def setUp(self):
GN = fwdpy11.GaussianNoise
self.w = fwdpy11.Multiplicative(2.0)
self.t = fwdpy11.Multiplicative(
2.0, fwdpy11.GSS(0.0, 1.0))
self.tn = fwdpy11.Multiplicative(1.0,
fwdpy11.GSS(
0.0, 1.0),
GN(mean=0.1, sd=2.0))
def testPopGenSim(self):
N = 1000
demography = np.array([N] * 10 * N, dtype=np.uint32)
rho = 1.
r = rho / (4 * N)
a = fwdpy11.Multiplicative(2.0)
p = {
'nregions': [],
'sregions': [fwdpy11.ExpS(0, 1, 1, 0.01)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.00005, r),
'gvalue': a,
'prune_selected': True,
'demography': demography
}
params = fwdpy11.ModelParams(**p)
rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
pop = fwdpy11.DiploidPopulation(N, 1.0)
fwdpy11.evolvets(rng, pop, params, 100, track_mutation_counts=True)
mc = fwdpy11.count_mutations(pop.tables, pop.mutations,
[i for i in range(2 * pop.N)])
assert len(pop.fixations) > 0, "Test is meaningless without fixations"
fixations = np.where(mc == 2 * pop.N)[0]
self.assertEqual(len(fixations), 0)
# Brute-force calculation of fixations
brute_force = np.zeros(len(pop.mutations), dtype=np.int32)
for g in pop.haploid_genomes:
if g.n > 0:
for k in g.smutations:
brute_force[k] += g.n
self.assertTrue(np.array_equal(brute_force, mc))
self.assertTrue(np.array_equal(brute_force, pop.mcounts))
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 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, simseed, npseed):
dg = demes.load(args.yaml)
final_demes = get_final_demes(dg)
demog = fwdpy11.discrete_demography.from_demes(dg, burnin=args.burnin)
final_deme_ids = sorted([
i for i in demog.metadata["deme_labels"]
if demog.metadata["deme_labels"][i] in final_demes
])
initial_sizes = [
demog.metadata["initial_sizes"][i]
for i in sorted(demog.metadata["initial_sizes"].keys())
]
recrate = RHO / (4.0 * initial_sizes[0])
pdict = {
"nregions": [],
"sregions": [],
"recregions": [fwdpy11.PoissonInterval(0, 1, recrate)],
"gvalue": fwdpy11.Multiplicative(2.0),
"rates": (0.0, 0.0, None),
"simlen": demog.metadata["total_simulation_length"],
"demography": demog,
}
params = fwdpy11.ModelParams(**pdict)
pop = fwdpy11.DiploidPopulation(initial_sizes, 1.0)
# FIXME: need seed as input argument to this fxn
rng = fwdpy11.GSLrng(simseed)
np.random.seed(npseed)
fwdpy11.evolvets(rng, pop, params, 100)
nmuts = fwdpy11.infinite_sites(rng, pop, THETA / (4.0 * initial_sizes[0]))
md = np.array(pop.diploid_metadata, copy=False)
sample_nodes = []
for i in final_deme_ids:
w = np.where(md["deme"] == i)
s = np.random.choice(w[0], args.nsam, replace=False)
sample_nodes.append(md["nodes"][s].flatten())
fs = pop.tables.fs(sample_nodes)
return fs
def setUp(self):
import numpy as np
N = 1000
demography = np.array([N] * 10 * N, dtype=np.uint32)
rho = 1.
r = rho / (4 * N)
a = fwdpy11.Multiplicative(2.0)
self.p = {
'nregions': [],
'sregions': [fwdpy11.ExpS(0, 1, 1, 0.01)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.00005, r),
'gvalue': a,
'demography': demography
}
self.pop = fwdpy11.DiploidPopulation(N)
self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
def mslike(pop, **kwargs):
"""
Function to establish default parameters
for a single-locus simulation for standard pop-gen
modeling scenarios.
:params pop: An instance of :class:`fwdpy11.DiploidPopulation`
:params kwargs: Keyword arguments.
"""
import fwdpy11
if isinstance(pop, fwdpy11.DiploidPopulation) is False:
raise ValueError("incorrect pop type: " + str(type(pop)))
defaults = {
'simlen': 10 * pop.N,
'beg': 0.0,
'end': 1.0,
'theta': 100.0,
'pneutral': 1.0,
'rho': 100.0,
'dfe': None
}
for key, value in kwargs.items():
if key in defaults:
defaults[key] = value
import numpy as np
params = {
'demography':
np.array([pop.N] * defaults['simlen'], dtype=np.uint32),
'nregions': [fwdpy11.Region(defaults['beg'], defaults['end'], 1.0)],
'recregions': [fwdpy11.Region(defaults['beg'], defaults['end'], 1.0)],
'rates':
((defaults['pneutral'] * defaults['theta']) / (4.0 * pop.N),
((1.0 - defaults['pneutral']) * defaults['theta']) / (4.0 * pop.N),
defaults['rho'] / (4.0 * float(pop.N))),
'gvalue':
fwdpy11.Multiplicative(2.0)
}
if defaults['dfe'] is None:
params['sregions'] = []
else:
params['sregions'] = [defaults['dfe']]
return params
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
import sys
import fwdpy11
N = int(sys.argv[1])
seed = int(sys.argv[2])
pdict = {
"nregions": [],
"sregions": [],
"recregions": [],
"rates": [0, 0, 0],
"gvalue": fwdpy11.Multiplicative(2.0),
"simlen": 5 * N,
}
mp = fwdpy11.ModelParams(**pdict)
pop = fwdpy11.DiploidPopulation(N, 1.0)
rng = fwdpy11.GSLrng(seed)
fwdpy11.evolvets(rng, pop, mp, 100, suppress_table_indexing=True)
ts = pop.dump_tables_to_tskit() # Copies all data from C++ to Python
ts.dump("fwdpy11.trees")