def test_maskarrayp_changeQ_correctentriesmasked(self):
tree = ivy.tree.read(u'(((A:1,B:1)C:1,D:2)E:1,F:3)root;')
chars = [1, 0, 0, 0]
Q1 = np.array([[-0.10, 0.10], [0.05, -0.05]])
Q2 = np.array([[-1.5, 1.5], [1., -1.]])
Q3 = np.array([[-0.15, 0.15], [0.1, -0.1]])
Qs1 = np.array([Q2, Q1])
Qs2 = np.array([Q2, Q3])
switchpoint = [(tree["C"], 0.75)]
ar = mk_mr.create_mkmr_mb_ar(tree, chars, 2)
l1 = mk_mr.mk_mr_midbranch(tree,
chars,
Qs1,
switchpoint,
ar=ar,
debug=False)
l2 = mk_mr.mk_mr_midbranch(tree,
chars,
Qs2,
switchpoint,
ar=ar,
debug=False)
ar2 = mk_mr.create_mkmr_mb_ar(tree, chars, 2)
l2True = mk_mr.mk_mr_midbranch(tree, chars, Qs2, switchpoint, ar=ar2)
self.assertEqual(l2, l2True)
def test_mkmr_largetree_correctlikelihood(self):
tree = ivy.tree.read(u"support/hrm_600tips.newick")
chars = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,
1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
]
Q0 = np.array([[-0.061457569587587413, 0.061457569587587413],
[0.061457569587587413, -0.061457569587587413]])
Q1 = np.array([[-1.4976115055655292, 1.4976115055655292],
[1.4976115055655292, -1.4976115055655292]])
Q2 = np.array([[-0.0014644343303779842, 0.0014644343303779842],
[0.0014644343303779842, -0.0014644343303779842]])
Qs = np.array([Q0, Q1, Q2])
switchpoint0 = (tree[579], 1.2)
switchpoint1 = (tree[329], 3.0)
true_L = -89.213330113632566
ar = mk_mr.create_mkmr_mb_ar(tree, chars, 3)
calculated_l = mk_mr.mk_mr_midbranch(tree,
chars,
Qs, [switchpoint0, switchpoint1],
ar=ar,
debug=True)
self.assertTrue(np.isclose(calculated_l, true_L))
def test_mkmr_benchmark(self):
tree = ivy.tree.read(u'(((A:1,B:1)C:1,D:2)E:1,F:3)root;')
chars = [1, 0, 0, 0]
Q1 = np.array([[-0.10, 0.10], [0.05, -0.05]])
Q2 = np.array([[-1.5, 1.5], [1., -1.]])
Q3 = np.array([[-0.15, 0.15], [0.1, -0.1]])
Qs1 = np.array([Q2, Q1])
Qs2 = np.array([Q2, Q3])
switchpoint_1 = [(tree["C"], 0.75)]
switchpoint_2 = [(tree["D"], 0.5)]
ar = mk_mr.create_mkmr_mb_ar(tree, chars, 2)
def foo(tree=tree,
chars=chars,
Qs1=Qs1,
switchpoint_1=switchpoint_1,
ar=ar):
mk_mr.mk_mr_midbranch(tree,
chars,
Qs1,
switchpoint_1,
ar=ar,
debug=False)
def dotime():
t = timeit.Timer("foo()")
time = t.timeit(1000)
print("1000 loops took %fs\n" % (time, ))
import __builtin__
__builtin__.__dict__.update(locals())
dotime()
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0]
data = dict(zip([n.label for n in tree.leaves()],mr_chars))
# The Q-matrices used to generate the character states
trueFastQ = np.array([[-.8,.8], [.8,-.8]])
trueSlowQ = np.array([[-.1,.1], [.1,-.1]])
trueQs = np.array([trueFastQ,trueSlowQ])
ar = mk_mr.create_mkmr_mb_ar(tree, mr_chars, 2)
switchpoint =[(tree[350],0.0)]
mods = [(2,2),(1,1)]
qidx = np.array([[0,0,1,0],
[0,1,0,0],
[1,0,1,1],
[1,1,0,1]])
switches = np.array([120])
lengths = np.array([1e-15])
f = cyexpokit.make_mklnl_func(tree, data, 2, 2, qidx)
f(np.array([0.1,0.05]),switches,lengths)