def make_plot2(name, data, params, tol=.01):
x, y = distrib(data, 40)
n = len(params[0])
y2 = [[spidir.gammaSumPdf(i, n,
params[0],
params[1], tol)
for i in x]]
a, b = params
mu = sum(u/v for u,v in zip(a, b))
var = sum(u/v/v for u,v in zip(a, b))
# if alpha > 1
a2 = mu*mu/var
b2 = mu/var
m = (a2-1)/b2
#y3 = [normalPdf(i, (m, sqrt(var))) for i in x]
y3 = [gammaPdf(i, (a2, b2)) for i in x]
rplot_start("test/output/gamma_add/%s.pdf" % name)
rplot("plot", x, y, t="l")
for i in xrange(len(y2)):
k = i/float(len(y2))
rp.lines(x, y2[i], col=rp.rgb(1-k, k, 0))
rp.lines(x, y3, col="blue")
rplot_end(True)
def test_branch_prior_simple1(self):
"""Test branch prior"""
tree = treelib.parse_newick("((a1:1, b1:1):2, c1:3);")
stree = treelib.parse_newick("((A:2, B:2):1, C:3);")
gene2species = lambda x: x[0].upper()
params = {
"A": (1.0, 1.0),
"B": (3.0, 3.0),
"C": (4, 3.5),
2: (2.0, 2.0),
1: (1.0, 1.0),
"baserate": (11.0, 10.0),
}
birth = 0.01
death = 0.02
pretime = 1.0
nsamples = 1
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, generate=1
)
tot = 0.0
gs = list(frange(0.0001, 4, 0.01))
gs = list(frange(1, 1.01, 0.01))
for g in gs:
pg = invgammaPdf(g, params["baserate"])
pa = gammaPdf(tree.nodes["a1"].dist, [params["A"][0], params["A"][1] / (g * stree.nodes["A"].dist)])
pb = gammaPdf(tree.nodes["b1"].dist, [params["B"][0], params["B"][1] / (g * stree.nodes["B"].dist)])
pc = spidir.gammaSumPdf(
tree.nodes["c1"].dist + tree.nodes[2].dist,
2,
[params["C"][0], params[2][0]],
[params["C"][1] / (g * stree.nodes["C"].dist), params[2][1] / (g * stree.nodes[2].dist)],
0.001,
)
print g, pg, pa, pb, pc
tot += pg * pa * pb * pc
tot /= len(gs)
print (
tree.nodes["c1"].dist + tree.nodes[2].dist,
[params["C"][0], params[2][0]],
[params["C"][1], params[2][1]],
)
print "C", p
print "P", log(tot)
def test_branch_prior_simple1(self):
"""Test branch prior"""
tree = treelib.parse_newick("((a1:1, b1:1):2, c1:3);")
stree = treelib.parse_newick("((A:2, B:2):1, C:3);")
gene2species = lambda x: x[0].upper()
params = {
"A": (1.0, 1.0),
"B": (3.0, 3.0),
"C": (4, 3.5),
2: (2.0, 2.0),
1: (1.0, 1.0),
"baserate": (11.0, 10.0)
}
birth = .01
death = .02
pretime = 1.0
nsamples = 1
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,
generate=1)
tot = 0.0
gs = list(frange(.0001, 4, .01))
gs = list(frange(1, 1.01, .01))
for g in gs:
pg = invgammaPdf(g, params["baserate"])
pa = gammaPdf(
tree.nodes["a1"].dist,
[params["A"][0], params["A"][1] / (g * stree.nodes["A"].dist)])
pb = gammaPdf(
tree.nodes["b1"].dist,
[params["B"][0], params["B"][1] / (g * stree.nodes["B"].dist)])
pc = spidir.gammaSumPdf(
tree.nodes["c1"].dist + tree.nodes[2].dist, 2,
[params["C"][0], params[2][0]], [
params["C"][1] / (g * stree.nodes["C"].dist),
params[2][1] / (g * stree.nodes[2].dist)
], .001)
print g, pg, pa, pb, pc
tot += pg * pa * pb * pc
tot /= len(gs)
print(tree.nodes["c1"].dist + tree.nodes[2].dist,
[params["C"][0], params[2][0]], [params["C"][1], params[2][1]])
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 = .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)