def test_ml_large(self):
"""Test ML code"""
# params
bgfreq = [.258,.267,.266,.209]
kappa = 1.59
# data
tree = treelib.readTree("test/data/verts/19520/19520.ensembl.tree")
align = fasta.readFasta("test/data/verts/19520/19520.nt.mfa")
likes = []
dists = []
nodes = sorted(tree.nodes.values(), key=lambda x: x.dist)
l = spidir.calc_seq_likelihood_hky(tree, align, bgfreq, kappa)
print l
self.assert_(l != -util.INF)
l = spidir.find_ml_branch_lengths_hky(
tree,
util.mget(align, tree.leafNames()),
bgfreq, kappa,
parsinit=False,
maxiter=1)
print l
self.assert_(l != -util.INF)
def test_ml_large(self):
"""Test ML code"""
# params
bgfreq = [.258, .267, .266, .209]
kappa = 1.59
# data
tree = treelib.readTree("test/data/verts/19520/19520.ensembl.tree")
align = fasta.readFasta("test/data/verts/19520/19520.nt.mfa")
likes = []
dists = []
nodes = sorted(tree.nodes.values(), key=lambda x: x.dist)
l = spidir.calc_seq_likelihood_hky(tree, align, bgfreq, kappa)
print l
self.assert_(l != -util.INF)
l = spidir.find_ml_branch_lengths_hky(tree,
util.mget(
align, tree.leafNames()),
bgfreq,
kappa,
parsinit=False,
maxiter=1)
print l
self.assert_(l != -util.INF)
def test_calc_hky_seq_likelihood(self):
bgfreq = [.258, .267, .266, .209]
kappa = 1.59
tree = treelib.parse_newick(
"((A:.1,B:.1):.1,((C:.1,D:.1):.2,E:.3):.1);")
align = {
"A":
"CGCAGACAACTCCCCCGACCACACATAGTACGAAATCCTCAGCCGCTGCCGACTCCGACGCGCGGACTGTCCGGGTTCAGCGAGGCTTAAGAGAACGGCC",
"B":
"CCCAAACAACTCCCCCGACCAGACATAGTACGAGATCCTCAGCCACTGGCGACTCGGACGCGCAGAGTGTCCCGCTTAAGCGAGGCTGCAGAGAACGGCC",
"C":
"GGCCAGCAATTCCTCCGACCACGCATAGTACGAGATCGTCTGCCTCCTGCGAATCGGACGCGCAGAGTGTTCCGGTTAAGGGAGACTTCAGAGACCTGGC",
"D":
"CGCTAACAATTCCCCCGACCACACTGAGTACGAGATACTCGGACTCCGGCGATCTCTACTCGCAGAGAGTCCCACTTAAGCGAGACTGACGAGCACGGGC",
"E":
"ATTCTTCCACACCTGCGTGTTCGTCACGTATCAAATGCGGAGCCCACGTCCAATGGCACACGAACAGTCGGCCACGGAATCGCAGACTCGTTGACCAACG"
}
draw_tree(tree)
l = spidir.calc_seq_likelihood_hky(tree, align, bgfreq, kappa)
l2 = spidir.find_ml_branch_lengths_hky(tree, align, bgfreq, kappa)
draw_tree(tree)
print "log lk", l, l2
self.assert_(l2 > l)
def test1(self):
# (0, 1):2
# P(x_0,x_1|t) = \sum_{x_2} P(x_0|x_2,t_0) * P(x_1|x_2,t_1) * P(x_2)
# P(x_0|x_1,t) = P(x_0,x_1|t) / P(x_1|t) = P(x_0,x_1|t) / P(x_1)
for t in frange(0.1, 2.0, 0.1):
bgfreq = [0.25, 0.25, 0.25, 0.25]
kappa = 1.0
tree = treelib.parse_newick("(a:%f,b:%f)" % (t * 0.5, t * 0.5))
align = {"a": "C", "b": "T"}
l = spidir.calc_seq_likelihood_hky(tree, align, bgfreq, kappa) - log(0.25)
mat = spidir.make_hky_matrix(bgfreq, kappa, t)
l2 = log(mat[0][1])
print l, l2
fequal(l, l2)
def test1(self):
# (0, 1):2
# P(x_0,x_1|t) = \sum_{x_2} P(x_0|x_2,t_0) * P(x_1|x_2,t_1) * P(x_2)
# P(x_0|x_1,t) = P(x_0,x_1|t) / P(x_1|t) = P(x_0,x_1|t) / P(x_1)
for t in frange(.1, 2.0, .1):
bgfreq = [.25, .25, .25, .25]
kappa = 1.0
tree = treelib.parse_newick("(a:%f,b:%f)" % (t * .5, t * .5))
align = {"a": "C", "b": "T"}
l = spidir.calc_seq_likelihood_hky(tree, align, bgfreq, kappa) \
- log(.25)
mat = spidir.make_hky_matrix(bgfreq, kappa, t)
l2 = log(mat[0][1])
print l, l2
fequal(l, l2)
def test_calc_hky_seq_likelihood(self):
bgfreq = [0.258, 0.267, 0.266, 0.209]
kappa = 1.59
tree = treelib.parse_newick("((A:.1,B:.1):.1,((C:.1,D:.1):.2,E:.3):.1);")
align = {
"A": "CGCAGACAACTCCCCCGACCACACATAGTACGAAATCCTCAGCCGCTGCCGACTCCGACGCGCGGACTGTCCGGGTTCAGCGAGGCTTAAGAGAACGGCC",
"B": "CCCAAACAACTCCCCCGACCAGACATAGTACGAGATCCTCAGCCACTGGCGACTCGGACGCGCAGAGTGTCCCGCTTAAGCGAGGCTGCAGAGAACGGCC",
"C": "GGCCAGCAATTCCTCCGACCACGCATAGTACGAGATCGTCTGCCTCCTGCGAATCGGACGCGCAGAGTGTTCCGGTTAAGGGAGACTTCAGAGACCTGGC",
"D": "CGCTAACAATTCCCCCGACCACACTGAGTACGAGATACTCGGACTCCGGCGATCTCTACTCGCAGAGAGTCCCACTTAAGCGAGACTGACGAGCACGGGC",
"E": "ATTCTTCCACACCTGCGTGTTCGTCACGTATCAAATGCGGAGCCCACGTCCAATGGCACACGAACAGTCGGCCACGGAATCGCAGACTCGTTGACCAACG",
}
draw_tree(tree)
l = spidir.calc_seq_likelihood_hky(tree, align, bgfreq, kappa)
l2 = spidir.find_ml_branch_lengths_hky(tree, align, bgfreq, kappa)
draw_tree(tree)
print "log lk", l, l2
self.assert_(l2 > l)
