def createGenome(size, numMSats, numSNPs):
maxAlleleN = 100
# print "Mutation model is most probably not correct", numMSats, numSNPs
loci = (numMSats + numSNPs) * [1]
initOps = []
for msat in range(numMSats):
diri = numpy.random.mtrand.dirichlet([1.0] * cfg.startAlleles)
if type(diri[0]) == float:
diriList = diri
else:
diriList = list(diri)
initOps.append(
sp.InitGenotype(freq=[0.0] * ((maxAlleleN + 1 - 8) // 2) +
diriList + [0.0] * ((maxAlleleN + 1 - 8) // 2),
loci=msat))
for snp in range(numSNPs):
freq = 0.5
initOps.append(
sp.InitGenotype(
# Position 0 is coded as 0, not good for genepop
freq=[0.0, freq, 1 - freq],
loci=numMSats + snp))
preOps = []
if cfg.mutFreq > 0:
preOps.append(sp.StepwiseMutator(rates=cfg.mutFreq,
loci=list(range(numMSats))))
return loci, initOps, preOps
def _create_genome(self, num_msats, mut=None, start_alleles=10):
init_ops = []
loci = num_msats * [1]
pre_ops = []
max_allele_msats = 100
for msat in range(num_msats):
diri = np.random.mtrand.dirichlet([1.0] * start_alleles)
if type(diri[0]) == float:
diri_list = diri
else:
diri_list = list(diri)
init_ops.append(
sp.InitGenotype(freq=[0.0] * ((max_allele_msats + 1 - 8) //
2) + diri_list + [0.0] *
((max_allele_msats + 1 - 8) // 2),
loci=msat))
if mut is not None:
pre_ops.append(sp.StepwiseMutator(rates=mut))
return loci, init_ops, pre_ops
import simuOpt
simuOpt.setOptions(quiet=True, alleleType='long')
import simuPOP as sim
pop = sim.Population(size=[2500]*10, loci=1)
simu = sim.Simulator(pop, rep=2)
simu.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(genotype=20),
],
preOps=[
sim.StepwiseMutator(rates=0.0001, reps=0),
sim.KAlleleMutator(k=10000, rates=0.0001, reps=1),
],
matingScheme=sim.RandomMating(),
postOps=[
# Use vars=['alleleFreq_sp'] to calculate allele frequency for
# each subpopulation
sim.Stat(alleleFreq=0, vars=['alleleFreq_sp'], step=200),
sim.PyEval('gen', step=200, reps=0),
sim.PyEval(r"'\t%.2f' % (sum([len(subPop[x]['alleleFreq'][0]) "
"for x in range(10)])/10.)", step=200),
sim.PyOutput('\n', reps=-1, step=200)
],
gen=601
)
pop.dvars().avgAllele = avg
return True
pop = sim.Population(10000, loci=[1, 1])
pop.setVirtualSplitter(sim.AffectionSplitter())
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(genotype=[50, 50])
],
matingScheme=sim.RandomMating(),
postOps=[
# determine affection sim.status for each offspring (duringMating)
sim.PyPenetrance(func=fragileX, loci=0),
# unaffected offspring, mutation rate is high to save some time
sim.StepwiseMutator(rates=1e-3, loci=1),
# unaffected offspring, mutation rate is high to save some time
sim.StepwiseMutator(rates=1e-3, loci=0, subPops=[(0, 0)]),
# affected offspring have high probability of mutating upward
sim.StepwiseMutator(rates=1e-2, loci=0, subPops=[(0, 1)],
incProb=0.7, mutStep=3),
# number of affected
sim.PyOperator(func=avgAllele, step=20),
sim.PyEval(r"'Gen: %3d #Aff: %d AvgRepeat: %.2f (unaff), %.2f (aff), %.2f (unrelated)\n'"
+ " % (gen, numOfAffected, avgAllele[0], avgAllele[1], avgAllele[2])",
step=20),
],
gen = 101
)
#
# 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, loci=[1, 1])
pop.evolve(
# all start from allele 50
initOps=[sim.InitSex(),
sim.InitGenotype(freq=[0] * 50 + [1])],
matingScheme=sim.RandomMating(),
preOps=[
sim.StepwiseMutator(rates=1e-3, loci=0),
sim.StepwiseMutator(rates=1e-3,
incProb=0.6,
loci=1,
mutStep=(sim.GEOMETRIC_DISTRIBUTION, 0.2)),
],
gen=100)
# count the average number tandem repeats at both loci
cnt0 = cnt1 = 0
for ind in pop.individuals():
cnt0 += ind.allele(0, 0) + ind.allele(0, 1)
cnt1 += ind.allele(1, 0) + ind.allele(1, 1)
print('Average number of repeats at two loci are %.2f and %.2f.' % \
(cnt0/2000., cnt1/2000.))
# 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(5000, loci=[1, 1])
pop.evolve(
initOps=[sim.InitSex(), sim.InitGenotype(genotype=[50, 50])],
preOps=[
# the first locus uses a pure stepwise mutation model
sim.StepwiseMutator(rates=0.001, loci=0),
# the second locus uses a mixed model
sim.MixedMutator(rates=0.001,
loci=1,
mutators=[
sim.KAlleleMutator(rates=1, k=100),
sim.StepwiseMutator(rates=1)
],
prob=[0.1, 0.9])
],
matingScheme=sim.RandomMating(),
gen=20)
# what alleles are there?
geno0 = []
geno1 = []
for ind in pop.individuals():