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
