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 = []
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)
