help=
"name of file to output selected locus information (default: (dir)/sel_loci.txt)"
)
parser.add_argument(
"--samples_file",
"-e",
help=
"name of file to output information on samples (default=(dir)/samples.tsv)"
)
args = parser.parse_args()
import simuOpt
import simuPOP as sim
sim.setRNG(seed=args.seed)
random.seed(args.seed)
if args.outfile is not None:
outfile = fileopt(args.outfile, "w")
logfile = fileopt(args.logfile, "w")
if args.selloci_file is None:
args.selloci_file = os.path.join(os.path.dirname(args.treefile),
"sel_loci.txt")
selloci_file = args.selloci_file
if args.samples_file is None:
args.samples_file = os.path.join(os.path.dirname(args.treefile),
"samples.tsv")
samples_file = fileopt(args.samples_file, "w")
# compute these here so they get recorded in the log
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
import random
def simulate():
pop = sim.Population(1000, loci=10, infoFields='age')
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5]),
sim.InitInfo(lambda: random.randint(0, 10), infoFields='age')
],
matingScheme=sim.RandomMating(),
finalOps=sim.Stat(alleleFreq=0),
gen=100
)
return pop.dvars().alleleFreq[0][0]
seed = sim.getRNG().seed()
random.seed(seed)
print('%.4f' % simulate())
# will yield different result
print('%.4f' % simulate())
sim.setRNG(seed=seed)
random.seed(seed)
# will yield identical result because the same seeds are used
print('%.4f' % simulate())
def simuAssortativeMatingWithFitness(e):
'''
Accepts:
e an Experiment object.
Returns a dict containing the results from each gen of the simulation:
gen generation number.
A frequency of the A allele.
a frequency of the a allele.
AA frequency of AA individuals.
Aa frequency of Aa individuals.
aa frequency of aa individuals.
deaf frequency of deaf individuals (incl adventitious).
AA_size size of the AA subpopulation.
Aa_size size of the Aa subpopulation.
aa_size size of the aa subpopulation.
deaf_size size of the deaf subpopulation (incl adventitious).
homogamy calculated actual homogamy.
F calculated inbreeding coefficient.
Adopted from: http://simupop.sourceforge.net/Cookbook/AssortativeMating
'''
sim.setRNG(random.seed(sim.getRNG().seed()))
pop = sim.Population(e.constant_pop_size * 1000, loci=[1])
# These variables need to be set in order to be available to customChooser().
# There appears to be no way to directly pass variables to customChooser().
pop.dvars().constant_pop_size = e.constant_pop_size
pop.dvars().a = e.a
pop.dvars().aa_fitness = e.aa_fitness
pop.dvars().aa_homogamy = e.aa_homogamy
pop.dvars().deaf = e.deaf
pop.dvars().adv_deaf_target = int(
round((e.deaf - e.a**2) * e.constant_pop_size * 1000))
# These will hold the final data
pop.dvars().headers = []
pop.dvars().row = []
pop.evolve(
initOps= [sim.InitGenotype(freq=[1-e.a, e.a])],
matingScheme = sim.HomoMating(
chooser = sim.PyParentsChooser(customChooser),
generator = sim.OffspringGenerator(sim.MendelianGenoTransmitter())),
postOps = [sim.Stat(alleleFreq=[0], genoFreq=[0]),
sim.PyExec(r"headers += ['gen','A', 'a',"\
"'AA', 'Aa', 'aa', 'deaf', 'AA_size', 'Aa_size', " \
"'aa_size', 'deaf_size', 'homogamy', 'F'] \n" \
"F = 1.0-((genoFreq[0][(0,1)]+genoFreq[0][(1,0)])/" # F \
"(2.0*alleleFreq[0][0]*alleleFreq[0][1])) "\
"if alleleFreq[0][0]*alleleFreq[0][1] > 0. "\
"else 0. \n" \
"deaf_size = min(genoNum[0][(1,1)] + adv_deaf_target, constant_pop_size*1000) \n"\
"row += [gen, " # generation \
"alleleFreq[0][0], " # A \
"alleleFreq[0][1], " # a \
"genoFreq[0][(0,0)]," # AA \
"genoFreq[0][(0,1)]+genoFreq[0][(1,0)], " # Aa \
"genoFreq[0][(1,1)], " # aa \
"deaf_size/(constant_pop_size*1000.), " # deaf \
"genoNum[0][(0,0)], " # AA_size \
"genoNum[0][(0,1)]+genoNum[0][(1,0)], " # Aa_size \
"genoNum[0][(1,1)], " # aa_size \
"deaf_size, " # deaf_size \
"homogamy, " # homogamy \
"F if F>0. else 0.]") # F \
],
gen = e.generations
)
return {'headers': pop.dvars().headers, 'row': pop.dvars().row}
def simuAssortativeMatingWithFitness(constant_pop_size, a, aa_fitness,
aa_homogamy, deafness_freq, generations):
'''
Accepts:
constant_pop_size population size, which remains constant throughout.
a starting frequency of the a allele.
aa_fitness _relative_ fitness of deaf (aa) individuals.
aa_homogamy the percent of assortative mating between
deaf individuals.
deafness_freq overall frequency of deaf individuals at the time of
reproductive age, including from causes other than
connexin 26.
generations number of generations.
Returns a dict containing the results from each gen of the simulation:
gen generation number.
A frequency of the A allele.
a frequency of the a allele.
AA frequency of AA individuals.
Aa frequency of Aa individuals.
aa frequency of aa individuals.
AA_size size of the AA subpopulation.
Aa_size size of the Aa subpopulation.
aa_size size of the aa subpopulation.
F calculated inbreeding coefficient.
num_deaf size of the deaf subpopulation (incl adventitious).
homogamy calculated actual homogamy.
Adopted from: http://simupop.sourceforge.net/Cookbook/AssortativeMating
'''
sim.setRNG(random.seed(sim.getRNG().seed()))
pop = sim.Population(constant_pop_size*1000, loci=[1])
pop.dvars().constant_pop_size = constant_pop_size
pop.dvars().a = a
pop.dvars().aa_fitness = aa_fitness
pop.dvars().aa_homogamy = aa_homogamy
pop.dvars().deafness_freq = deafness_freq
pop.dvars().generations = generations
pop.dvars().headers = []
pop.dvars().row = []
pop.evolve(
initOps= [sim.InitSex(),
# Assigns individuals randomly to be male or female.
# This can result in slightly more males or females,
# which can cause errors if the wrong mating scheme is
# selected.
sim.InitGenotype(freq=[1-a, a])
],
matingScheme = sim.HomoMating(
chooser = sim.PyParentsChooser(customChooser),
generator = sim.OffspringGenerator(sim.MendelianGenoTransmitter())),
postOps = [sim.Stat(alleleFreq=[0], genoFreq=[0]),
sim.PyExec(r"headers += ['gen','A', 'a',"\
"'AA', 'Aa', 'aa',"\
"'AA_size', 'Aa_size', 'aa_size',"\
"'F', 'nm_deaf', 'homogmy']"),
sim.PyExec(r"F = 1.0-((genoFreq[0][(0,1)]+genoFreq[0][(1,0)])/" # F \
"(2.0*alleleFreq[0][0]*alleleFreq[0][1]))"\
"if alleleFreq[0][0] != 0.0 and alleleFreq[0][1]"\
"!= 0.0 else 0.0"),
sim.PyExec(r"row += [gen, "\
"alleleFreq[0][0]," # A \
"alleleFreq[0][1]," # a \
"genoFreq[0][(0,0)]," # AA \
"genoFreq[0][(0,1)]+genoFreq[0][(1,0)]," # Aa \
"genoFreq[0][(1,1)]," # aa \
"genoNum[0][(0,0)]," # AA_size \
"genoNum[0][(0,1)]+genoNum[0][(1,0)]," # Aa_size \
"genoNum[0][(1,1)]," # aa_size \
"F if F>0.0 else 0.0," # F \
"num_deaf," # nm_deaf \
"homogamy]") # homogmy
],
gen = generations
)
return {'headers':pop.dvars().headers, 'row':pop.dvars().row}
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
