def compare_trees(tree_filename1, tree_filename2):
from dendropy import Tree, TreeList
from dendropy.treecalc import symmetric_difference, euclidean_distance, robinson_foulds_distance as rbd, PatristicDistanceMatrix as pdm
c = TreeList([g(tree_filename1), g(tree_filename2)])
pp1 = pdm(c[0]).distances()
pp2 = pdm(c[1]).distances()
sumbl1 = sum(n.edge_length for n in c[0].nodes() if n.edge_length is not None)
sumbl2 = sum(n.edge_length for n in c[1].nodes() if n.edge_length is not None)
e = [n.edge_length for n in c[0].nodes() if n.edge_length is not None]
return {'nBSD':euclidean_distance(c[0], c[1]), 'SDD':symmetric_difference(c[0], c[1]), 'RBD':rbd(c[0], c[1]), 'edgeDelta1': max(pp1)-min(pp1), 'edgeStd1': np.std(pp1),\
'edgeDelta2': max(pp2)-min(pp2), 'edgeStd2': np.std(pp2), 'SumBranchLen1': sumbl1, 'SumBranchLen2': sumbl2}
def runTest(self):
tree_list = dendropy.TreeList(stream=StringIO(
"""((t5:0.161175,t6:0.161175):0.392293,((t4:0.104381,(t2:0.075411,t1:0.075411):1):0.065840,t3:0.170221):0.383247);
((t5:2.161175,t6:0.161175):0.392293,((t4:0.104381,(t2:0.075411,t1:0.075411):1):0.065840,t3:0.170221):0.383247);
((t5:0.161175,t6:0.161175):0.392293,((t2:0.075411,(t4:0.104381,t1:0.075411):1):0.065840,t3:0.170221):0.383247);
((t5:0.161175,t6:0.161175):0.392293,((t4:0.104381,(t2:0.075411,t1:0.075411):0.028969):0.065840,t3:0.170221):0.383247);
"""),
schema="newick")
for i in tree_list:
encode_splits(i)
self.assertAlmostEqual(
treecalc.euclidean_distance(tree_list[0], tree_list[1]), 2.0)
self.assertAlmostEqual(
treecalc.euclidean_distance(tree_list[0], tree_list[2]),
math.sqrt(2.0))
self.assertAlmostEqual(
treecalc.euclidean_distance(tree_list[0], tree_list[3]),
0.97103099999999998)
self.assertAlmostEqual(
treecalc.euclidean_distance(tree_list[1], tree_list[2]),
math.sqrt(6.0))
self.assertAlmostEqual(
treecalc.euclidean_distance(tree_list[1], tree_list[3]),
2.2232636377544162)
self.assertAlmostEqual(
treecalc.euclidean_distance(tree_list[2], tree_list[3]),
1.000419513484718)
def calc_dtcs(self):
for cfactor in self.clist:
min_bic=self.min_criterion['BIC'][cfactor]
for model in self.models:
dtc_sum=0.0
model_tree=self.models[model].tree
for other_model in self.models:
if other_model==model: continue
other_tree=self.models[other_model].tree
distance=euclidean_distance(model_tree,other_tree)
if distance <= 0.0: continue
other_bic=self.models[other_model].criterion['BIC'][cfactor]
power = log(distance) - other_bic + min_bic
if power > -30.0:
dtc_sum+=exp(power)
self.models[model].criterion['DTC'][cfactor]=dtc_sum
def calc_dtcs(self):
for cfactor in self.clist:
min_bic = self.min_criterion['BIC'][cfactor]
for model in self.models:
dtc_sum = 0.0
model_tree = self.models[model].tree
for other_model in self.models:
if other_model == model: continue
other_tree = self.models[other_model].tree
distance = euclidean_distance(model_tree, other_tree)
if distance <= 0.0: continue
other_bic = self.models[other_model].criterion['BIC'][
cfactor]
power = log(distance) - other_bic + min_bic
if power > -30.0:
dtc_sum += exp(power)
self.models[model].criterion['DTC'][cfactor] = dtc_sum
def runTest(self):
tree_list = dendropy.TreeList(
stream=StringIO("""((t5:0.161175,t6:0.161175):0.392293,((t4:0.104381,(t2:0.075411,t1:0.075411):1):0.065840,t3:0.170221):0.383247);
((t5:2.161175,t6:0.161175):0.392293,((t4:0.104381,(t2:0.075411,t1:0.075411):1):0.065840,t3:0.170221):0.383247);
((t5:0.161175,t6:0.161175):0.392293,((t2:0.075411,(t4:0.104381,t1:0.075411):1):0.065840,t3:0.170221):0.383247);
((t5:0.161175,t6:0.161175):0.392293,((t4:0.104381,(t2:0.075411,t1:0.075411):0.028969):0.065840,t3:0.170221):0.383247);
"""),
schema="newick")
for i in tree_list:
encode_splits(i)
self.assertAlmostEqual(treecalc.euclidean_distance(tree_list[0], tree_list[1]), 2.0)
self.assertAlmostEqual(treecalc.euclidean_distance(tree_list[0], tree_list[2]), math.sqrt(2.0))
self.assertAlmostEqual(treecalc.euclidean_distance(tree_list[0], tree_list[3]), 0.97103099999999998)
self.assertAlmostEqual(treecalc.euclidean_distance(tree_list[1], tree_list[2]), math.sqrt(6.0))
self.assertAlmostEqual(treecalc.euclidean_distance(tree_list[1], tree_list[3]), 2.2232636377544162)
self.assertAlmostEqual(treecalc.euclidean_distance(tree_list[2], tree_list[3]), 1.000419513484718)
def compare_trees(tree_filename1, tree_filename2):
from dendropy import Tree, TreeList
from dendropy.treecalc import symmetric_difference, euclidean_distance, robinson_foulds_distance as rbd
c = TreeList([g(tree_filename1), g(tree_filename2)])
return {'nBSD':euclidean_distance(c[0],c[1]), 'SDD':symmetric_difference(c[0], c[1]), \
'RBD':rbd(c[0],c[1])}
