def runsim(argtuple):
seed = argtuple
rng = fwdpy11.GSLrng(seed)
pdict = {
'gvalue':
fwdpy11.Multiplicative(2.),
'rates': (0., U / 2., R), # The U/2. is from their eqn. 2.
'nregions': [],
'sregions': [
fwdpy11.ConstantS(0, 1. / 3., 1, -0.02, 1.),
fwdpy11.ConstantS(2. / 3., 1., 1, -0.02, 1.)
],
'recregions':
[fwdpy11.Region(0, 1. / 3., 1),
fwdpy11.Region(2. / 3., 1., 1)],
'demography':
np.array([N] * 20 * N, dtype=np.uint32)
}
params = fwdpy11.ModelParams(**pdict)
pop = fwdpy11.DiploidPopulation(N, GENOME_LENGTH)
fwdpy11.evolvets(rng, pop, params, 100, suppress_table_indexing=True)
rdips = np.random.choice(N, NSAM, replace=False)
md = np.array(pop.diploid_metadata, copy=False)
rdip_nodes = md['nodes'][rdips].flatten()
nodes = np.array(pop.tables.nodes, copy=False)
# Only visit trees spanning the
# mutation-free segment of the genome
tv = fwdpy11.TreeIterator(pop.tables,
rdip_nodes,
begin=1. / 3.,
end=2. / 3.)
plist = np.zeros(len(nodes), dtype=np.int8)
sum_pairwise_tmrca = 0
for t in tv:
for i in range(len(rdip_nodes) - 1):
u = rdip_nodes[i]
while u != fwdpy11.NULL_NODE:
plist[u] = 1
u = t.parent(u)
for j in range(i + 1, len(rdip_nodes)):
u = rdip_nodes[j]
while u != fwdpy11.NULL_NODE:
if plist[u] == 1:
sum_pairwise_tmrca += 2 * \
(pop.generation-nodes['time'][u])
u = fwdpy11.NULL_NODE
else:
u = t.parent(u)
plist.fill(0)
return 2 * sum_pairwise_tmrca / (len(rdip_nodes) * (len(rdip_nodes) - 1))
def runsim(args):
rng = fwdpy11.GSLrng(args.seed)
pdict = {'gvalue': fwdpy11.Multiplicative(FITNESS_SCALING),
'rates': (0., args.mu, None),
'nregions': [],
'sregions': [fwdpy11.GammaS(0., GENOME_LENGTH, 1.0-args.proportion,
args.mean, args.shape, FITNESS_SCALING/2.0,
scaling=2*args.popsize, label=1),
fwdpy11.ConstantS(0, GENOME_LENGTH, args.proportion, TWONS,
FITNESS_SCALING/2.0, label=2,
scaling=2*args.popsize)],
'recregions': [fwdpy11.PoissonInterval(0, GENOME_LENGTH, args.recrate)],
'demography': np.array([args.popsize]*SIMLEN*args.popsize, dtype=np.uint32),
# This could easily be True for these sims:
'prune_selected': False
}
params = fwdpy11.ModelParams(**pdict)
pop = fwdpy11.DiploidPopulation(args.popsize, GENOME_LENGTH)
sampler = fwdpy11.RandomAncientSamples(
args.seed, args.popsize, [i for i in range(10*pop.N, SIMLEN*pop.N)])
# With a lot of ancient samples:
# 1. RAM use already skyrockets
# 2. Simplification slows down
# So, we should do it a little less often:
fwdpy11.evolvets(rng, pop, params, 1000, sampler,
suppress_table_indexing=True)
return pop
def build_parameters_dict(args):
"""
Returns sim params and the final sizes
in each deme
"""
demog, simlen, finalNs = build_demography(args)
nregions = []
sregions = []
recregions = [fwdpy11.PoissonInterval(0, 1, args.rho / (4.0 * args.Nref))]
rates = (0, 0, None)
if args.gamma is not None:
sregions = [
fwdpy11.ConstantS(0,
1,
1,
args.gamma,
args.H,
scaling=2 * args.Nref)
]
rates = (0, args.theta / (4.0 * args.Nref), None)
pdict = {
"nregions": nregions,
"sregions": sregions,
"recregions": recregions,
"rates": rates,
"gvalue": fwdpy11.Multiplicative(2.0),
"demography": demog,
"simlen": simlen,
"prune_selected": True,
}
return pdict, simlen, finalNs
def evolve_track_wrapper(popsize=1000,
rho=10000.0,
mu=1e-2,
seed=42,
gc_interval=10,
dfe=fwdpy11.ConstantS(0, 1, 1, -0.025, 1.0)):
if isinstance(dfe, fwdpy11.Sregion) is False:
raise TypeError("dfe must be a fwdpy11.Sregion")
if dfe.b != 0.0 or dfe.e != 1.0:
raise ValueError("DFE beg/end must be 0.0/1.0, repsectively")
pop = fwdpy11.SlocusPop(popsize)
recrate = float(rho) / (4.0 * float(popsize))
pdict = {
'rates': (0.0, mu, recrate),
'nregions': [],
'sregions': [dfe],
'recregions': [fwdpy11.Region(0, 1, 1)],
'gvalue': fwdpy11.fitness.SlocusMult(2.0),
'demography': np.array([popsize] * 10 * popsize, dtype=np.uint32)
}
params = fwdpy11.model_params.SlocusParams(**pdict)
rng = fwdpy11.GSLrng(seed)
ancestry = evolve_track(rng, pop, params, gc_interval)
return (pop, ancestry)
def setUp(self):
self.pop = fwdpy11.DiploidPopulation(1000)
self.pdict = fwdpy11.ezparams.mslike(self.pop,
dfe=fwdpy11.ConstantS(
0, 1, 1, -0.05, 0.05),
pneutral=0.95,
simlen=10)
self.pdict['gvalue'] = general.GeneralW()
self.rng = fwdpy11.GSLrng(42)
self.params = fwdpy11.ModelParams(**self.pdict)
def set_up_standard_pop_gen_model():
"""
For this sort of model, when mutations fix, they are
removed from the simulation, INCLUDING THE TREE
SEQUENCES. The fact of their existence gets
recorded in pop.fixations and pop.fixation_times
"""
# TODO add neutral variants
N = 1000
demography = np.array([N] * 10 * N, dtype=np.uint32)
rho = 1.
