def _test_branch_prior_predup(self):
"""Test branch prior"""
prep_dir("test/output/branch_prior_predup")
# out = open("test/output/branch_prior/flies.nt.approx.txt", "w")
out = sys.stderr
treeid = "predup"
tree = read_tree("test/data/flies.predup.tree")
drawTree(tree)
stree = read_tree("test/data/flies.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.param")
birth = 0.4
death = 0.39
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
p = [
spidir.branch_prior(tree, stree, recon, events, params, birth, death, pretime, nsamples, True)
for i in xrange(30)
]
p2 = [
spidir.branch_prior(tree, stree, recon, events, params, birth, death, pretime, nsamples, False)
for i in xrange(30)
]
print >> out, "\t".join(map(str, [treeid, mean(p), sdev(p), mean(p2), sdev(p2)]))
def _test_branch_prior_predup(self):
"""Test branch prior"""
prep_dir("test/output/branch_prior_predup")
#out = open("test/output/branch_prior/flies.nt.approx.txt", "w")
out = sys.stderr
treeid = "predup"
tree = read_tree("test/data/flies.predup.tree")
drawTree(tree)
stree = read_tree("test/data/flies.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.param")
birth = .4
death = .39
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
p = [
spidir.branch_prior(tree, stree, recon, events, params, birth,
death, pretime, nsamples, True)
for i in xrange(30)
]
p2 = [
spidir.branch_prior(tree, stree, recon, events, params, birth,
death, pretime, nsamples, False)
for i in xrange(30)
]
print >> out, "\t".join(
map(str,
[treeid, mean(p), sdev(p),
mean(p2), sdev(p2)]))
def calc_birth_death_prior(tree, stree, recon, birth, death,
events=None):
"""Returns the topology prior of a gene tree"""
from rasmus.bio import phylo
if events is None:
events = phylo.label_events(tree, recon)
ptree, nodes, nodelookup = make_ptree(tree)
pstree, snodes, snodelookup = make_ptree(stree)
ctree = tree2ctree(tree)
cstree = tree2ctree(stree)
recon2 = make_recon_array(tree, recon, nodes, snodelookup)
events2 = make_events_array(nodes, events)
doomtable = c_list(c_double, [0] * len(stree.nodes))
calcDoomTable(cstree, birth, death, doomtable)
p = birthDeathTreePriorFull(ctree, cstree,
c_list(c_int, recon2),
c_list(c_int, events2),
birth, death, doomtable)
deleteTree(ctree)
deleteTree(cstree)
return p
def _test_branch_prior_samples(self):
"""Test branch prior"""
prep_dir("test/output/branch_prior")
treeids = os.listdir("test/data/flies")
treeids = ["3"]
for treeid in treeids:
tree = read_tree("test/data/flies-duploss/%s/%s.tree" % (treeid, treeid))
print treeid
draw_tree(tree)
stree = read_tree("test/data/flies.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.param")
birth = 0.0012
death = 0.0013
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
p = [
spidir.branch_prior(tree, stree, recon, events, params, birth, death, nsamples=nsamples, approx=True)
for i in xrange(30)
]
# row = [treeid,
# mean(p), exc_default(lambda: sdev(p), INF)]
print treeid, p
def calc_birth_death_prior(tree, stree, recon, birth, death, events=None):
"""Returns the topology prior of a gene tree"""
from rasmus.bio import phylo
if events is None:
events = phylo.label_events(tree, recon)
ptree, nodes, nodelookup = make_ptree(tree)
pstree, snodes, snodelookup = make_ptree(stree)
ctree = tree2ctree(tree)
cstree = tree2ctree(stree)
recon2 = make_recon_array(tree, recon, nodes, snodelookup)
events2 = make_events_array(nodes, events)
doomtable = c_list(c_double, [0] * len(stree.nodes))
calcDoomTable(cstree, birth, death, doomtable)
p = birthDeathTreePriorFull(ctree, cstree, c_list(c_int, recon2),
c_list(c_int, events2), birth, death,
doomtable)
deleteTree(ctree)
deleteTree(cstree)
return p
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_all_terms(self):
