def generate_f_one(self, pop: sim.Population):
"""
A very basic implementation of the F_1 cross between pairs of individuals. Relies on
pre-formatting of desired mating pairs into an ordered list.
[1, 3, 5, 11, 12, 9, 22, 2]
The mating pattern would be:
1x3, 5x11, 12x9, 22x2. Rearranging the order of the indices would change the mating
pairs.
:param pop:
:type pop:
:return:
:rtype:
"""
pop.dvars().generations[1] = 'F_1'
pop.dvars().gen = 1
pairs_of_founders = int(pop.popSize() / 2)
self.odd_to_even(pop)
print("Creating the F_one population from selected founders.")
return pop.evolve(
preOps=[
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.CalcTripletFrequencies(),
sim.PyExec('triplet_freq[gen]=tripletFreq'),
sim.SplitSubPops(sizes=[2] * pairs_of_founders, randomize=False),
],
matingScheme=sim.RandomMating(subPopSize=[1] * pairs_of_founders,
ops=[sim.Recombinator(rates=0.01), sim.IdTagger(), sim.PedigreeTagger()]),
gen=1,
)
def _mate_and_merge(self, pop: sim.Population):
starting_gen = pop.vars()['gen']
print("Initiating recombinatorial convergence at generation: %d" % pop.dvars().gen)
while pop.numSubPop() > 1:
pop.vars()['generations'][pop.vars()['gen']] = 'IG'+str(pop.vars()['gen'] - starting_gen)
self.pop_halver(pop)
self.odd_to_even(pop)
self.pairwise_merge_protocol(pop)
sub_pop_sizes = list(pop.subPopSizes())
pop.evolve(
preOps=[
sim.MergeSubPops(),
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.CalcTripletFreq(),
sim.PyExec('triplet_freq[gen]=tripletFreq'),
sim.SplitSubPops(sizes=sub_pop_sizes, randomize=False),
],
matingScheme=sim.RandomMating(ops=[sim.Recombinator(rates=0.01),
sim.IdTagger(), sim.PedigreeTagger()]),
gen=1,
)
def _generate_f_two(self, pop: sim.Population) -> sim.Population:
"""
Creates an F2 subpopulations generation by selfing the individuals of 'pop'. Works on a population with one
or more subpopulations.
"""
pop.vars()['generations'][2] = 'F_2'
self.odd_to_even(pop)
num_sub_pops = pop.numSubPop()
progeny_per_individual = int(self.selected_population_size/2)
print("Creating the F_two population.")
return pop.evolve(
preOps=[
sim.MergeSubPops(),
sim.PyEval(r'"Generation: %d\n" % gen'),
operators.CalcTripletFreq(),
sim.PyExec('triplet_freq[gen]=tripletFreq'),
sim.SplitSubPops(sizes=[1]*num_sub_pops, randomize=False),
],
matingScheme=sim.SelfMating(subPopSize=[progeny_per_individual] * num_sub_pops,
numOffspring=progeny_per_individual,
ops=[sim.Recombinator(rates=0.01), sim.IdTagger(), sim.PedigreeTagger()],
),
gen=1,
)
# 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')
]
)
pop = sim.Population(size=100, loci=1)
#Define the demographic trajectory
demo_function = serf.demo_dynamic(101, 100., np.log(2), 100000., 10., 30,
sampling)
#Initiate the simulation
simu = sim.Simulator(pop, rep=100)
#Evolve population
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(
freq=[0.95, 0.05]), #proportion of minor to major allele
sim.PyExec(
'traj=[]') #Initiate a recipient list for frequency outputs
],
matingScheme=sim.RandomSelection(
subPopSize=demo_function), #random binary fission
postOps=[
sim.Stat(alleleFreq=0),
sim.PyExec('traj.append(alleleFreq[0][1])',
step=10), #record state of the simulation
],
gen=101 #number of generations over which to run the simulation
)
#Store simulation output, specifically the state of the alleles at the
#last recorded simulated timepoint.
