def test_single_slice(self):
dm = fwdpy11.DataMatrixIterator(self.pop.tables, self.all_samples,
[(0.1, 0.2)], True, True)
dm = next(dm)
rows = np.where((self.spos >= 0.1) & (self.spos < 0.2))[0]
pos_slice = self.spos[rows]
selected_slice = self.selected[rows, ]
self.assertTrue(np.array_equal(dm.selected_positions, pos_slice))
self.assertTrue(np.array_equal(dm.selected, selected_slice))
def test_nested_slices(self):
slices = [(0.1, 0.2), (0.15, 0.19), (0.21, 0.37), (0.38, 0.5337),
(0.39, 0.432), (0.5, 0.55)]
dmi = fwdpy11.DataMatrixIterator(self.pop.tables, self.all_samples,
slices, True, True)
for r, dm in zip(slices, dmi):
rows = np.where((self.spos >= r[0]) & (self.spos < r[1]))[0]
pos_slice = self.spos[rows]
selected_slice = self.selected[rows, ]
self.assertTrue(np.array_equal(dm.selected_positions, pos_slice))
self.assertTrue(np.array_equal(dm.selected, selected_slice))
def test_nonoverlapping_slices(self):
slices = [(0.1, 0.2), (0.21, 0.37), (0.5, 0.55)]
dmi = fwdpy11.DataMatrixIterator(self.pop.tables, self.all_samples,
slices, True, True)
niterations = 0
for r, dm in zip(slices, dmi):
niterations += 1
rows = np.where((self.spos >= r[0]) & (self.spos < r[1]))[0]
pos_slice = self.spos[rows]
selected_slice = self.selected[rows, ]
self.assertTrue(np.array_equal(dm.selected_positions, pos_slice))
self.assertTrue(np.array_equal(dm.selected, selected_slice))
self.assertEqual(niterations, len(slices))
def test_entire_matrix(self):
dmi = fwdpy11.DataMatrixIterator(self.pop.tables, self.all_samples,
[(0, 1)], True, True)
niterations = 0
for dm in dmi:
niterations += 1
for i in dm.selected_keys:
self.assertFalse(self.pop.mutations[i].neutral)
self.assertTrue(
np.array_equal(np.array(self.dm.selected_keys),
dm.selected_keys))
self.assertTrue(np.array_equal(dm.neutral, self.neutral))
self.assertTrue(np.array_equal(dm.selected, self.selected))
self.assertEqual(niterations, 1)
def get_stats(tables, samples):
tables_for_sample, idmap = fwdpy11.simplify_tables(tables, samples)
stats = []
for k, l in zip(enumerate(fwdpy11.DataMatrixIterator(tables_for_sample,
idmap[samples],
WINDOWS, True, True)),
WINDOW_LEFTS):
i, dm = k
locus = int(i*STEPSIZE / LOCUS_LENGTH)
window = i % (LOCUS_LENGTH/STEPSIZE)
window_in_paper = int(window*STEPSIZE)
pos, data = merge_matrix(dm)
vm = libsequence.VariantMatrix(data, pos)
ac = vm.count_alleles()
pi = libsequence.thetapi(ac)
D = libsequence.tajd(ac)
Hp = libsequence.hprime(ac, 0)
nhaps = libsequence.number_of_haplotypes(vm)
hdiv = libsequence.haplotype_diversity(vm)
stats.append(Datum(l, locus, window, window_in_paper, pi, D, Hp, nhaps, hdiv))
return stats