"""Test all terms"""
prep_dir("test/output/all_terms")
out = open("test/output/all_terms/flies.txt", "w")
#out = sys.stderr
treeids = os.listdir("test/data/flies")[:100]
#treeids = ["0"]
for treeid in treeids:
tree = read_tree("test/data/flies/%s/%s.nt.tree" %
(treeid, treeid))
align = read_fasta("test/data/flies/%s/%s.nt.align" %
(treeid, treeid))
print >> out, treeid
draw_tree(tree, out=out)
stree = read_tree("test/data/flies.norm.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.nt.param")
birth = .4
death = .39
pretime = 1.0
nsamples = 100
maxdoom = 20
bgfreq = [.258, .267, .266, .209]
kappa = 1.59
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
branchp, topp, seqlk = spidir.calc_joint_prob(align,
tree,
stree,
recon,
events,
params,
birth,
death,
pretime,
bgfreq,
kappa,
maxdoom=maxdoom,
terms=True)
joint = topp + branchp + seqlk
print >> out, "topp ", topp
print >> out, "branchp", branchp
print >> out, "seqlk ", seqlk
print >> out, "joint ", joint
out.close()
def evalUserTree(tree):
setTreeDistances(conf, tree, distmat, labels)
logl = treeLogLikelihood(conf, tree, stree, gene2species, params)
thash = phylo.hash_tree(tree)
if thash in visited:
a, b, count = visited[thash]
else:
count = 0
visited[thash] = [logl, tree.copy(), count+1]
if isDebug(DEBUG_LOW):
debug("\nuser given tree:")
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
drawTreeLogl(tree, events=events)
def test_all_terms(self):
"""Test all terms"""
prep_dir("test/output/all_terms")
out = open("test/output/all_terms/flies.txt", "w")
#out = sys.stderr
treeids = os.listdir("test/data/flies")[:100]
#treeids = ["0"]
for treeid in treeids:
tree = read_tree("test/data/flies/%s/%s.nt.tree" % (treeid, treeid))
align = read_fasta("test/data/flies/%s/%s.nt.align" % (treeid, treeid))
print >>out, treeid
draw_tree(tree, out=out)
stree = read_tree("test/data/flies.norm.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.nt.param")
birth = .4
death = .39
pretime = 1.0
nsamples = 100
maxdoom = 20
bgfreq = [.258,.267,.266,.209]
kappa = 1.59
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
branchp, topp, seqlk = spidir.calc_joint_prob(
align, tree, stree, recon, events, params,
birth, death, pretime,
bgfreq, kappa, maxdoom=maxdoom, terms=True)
joint = topp + branchp + seqlk
print >>out, "topp ", topp
print >>out, "branchp", branchp
print >>out, "seqlk ", seqlk
print >>out, "joint ", joint
out.close()
def _test_branch_prior_samples(self):
"""Test branch prior"""
prep_dir("test/output/branch_prior")
treeids = os.listdir("test/data/flies")
treeids = ["3"]
for treeid in treeids:
tree = read_tree("test/data/flies-duploss/%s/%s.tree" %
(treeid, treeid))
print treeid
draw_tree(tree)
stree = read_tree("test/data/flies.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.param")
birth = .0012
death = .0013
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
p = [
spidir.branch_prior(tree,
stree,
recon,
events,
params,
birth,
death,
nsamples=nsamples,
approx=True) for i in xrange(30)
]
#row = [treeid,
# mean(p), exc_default(lambda: sdev(p), INF)]
print treeid, p
def _test_branch_prior_approx(self):
"""Test branch prior"""
prep_dir("test/output/branch_prior")
out = open("test/output/branch_prior/flies.approx.txt", "w")
out = sys.stderr
treeids = os.listdir("test/data/flies")
for treeid in treeids:
tree = read_tree("test/data/flies-duploss/%s/%s.nt.tree" % (treeid, treeid))
print treeid
draw_tree(tree)
stree = read_tree("test/data/flies.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.param")
birth = 0.0012
death = 0.0013
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
p = [
spidir.branch_prior(tree, stree, recon, events, params, birth, death, nsamples=nsamples, approx=False)
for i in xrange(30)
]
