def test_birth_wait_time(self):
"""ensure reconstructed birth wait time has right distribution"""
T = 1.2
birth = 2.0
death = 1.2
times = [birthdeath.sample_birth_wait_time(1, T, birth, death)
for i in xrange(10000)]
hx, hy = util.distrib(times, 30)
cond = 1.0 - birthdeath.prob_no_birth(1, T, birth, death)
x = list(frange(0, T, T/40.0))
y = [birthdeath.birth_wait_time(t, 1, T, birth, death) / cond
for t in x]
print sum(y) * T / 40.0
prep_dir("test/output/sim-wait")
rplot_start("test/output/sim-wait/wait_time.pdf")
rplot("plot", hx, hy, t="l", ylim=[0, max(hy)])
rp.lines(x, y, col="red")
rplot_end(False)
def test_branch_prior_simple2(self):
"""Test branch prior 2"""
tree = treelib.parse_newick("((a1:2, a2:3):.4, b1:2);")
stree = treelib.parse_newick("(A:2, B:2);")
gene2species = lambda x: x[0].upper()
params = {
"A": (1.0, 1.0),
"B": (3.0, 3.0),
1: (1.0, 1.0),
"baserate": (11.0, 10.0)
}
birth = .01
death = .02
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
#pd(mapdict(recon, key=lambda x: x.name, val=lambda x: x.name))
#pd(mapdict(events, key=lambda x: x.name))
p = spidir.branch_prior(tree,
stree,
recon,
events,
params,
birth,
death,
nsamples=nsamples,
approx=False)
tot = 0.0
gstart = 0.01
gend = 3.0
step = (gend - gstart) / 20.0
s2 = step / 2.0
gs = list(frange(gstart + s2, gend + s2, step))
for g in gs:
pg = invgammaPdf(g, params["baserate"])
pa = 0.0
for i in range(nsamples):
t = birthdeath.sample_birth_wait_time(1, stree.nodes["A"].dist,
birth, death)
#print t
t2 = stree.nodes["A"].dist - t
pa1 = gammaPdf(tree.nodes["a1"].dist,
[params["A"][0], params["A"][1] / (g * t2)])
pa2 = gammaPdf(tree.nodes["a2"].dist,
[params["A"][0], params["A"][1] / (g * t2)])
pb = spidir.gammaSumPdf(
tree.nodes["b1"].dist + tree.nodes[2].dist, 2,
[params["B"][0], params["A"][0]], [
params["B"][1] /
(g * stree.nodes["B"].dist), params["A"][1] / (g * t)
], .001)
if "nan" not in map(str, [pa1, pa2, pb]):
pa += pa1 * pa2 * pb / nsamples
tot += pg * pa * step
#tot /= len(gs)
print "unfold", (tree.nodes["b1"].dist + tree.nodes[2].dist,
[params["B"][0], params["A"][0]], [
params["B"][1] / (g * stree.nodes["B"].dist),
params["A"][1] / (g * t)
])
print "C", p
print "P", log(tot)
def test_branch_prior_simple2(self):
"""Test branch prior 2"""
tree = treelib.parse_newick("((a1:2, a2:3):.4, b1:2);")
stree = treelib.parse_newick("(A:2, B:2);")
gene2species = lambda x: x[0].upper()
params = {"A": (1.0, 1.0), "B": (3.0, 3.0), 1: (1.0, 1.0), "baserate": (11.0, 10.0)}
birth = 0.01
death = 0.02
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
# pd(mapdict(recon, key=lambda x: x.name, val=lambda x: x.name))
# pd(mapdict(events, key=lambda x: x.name))
p = spidir.branch_prior(tree, stree, recon, events, params, birth, death, nsamples=nsamples, approx=False)
tot = 0.0
gstart = 0.01
gend = 3.0
step = (gend - gstart) / 20.0
s2 = step / 2.0
gs = list(frange(gstart + s2, gend + s2, step))
for g in gs:
pg = invgammaPdf(g, params["baserate"])
pa = 0.0
for i in range(nsamples):
t = birthdeath.sample_birth_wait_time(1, stree.nodes["A"].dist, birth, death)
# print t
t2 = stree.nodes["A"].dist - t
pa1 = gammaPdf(tree.nodes["a1"].dist, [params["A"][0], params["A"][1] / (g * t2)])
pa2 = gammaPdf(tree.nodes["a2"].dist, [params["A"][0], params["A"][1] / (g * t2)])
pb = spidir.gammaSumPdf(
tree.nodes["b1"].dist + tree.nodes[2].dist,
2,
[params["B"][0], params["A"][0]],
[params["B"][1] / (g * stree.nodes["B"].dist), params["A"][1] / (g * t)],
0.001,
)
if "nan" not in map(str, [pa1, pa2, pb]):
pa += pa1 * pa2 * pb / nsamples
tot += pg * pa * step
# tot /= len(gs)
print "unfold", (
tree.nodes["b1"].dist + tree.nodes[2].dist,
[params["B"][0], params["A"][0]],
[params["B"][1] / (g * stree.nodes["B"].dist), params["A"][1] / (g * t)],
)
print "C", p
print "P", log(tot)
