def test_internal_branch(self):
duprate = .4
lossrate = .01
stree = treelib.parse_newick("((A:.05,B:.01):1,C:.01);")
def gene2species(gene):
return gene[:1].upper()
tree = treelib.parse_newick("((((A1,B3),(A2,B2)),(A3,B1)),C1)")
recon = phylo.reconcile(tree, stree, gene2species)
#p = exp(calcBirthDeathPrior(tree, stree, recon, duprate, lossrate,
# maxdoom))
p = exp(c_calcBirthDeathPrior(tree, stree, recon,
duprate, lossrate))
print p, 0.0012 * 3
def do_test_birth_death_gene_sim(self, stree, gene2species,
duprate, lossrate,
ntrees=10000, tabsize=30):
"""Perform a birth death gene tree simulation test"""
doomtable = calcDoomTable(stree, duprate, lossrate)
tops = []
lookup = {}
def rename_tree(tree, gene2species):
if len(tree.nodes) == 0:
return
spcounts = util.hist_dict(map(gene2species, tree.leaf_names()))
names = {}
for sp, c in spcounts.items():
names[sp] = range(1, c+1)
random.shuffle(names[sp])
for node in tree.leaves():
sp = gene2species(node.name)
tree.rename(node.name, sp + "." + str(names[sp].pop()))
util.tic("simulating %d trees" % ntrees)
for i in xrange(ntrees):
tree, recon, events = birthdeath.sample_birth_death_gene_tree(
stree, duprate, lossrate,
removeloss=True)
phylo.add_implied_spec_nodes(tree, stree, recon, events)
if len(tree.nodes) == 1 and recon[tree.root] == stree.root:
tops.append("()")
lookup["()"] = (None, None, None)
else:
rename_tree(tree, gene2species)
tops.append(phylo.hash_tree(tree))
lookup[tops[-1]] = (tree, recon, events)
util.toc()
hist = histtab(tops)
probs = []
for row in hist:
tree, recon, events = lookup[row["item"]]
if tree is None:
probs.append(exp(doomtable[-1]))
else:
p = c_calcBirthDeathPrior(tree, stree, recon,
duprate, lossrate,
events=events)
p2 = calcBirthDeathPrior(tree, stree, recon,
duprate, lossrate,
events=events)
fequal(p, p2)
probs.append(exp(p))
return hist, probs