def prepare_sim(self, params):
for view in self._views:
for info in view.info_fields:
self._info_fields.add(info)
nloci = 1 + params['neutral_loci']
pop, init_ops, pre_ops, post_ops = \
self._create_single_pop(params['pop_size'], nloci)
view_ops = []
for view in self._views:
view.pop = pop
view_ops.extend(view.view_ops)
for view in self._views:
post_ops.append(sp.PyOperator(func=_hook_view, param=view))
post_ops = view_ops + post_ops
loci, genome_init = self._create_snp_genome(
nloci, freq=params['snp_freq'])
sim = sp.Simulator(pop, 1, True)
if params['sel_type'] == 'hz_advantage':
ms = sp.MapSelector(loci=0, fitness={
(0, 0): 1 - params['sel'],
(0, 1): 1,
(1, 1): 1 - params['sel']})
elif params['sel_type'] == 'recessive':
ms = sp.MapSelector(loci=0, fitness={
(0, 0): 1 - params['sel'],
(0, 1): 1 - params['sel'],
(1, 1): 1})
else: # dominant
ms = sp.MapSelector(loci=0, fitness={
(0, 0): 1 - params['sel'],
(0, 1): 1,
(1, 1): 1})
return {'sim': sim, 'pop': pop, 'init_ops': init_ops + genome_init,
'pre_ops': pre_ops, 'post_ops': post_ops,
'mating_scheme': sp.RandomMating(
ops=[sp.MendelianGenoTransmitter(), ms])}
# 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=1, infoFields='fitness')
s1 = .1
s2 = .2
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[.2, .8])
],
preOps=sim.MapSelector(loci=0, fitness={(0,0):1-s1, (0,1):1, (1,1):1-s2}),
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.PyEval(r"'%.4f\n' % alleleFreq[0][0]", step=100)
],
gen=301
)
# 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(1000, loci=[5]*4,
# one autosome, two sex chromosomes, and one mitochondrial chromosomes
chromTypes=[sim.AUTOSOME, sim.CHROMOSOME_X, sim.CHROMOSOME_Y, sim.MITOCHONDRIAL],
infoFields=['fitness'])
pop.evolve(
initOps=[
sim.InitSex(),
sim.InitGenotype(freq=[0.25]*4)
],
preOps=[
sim.MapSelector(loci=17, fitness={(0,): 1, (1,): 1, (2,): 1, (3,): 0.4})
],
matingScheme=sim.RandomMating(ops= [
sim.Recombinator(rates=0.1),
sim.MitochondrialGenoTransmitter(),
]),
postOps=[
sim.Stat(alleleFreq=17, step=10),
sim.PyEval(r'"%.2f %.2f %.2f %.2f\n" % (alleleNum[17][0],'
'alleleNum[17][1], alleleNum[17][2], alleleNum[17][3])', step=10),
],
gen = 100
)
# 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(4000, loci=1, infoFields='fitness')
simu = sim.Simulator(pop, rep=3)
simu.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(freq=[0.5, 0.5])],
preOps=sim.MapSelector(loci=0,
fitness={
(0, 0): 1,
(0, 1): 0.98,
(1, 1): 0.97
}),
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0, step=10),
sim.PyEval("'Gen:%3d ' % gen", reps=0, step=10),
sim.PyEval(r"'%.3f\t' % alleleFreq[0][1]", step=10),
sim.PyOutput('\n', reps=-1, step=10)
],
gen=50)
import simuPOP as sim
from simuPOP.plotter import VarPlotter
pop = sim.Population(size=10000, loci=1, infoFields='fitness')
simu = sim.Simulator(pop, rep=3)
h = [0.5, 0.2, -0.5]
s = [0.1, -0.1, 0.1]
simu.evolve(initOps=[sim.InitSex(),
sim.InitGenotype(freq=(0.5, 0.5))],
preOps=[
sim.MapSelector(loci=0,
fitness={
(0, 0): 1,
(0, 1): 1 - h[x] * s[x],
(1, 1): 1 - s[x]
},
reps=x) for x in range(3)
],
matingScheme=sim.RandomMating(),
postOps=[
sim.Stat(alleleFreq=0),
sim.PyEval(r'"%.3f\t" % alleleFreq[0][1]', step=50),
sim.PyOutput('\n', reps=-1, step=50),
VarPlotter(
'alleleFreq[0][0]',
update=200,
legend=['h=%.1f s=%.1f' % (x, y) for x, y in zip(h, s)],
saveAs='Figures/selection.pdf',
lines_lty_rep=[1, 2, 3],
lines_col='black',
lines_lwd=2,
legend_x=120,
# Case 1: produce the given number of offspring
checkNumOffspring(numOffspring=2)
# Case 2: Use a Python function
import random
def func(gen):