p2 = [
spidir.branch_prior(tree, stree, recon, events, params, birth, death, nsamples=nsamples, approx=True)
for i in xrange(30)
]
row = [treeid, mean(p), exc_default(lambda: sdev(p), INF), mean(p2), exc_default(lambda: sdev(p2), INF)]
print >> out, "\t".join(map(str, row))
self.assert_(INF not in row and -INF not in row)
out.close()
def treeLogLikelihood_python(conf, tree, stree, gene2species, params,
baserate=None, integration="fastsampling"):
# debug info
if isDebug(DEBUG_MED):
util.tic("find logl")
# derive relative branch lengths
tree.clear_data("logl", "extra", "fracs", "params", "unfold")
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
# determine if top branch unfolds
if recon[tree.root] == stree.root and \
events[tree.root] == "dup":
for child in tree.root.children:
if recon[child] != stree.root:
child.data["unfold"] = True
if baserate == None:
baserate = getBaserate(tree, stree, params, recon=recon)
phylo.midroot_recon(tree, stree, recon, events, params, baserate)
# top branch is "free"
params[stree.root.name] = [0,0]
this = util.Bundle(logl=0.0)
# recurse through indep sub-trees
def walk(node):
if events[node] == "spec" or \
node == tree.root:
this.logl += subtreeLikelihood(conf, node, recon, events,
stree, params, baserate,
integration=integration)
node.recurse(walk)
walk(tree.root)
# calc probability of rare events
tree.data["eventlogl"] = rareEventsLikelihood(conf, tree, stree, recon, events)
this.logl += tree.data["eventlogl"]
# calc penality of error
tree.data["errorlogl"] = tree.data.get("error", 0.0) * \
conf.get("errorcost", 0.0)
this.logl += tree.data["errorlogl"]
# family rate likelihood
if conf["famprob"]:
this.logl += log(stats.gammaPdf(baserate, params["baserate"]))
tree.data["baserate"] = baserate
tree.data["logl"] = this.logl
if isDebug(DEBUG_MED):
util.toc()
debug("\n\n")
drawTreeLogl(tree, events=events)
return this.logl
def spidir(conf, distmat, labels, stree, gene2species, params):
"""Main function for the SPIDIR algorithm"""
setDebug(conf["debug"])
if isDebug(DEBUG_HIGH) and pyspidir:
pyspidir.set_log(3, "")
if "out" in conf:
# create debug table
conf["debugtab_file"] = file(conf["out"] + ".debug.tab", "w")
debugtab = tablelib.Table(headers=["correct",
"logl", "treelen", "baserate",
"error", "errorlogl",
"eventlogl", "tree",
"topology", "species_hash"],
types={"correct": bool,
"logl": float,
"treelen": float,
"baserate": float,
"error": float,
"errorlogl": float,
"eventlogl": float,
"tree": str,
"topology": str,
"species_hash": str})
debugtab.writeHeader(conf["debugtab_file"])
conf["debugtab"] = debugtab
else:
conf["debugfile"] = None
trees = []
logls = []
tree = None
visited = {}
util.tic("SPIDIR")
# do auto searches
for search in conf["search"]:
util.tic("Search by %s" % search)
if search == "greedy":
tree, logl = Search.searchGreedy(conf, distmat, labels, stree,
gene2species, params,
visited=visited)
elif search == "mcmc":
tree, logl = Search.searchMCMC(conf, distmat, labels, stree,
gene2species, params, initTree=tree,
visited=visited)
elif search == "regraft":
tree, logl = Search.searchRegraft(conf, distmat, labels, stree,
gene2species, params, initTree=tree,
visited=visited, proposeFunc=Search.proposeTree3)
elif search == "exhaustive":
if tree == None:
tree = phylo.neighborjoin(distmat, labels)
tree = phylo.recon_root(tree, stree, gene2species)
tree, logl = Search.searchExhaustive(conf, distmat, labels, tree, stree,
gene2species, params,
depth=conf["depth"],
visited=visited)
elif search == "hillclimb":
tree, logl = Search.searchHillClimb(conf, distmat, labels, stree,
gene2species, params, initTree=tree,
visited=visited)
elif search == "none":
break
else:
raise SindirError("unknown search '%s'" % search)
util.toc()
