def __init__(self, nodeid, alg_phylip_file, constrain_id, model,
seqtype, conf, confname, parts_id=None):
GLOBALS["citator"].add(PHYML_CITE)
base_args = OrderedDict({
"--model": "",
"--no_memory_check": "",
"--quiet": "",
"--constraint_tree": ""})
self.confname = confname
self.conf = conf
self.constrain_tree = None
if constrain_id:
self.constrain_tree = db.get_dataid(constrain_id, DATATYPES.constrain_tree)
self.alg_phylip_file = alg_phylip_file
TreeTask.__init__(self, nodeid, "tree", "Phyml",
base_args, conf[confname])
if seqtype == "aa":
self.model = model or conf[confname]["_aa_model"]
elif seqtype == "nt":
self.model = model or conf[confname]["_nt_model"]
self.seqtype = seqtype
self.lk = None
self.init()
def __init__(self, nodeid, alg_file, constrain_id, model, seqtype, conf, confname, parts_id=None):
GLOBALS["citator"].add(FASTTREE_CITE)
self.confname = confname
self.conf = conf
self.alg_phylip_file = alg_file
self.constrain_tree = None
if constrain_id:
self.constrain_tree = db.get_dataid(constrain_id, DATATYPES.constrain_alg)
self.alg_basename = basename(self.alg_phylip_file)
self.seqtype = seqtype
self.tree_file = ""
if model:
log.warning("FastTree does not support model selection")
self.model = None
self.lk = None
base_args = OrderedDict()
base_args["-nopr"] = ""
if self.seqtype == "nt":
base_args["-gtr -nt"] = ""
elif self.seqtype == "aa":
pass
else:
raise ValueError("Unknown seqtype %s" % self.seqtype)
TreeTask.__init__(self, nodeid, "tree", "FastTree", base_args, self.conf[confname])
self.init()
def finish(self):
if self.conf[self.confname]["_alg_trimming"]:
# If trimming happened after mcoffee, let's save the
# resulting output
trim_job = self.jobs[-1]
alg = SeqGroup(pjoin(trim_job.jobdir, trim_job.alg_fasta_file))
fasta = alg.write(format="fasta")
phylip = alg.write(format="iphylip_relaxed")
AlgTask.store_data(self, fasta, phylip)
else:
# If no post trimming, output is just what Mcoffee
# produced, so we can recycle its data ids.
mc_task = self.jobs[-1]
fasta_id = db.get_dataid(mc_task.taskid, DATATYPES.alg_fasta)
phylip_id = db.get_dataid(mc_task.taskid, DATATYPES.alg_phylip)
db.register_task_data(self.taskid, DATATYPES.alg_fasta, fasta_id)
db.register_task_data(self.taskid, DATATYPES.alg_phylip, phylip_id)
def get_stored_data(fileid):
try:
_tid, _did = fileid.split(".")
_did = int(_did)
except (IndexError, ValueError):
dataid = fileid
else:
dataid = db.get_dataid(_tid, _did)
return db.get_data(dataid)
def __init__(self, nodeid, alg_file, constrain_id, model,
seqtype, conf, confname, parts_id=None):
GLOBALS["citator"].add(RAXML_CITE)
base_args = OrderedDict()
self.bootstrap = conf[confname].get("_bootstrap", None)
model = model or conf[confname]["_aa_model"]
self.confname = confname
self.conf = conf
self.alg_phylip_file = alg_file
try:
self.constrain_tree = db.get_dataid(constrain_id, DATATYPES.constrain_tree)
except ValueError:
self.constrain_tree = None
self.partitions_file = parts_id
TreeTask.__init__(self, nodeid, "tree", "RaxML",
base_args, conf[confname])
max_cores = GLOBALS["_max_cores"]
appname = conf[confname]["_app"]
if max_cores > 1:
threads = conf["threading"].get("raxml-pthreads")
if threads > 1:
appname = appname.replace("raxml", "raxml-pthreads")
raxml_bin = conf["app"][appname]
else:
appname = appname.replace("raxml-pthreads", "raxml")
threads = 1
raxml_bin = conf["app"][appname]
self.raxml_bin = raxml_bin
self.threads = threads
self.seqtype = seqtype
# Process raxml options
method = conf[confname].get("_method", "GAMMA").upper()
if seqtype.lower() == "aa":
self.model_string = 'PROT%s%s' %(method, model.upper())
self.model = model
elif seqtype.lower() == "nt":
