def check_random_tensor(demo, *args, **kwargs):
leaves = demo.sampled_pops
ranges = [list(range(n + 1)) for n in demo.sampled_n]
config_list = momi.data.configurations.build_full_config_list(
demo.sampled_pops, demo.sampled_n)
esfs = expected_sfs(demo, config_list, *args, **kwargs)
tensor_components = [
np.random.normal(size=(1, n + 1)) for n in demo.sampled_n
]
#tensor_components_list = tuple(v[0,:] for _,v in sorted(tensor_components.iteritems()))
#prod1 = sfs_tensor_prod(dict(list(zip(config_list,esfs))), tensor_components)
# sfs = momi.site_freq_spectrum(demo.sampled_pops, [dict(zip((tuple(map(tuple,c)) for c in config_list),
# esfs))])
sfs = momi.site_freq_spectrum(
demo.sampled_pops,
[{tuple(map(tuple, c)): s
for c, s in zip(config_list, esfs)}])
#assert sfs.get_dict() == {tuple(map(tuple,c)): s for c,s in zip(config_list, esfs)}
prod1 = sfs_tensor_prod(sfs, tensor_components)
# prod1 = sfs_tensor_prod({tuple(map(tuple,c)): s for c,s in zip(config_list, esfs)},
# tensor_components)
prod2 = expected_sfs_tensor_prod(tensor_components,
demo,
sampled_pops=demo.sampled_pops)
assert np.allclose(prod1, prod2)
def check_tmrca(demo):
tmrca = expected_tmrca(demo)
print(tmrca)
for i in range(len(demo.sampled_pops)):
vecs = [np.ones(n + 1) for n in demo.sampled_n]
vecs[0] = np.arange(len(vecs[0])) / (len(vecs[0]) - 1.0)
tmrca2 = expected_sfs_tensor_prod(vecs,
demo,
sampled_pops=demo.sampled_pops)
print(tmrca2)
assert np.isclose(tmrca, tmrca2)
def test_pseudoinverse():
demo0 = simple_admixture_demo()._get_demo({"b": 2, "a": 3})
demo1 = NoLookdownDemography(demo0)
p = 20
vecs = [np.random.normal(size=(p, n + 1)) for n in demo0.sampled_n]
vals0, vals1 = [expected_sfs_tensor_prod(vecs, d) for d in (demo0, demo1)]
assert np.allclose(vals0, vals1)
n_lins_diff = np.array(
[demo0._n_at_node(v) - demo1._n_at_node(v) for v in demo0._G])
assert all(n_lins_diff <= 0)
assert any(n_lins_diff < 0)
def test_P():
t1 = np.random.exponential(.25)
t2 = np.random.exponential(.25) + t1
t3 = np.random.exponential(.5) + t2
p1 = np.random.uniform(0, 1)
p2 = np.random.uniform(0, 1)
i = np.random.choice([0, 1])
j = 1 - i
demo0 = momi.DemographicModel(1.0, .25)
demo1 = momi.DemographicModel(1.0, .25)
for d in (demo0, demo1):
d.add_leaf(0)
d.add_leaf(1)
d.move_lineages(0, 1, t3)
demo0.move_lineages(0, 1, t=t1, p=p1)
demo0.move_lineages(i, j, t=t2, p=p2)
demo1.move_lineages(0, 'x', t=t1, p=p1)
demo1.move_lineages('x', 1, t=t1)
demo1.move_lineages(i, 'y', t=t2, p=p2)
demo1.move_lineages('y', j, t=t2)
demo0 = demo0._get_demo({0:5,1:6})
demo1 = demo1._get_demo({0:5,1:6})
#root_event = ('-ej', t3, 0, 1)
#pulse_events0 = [('-ep', t1, 0, 1, p1),
# ('-ep', t2, i, j, p2)]
#pulse_events1 = [('-ep', t1, 0, 'x', p1), ('-ej', t1, 'x', 1),
# ('-ep', t2, i, 'y', p2), ('-ej', t2, 'y', j)]
#demo0 = make_demography(pulse_events0 + [root_event],
# (0, 1), (5, 6))
#demo1 = make_demography(pulse_events1 + [root_event],
# (0, 1), (5, 6))
p = 20
vecs = [np.random.normal(size=(p, n + 1)) for n in demo0.sampled_n]
vals0, vals1 = [expected_sfs_tensor_prod(vecs, d)
for d in (demo0, demo1)]
assert np.allclose(vals0, vals1)
def test_events_before_sample():
n_events = 4
t = [0.0]
for i in range(n_events):
t += [np.random.exponential(1. / float(n_events)) + t[-1]]
t = t[1:]
demo0 = momi.DemographicModel(1.0, .25)
demo1 = momi.DemographicModel(1.0, .25)
p = np.random.uniform(0, 1)
for d in (demo0, demo1):
d.add_leaf("a")
d.add_leaf("b", t=t[3])
d.move_lineages("a", "b", t=t[0], p=p)
demo0.set_size("c", t=0, N=10, g=1)
demo0.move_lineages("a", "c", t=t[1])
demo0.move_lineages("c", "b", t=t[2])
demo1.set_size("a", t=t[1], N=10*np.exp(-t[1]), g=1.0)
demo1.move_lineages("a", "b", t=t[2])
demo0, demo1 = [d._get_demo({"a":7,"b":5}) for d in (demo0, demo1)]
#events = [('-ep', t[0], 'a', 'b', np.random.uniform(0, 1))]
#demo0 = make_demography(events + [('-en', 0.0, 'c', 10.0), ('-eg', 0.0, 'c', 1.0),
# ('-ej', t[1], 'a', 'c'),
# ('-ej', t[2], 'c', 'b')],
# sampled_pops=('a', 'b'), sampled_n=(7, 5),
# sampled_t=(0., t[3]))
#demo1 = make_demography(events + [('-en', t[1], 'a', 10.0 * np.exp(-t[1])), ('-eg', t[1], 'a', 1.0),
# ('-ej', t[2], 'a', 'b')],
# sampled_pops=('a', 'b'), sampled_n=(7, 5),
# sampled_t=(0., t[3]))
vecs = [np.random.normal(size=(10, n + 1)) for n in demo0.sampled_n]
val0, val1 = [expected_sfs_tensor_prod(vecs, d) for d in (demo0, demo1)]
assert np.allclose(val0, val1)
