def evolve_and_return(args):
"""
This function runs our simulation.
The input arguments come in a tuple,
which is required by many of Python's
functions for execution in separate processes.
For this function, the arguments are the population
size and a random number seed.
"""
from fwdpy11 import Multiplicative
N, seed = args
# Construct as single-deme object
# with N diploids
pop = fp11.DiploidPopulation(N)
theta = 100.0
# Initialize a random number generator
rng = fp11.GSLrng(seed)
p = fp11ez.mslike(pop,
simlen=100,
rates=(theta / float(4 * pop.N), 1e-3,
theta / float(4 * pop.N)))
p['gvalue'] = Multiplicative(2.)
params = fp11.ModelParams(**p)
fp11.evolve_genomes(rng, pop, params)
# The population is picklable, and so
# we can return it from another process
return pop
def evolve_snowdrift(args):
"""
We write the function taking a tuple
out of habit, simplifying later
integration with multiprocessing or
concurrent.futures.
"""
N, seed = args
# Construct as single-deme object
# with N diploids
pop = fp11.DiploidPopulation(N)
# Initialize a random number generator
rng = fp11.GSLrng(seed)
p = {
'sregions': [fp11.ExpS(0, 1, 1, -0.1, 1.0)],
'recregions': [fp11.Region(0, 1, 1)],
'nregions': [],
'gvalue': snowdrift.DiploidSnowdrift(0.2, -0.2, 1, -2),
# evolve for 100 generations so that unit tests are
# fast
'demography': np.array([N] * 100, dtype=np.uint32),
'rates': (0.0, 0.0025, 0.001),
'prune_selected': False
}
params = fwdpy11.ModelParams(**p)
sampler = SamplePhenotypes(params.gvalue)
fp11.evolve_genomes(rng, pop, params, sampler)
# return our pop
return pop
def testMutationLookupTable(self):
params = fwdpy11.ModelParams(**self.pdict)
fwdpy11.evolve_genomes(self.rng, self.pop, params)
lookup = self.pop.mut_lookup
for i in range(len(self.pop.mcounts)):
if self.pop.mcounts[i] > 0:
self.assertTrue(self.pop.mutations[i].pos in lookup)
self.assertTrue(i in lookup[self.pop.mutations[i].pos])
def testParentalData(self):
params = fwdpy11.ModelParams(**self.pdict)
fwdpy11.evolve_genomes(self.rng, self.pop, params)
parents = [i.parents for i in self.pop.diploid_metadata]
for i in parents:
self.assertTrue(i is not None)
self.assertTrue(len(i) == 2)
self.assertTrue(i[0] < self.pop.N)
self.assertTrue(i[1] < self.pop.N)
def testMutationIndices(self):
params = fwdpy11.ModelParams(**self.pdict)
fwdpy11.evolve_genomes(self.rng, self.pop, params)
lookup = self.pop.mut_lookup
for key, val in lookup.items():
indexes = self.pop.mutation_indexes(key)
self.assertTrue(indexes is not None)
for i in indexes:
self.assertTrue(i in val)
def testPopGenSimWithPruning(self):
import fwdpy11
import numpy as np
self.p['prune_selected'] = True
params = fwdpy11.ModelParams(**self.p)
fwdpy11.evolve_genomes(self.rng, self.pop, params)
assert len(
self.pop.fixations) > 0, "Test is meaningless without fixations"
mc = np.array(self.pop.mcounts)
self.assertEqual(len(np.where(mc == 2 * self.pop.N)[0]), 0)
def quick_neutral_slocus(N=1000, simlen=100):
from fwdpy11.ezparams import mslike
from fwdpy11 import ModelParams
from fwdpy11 import DiploidPopulation, GSLrng
from fwdpy11 import Multiplicative
from fwdpy11 import evolve_genomes
pop = DiploidPopulation(N)
params_dict = mslike(pop, simlen=simlen)
params_dict['gvalue'] = Multiplicative(2.)
params = ModelParams(**params_dict)
rng = GSLrng(42)
evolve_genomes(rng, pop, params)
return pop
def quick_nonneutral_slocus(N=1000, simlen=100, dfe=None):
from fwdpy11.ezparams import mslike
from fwdpy11 import ModelParams
from fwdpy11 import DiploidPopulation, GSLrng
from fwdpy11 import evolve_genomes
from fwdpy11 import ExpS
from fwdpy11 import Multiplicative
pop = DiploidPopulation(N)
if dfe is None:
dfe = ExpS(0, 1, 1, -0.1)
params_dict = mslike(pop, simlen=simlen, dfe=dfe, pneutral=0.95)
params_dict['gvalue'] = Multiplicative(2.0)
params = ModelParams(**params_dict)
rng = GSLrng(42)
evolve_genomes(rng, pop, params)
return pop
def test_nodes_after_evolution(self):
params = fwdpy11.ModelParams(**self.pdict)
fwdpy11.evolve_genomes(self.rng, self.pop, params)
for i in self.pop.diploid_metadata:
self.assertEqual(i.nodes[0], fwdpy11.NULL_NODE)
self.assertEqual(i.nodes[1], fwdpy11.NULL_NODE)
def testEvolve(self):
fwdpy11.evolve_genomes(self.rng, self.pop, self.params)