self.model_string = 'GTR%s' %method
self.model = "GTR"
else:
raise ValueError("Unknown seqtype %s", seqtype)
#inv = conf[confname].get("pinv", "").upper()
#freq = conf[confname].get("ebf", "").upper()
self.init()
def __init__(self, nodeid, alg_file, constrain_id, model, seqtype,
conf, confname, parts_id=None):
self.confname = confname
self.conf = conf
self.alg_phylip_file = alg_file
self.constrain_tree = None
if constrain_id:
self.constrain_tree = db.get_dataid(constrain_id, DATATYPES.constrain_alg)
self.alg_basename = basename(self.alg_phylip_file)
self.seqtype = seqtype
self.tree_file = ""
self.model = None
self.lk = None
TreeTask.__init__(self, nodeid, "tree", "DummyTree", {}, {})
self.init()
def process_task(task, wkname, npr_conf, nodeid2info):
cogconf, cogclass = npr_conf.cog_selector
concatconf, concatclass = npr_conf.alg_concatenator
treebuilderconf, treebuilderclass = npr_conf.tree_builder
splitterconf, splitterclass = npr_conf.tree_splitter
threadid, nodeid, seqtype, ttype = (task.threadid, task.nodeid,
task.seqtype, task.ttype)
cladeid, targets, outgroups = db.get_node_info(threadid, nodeid)
if not treebuilderclass or task.size < 4:
# Allows to dump algs in workflows with no tree tasks or if tree
# inference does not make sense given the number of sequences. DummyTree
# will produce a fake fully collapsed newick tree.
treebuilderclass = DummyTree
if outgroups and len(outgroups) > 1:
constrain_id = nodeid
else:
constrain_id = None
node_info = nodeid2info[nodeid]
conf = GLOBALS[task.configid]
new_tasks = []
if ttype == "cog_selector":
# Generates a md5 id based on the genetree configuration workflow used
# for the concat alg task. If something changes, concat alg will change
# and the associated tree will be rebuilt
config_blocks = set([wkname])
for key, value in conf[wkname].iteritems():
if isinstance(value, list) or isinstance(value, tuple) \
or isinstance(value, set):
for elem in value:
config_blocks.add(elem[1:]) if isinstance(elem, str) and elem.startswith("@") else None
elif isinstance(value, str):
config_blocks.add(value[1:]) if value.startswith("@") else None
config_checksum = md5(''.join(["[%s]\n%s" %(x, dict_string(conf[x]))
for x in sorted(config_blocks)]))
# THIS PART HAS BEEN MOVED TO COG_SELECTOR TASK
# Check that current selection of cogs will cover all target and
# outgroup species
#cog_hard_limit = int(conf[concatconf]["_max_cogs"])
#sp_repr = defaultdict(int)
#for co in task.raw_cogs[:cog_hard_limit]:
# for sp, seq in co:
# sp_repr[sp] += 1
#missing_sp = (targets | outgroups) - set(sp_repr.keys())
#if missing_sp:
# raise TaskError("missing species under current cog selection: %s" %missing_sp)
#else:
# log.log(28, "Analysis of current COG selection:")
# for sp, ncogs in sorted(sp_repr.items(), key=lambda x:x[1]):
# log.log(28, " % 30s species present in % 6d COGs" %(sp, ncogs))
# register concat alignment task. NodeId associated to concat_alg tasks
# and all its children jobs should take into account cog information and
# not only species and outgroups included.
concat_job = concatclass(task.cogs, seqtype, conf, concatconf,
config_checksum)
db.add_node(threadid,
concat_job.nodeid, cladeid,
targets, outgroups)
# Register Tree constrains
constrain_tree = "(%s, (%s));" %(','.join(sorted(outgroups)),
','.join(sorted(targets)))
_outs = "\n".join(map(lambda name: ">%s\n0" %name, sorted(outgroups)))
_tars = "\n".join(map(lambda name: ">%s\n1" %name, sorted(targets)))
constrain_alg = '\n'.join([_outs, _tars])
db.add_task_data(concat_job.nodeid, DATATYPES.constrain_tree, constrain_tree)
db.add_task_data(concat_job.nodeid, DATATYPES.constrain_alg, constrain_alg)