Search.printMCMC(conf, "N/A", tree, stree, gene2species, visited)
printVisitedTrees(visited)
def evalUserTree(tree):
setTreeDistances(conf, tree, distmat, labels)
logl = treeLogLikelihood(conf, tree, stree, gene2species, params)
thash = phylo.hash_tree(tree)
if thash in visited:
a, b, count = visited[thash]
else:
count = 0
visited[thash] = [logl, tree.copy(), count+1]
if isDebug(DEBUG_LOW):
debug("\nuser given tree:")
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
drawTreeLogl(tree, events=events)
# eval the user given trees
for treefile in conf["tree"]:
tree = treelib.read_tree(treefile)
evalUserTree(tree)
for topfile in conf["tops"]:
infile = file(topfile)
strees = []
while True:
try:
strees.append(treelib.read_tree(infile))
except:
break
print len(strees)
for top in strees:
tree = phylo.stree2gtree(top, labels, gene2species)
evalUserTree(tree)
if len(conf["tops"]) > 0:
printVisitedTrees(visited)
# eval correcttree for debug only
if "correcttree" in conf:
tree = conf["correcttree"]
setTreeDistances(conf, tree, distmat, labels)
logl = treeLogLikelihood(conf, tree, stree, gene2species, params)
if isDebug(DEBUG_LOW):
debug("\ncorrect tree:")
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
drawTreeLogl(tree, events=events)
util.toc()
if len(visited) == 0:
raise SindirError("No search or tree topologies given")
if "correcthash" in conf:
if conf["correcthash"] in visited:
debug("SEARCH: visited correct tree")
else:
debug("SEARCH: NEVER saw correct tree")
# return ML tree
trees = [x[1] for x in visited.itervalues()]
i = util.argmax([x.data["logl"] for x in trees])
return trees[i], trees[i].data["logl"]
def treeLogLikelihood(conf, tree, stree, gene2species, params, baserate=None):
conf.setdefault("bestlogl", -util.INF)
if pyspidir == None or conf.get("python_only", False):
return Likelihood.treeLogLikelihood_python(conf, tree, stree,
gene2species, params,
baserate=baserate,
integration="fastsampling")
# debug info
if isDebug(DEBUG_MED):
util.tic("find logl")
# derive relative branch lengths
#tree.clearData("logl", "extra", "fracs", "params", "unfold")
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
# determine if top branch unfolds
if recon[tree.root] == stree.root and \
events[tree.root] == "dup":
for child in tree.root.children:
if recon[child] != stree.root:
child.data["unfold"] = True
# top branch is "free"
params[stree.root.name] = [0,0]
this = util.Bundle(logl=0.0)
if conf.get("generate_int", False):
baserate = -99.0 # indicates in integration over gene rates is requested
elif baserate == None:
baserate = Likelihood.getBaserate(tree, stree, params, recon=recon)
phylo.midroot_recon(tree, stree, recon, events, params, baserate)
# calc likelihood in C
this.logl = treeLikelihood_C(conf, tree, recon, events, stree, params,
baserate, gene2species)
# calc probability of rare events
tree.data["eventlogl"] = Likelihood.rareEventsLikelihood(conf, tree, stree, recon, events)
# calc penality of error
tree.data["errorlogl"] = tree.data.get("error", 0.0) * \
conf.get("errorcost", 0.0)
this.logl += tree.data["errorlogl"]
# add logl of sequence evolution
this.logl += tree.data.get("distlogl", 0.0)
if baserate == -99.0: # indicates in integration over gene rates is requested
baserate = Likelihood.getBaserate(tree, stree, params, recon=recon)
tree.data["baserate"] = baserate
tree.data["logl"] = this.logl
if isDebug(DEBUG_MED):
util.toc()
debug("\n\n")
drawTreeLogl(tree, events=events)
return this.logl
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_approx(self):
"""Test branch prior"""
prep_dir("test/output/branch_prior")
out = open("test/output/branch_prior/flies.approx.txt", "w")
out = sys.stderr
treeids = os.listdir("test/data/flies")
for treeid in treeids:
