 or 1 at locus 0.
sim.TerminateIf('alleleFreq[0][0] in (0, 1)'),
],
)
# find out populations with or without allele 1
gen_lost = []
gen_fixed = []
for gen, pop in zip(gens, simu.populations()):
if pop.dvars().alleleFreq[0][0] == 0:
gen_lost.append(gen)
else:
gen_fixed.append(gen)
print('''\nMean persistence time: %.2f (expected: %.2f)
Lost pops: %d (expected: %.1f), Mean persistence time: %.2f (expected: %.2f)
Fixed pops: %d (expected: %.1f), Mean persistence time: %.2f (expected: %.2f)'''\
% (float(sum(gens)) / len(gens), -4*N*((1-p)*log(1-p) + p*log(p)),
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# 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
simu = sim.Simulator(sim.Population(size=100, loci=1), rep=10)
simu.evolve(initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.TerminateIf('len(alleleFreq[0]) == 1', stopAll=True)
])
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
