def get_gynodioecious_mating(r_rate,
weight,
size,
sex_ratio,
loci,
allele_length,
field='self_gen'):
"""
Sets up gynodioecious mating.
"""
sex_mode = (simu.PROB_OF_MALES, sex_ratio)
rec_loci = [allele_length * i - 1 for i in range(1, loci + 1)]
selfing = simu.SelfMating(ops=[
simu.Recombinator(rates=r_rate, loci=rec_loci),
cf.MySelfingTagger(field)
],
sexMode=sex_mode,
subPops=[(0, 0)],
weight=weight)
outcross = simu.RandomMating(ops=[
simu.Recombinator(rates=r_rate, loci=rec_loci),
cf.MySelfingTagger(field)
],
sexMode=sex_mode,
weight=weight)
return simu.HeteroMating(matingSchemes=[selfing, outcross],
subPopSize=size)
def test_selfing_after_outcrossing(self):
"""Test scinario: generations of selfing after generations of outcrossing."""
# outcrossing
self.sim.evolve(initOps=self.initOps,
matingScheme=simu.RandomMating(
subPopSize=10,
sexMode=self.sexMode,
ops=[
simu.MendelianGenoTransmitter(),
cf.MyOutcrossingTagger()
]),
gen=10)
for pop in self.sim.populations():
for ind in pop.individuals():
assert ind.info('self_gen') == 0
assert pop.dvars().gen == 10
# selfing
self.sim.evolve(
initOps=self.initOps,
matingScheme=simu.SelfMating(
subPopSize=10,
sexMode=self.sexMode,
ops=[simu.SelfingGenoTransmitter(),
cf.MySelfingTagger()]),
gen=10)
for pop in self.sim.populations():
for ind in pop.individuals():
assert ind.info('self_gen') == 10
assert pop.dvars().gen == 20
def get_pure_hermaphrodite_mating(r_rate,
weight,
size,
loci,
allele_length,
field='self_gen'):
"""
Construct mating scheme for pure hermaphrodite with partial selfing under the
infinite sites model.
A fraction, 0 <= weight <= 1, of offspring is generated by selfing, and others are
generated by outcrossing. In this model, there is no specific sex so that any
individual can mate with any other individuals in a population.
Furthermore, a parent can participate in both selfing and outcrossing.
"""
field = str(field)
# Index of sites, after which recombinations happen.
rec_loci = [allele_length * i - 1 for i in range(1, loci + 1)]
selfing = simu.SelfMating(ops=[
simu.Recombinator(rates=r_rate, loci=rec_loci),
cf.MySelfingTagger(field)
],
weight=weight)
outcross = simu.HomoMating(
chooser=simu.PyParentsChooser(generator=cf.pickTwoParents),
generator=simu.OffspringGenerator(ops=[
simu.Recombinator(rates=r_rate, loci=rec_loci),
cf.MyOutcrossingTagger(field)
]),
weight=1.0 - weight)
return simu.HeteroMating(matingSchemes=[selfing, outcross],
subPopSize=size)
def expand_by_selfing(self, pop, recombination_rates):
"""
Specific for plant populations capable of selfing.
Creates an F2 subpopulations generation by selfing the individuals of
'pop'. Works on a population with one or more subpopulations.
:param pop:
"""
# self.odd_to_even(pop)
num_sub_pops = pop.numSubPop()
progeny_per_individual = int(self.operating_population_size / 2)
return pop.evolve(
preOps=[
sim.MergeSubPops(),
sim.PyEval(r'"Generation: %d\n" % gen'),
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=recombination_rates),
sim.IdTagger(),
sim.PedigreeTagger()
],
),
gen=1,
)
def create_self_crosses(self, existing_pop, offspring_per_individual):
new_pop_size = offspring_per_individual * existing_pop.popSize()
existing_pop.evolve(
matingScheme=sim.SelfMating(
replacement=False,
numOffspring=offspring_per_individual,
subPopSize=new_pop_size,
ops=[
sim.IdTagger(),
sim.PedigreeTagger(),
sim.Recombinator(rates=0.01)
],
),
gen=1,
)
def test_pure_selfing(self):
"""Test pure inbreeding population.
When all individuals undergo selfing, the values in `self_gen` should be identical
to the number of generations since simulations started.
"""
self.sim.evolve(
initOps=self.initOps,
matingScheme=simu.SelfMating(
subPopSize=10,
sexMode=self.sexMode,
ops=[simu.SelfingGenoTransmitter(),
cf.MySelfingTagger()]),
gen=10)
for pop in self.sim.populations():
for ind in pop.individuals():
assert ind.info('self_gen') == 10
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,
)
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)
# 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],
loci=2,
infoFields=['father_idx', 'mother_idx'])
pop.setVirtualSplitter(sim.ProportionSplitter([0.2, 0.8]))
pop.evolve(
initOps=sim.InitSex(),
matingScheme=sim.HeteroMating(matingSchemes=[
sim.SelfMating(subPops=[(0, 0)],
ops=[sim.SelfingGenoTransmitter(),
sim.ParentsTagger()]),
sim.RandomMating(
subPops=[(0, 1)],
ops=[sim.SelfingGenoTransmitter(),
sim.ParentsTagger()])
]),
gen=10)
[int(ind.father_idx) for ind in pop.individuals(0)][:15]
[int(ind.mother_idx) for ind in pop.individuals(0)][:15]
# 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(20, loci=8)
# every chromosomes are different. :-)
for idx, ind in enumerate(pop.individuals()):
ind.setGenotype([idx*2], 0)
ind.setGenotype([idx*2+1], 1)
pop.evolve(
matingScheme=sim.SelfMating(ops=sim.Recombinator(rates=0.01)),
gen = 1
)
sim.dump(pop, width=3, structure=False, max=10)