def test_birth_death_single2_sim(self):
"""test the single branch prior"""
duprate = 2.0
lossrate = .5
T = 1.0
stree = treelib.parse_newick("(A:1,B:1);")
def gene2species(gene):
return gene[:1].upper()
s = stree.leaves()[0]
b = birthDeathCount(1, T, duprate, lossrate)
# 1
tree = treelib.parse_newick("(a,b);")
recon = phylo.reconcile(tree, stree, gene2species)
p = birthDeathCount(1, T, duprate, lossrate) * b
p2 = exp(calcBirthDeathPrior(tree, stree, recon, duprate, lossrate))
p2 *= numRedunantTopology(tree.root, gene2species)
print p, p2
fequal(p, p2)
# 2
tree = treelib.parse_newick("((a,a),b);")
recon = phylo.reconcile(tree, stree, gene2species)
p = birthDeathCount(2, T, duprate, lossrate) * b
p2 = exp(calcBirthDeathPrior(tree, stree, recon, duprate, lossrate))
p2 *= numRedunantTopology(tree.root, gene2species)
print p, p2
fequal(p, p2)
# 3
tree = treelib.parse_newick("(((a,a),a),b);")
recon = phylo.reconcile(tree, stree, gene2species)
p = birthDeathCount(3, T, duprate, lossrate) * b
p2 = exp(calcBirthDeathPrior(tree, stree, recon, duprate, lossrate))
p2 *= numRedunantTopology(tree.root, gene2species)
print p, p2
fequal(p, p2)
# 4
tree = treelib.parse_newick("(((a,a),(a,a)),b);")
recon = phylo.reconcile(tree, stree, gene2species)
p = birthDeathCount(4, T, duprate, lossrate) * b / 3.0
p2 = exp(calcBirthDeathPrior(tree, stree, recon, duprate, lossrate))
p2 *= numRedunantTopology(tree.root, gene2species)
print p, p2
fequal(p, p2)
def test_birth_death_single_sim(self):
"""test the single branch prior"""
duprate = 2.0
lossrate = .5
ntrees = 1000
tabsize = 100
T = 1.0
tops = []
survivors = []
lookup = {}
# define species tree
stree = treelib.parse_newick("(A:1);")
def gene2species(gene):
return gene[:1].upper()
# simulate gene trees
util.tic("simulating %d trees" % ntrees)
for i in xrange(ntrees):
tree, doom = birthdeath.sample_birth_death_tree(
T, duprate, lossrate)
if tree.root in doom:
tops.append("()")
survivors.append(0)
else:
rename_leaves(tree, stree, lambda x: "A")
tops.append(phylo.hash_tree(tree, gene2species))
survivors.append(len(tree.leaves()))
lookup[tops[-1]] = tree
util.toc()
# setup test output
outdir = "test/output/birthdeath_sim_simple"
prep_dir(outdir)
# histogram of topologies and survivors (# leaves)
hist_tops = histtab(tops)
hist_num = histtab(survivors)
# compute survivor prior
probs = []
for row in hist_num:
ngenes = row["item"]
probs.append(birthDeathCount(ngenes, T, duprate, lossrate))
# compute topologie priors
probs_tops = []
for row in hist_tops:
tree = lookup[row["item"]]
if tree.root.is_leaf():
p = log(birthdeath.prob_birth_death1(
0, T, duprate, lossrate))
else:
nhist = numTopologyHistories(tree.root)
s = len(tree.leaves())
thist = factorial(s) * factorial(s-1) / 2**(s-1)
r = numRedunantTopology(tree.root, gene2species,
all_leaves=True)
p = log(r * nhist / thist * birthdeath.prob_birth_death1(
s, T, duprate, lossrate))
probs_tops.append(exp(p))
self.calc_fit(outdir + "/sim_prior_ngenes", hist_num, probs)
self.calc_fit(outdir + "/sim_prior_top", hist_tops, probs_tops)