# theta = 100.
# nreps = 500
# mu = theta/(4*N)
r = rho / (4 * N)
a = fwdpy11.Multiplicative(2.0)
pselected = 1e-3
p = {
'nregions': [],
'sregions': [
fwdpy11.GammaS(0,
1,
1. - pselected,
mean=-5,
shape=1,
scaling=2 * N),
fwdpy11.ConstantS(0, 1, pselected, 1000, scaling=2 * N)
],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.001, r),
'gvalue':
a,
'prune_selected':
True,
'demography':
demography
}
params = fwdpy11.ModelParams(**p)
rng = fwdpy11.GSLrng(666**2)
pop = fwdpy11.DiploidPopulation(N, 1.0)
return params, rng, pop
def evolve_track_wrapper(popsize=1000, rho=10000.0, mu=1e-2, seed=42,
gc_interval=10,
dfe=fwdpy11.ConstantS(0, 1, 1, -0.025, 1.0)):
"""
Wrapper around evolve_track to facilitate testing.
:param popsize: Diploid population size.
:param rho: 4Nr
:param mu: Mutation rate to selected alleles
:param seed: RNG seed
:param gc_interval: Garbage collection interval.
:param dfe: An instance of a fwdpy11.Sregion
:rtype: tuple
:return: See evolve_track for details.
"""
if isinstance(dfe, fwdpy11.Sregion) is False:
raise TypeError("dfe must be a fwdpy11.Sregion")
if dfe.b != 0.0 or dfe.e != 1.0:
raise ValueError("DFE beg/end must be 0.0/1.0, repsectively")
pop = fwdpy11.SlocusPop(popsize)
recrate = float(rho) / (4.0 * float(popsize))
pdict = {'rates': (0.0, mu, recrate),
'nregions': [],
'sregions': [dfe],
'recregions': [fwdpy11.Region(0, 1, 1)],
'gvalue': fwdpy11.fitness.SlocusMult(2.0),
'demography': np.array([popsize] * 20 * popsize, dtype=np.uint32)
}
params = fwdpy11.model_params.SlocusParams(**pdict)
rng = fwdpy11.GSLrng(seed)
return evolve_track(rng, pop, params, gc_interval)
def setUp(self):
self.nregions = [fwdpy11.Region(0, 1, 1)]
self.sregions = [fwdpy11.ConstantS(
0, 1, 1, -0.1, 0.25), fwdpy11.ExpS(0, 1, 0.001, 0.01, 1.0)]
def setUp(self):
self.r = fwdpy11.ConstantS(BEG, END, WEIGHT,
0.5, DOM, COUPLED, LABEL)
def testBadDominance(self):
with self.assertRaises(ValueError):
fwdpy11.ConstantS(0, 1, 1, 1, np.nan)
print(models) Nstart = 20000#int(demog.item(0)) mu = 1.66e-8 s_value= float(float(gamma_value)/(2*float(Nstart))) print(s_value) ################## simulate BGS ############################ recregion =[fp11.Region(0,55,1., coupled=True)] sregion = [fp11.ConstantS(0, 50, 1., s_value, h=1.0, coupled=True)] #sregion = [fp11.GammaS(-1, 0, 1, -0.83, 0.01514, h=1.0, coupled=True)] nregion = [fp11.Region(50, 55, 1., coupled=True)] # Mutation rate rec = 8.2e-10 * 40000 * 55 mu_s = mu * 40000 * 50 * 0.2 mu_n = mu * 40000 * 5 rates = [mu_n, mu_s, rec] # constant size for 10 N generations