simulation_outcome[sampling] = [simu.dvars(x).traj[-1] for x in range(100)]
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()])
###############################################################
# SIMULATION #
###############################################################
simu = sim.Simulator(pop, rep=args.replicates)
simu.evolve(
initOps = [
sim.InitInfo([0], infoFields = 'age'),
sim.InitInfo([args.a], infoFields = 'a'),
sim.InitInfo([args.b], infoFields = 'b'),
sim.InitInfo(lambda: random.random(), infoFields = 'luck'),
sim.InfoExec("t0 = -ind.b / ind.a", exposeInd = 'ind'),
sim.InfoExec("smurf = 1 if (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.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')
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}
initOps=[
],
preOps=[
],
matingScheme=sim.RandomMating(subPopSize=sizeChange,numOffspring=(sim.POISSON_DISTRIBUTION, Offspring_Poisson_dist_mean),ops=sim.Recombinator(loci=[0, 1, 2, 3, 4, 5, 6, 7], rates=[0.095162582, 0.075960076, 0.094257292, 0.154646165, 0.005, 0.104165865, 0.071328306,0.5]))
,
postOps=[
sim.PySelector(loci=[4], func=sel),
sim.Stat(alleleFreq=[0,1,2,3,4,5,6,7],numOfMales=True,popSize=True),
#Output H per replicate per generation to file
sim.PyEval(r"'%d\t%d\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\n' % (rep,gen, 1-sum([x*x for x in alleleFreq[0].values()]), 1-sum([x*x for x in alleleFreq[1].values()]), 1-sum([x*x for x in alleleFreq[2].values()]), 1-sum([x*x for x in alleleFreq[3].values()]), 1-sum([x*x for x in alleleFreq[4].values()]), 1-sum([x*x for x in alleleFreq[5].values()]), 1-sum([x*x for x in alleleFreq[6].values()]), 1-sum([x*x for x in alleleFreq[7].values()]))", step=1,reps=(range(0,Replicates,1)),output=Output_file),
sim.PyExec('from math import log'),
#sim.Dumper(output='!"./Simulation_output_pops/pop%d.pop"%rep',step=69,max=500,width=3),
#allelic richness for given locus
#sim.PyEval(r"'%d\n' % (len(alleleNum[0].keys()))"),
#Output Shannon E per replicate per generation to file
#sim.PyEval(r"'%d\t%d\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\n' % (rep,gen, (0-sum([(x * log(x)) for x in alleleFreq[0].values()])/log(len(alleleNum[0].keys())) if len(alleleNum[0].keys()) > 1 else 0) , (0-sum([(x * log(x)) for x in alleleFreq[1].values()])/log(len(alleleNum[1].keys())) if len(alleleNum[1].keys()) > 1 else 0) , (0-sum([(x * log(x)) for x in alleleFreq[2].values()])/log(len(alleleNum[2].keys())) if len(alleleNum[2].keys()) > 1 else 0) , (0-sum([(x * log(x)) for x in alleleFreq[3].values()])/log(len(alleleNum[3].keys())) if len(alleleNum[3].keys()) > 1 else 0) , (0-sum([(x * log(x)) for x in alleleFreq[4].values()])/log(len(alleleNum[4].keys())) if len(alleleNum[4].keys()) > 1 else 0) , (0-sum([(x * log(x)) for x in alleleFreq[5].values()])/log(len(alleleNum[5].keys())) if len(alleleNum[5].keys()) > 1 else 0) , (0-sum([(x * log(x)) for x in alleleFreq[6].values()])/log(len(alleleNum[6].keys())) if len(alleleNum[6].keys()) > 1 else 0), (0-sum([(x * log(x)) for x in alleleFreq[7].values()])/log(len(alleleNum[7].keys())) if len(alleleNum[7].keys()) > 1 else 0))", step=1,reps=(range(0,Replicates,1)),output=Output_file2)
#Output AlleleNum
sim.PyEval(r"'%d\t%d\t%.d\t%d\t%d\t%d\t%d\t%d\t%d\t%d\n' % (rep,gen, len(alleleNum[0].keys()),len(alleleNum[1].keys()),len(alleleNum[2].keys()),len(alleleNum[3].keys()),len(alleleNum[4].keys()),len(alleleNum[5].keys()),len(alleleNum[6].keys()),len(alleleNum[7].keys()))", step=1,reps=(range(0,Replicates,1)),output=Output_file3)
#Output sex ratio
#sim.PyExec('ratio= (numOfMales/popSize)'),
#sim.PyEval("'gen=%d' % gen", reps=0),
# 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=2)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.2, 0.8]),
sim.PyExec('traj=[]')
],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.PyExec('traj.append(alleleFreq[0][1])'),
],
gen=5
)
# print Trajectory
print(', '.join(['%.3f' % x for x in simu.dvars(0).traj]))
def simulateBySimuPOP():
#starting variables
directory = '/data/new/javi/toxo/simulations4/'
input_path = 'Toxo20.txt'
output_path = 'SimulatedToxo.txt'
input_path = directory + input_path
output_path = directory + output_path
parents_path = directory + '/parents.txt'
pedigree_path = directory + 'pedigree.txt'
number_of_ancestors = 3
expansion_pop_size = 15
offsprings_sampled = number_of_ancestors + expansion_pop_size
gen = 3
translate_mode = 'toxoplasma'
structure_mode = 'simupop'
#parsing input
init_info = parseSNPInput(input_path, number_of_ancestors)
ancestral_genomes = init_info[0]
ancestor_names = ancestral_genomes.keys()
loci_positions = init_info[1]
chromosome_names = sorted(loci_positions.keys(),
key=lambda x: cns.getValue(x, translate_mode))
list_of_loci = [len(loci_positions[chr]) for chr in chromosome_names]
lociPos = fc.reduce(lambda x, y: x + y,
[loci_positions[x] for x in chromosome_names])
sp.turnOnDebug(code="DBG_GENERAL")
#initializing
print('Initializaing Population')
population = sp.Population(size=[number_of_ancestors], loci=list_of_loci, ancGen = 5, lociPos = lociPos, \
chromNames = chromosome_names, lociNames = [], alleleNames = ['A','T','G','C'],\
infoFields=['name', 'ind_id', 'father_id', 'mother_id'])
for individual, sample, ind_id in zip(population.individuals(),
ancestral_genomes,
range(len(ancestral_genomes))):
individual.setInfo(ancestor_names.index(sample), 'name')
individual.setInfo(ind_id, 'ind_id')
for ind, chr in enumerate(chromosome_names):
individual.setGenotype(ancestral_genomes[sample][chr],
chroms=[ind])
#Alternating rounds of recombination with clonal expansion. Clonal expansion gives + 2.
