def test_xpehh():
n_variants = 1000
n_haplotypes = 20
h1 = np.random.randint(0, 2, size=(n_variants, n_haplotypes),
dtype='i1')
h2 = np.random.randint(0, 2, size=(n_variants, n_haplotypes),
dtype='i1')
pos = np.arange(0, n_variants * 10, 10)
for use_threads in True, False:
for min_ehh in 0, 0.05, 0.5:
for include_edges in True, False:
score = xpehh(h1, h2, pos, min_ehh=min_ehh,
include_edges=include_edges,
use_threads=use_threads)
assert_is_instance(score, np.ndarray)
eq((n_variants,), score.shape)
eq(np.dtype('f8'), score.dtype)
with assert_raises(ValueError):
xpehh(h1, h2[1:], pos)
with assert_raises(ValueError):
xpehh(h1[1:], h2, pos)
with assert_raises(ValueError):
xpehh(h1, h2, pos[1:])
with assert_raises(ValueError):
xpehh(h1, h2, pos, map_pos=pos[1:])
def test_xpehh_data():
pos = np.arange(1, hap1.shape[0] + 1)
expect = -np.log(5.5/1.5)
for use_threads in True, False:
for include_edges in True, False:
score = xpehh(hap1, hap2, pos, include_edges=include_edges,
use_threads=use_threads)
actual = score[9]
eq(expect, actual)
