def get_mean_r2(Ne, S, n_loci, gens, n_subpops, initial_frequencies, m):
"""Returns the mean r2 value for each subpopulation, in list of length n_subpops"""
# make pairwise migration matrix
M = get_migration_matrix(m, n_subpops)
# initialise population
n_alleles = 2
pop = sim.Population(size=[Ne] * n_subpops,
ploidy=2,
loci=[1] * n_loci,
alleleNames=[str(i) for i in range(n_alleles)],
infoFields='migrate_to')
sim.initGenotype(pop, freq=[initial_frequencies, 1 - initial_frequencies])
#sim.initGenotype(pop, freq = [1/n_alleles for i in range(n_alleles)])
sim.initSex(pop)
print(M)
# run burn in generations
pop.evolve(initOps=[],
preOps=sim.Migrator(M, mode=sim.BY_PROBABILITY),
matingScheme=sim.RandomMating(),
gen=gens)
# take sample from each subpopulation
sample_pop = drawRandomSample(pop, sizes=[S] + [0] * (n_subpops - 1))
#sim.dump(sample_pop)
# get allele frequencies
sim.stat(sample_pop, alleleFreq=range(0, n_loci), vars=['alleleFreq_sp'])
#print(sample_pop.dvars(0).alleleFreq)
# calculate r2 values
sim.stat(sample_pop,
LD=list(itertools.combinations(list(range(n_loci)), r=2)),
vars=['R2_sp'])
#print(sample_pop.dvars(0).R2)
r2s = []
for sp in [0]: #range(n_subpops*0):
allele_freqs = sample_pop.dvars(sp).alleleFreq
seg_alleles = [
k for k in range(n_loci)
if np.abs(.5 - allele_freqs[k][0]) < .5 - 0.05
]
if len(seg_alleles) < 2: raise Exception("<2 segregating alleles")
r2_sum = 0
count = 0
for pairs in itertools.combinations(seg_alleles, r=2):
r2_sum += sample_pop.dvars(sp).R2[pairs[0]][pairs[1]]
count += 1
mean_r2 = r2_sum / count
r2s.append(mean_r2)
return r2s
def setUp(self):
self.fs = 0.9
self.ms = 0.5
self.pop = sim.Population(size=[4, 4],
loci=[1, 1, 1, 1],
chromTypes=[sim.AUTOSOME,
sim.CHROMOSOME_X,
sim.CHROMOSOME_Y,
sim.CUSTOMIZED],
infoFields=['fitness'])
self.pop.setVirtualSplitter(sim.SexSplitter())
sim.initSex(self.pop, sex=[sim.MALE, sim.FEMALE])
for i in range(2):
for j in range(2):
sim.initGenotype(self.pop,
loci=[0], # Autosome
prop=[0.5, 0.5],
subPops=[(i,j)])
sim.initGenotype(self.pop,
loci=[3], # Mitochondria
ploidy=0,
prop=[0.5, 0.5],
subPops=[(i,j)])
sim.initGenotype(self.pop,
loci=[1], # Chromosome X
prop=[0.5, 0.5],
subPops=[(i,1)])
sim.initGenotype(self.pop,
loci=[1], # Chromosome X
prop=[0.5, 0.5],
ploidy=0,
subPops=[(i,0)])
sim.initGenotype(self.pop,
loci=[2], # Chromosome Y
prop=[0.5, 0.5],
ploidy=1,
subPops=[(i,0)])
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed # description of this example. # import simuPOP as sim import random pop = sim.Population(size=[200, 200], loci=[5, 5], infoFields='age') sim.initGenotype(pop, genotype=range(10)) sim.initInfo(pop, lambda: random.randint(0, 75), infoFields='age') pop.setVirtualSplitter(sim.InfoSplitter(field='age', cutoff=[20, 60])) # remove individuals pop.removeIndividuals(indexes=range(0, 300, 10)) print(pop.subPopSizes()) # remove individuals using IDs pop.setIndInfo([1, 2, 3, 4], field='age') pop.removeIndividuals(IDs=[2, 4], idField='age') # remove indiviuals using a filter function sim.initSex(pop) pop.removeIndividuals(filter=lambda ind: ind.sex() == sim.MALE) print([pop.individual(x).sex() for x in range(8)]) # # remove subpopulation pop.removeSubPops(1) print(pop.subPopSizes()) # remove virtual subpopulation (people with age between 20 and 60) pop.removeSubPops([(0, 1)]) print(pop.subPopSizes()) # extract another virtual subpopulation (people with age greater than 60) pop1 = pop.extractSubPops([(0, 2)]) sim.dump(pop1, structure=False, max=10)
