def _test_ml_speed(self):
# params
bgfreq = [.258,.267,.266,.209]
kappa = 1.59
# data
tree = treelib.readTree("test/data/flies.nt/0/0.tree")
align = fasta.readFasta("test/data/flies.nt/0/0.align")
likes = []
dists = []
nodes = sorted(tree.nodes.values(), key=lambda x: x.dist)
util.tic("find ML")
for i in xrange(10):
l = spidir.find_ml_branch_lengths_hky(
tree,
util.mget(align, tree.leafNames()),
bgfreq, kappa,
maxiter=10)
util.toc()
dists.append([n.dist for n in nodes])
likes.append(l)
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_ml_speed(self):
# params
bgfreq = [.258, .267, .266, .209]
kappa = 1.59
# data
tree = treelib.readTree("test/data/flies.nt/0/0.tree")
align = fasta.readFasta("test/data/flies.nt/0/0.align")
likes = []
dists = []
nodes = sorted(tree.nodes.values(), key=lambda x: x.dist)
util.tic("find ML")
for i in xrange(10):
l = spidir.find_ml_branch_lengths_hky(tree,
util.mget(
align, tree.leafNames()),
bgfreq,
kappa,
maxiter=10)
util.toc()
dists.append([n.dist for n in nodes])
likes.append(l)
def _test_ml(self):
"""Test ML code"""
# params
bgfreq = [.258, .267, .266, .209]
kappa = 1.59
# data
tree = treelib.readTree("test/data/flies.nt/0/0.tree")
align = fasta.readFasta("test/data/flies.nt/0/0.align")
likes = []
dists = []
nodes = sorted(tree.nodes.values(), key=lambda x: x.dist)
util.tic("find ML")
for i in range(40):
l = spidir.find_ml_branch_lengths_hky(tree,
util.mget(
align, tree.leafNames()),
bgfreq,
kappa,
parsinit=False,
maxiter=1)
dists.append([n.dist for n in nodes])
likes.append(l)
util.toc()
print likes
prep_dir("test/output/ml/")
# distances plot
util.rplot_start("test/output/ml/ml_branches.pdf")
util.rplot("plot",
util.cget(dists, 0),
ylim=[0, max(dists[0])],
t="l",
main="branch length convergence",
xlab="iterations",
ylab="branch lengths (sub/site)")
for d in zip(*dists):
util.rplot("lines", d)
util.rplot_end(True)
print util.cget(dists, 4)
# likelihood plot
util.rplot_start("test/output/ml/ml_likelihood.pdf")
util.rplot("plot",
likes,
t="l",
xlab="iterations",
ylab="log likelihood",
main="likelihood convergence")
util.rplot_end(True)
def _test_ml(self):
"""Test ML code"""
# params
bgfreq = [.258,.267,.266,.209]
kappa = 1.59
# data
tree = treelib.readTree("test/data/flies.nt/0/0.tree")
align = fasta.readFasta("test/data/flies.nt/0/0.align")
likes = []
dists = []
nodes = sorted(tree.nodes.values(), key=lambda x: x.dist)
util.tic("find ML")
for i in range(40):
l = spidir.find_ml_branch_lengths_hky(
tree,
util.mget(align, tree.leafNames()),
bgfreq, kappa,
parsinit=False,
maxiter=1)
dists.append([n.dist for n in nodes])
likes.append(l)
util.toc()
print likes
prep_dir("test/output/ml/")
# distances plot
util.rplot_start("test/output/ml/ml_branches.pdf")
util.rplot("plot", util.cget(dists, 0),
ylim=[0, max(dists[0])], t="l",
main="branch length convergence",
xlab="iterations",
ylab="branch lengths (sub/site)")
for d in zip(* dists):
util.rplot("lines", d)
util.rplot_end(True)
print util.cget(dists, 4)
# likelihood plot
util.rplot_start("test/output/ml/ml_likelihood.pdf")
util.rplot("plot", likes, t="l",
xlab="iterations",
ylab="log likelihood",
main="likelihood convergence")
util.rplot_end(True)
def readOptions(conf):
"""Setup data paths and parse common options"""
# read species map
if "smap" in conf:
conf["gene2species"] = readGene2species(*conf["smap"])
else:
conf["gene2species"] = gene2species
if "stree" in conf:
conf["stree"] = treelib.readTree(conf["stree"])
def gene2speciesArray(tree, stree, gene2species):
ptree, nodes, nodelookup = Spidir.makePtree(tree)
sptree, snodes, snodelookup = Spidir.makePtree(stree)
gene2speciesarray = []
for node in nodes:
if node.isLeaf():
gene2speciesarray.append(snodelookup[
stree.nodes[gene2species(node.name)]])
else:
gene2speciesarray.append(-1)
return gene2speciesarray
tree = treelib.readTree("../data/0.nt.tree")
tree = treelib.parseNewick("((dmoj_sim,(dvir_sim,dgri_sim)),(dwil_sim,(dpse_sim,(dana_sim,(dmel_sim,(dyak_sim,dere_sim))))));")
tree = treelib.parseNewick("((dwil_sim,(dvir_sim,dgri_sim)),(dmoj_sim,(dpse_sim,(dana_sim,(dmel_sim,(dyak_sim,dere_sim))))));")
stree = treelib.readTree("../data/flies.stree")
gene2species = genomeutil.readGene2species("../data/flies.smap")
params = Spidir.readParams("../data/flies.nt.param")
ptree, nodes, nodelookup = Spidir.makePtree(tree)
pstree, snodes, snodelookup = Spidir.makePtree(stree)
g2s = gene2speciesArray(tree, stree, gene2species)
mu = [float(params[snode.name][0]) for snode in snodes]
sigma = [float(params[snode.name][1]) for snode in snodes]
alpha = float(params['baserate'][0])
beta = float(params['baserate'][1])
def gene2speciesArray(tree, stree, gene2species):
ptree, nodes, nodelookup = Spidir.makePtree(tree)
sptree, snodes, snodelookup = Spidir.makePtree(stree)
gene2speciesarray = []
for node in nodes:
if node.isLeaf():
gene2speciesarray.append(snodelookup[stree.nodes[gene2species(
node.name)]])
else:
gene2speciesarray.append(-1)
return gene2speciesarray
tree = treelib.readTree("../data/0.nt.tree")
tree = treelib.parseNewick(
"((dmoj_sim,(dvir_sim,dgri_sim)),(dwil_sim,(dpse_sim,(dana_sim,(dmel_sim,(dyak_sim,dere_sim))))));"
)
tree = treelib.parseNewick(
"((dwil_sim,(dvir_sim,dgri_sim)),(dmoj_sim,(dpse_sim,(dana_sim,(dmel_sim,(dyak_sim,dere_sim))))));"
)
stree = treelib.readTree("../data/flies.stree")
gene2species = genomeutil.readGene2species("../data/flies.smap")
params = Spidir.readParams("../data/flies.nt.param")
ptree, nodes, nodelookup = Spidir.makePtree(tree)
pstree, snodes, snodelookup = Spidir.makePtree(stree)
g2s = gene2speciesArray(tree, stree, gene2species)
mu = [float(params[snode.name][0]) for snode in snodes]