#Mutation prior to each round
simulator = sp.Simulator(population)
rate_matrix = createRateMatrix(len(ancestor_names),
0.0002) #10,000 times the mutation rate.
id_tagger = sp.IdTagger()
ped_tagger = sp.PedigreeTagger(output='>>' + pedigree_path,
outputFields=['name', 'ind_id'])
inherit_tagger = sp.InheritTagger(infoFields='name')
initOps1 = [sp.PyExec('print("Starting random selection")'), ped_tagger]
initOps2 = [sp.PyExec('print("Starting random mating")'), ped_tagger]
preOps1 = [sp.MatrixMutator(rate=rate_matrix)]
preOps2 = [sp.InitSex(sex=[sp.MALE, sp.FEMALE])]
matingScheme1 = sp.RandomSelection(
ops=[sp.CloneGenoTransmitter(), inherit_tagger, id_tagger, ped_tagger],
subPopSize=expansion_pop_size)
matingScheme2 = sp.RandomMating(
ops=[
sp.Recombinator(intensity=0.01 / 105000,
convMode=(sp.GEOMETRIC_DISTRIBUTION, 0.001,
0.01)), #10x normal
sp.PyTagger(func=addNames),
id_tagger,
ped_tagger
],
subPopSize=expansion_pop_size)
postOps = []
finalOps = []
print('Starting Evolution Cycles.')
try:
os.remove(pedigree_path)
except:
pass
simulator.evolve(
initOps=[id_tagger, ped_tagger],
matingScheme=sp.CloneMating(ops=[
sp.CloneGenoTransmitter(), ped_tagger, id_tagger, inherit_tagger
]),
gen=1)
for x in range(gen):
simulator.evolve(initOps=initOps1,
preOps=preOps1,
matingScheme=matingScheme1,
postOps=postOps,
finalOps=finalOps,
gen=1)
simulator.evolve(initOps=initOps2,
preOps=preOps2,
matingScheme=matingScheme2,
postOps=postOps,
finalOps=finalOps,
gen=1)
offsprings = {
''.join([
str(int(x.info('name'))),
generateID(3),
str(int(x.info('ind_id')))
]): x.info('ind_id')
for x in simulator.population(0).individuals()
}
sampled_ones = rand.sample(offsprings.keys(), offsprings_sampled)
#reorganizes the offspring genome. Extract info by chr.
offspring_genomes = {name: {} for name in sampled_ones}
for name in sampled_ones:
for ind, chr in enumerate(chromosome_names):
offspring_genomes[name][chr] = simulator.population(0).indByID(
offsprings[name], idField='ind_id').genotype(ploidy=0,
chroms=[ind])
offspring_genomes.update(ancestral_genomes)
print('Parent Guide:')
for ind, id in enumerate(ancestor_names):
print(" : ".join([str(ind), str(id)]))
print('Complete. Generating Output.')
with open(parents_path, 'w') as parent_output:
parent_output.write('Parent Guide:\n')
for ind, id in enumerate(ancestor_names):
parent_output.write(" : ".join([str(ind), str(id)]) + '\n')
#output
offspring_genomes = snp.restructure((offspring_genomes, loci_positions),
structure_mode)
snp.outputGriggFormat(offspring_genomes, output_path)
print('Simulation Complete.')
# At this point, even males are diploids! Only 1/1 and 1/0 (but not 0/1) males get 'a' increased.
sim.InfoExec(
"a = min_a + meffect if ind.sex() == 1 and ind.allele(0,0) == 1 else min_a",
exposeInd='ind'),
sim.InitInfo([args.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 (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.PedigreeTagger(output='>>{}.ped'.format(args.output),
outputFields=['a', 'birthday'],
outputLoci=[0]),
sim.PyExec(
"AccumAges = {1: {0: {x: 0 for x in range(maxAge)}, 1: {x: 0 for x in range(maxAge)}, 2: {x: 0 for x in range(maxAge)}}, 2: {0: {x: 0 for x in range (maxAge)}, 1: {x: 0 for x in range(maxAge)}, 2: {x: 0 for x in range(maxAge)}}}"
)
],
preOps=[
sim.InfoExec("luck = random.random()"),
sim.InfoExec(
"smurf = 1.0 if (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(aging_model(args.model)),
sim.InfoExec("age += 1"),
# Here, ind.allele(0,1) is 0 for all males, except in first generation.
sim.InfoExec(
"AccumAges[ind.sex()][ind.allele(0,0) + ind.allele(0,1)][int(ind.age)] += 1",
usePopVars=True,
exposeInd='ind'),
sim.PySelector(loci=[0], func=fitness_func)
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