db.dataconn.commit() # since the creation of some Task objects
# may require this info, I need to commit
# right now.
concat_job.size = task.size
new_tasks.append(concat_job)
elif ttype == "concat_alg":
# register tree for concat alignment, using constraint tree if
# necessary
alg_id = db.get_dataid(task.taskid, DATATYPES.concat_alg_phylip)
try:
parts_id = db.get_dataid(task.taskid, DATATYPES.model_partitions)
except ValueError:
parts_id = None
nodeid2info[nodeid]["size"] = task.size
nodeid2info[nodeid]["target_seqs"] = targets
nodeid2info[nodeid]["out_seqs"] = outgroups
tree_task = treebuilderclass(nodeid, alg_id,
constrain_id, None,
task.seqtype, conf, treebuilderconf,
parts_id=parts_id)
tree_task.size = task.size
new_tasks.append(tree_task)
elif ttype == "tree":
merger_task = splitterclass(nodeid, seqtype, task.tree_file, conf, splitterconf)
merger_task.size = task.size
new_tasks.append(merger_task)
elif ttype == "treemerger":
# Lets merge with main tree
if not task.task_tree:
task.finish()
log.log(24, "Saving task tree...")
annotate_node(task.task_tree, task)
db.update_node(nid=task.nodeid, runid=task.threadid,
newick=db.encode(task.task_tree))
db.commit()
if not isinstance(treebuilderclass, DummyTree) and npr_conf.max_iters > 1:
current_iter = get_iternumber(threadid)
if npr_conf.max_iters and current_iter >= npr_conf.max_iters:
log.warning("Maximum number of iterations reached!")
else:
# Add new nodes
source_seqtype = "aa" if "aa" in GLOBALS["seqtypes"] else "nt"
ttree, mtree = task.task_tree, task.main_tree
log.log(26, "Processing tree: %s seqs, %s outgroups",
len(targets), len(outgroups))
target_cladeids = None
if tobool(conf[splitterconf].get("_find_ncbi_targets", False)):
tcopy = mtree.copy()
ncbi.connect_database()
tax2name, tax2track = ncbi.annotate_tree_with_taxa(tcopy, None)
#tax2name, tax2track = ncbi.annotate_tree_with_taxa(tcopy, "fake") # for testing sptree example
n2content = tcopy.get_cached_content()
broken_branches, broken_clades, broken_clade_sizes, tax2name = ncbi.get_broken_branches(tcopy, n2content)
log.log(28, 'restricting NPR to broken clades: '+
colorify(', '.join(map(lambda x: "%s"%tax2name[x], broken_clades)), "wr"))
target_cladeids = set()
for branch in broken_branches:
print branch.get_ascii(attributes=['spname', 'taxid'], compact=True)
print map(lambda x: "%s"%tax2name[x], broken_branches[branch])
target_cladeids.add(branch.cladeid)
for node, seqs, outs, wkname in get_next_npr_node(task.configid, ttree,
task.out_seqs, mtree, None,
npr_conf, target_cladeids): # None is to avoid alg checks
log.log(24, "Adding new node: %s seqs, %s outgroups",
len(seqs), len(outs))
new_task_node = cogclass(seqs, outs,
source_seqtype, conf, cogconf)
new_task_node.target_wkname = wkname
new_tasks.append(new_task_node)
db.add_node(threadid,
new_task_node.nodeid, new_task_node.cladeid,
new_task_node.targets,
new_task_node.outgroups)
return new_tasks
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):
# Assumes tasks resulting from genetree workflow, in which
# only Alg and Acleaner tasks could contain the results
log.log(26, "Collecting supermatrix data")
# jobtypes = set()
# job2alg, job2acleaner = {}, {}
# for job in self.jobs:
# jobtypes.add(job.ttype)
# if job.ttype == "alg" and job.nodeid not in self.job2alg:
# dataid = db.get_dataid(*job.alg_fasta_file.split("."))
# job2alg[job.nodeid] = db.get_data(dataid)
# elif job.ttype == "acleaner":
# a, b = job.clean_alg_fasta_file.split(".")
# dataid = db.get_dataid(*job.clean_alg_fasta_file.split("."))
# job2acleaner[job.nodeid] = db.get_data(dataid)
# elif job.ttype == "mchooser":
# self.job2model[job.nodeid] = job.best_model
# Let's extract alignments from the tree job in the genetree workflow,
# so I can make sure I am using the correct version, either raw, trimmed version, or even the
# switched nt version
observed_seqtypes = set()
self.job2alg = {}
for job in self.jobs:
if job.ttype == "tree":
observed_seqtypes.add(job.seqtype)
taskid, datatype = job.alg_phylip_file.split(".")