return random.randint(5, 8)
checkNumOffspring(numOffspring=func)
# Case 3: A geometric distribution
checkNumOffspring(numOffspring=(sim.GEOMETRIC_DISTRIBUTION, 0.3))
# Case 4: A Possition distribution
checkNumOffspring(numOffspring=(sim.POISSON_DISTRIBUTION, 1.6))
# Case 5: A Binomial distribution
checkNumOffspring(numOffspring=(sim.BINOMIAL_DISTRIBUTION, 0.1, 10))
# Case 6: A uniform distribution
checkNumOffspring(numOffspring=(sim.UNIFORM_DISTRIBUTION, 2, 6))
# Case 7: With selection on offspring
checkNumOffspring(numOffspring=8,
ops=[
sim.MapSelector(loci=0,
fitness={
(0, 0): 1,
(0, 1): 0.8,
(1, 1): 0.5
})
])
#
# 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=10000, loci=2, infoFields='fitness')
pop.evolve(
initOps=[sim.InitSex(), sim.InitGenotype(freq=[.5, .5])],
preOps=[
sim.MlSelector([
sim.MapSelector(loci=0, fitness={
(0, 0): 1,
(0, 1): 1,
(1, 1): .8
}),
sim.MapSelector(loci=1,
fitness={
(0, 0): 1,
(0, 1): 0.9,
(1, 1): .8
}),
],
mode=sim.ADDITIVE,
reps=0),
sim.MapSelector(loci=0,
fitness={
(0, 0): 1,
(0, 1): 1,
def simu(w, m1, m2, psize, afr_size, fl):
print(fl)
if os.path.exists(fl):
os.remove(fl)
matingScheme = sim.HaplodiploidMating(sexMode=sex_func,
subPopSize=get_sizes)
fitness = {
(0, ): 1.0,
(1, ): 1.0,
(0, 0): w,
(0, 1): (1 + w) / 2,
(1, 1): 1.0
}
migrator = sim.BackwardMigrator(rate=[[0, m1, m2], [m1, 0, m2], [0, 0, 0]],
begin=4,
step=1)
count_pop = {}
sg = sample_genes()
for i in range(len(sg)):
(n_loci, gene_prop_az, gene_prop_tx) = sg[i]
selector = sim.MlSelector(
[sim.MapSelector(loci=x, fitness=fitness) for x in range(n_loci)],
mode=sim.ADDITIVE,
begin=4,
step=1)
pre_ops = [
migrator, selector,
sim.InitGenotype(prop=(0.1, 0.9), subPops=[2])
]
post_ops = [
sim.ResizeSubPops(subPops=[2],
sizes=[math.ceil(psize * afr_size)],
propagate=True,
at=g) for g in range(1, 11)
]
pop = sim.Population(
size=[psize, psize, math.ceil(afr_size * psize)],
ploidy=2,
loci=n_loci,
subPopNames=['AZ', 'TX', 'AFR'],
ancGen=-1,
infoFields=['fitness', 'migrate_to',
'migrate_from']) # store all past generations
pop.evolve(initOps=[
sim.InitSex(maleFreq=0.9),
sim.InitGenotype(prop=(gene_prop_az, 1 - gene_prop_az),
subPops=[0]),
sim.InitGenotype(prop=(gene_prop_tx, 1 - gene_prop_tx),
subPops=[1]),
sim.InitGenotype(prop=(0.1, 0.9), subPops=[2])
],
matingScheme=matingScheme,
preOps=[],
postOps=[],
gen=1)
sim.stat(pop, alleleFreq=list(range(n_loci)), subPops=[0])
az1 = np.mean(
np.array([pop.dvars().alleleFreq[loc][1]
for loc in range(n_loci)]))
sim.stat(pop, alleleFreq=list(range(n_loci)), subPops=[1])
tx1 = np.mean(
np.array([pop.dvars().alleleFreq[loc][1]
for loc in range(n_loci)]))
cp = export(pop, fl, False)
count_pop["AZ_early"] = cp["AZ"]
count_pop["TX_early"] = cp["TX"]
pop.evolve(initOps=[],
matingScheme=matingScheme,
preOps=pre_ops,
postOps=post_ops,
gen=10)
sim.stat(pop, alleleFreq=list(range(n_loci)), subPops=[0])
az2 = np.mean(
np.array([pop.dvars().alleleFreq[loc][1]
for loc in range(n_loci)]))
sim.stat(pop, alleleFreq=list(range(n_loci)), subPops=[1])
tx2 = np.mean(
np.array([pop.dvars().alleleFreq[loc][1]
for loc in range(n_loci)]))
cp = export(pop, fl, True)
count_pop["AZ_late"] = cp["AZ"]
count_pop["TX_late"] = cp["TX"]
print("%i) nloci: %i AZ : %.3f->%.3f TX : %.3f->%.3f" %
(i, n_loci, az1, az2, tx1, tx2))
n = count_pop["AZ_early"] + count_pop["TX_early"] + count_pop[
"AZ_late"] + count_pop["TX_late"]
with open(fl, 'r+') as f:
lns = f.readlines()
lns.insert(0, '30164 48394 29292\n')
lns.insert(0, 'simuPOP_export %d %d\n' % (n, len(sg)))
f.seek(0) # readlines consumes the iterator, so we need to start over
f.writelines(lns)
