def test_genotype_matrix_ranges(self):
dm = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)], False, True)
spos = np.array(dm.selected.positions)
for i in np.arange(0, self.pop.tables.genome_length, 0.1):
dmi = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)],
record_neutral=False,
record_selected=True,
begin=i,
end=i + 0.1)
w = np.where((spos >= i) & (spos < i + 0.1))[0]
self.assertTrue(
np.array_equal(spos[w], np.array(dmi.selected.positions)))
def test_genotype_matrix(self):
"""
Make data matrix objects from the tree sequences
and compare their contents to those of tskit
as well as to an explicit calculation of mutation counts.
"""
dm = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)], False, True,
True)
sa = np.array(dm.selected)
cs = np.sum(sa, axis=1)
dumped_ts = self.pop.dump_tables_to_tskit()
mm = dumped_ts.genotype_matrix()
mc = np.sum(mm, axis=1)
ec = np.zeros(len(self.pop.mutations), dtype=np.uint32)
for d in self.pop.diploids:
for m in self.pop.haploid_genomes[d.first].smutations:
ec[m] += 1
for m in self.pop.haploid_genomes[d.second].smutations:
ec[m] += 1
for i, j, k in zip(self.pop.tables.mutations, cs, mc):
self.assertEqual(self.pop.mcounts[i.key], ec[i.key])
self.assertEqual(ec[i.key], j)
self.assertEqual(ec[i.key], k)
self.assertTrue(np.array_equal(ec, np.array(self.pop.mcounts)))
self.assertEqual(ec.sum(), cs.sum())
self.assertEqual(ec.sum(), mc.sum())
self.assertTrue(np.array_equal(sa, mm))
self.assertTrue(np.array_equal(cs, mc))
def setUpClass(self):
# TODO add neutral variants
self.N = 1000
self.demography = np.array([self.N] * self.N, dtype=np.uint32)
self.rho = 1.
self.theta = 100.
self.nreps = 500
self.mu = self.theta / (4 * self.N)
self.r = self.rho / (4 * self.N)
self.GSS = fwdpy11.GSS(VS=1, opt=0)
a = fwdpy11.Additive(2.0, self.GSS)
self.p = {
'nregions': [],
'sregions': [fwdpy11.GaussianS(0, 1, 1, 0.25)],
'recregions': [fwdpy11.Region(0, 1, 1)],
'rates': (0.0, 0.025, self.r),
'gvalue': a,
'prune_selected': False,
'demography': self.demography
}
self.params = fwdpy11.ModelParams(**self.p)
self.rng = fwdpy11.GSLrng(101 * 45 * 110 * 210)
self.pop = fwdpy11.DiploidPopulation(self.N, 1.0)
self.all_samples = [i for i in range(2 * self.N)]
fwdpy11.evolvets(self.rng, self.pop, self.params, 100)
self.dm = fwdpy11.data_matrix_from_tables(self.pop.tables,
self.all_samples, True, True)
self.neutral = np.array(self.dm.neutral)
self.npos = np.array(self.dm.neutral.positions)
self.selected = np.array(self.dm.selected)
self.spos = np.array(self.dm.selected.positions)
def test_genotype_matrix(self):
dm = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)], False, True,
True)
rc = np.sum(dm.selected, axis=1)
index = [i for i in range(len(self.pop.mcounts))]
index = sorted(index, key=lambda x: self.pop.mutations[x].pos)
mc = [self.pop.mcounts[i] for i in index if self.pop.mcounts[i] > 0]
self.assertTrue(np.array_equal(rc, np.array(mc)))
def test_VariantIteratorFromPopulation(self):
dm = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)], False, True,
True)
sa = np.array(dm.selected)
cs = np.sum(sa, axis=1)
i = 0
vi = fwdpy11.VariantIterator(self.pop)
for v in vi:
c = self.pop.mcounts[self.pop.tables.mutations[i].key]
self.assertEqual(c, cs[i])
self.assertEqual(c, v.genotypes.sum())
i += 1
mc = np.array(self.pop.mcounts)
self.assertEqual(i, len(np.where(mc > 0)[0]))
self.assertEqual(i, len(self.pop.tables.mutations))
def test_VariantIterator(self):
"""
Test VariantIterator by asserting
that sum of genotypes equal values in
the corresponding DataMatrix and
those in pop.mcounts
"""
dm = fwdpy11.data_matrix_from_tables(
self.pop.tables, [i for i in range(2 * self.pop.N)], False, True,
True)
sa = np.array(dm.selected)
cs = np.sum(sa, axis=1)
i = 0
vi = fwdpy11.VariantIterator(self.pop.tables,
[i for i in range(2 * self.pop.N)])
for v in vi:
c = self.pop.mcounts[self.pop.tables.mutations[i].key]
self.assertEqual(c, cs[i])
self.assertEqual(c, v.genotypes.sum())
i += 1
mc = np.array(self.pop.mcounts)
self.assertEqual(i, len(np.where(mc > 0)[0]))
self.assertEqual(i, len(self.pop.tables.mutations))