pop = simuPOP.population(size=[n_ind] * n_pop,
ploidy=n_ploidy,
loci=[n_loci],
lociPos=[15, 25],
chromNames=['chr3', 'chr5'],
chromTypes=[simuPOP.Autosome],
alleleNames=["A", "T"],
infoFields='migrate_to')
simu = simuPOP.simulator(pop, simuPOP.randomMating())
myInitOps, myPreOps, myPostOps = [], [], []
#iniate sex & frequency
myInitOps += [
simuPOP.initSex(sex=[simuPOP.Male, simuPOP.Female]),
simuPOP.initByFreq(loci=[0], alleleFreq=init_freq_2)
]
#parameter transfer
myInitOps += [
simuPOP.pyExec("traj_gen=[0] * n_pop"),
simuPOP.pyExec("traj_pop=range(n_pop)"),
simuPOP.pyExec("traj_af=init_freq"),
simuPOP.pyExec("traj_popsize=[n_ind] * n_pop")
]
myPostOps += [
simuPOP.stat(alleleFreq=[0], subPops=range(n_pop), vars=['alleleFreq_sp']),
simuPOP.pyExec("traj_gen += [gen+1] * n_pop"),
simuPOP.pyExec("traj_pop += range(n_pop)"),
simuPOP.pyExec(
"traj_af += [subPop[x]['alleleFreq'][0][0] for x in range(n_pop)]"),
# contribute genotype to the last generation?
anc = ped.identifyAncestors()
len(anc)
# remove individuals who do not contribute genotype to the last generation
allIDs = [x.ind_id for x in ped.allIndividuals()]
removedIDs = list(set(allIDs) - set(anc))
ped.removeIndividuals(IDs=removedIDs)
# now create a top most population, but we do not need all of them
# so we record only used individuals
IDs = [x.ind_id for x in ped.allIndividuals(ancGens=N)]
sex = [x.sex() for x in ped.allIndividuals(ancGens=N)]
# create a population, this time with genotype. Note that we do not need
# populaton structure because PedigreeMating disregard population structure.
pop = sim.Population(size=len(IDs), loci=1000, infoFields='ind_id')
# manually initialize ID and sex
sim.initInfo(pop, IDs, infoFields='ind_id')
sim.initSex(pop, sex=sex)
pop.evolve(
initOps=sim.InitGenotype(freq=[0.4, 0.6]),
# we do not need migration, or set number of offspring,
# or demographic model, but we do need a genotype transmitter
matingScheme=sim.PedigreeMating(ped, ops=sim.MendelianGenoTransmitter()),
gen=100)
# let us compare the pedigree and the population object
print(ped.indInfo('ind_id')[:5])
print(pop.indInfo('ind_id')[:5])
print([ped.individual(x).sex() for x in range(5)])
print([pop.individual(x).sex() for x in range(5)])
print(ped.subPopSizes())
print(pop.subPopSizes())
# # Copyright (C) 2004 - 2010 Bo Peng ([email protected]) # # 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 pop = sim.Population(size=[1000, 1000]) sim.initSex(pop, maleFreq=0.3, subPops=0) sim.initSex(pop, sex=[sim.MALE, sim.FEMALE, sim.FEMALE], subPops=1) sim.stat(pop, numOfMales=True, vars='numOfMales_sp') print(pop.dvars(0).numOfMales) print(pop.dvars(1).numOfMales)
