def pre_sel_sort(self, pop):
"""
Meant to set up the population for truncation selection.
Can be used in an evolutionary process to provide easy
way to implement non-random mating schemes.
:warning: Replaces current virtual sub-population split
:param pop:
:return:
"""
pop.sortIndividuals('p')
pop.setVirtualSplitter(sim.ProportionSplitter(self.proportions,
names=['not_selected','selected']))
return True
import simuPOP as sim
pop = sim.Population(size=[10000, 10000], loci=1)
pop.setVirtualSplitter(sim.ProportionSplitter([0.8, 0.2]))
pop.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5])],
preOps=[
sim.Stat(homoFreq=0, subPops=[0, 1], vars='homoFreq_sp'),
sim.PyEval(r"'(%.2f, %.2f)\n' % (subPop[0]['homoFreq'][0], "
"subPop[1]['homoFreq'][0])"),
],
matingScheme=sim.HeteroMating(matingSchemes=[
sim.RandomMating(subPops=[(0, 0), 1]),
sim.SelfMating(subPops=[(0, 1)]),
]),
gen=3)
pop = sim.Population(size=[2, 3], loci=[5, 7])
sim.initGenotype(pop, genotype=[1] * 5 + [2] * 7 + [3] * 5 + [4] * 7)
sim.dump(pop, structure=False)
#
# use virtual subpopulation
pop = sim.Population(size=[2000, 3000], loci=[5, 7])
pop.setVirtualSplitter(sim.SexSplitter())
sim.initSex(pop)
sim.initGenotype(pop, genotype=range(10), loci=range(5))
# initialize all males
sim.initGenotype(pop,
genotype=[2] * 7,
loci=range(5, 12),
subPops=[(0, 0), (1, 0)])
# assign genotype by proportions
pop.setVirtualSplitter(sim.ProportionSplitter([0.4, 0.6]))
sim.initGenotype(pop, freq=[0.2, 0.8], subPops=[(0, 0)])
sim.initGenotype(pop, freq=[0.5, 0.5], subPops=[(0, 1)])
#
# initialize by random allele frequency
import random
sim.initGenotype(pop, freq=lambda: random.random())
printFreq(pop, range(5))
# initialize with loci specific frequency. here
# lambda loc: 0.01*loc is equivalent to
# lambda loc: [0.01*loc, 1-0.01*loc]
sim.initGenotype(pop, freq=lambda loc: 0.01 * loc)
printFreq(pop, range(5))
# initialize with VSP-specific frequency
sim.initGenotype(pop,
freq=lambda vsp: [[0.2, 0.8], [0.5, 0.5]][vsp[1]],
from simuPOP.sampling import drawCaseControlSample
def assoTest(pop):
'Draw case-control sample and apply association tests'
sample = drawCaseControlSample(pop, cases=500, controls=500)
sim.stat(sample,
association=(0, 2),
vars=['Allele_ChiSq_p', 'Geno_ChiSq_p', 'Armitage_p'])
print('Allele test: %.2e, %.2e, Geno test: %.2e, %.2e, Trend test: %.2e, %.2e' \
% (sample.dvars().Allele_ChiSq_p[0], sample.dvars().Allele_ChiSq_p[2],
sample.dvars().Geno_ChiSq_p[0], sample.dvars().Geno_ChiSq_p[2],
sample.dvars().Armitage_p[0], sample.dvars().Armitage_p[2]))
return True
pop = sim.Population(size=100000, loci=3)
pop.setVirtualSplitter(sim.ProportionSplitter([0.5, 0.5]))
pop.evolve(initOps=[
sim.InitSex(),
sim.InitGenotype(genotype=[0] * 3, subPops=[(0, 0)]),
sim.InitGenotype(genotype=[1] * 3, subPops=[(0, 1)]),
],
matingScheme=sim.RandomMating(
ops=sim.Recombinator(loci=[0, 1], rates=[0.01, 0.005])),
postOps=[
sim.MaPenetrance(loci=1, penetrance=[0.1, 0.2, 0.4]),
sim.PyOperator(func=assoTest, step=20),
],
gen=100)