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 test_count_mutations_preserved_samples(self):
mc = fwdpy11.count_mutations(self.pop, self.pop.tables.preserved_nodes)
pmc = np.array(self.pop.mcounts_ancient_samples)
self.assertTrue(np.array_equal(mc, pmc))
def test_count_mutations(self):
mc = fwdpy11.count_mutations(self.pop,
[i for i in range(2 * self.pop.N)])
pmc = np.array(self.pop.mcounts)
self.assertTrue(np.array_equal(mc, pmc))