def _twopop_IM(engine_id,
out_dir,
seed,
NA=1000,
N1=500,
N2=5000,
T=1000,
M12=0,
M21=0,
pulse=None,
**sim_kwargs):
species = stdpopsim.get_species("AraTha")
contig = species.get_contig("chr5", length_multiplier=0.01) # ~270 kb
model = stdpopsim.IsolationWithMigration(NA=NA,
N1=N1,
N2=N2,
T=T,
M12=M12,
M21=M21)
if pulse is not None:
model.demographic_events.append(pulse)
model.demographic_events.sort(key=lambda x: x.time)
model.generation_time = species.generation_time
samples = model.get_samples(50, 50, 0)
engine = stdpopsim.get_engine(engine_id)
t0 = time.perf_counter()
ts = engine.simulate(model, contig, samples, seed=seed, **sim_kwargs)
t1 = time.perf_counter()
out_file = out_dir / f"{seed}.trees"
ts.dump(out_file)
return out_file, t1 - t0
def test_bad_population_size_addSubpopSplit(self):
engine, species, contig = self.triplet()
model = stdpopsim.IsolationWithMigration(NA=1000,
N1=100,
N2=1000,
T=1000,
M12=0,
M21=0)
samples = model.get_samples(2)
with self.assertWarns(stdpopsim.UnspecifiedSLiMWarning):
engine.simulate(
demographic_model=model,
contig=contig,
samples=samples,
slim_scaling_factor=10,
dry_run=True,
)
with self.assertRaises(stdpopsim.SLiMException):
engine.simulate(
demographic_model=model,
contig=contig,
samples=samples,
slim_scaling_factor=200,
dry_run=True,
)
def test_bad_population_size_addSubpopSplit(self):
engine, species, contig = self.triplet()
model = stdpopsim.IsolationWithMigration(
NA=1000, N1=100, N2=1000, T=1000, M12=0, M21=0)
samples = model.get_samples(2)
with mock.patch("warnings.warn", autospec=True) as mock_warning:
engine.simulate(
demographic_model=model, contig=contig, samples=samples,
slim_scaling_factor=10, dry_run=True)
mock_warning.assert_called_once()
with self.assertRaises(stdpopsim.SLiMException):
engine.simulate(
demographic_model=model, contig=contig, samples=samples,
slim_scaling_factor=200, dry_run=True)
def _twopop_IM(
engine_id,
out_dir,
seed,
NA=1000,
N1=500,
N2=5000,
T=1000,
M12=0,
M21=0,
pulse=None,
samples=None,
**sim_kwargs,
):
species = stdpopsim.get_species("AraTha")
contig = species.get_contig("chr5", length_multiplier=0.01) # ~270 kb
contig = irradiate(contig)
model = stdpopsim.IsolationWithMigration(NA=NA,
N1=N1,
N2=N2,
T=T,
M12=M12,
M21=M21)
if pulse is not None:
model.demographic_events.append(pulse)
model.demographic_events.sort(key=lambda x: x.time)
# XXX: AraTha has species.generation_time == 1, but there is the potential
# for this to mask bugs related to generation_time scaling, so we use 3 here.
model.generation_time = 3
if samples is None:
samples = model.get_samples(50, 50, 0)
engine = stdpopsim.get_engine(engine_id)
t0 = time.perf_counter()
ts = engine.simulate(model, contig, samples, seed=seed, **sim_kwargs)
t1 = time.perf_counter()
out_file = out_dir / f"{seed}.trees"
ts.dump(out_file)
return out_file, t1 - t0
import stdpopsim,sys
## I would like to simulate Drosophila melanogaster please
species = stdpopsim.get_species("DroMel")
## I have specified the desired chromosome arm at the command line, let's
contig = species.get_contig(sys.argv[1], genetic_map = "ComeronCrossover_dm6")
## For testing, it is good to model a lil chunk of chromosome
#contig = species.get_contig(sys.argv[1], length_multiplier = 0.10)
## You can grab the genetic map out of the simulations using:
#for p, r in zip( contig.recombination_map.get_positions() , contig.recombination_map.get_rates() ):
# print( p , r )
Ne = species.population_size/10
#Ne = 10000
model = stdpopsim.IsolationWithMigration(2*Ne, Ne, Ne, Ne, 1.5/Ne, 1.5/Ne)
print("NA", "N1", "N2", "T", "M12", "M21")
print( 2*Ne, Ne, Ne, Ne, 1.5/Ne, 1.5/Ne)
## I want to simulate 20 samples from each population
samples = model.get_samples(20,20)
## I will simulate using msprime
engine = stdpopsim.get_default_engine()
print("running simulation")
ts = engine.simulate(model, contig, samples)
## Save the simulated data to a VCF
with open("drosophilaSimulated."+ sys.argv[1] + ".vcf", "w") as vcf_file: