def finish(self):
# first job is the raxml tree
best_model = None
best_model_in_next_line = False
t = None
for line in open(self.jobs[-1].stdout_file, "rU"):
line = line.strip()
if best_model_in_next_line and line.startswith("Model"):
pass#best_model = line.split("=")[1].strip()
elif best_model_in_next_line and line.startswith("partition"):
best_model = line.split("=")[1].strip()
best_model_in_next_line = False
elif line.startswith("Model selected:"):
best_model_in_next_line = True
elif line.startswith("ML tree (NNI) for the best AIC model ="):
nw = line.replace("ML tree (NNI) for the best AIC model =", "")
t = PhyloTree(nw)
open(self.best_model_file, "w").write(best_model)
log.log(26, "Best model: %s" %best_model)
if self.ttype == "tree":
tree_job = self.jobs[-1]
tree_file = os.path.join(tree_job.jobdir,
"jModelTest_tree."+self.nodeid)
t.write(outfile=self.tree_file)
self.model = best_model
ModelTesterTask.finish(self)
def finish(self):
lks = []
if self.lk_mode == "phyml":
for job in self.jobs:
if job.jobcat != "bionj": continue
phyml_job = job
tree_file = pjoin(phyml_job.jobdir,
self.alg_phylip_file+"_phyml_tree.txt")
stats_file = pjoin(phyml_job.jobdir,
self.alg_phylip_file+"_phyml_stats.txt")
tree = PhyloTree(tree_file)
m = re.search('Log-likelihood:\s+(-?\d+\.\d+)',
open(stats_file).read())
lk = float(m.groups()[0])
tree.add_feature("lk", lk)
tree.add_feature("model", phyml_job.args["--model"])
lks.append([float(tree.lk), tree.model, tree])
elif self.lk_mode == "raxml":
for job in self.jobs:
if job.jobcat != "raxml": continue
raxml_job = job
lk = open(pjoin(raxml_job.jobdir, "RAxML_log.%s"
%raxml_job.args["-n"])).readline().split()[1]
tree = PhyloTree(raxml_job.args["-t"])
tree.add_feature("lk", lk)
tree.add_feature("model", raxml_job.model)
lks.append([float(tree.lk), tree.model, tree])
# sort lks in ASC order
lks.sort()
# choose the model with higher likelihood, the lastone in the list
best_model = lks[-1][1]
best_tree = lks[-1][2]
log.log(22, "%s model selected from the following lk values:\n%s" %(best_model, '\n'.join(map(str, lks))))
ModelTesterTask.store_data(self, best_model, lks)
def finish(self):
lks = []
j = self.jobs[0]
tree_file = os.path.join(j.jobdir,
self.alg_phylip_file+"_phyml_tree.txt")
stats_file = os.path.join(j.jobdir,
self.alg_phylip_file+"_phyml_stats.txt")
m = re.search('Log-likelihood:\s+(-?\d+\.\d+)',
open(stats_file).read())
lk = float(m.groups()[0])
stats = {"lk": lk}
tree = PhyloTree(tree_file)
TreeTask.store_data(self, tree.write(), stats)
def finish(self):
def euc_dist(x, y):
return len(x.symmetric_difference(y)) / float((len(x) + len(y)))
dataid = db.get_dataid(*self.task_tree_file.split("."))
ttree = PhyloTree(db.get_data(dataid))
mtree = self.main_tree
ttree.dist = 0
cladeid, target_seqs, out_seqs = db.get_node_info(self.threadid, self.nodeid)
self.out_seqs = out_seqs
self.target_seqs = target_seqs
ttree_content = ttree.get_cached_content()
if mtree and not out_seqs:
mtree_content = mtree.get_cached_content()
log.log(24, "Finding best scoring outgroup from previous iteration.")
for _n in mtree_content:
if _n.cladeid == cladeid:
orig_target = _n
target_left = set([_n.name for _n in mtree_content[orig_target.children[0]]])
target_right = set([_n.name for _n in mtree_content[orig_target.children[1]]])
partition_pairs = []
everything = set([_n.name for _n in ttree_content[ttree]])
for n, content in ttree_content.iteritems():
if n is ttree:
continue
left = set([_n.name for _n in content])
right = everything - left
d1 = euc_dist(left, target_left)
d2 = euc_dist(left, target_right)
best_match = min(d1, d2)
partition_pairs.append([best_match, left, right, n])
partition_pairs.sort()
self.outgroup_match_dist = partition_pairs[0][0]
#self.outgroup_match = '#'.join( ['|'.join(partition_pairs[0][1]),
# '|'.join(partition_pairs[0][2])] )
outgroup = partition_pairs[0][3]
ttree.set_outgroup(outgroup)
ttree.dist = orig_target.dist
ttree.support = orig_target.support
# Merge task and main trees
parent = orig_target.up
orig_target.detach()
parent.add_child(ttree)
elif mtree and out_seqs:
log.log(26, "Rooting tree using %d custom seqs" %
len(out_seqs))
self.outgroup_match = '|'.join(out_seqs)
#log.log(22, "Out seqs: %s", len(out_seqs))
#log.log(22, "Target seqs: %s", target_seqs)
if len(out_seqs) > 1:
#first root to a single seqs outside the outgroup
#(should never fail and avoids random outgroup split
#problems in unrooted trees)
ttree.set_outgroup(ttree & list(target_seqs)[0])
# Now tries to get the outgroup node as a monophyletic clade
outgroup = ttree.get_common_ancestor(out_seqs)
if set(outgroup.get_leaf_names()) ^ out_seqs:
msg = "Monophyly of the selected outgroup could not be granted! Probably constrain tree failed."
#dump_tree_debug(msg, self.taskdir, mtree, ttree, target_seqs, out_seqs)
raise TaskError(self, msg)
else:
outgroup = ttree & list(out_seqs)[0]
ttree.set_outgroup(outgroup)
orig_target = self.main_tree.get_common_ancestor(target_seqs)
found_target = outgroup.get_sisters()[0]
ttree = ttree.get_common_ancestor(target_seqs)
outgroup.detach()
self.pre_iter_support = orig_target.support
# Use previous dist and support
ttree.dist = orig_target.dist
ttree.support = orig_target.support
parent = orig_target.up
orig_target.detach()
parent.add_child(ttree)
else:
# ROOTS FIRST ITERATION
log.log(24, "Getting outgroup for first NPR split")
# if early split is provided in the command line, it
# overrides config file
mainout = GLOBALS.get("first_split_outgroup", "midpoint")
if mainout.lower() == "midpoint":
log.log(26, "Rooting to midpoint.")
best_outgroup = ttree.get_midpoint_outgroup()
if best_outgroup:
ttree.set_outgroup(best_outgroup)
else:
log.warning("Midpoint outgroup could not be set!")
ttree.set_outgroup(ttree.iter_leaves().next())
else:
if mainout.startswith("~"):
# Lazy defined outgroup. Will trust in the common
# ancestor of two or more OTUs
strict_common_ancestor = False
outs = set(mainout[1:].split())
if len(outs) < 2:
raise TaskError(self, "First split outgroup error: common "
"ancestor calculation requires at least two OTU names")
else:
strict_common_ancestor = True
outs = set(mainout.split())
if outs - target_seqs:
raise TaskError(self, "Unknown seqs cannot be used to set first split rooting:%s" %(outs - target_seqs))
if len(outs) > 1:
anchor = list(set(target_seqs) - outs)[0]
ttree.set_outgroup(ttree & anchor)
common = ttree.get_common_ancestor(outs)
out_seqs = common.get_leaf_names()
if common is ttree:
msg = "First split outgroup could not be granted:%s" %out_seqs
#dump_tree_debug(msg, self.taskdir, mtree, ttree, target_seqs, outs)
raise TaskError(self, msg)
if strict_common_ancestor and set(out_seqs) ^ outs:
msg = "Monophyly of first split outgroup could not be granted:%s" %out_seqs
#dump_tree_debug(msg, self.taskdir, mtree, ttree, target_seqs, outs)
raise TaskError(self, msg)
log.log(26, "@@8:First split rooting to %d seqs@@1:: %s" %(len(out_seqs),out_seqs))
ttree.set_outgroup(common)
else:
single_out = outs.pop()
common = ttree.set_outgroup(single_out)
log.log(26, "@@8:First split rooting to 1 seq@@1:: %s" %(single_out))
self.main_tree = ttree
orig_target = ttree
tn = orig_target.copy()
self.pre_iter_task_tree = tn
self.rf = orig_target.robinson_foulds(ttree)
self.pre_iter_support = orig_target.support
# Reloads node2content of the rooted tree and generate cladeids
ttree_content = self.main_tree.get_cached_content()
for n, content in ttree_content.iteritems():
cid = generate_id([_n.name for _n in content])
n.add_feature("cladeid", cid)
#ttree.write(outfile=self.pruned_tree)
self.task_tree = ttree
def finish(self):
lks = []
if self.lk_mode == "phyml":
for job in [j for j in self.jobs if j.flag == "phyml"]:
tree_file = os.path.join(job.jobdir,
self.alg_basename+"_phyml_tree.txt")
stats_file = os.path.join(j.jobdir,
self.alg_basename+"_phyml_stats.txt")
tree = PhyloTree(tree_file)
m = re.search('Log-likelihood:\s+(-?\d+\.\d+)',
open(stats_file).read())
lk = float(m.groups()[0])
tree.add_feature("lk", lk)
tree.add_feature("model", job.args["--model"])
lks.append([float(tree.lk), tree.model, tree])
elif self.lk_mode == "raxml":
for job in [j for j in self.jobs if j.flag == "raxml"]:
lk = open(os.path.join(job.jobdir, "RAxML_log.%s"
%job.args["-n"])).readline().split()[1]
tree = PhyloTree(job.args["-t"])
tree.add_feature("lk", lk)
tree.add_feature("model", job.model)
lks.append([lk, tree.model, tree])
lks.sort()
lks.reverse()
# choose the model with higher likelihood
best_model = lks[-1][1]
best_tree = lks[-1][2]
open(self.best_model_file, "w").write(best_model)
if self.tree_file:
tree.write(self.tree_file)
ModelTesterTask.finish(self)