tree = read_tree("test/data/flies-duploss/%s/%s.nt.tree" %
(treeid, treeid))
print treeid
draw_tree(tree)
stree = read_tree("test/data/flies.stree")
gene2species = phylo.read_gene2species("test/data/flies.smap")
params = spidir.read_params("test/data/flies.param")
birth = .0012
death = .0013
pretime = 1.0
nsamples = 100
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
p = [
spidir.branch_prior(tree,
stree,
recon,
events,
params,
birth,
death,
nsamples=nsamples,
approx=False) for i in xrange(30)
]
p2 = [
spidir.branch_prior(tree,
stree,
recon,
events,
params,
birth,
death,
nsamples=nsamples,
approx=True) for i in xrange(30)
]
row = [
treeid,
mean(p),
exc_default(lambda: sdev(p), INF),
mean(p2),
exc_default(lambda: sdev(p2), INF)
]
print >> out, "\t".join(map(str, row))
self.assert_(INF not in row and -INF not in row)
out.close()
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 draw_tree(tree, labels={}, xscale=100, yscale=20, canvas=None,
leafPadding=10,
labelOffset=None, fontSize=10, labelSize=None,
minlen=1, maxlen=util.INF, filename=sys.stdout,
rmargin=150, lmargin=10, tmargin=0, bmargin=None,
colormap=None,
stree=None,
layout=None,
gene2species=None,
lossColor=(0, 0, 1),
dupColor=(1, 0, 0),
eventSize=4,
legendScale=False, autoclose=None):
# set defaults
fontRatio = 8. / 11.
if labelSize == None:
labelSize = .7 * fontSize
if labelOffset == None:
labelOffset = -1
if bmargin == None:
bmargin = yscale
if sum(x.dist for x in tree.nodes.values()) == 0:
legendScale = False
minlen = xscale
if colormap == None:
for node in tree:
node.color = (0, 0, 0)
else:
colormap(tree)
if stree and gene2species:
recon = phylo.reconcile(tree, stree, gene2species)
events = phylo.label_events(tree, recon)
losses = phylo.find_loss(tree, stree, recon)
else:
events = None
losses = None
# layout tree
if layout is None:
coords = treelib.layout_tree(tree, xscale, yscale, minlen, maxlen)
else:
coords = layout
xcoords, ycoords = zip(* coords.values())
maxwidth = max(xcoords)
maxheight = max(ycoords) + labelOffset
# initialize canvas
if canvas == None:
canvas = svg.Svg(util.open_stream(filename, "w"))
width = int(rmargin + maxwidth + lmargin)
height = int(tmargin + maxheight + bmargin)
canvas.beginSvg(width, height)
if autoclose == None:
autoclose = True
else:
if autoclose == None:
autoclose = False
# draw tree
def walk(node):
x, y = coords[node]
if node.parent:
parentx = coords[node.parent][0]
else:
parentx = 0
# draw branch
canvas.line(parentx, y, x, y, color=node.color)
if node.name in labels:
branchlen = x - parentx
lines = str(labels[node.name]).split("\n")
labelwidth = max(map(len, lines))
labellen = min(labelwidth * fontRatio * fontSize,
max(int(branchlen-1), 0))
for i, line in enumerate(lines):
canvas.text(line,
parentx + (branchlen - labellen)/2.,
y + labelOffset
+(-len(lines)+1+i)*(labelSize+1),
labelSize)
if node.isLeaf():
canvas.text(str(node.name),
x + leafPadding, y+fontSize/2., fontSize,
fillColor=node.color)
else:
top = coords[node.children[0]][1]
bot = coords[node.children[-1]][1]
# draw children
canvas.line(x, top, x, bot, color=node.color)
for child in node.children:
walk(child)
canvas.beginTransform(("translate", lmargin, tmargin))
walk(tree.root)
if stree and gene2species:
draw_events(canvas, tree, coords, events, losses,
lossColor=lossColor,
dupColor=dupColor,
size=eventSize)
canvas.endTransform()
# draw legend
if legendScale:
if legendScale == True:
# automatically choose a scale
length = maxwidth / float(xscale)
order = math.floor(math.log10(length))
length = 10 ** order
drawScale(lmargin, tmargin + maxheight + bmargin - fontSize,
length, xscale, fontSize, canvas=canvas)
if autoclose:
canvas.endSvg()
return canvas
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)