dataid = db.get_dataid(taskid, datatype)
self.job2alg[job.nodeid] = db.get_data(dataid)
elif job.ttype == "mchooser":
self.job2model[job.nodeid] = job.best_model
# if all alignments are nt, then set it as seqtype for concat alg
if len(observed_seqtypes) > 1:
raise TaskError('Mixed data types not supported in super-matrix workflow')
elif "nt" in observed_seqtypes:
self.seqtype = "nt"
self.default_model = self.conf[self.confname]["_default_nt_model"]
else:
self.seqtype = "aa"
self.default_model = self.conf[self.confname]["_default_aa_model"]
if self.cog_ids - set(self.job2alg):
log.error("Missing %s algs", len(self.cog_ids -
set(self.job2alg)))
missing = self.cog_ids - set(self.job2alg)
raise TaskError(self, "Missing algs (%d): i.e. %s" %(len(missing),missing[:10]))
alg_data = [(self.job2alg[nid],
self.job2model.get(nid, self.default_model))
for nid in self.job2alg]
filenames, models = zip(*alg_data)
mainalg, partitions, sp2alg, species, alg_lenghts = get_concatenated_alg(
filenames,
models, sp_field=0,
sp_delimiter=GLOBALS["spname_delimiter"])
log.log(20, "Done concat alg, now writting fasta format")
fasta = mainalg.write(format="fasta")
log.log(20, "Done concat alg, now writting phylip format")
phylip = mainalg.write(format="iphylip_relaxed")
txt_partitions = '\n'.join(partitions)
log.log(26, "Modeled regions: \n"+'\n'.join(partitions))
ConcatAlg.store_data(self, fasta, phylip, txt_partitions)
def process_task(task, wkname, npr_conf, nodeid2info):
alignerconf, alignerclass = npr_conf.aligner
cleanerconf, cleanerclass = npr_conf.alg_cleaner
mtesterconf, mtesterclass = npr_conf.model_tester
treebuilderconf, treebuilderclass = npr_conf.tree_builder
if not treebuilderclass:
# Allows to dump algs in workflows with no tree tasks
treebuilderclass = DummyTree
splitterconf, splitterclass = npr_conf.tree_splitter
conf = GLOBALS[task.configid]
seqtype = task.seqtype
nodeid = task.nodeid
ttype = task.ttype
taskid = task.taskid
threadid = task.threadid
node_info = nodeid2info[nodeid]
size = task.size#node_info.get("size", 0)
target_seqs = node_info.get("target_seqs", [])
out_seqs = node_info.get("out_seqs", [])
if not treebuilderclass or size < 4:
# Allows to dump algs in workflows with no tree tasks or if tree
# inference does not make sense given the number of sequences. DummyTree
# will produce a fake fully collapsed newick tree.
treebuilderclass = DummyTree
mtesterclass = None
# If more than one outgroup are used, enable the use of constrain
if out_seqs and len(out_seqs) > 1:
constrain_id = nodeid
else:
constrain_id = None
new_tasks = []
if ttype == "msf":
# Register Tree constrains
constrain_tree = "(%s, (%s));" %(','.join(sorted(task.out_seqs)),
','.join(sorted(task.target_seqs)))
_outs = "\n".join(map(lambda name: ">%s\n0" %name, sorted(task.out_seqs)))
_tars = "\n".join(map(lambda name: ">%s\n1" %name, sorted(task.target_seqs)))
constrain_alg = '\n'.join([_outs, _tars])
db.add_task_data(nodeid, DATATYPES.constrain_tree, constrain_tree)
db.add_task_data(nodeid, DATATYPES.constrain_alg, constrain_alg)
db.dataconn.commit() # since the creation of some Task
# objects may require this info, I need
# to commit right now.
# Register node
db.add_node(task.threadid,
task.nodeid, task.cladeid,
task.target_seqs,
task.out_seqs)
nodeid2info[nodeid]["size"] = task.size
nodeid2info[nodeid]["target_seqs"] = task.target_seqs
nodeid2info[nodeid]["out_seqs"] = task.out_seqs
alg_task = alignerclass(nodeid, task.multiseq_file,
seqtype, conf, alignerconf)
alg_task.size = task.size
new_tasks.append(alg_task)
elif ttype == "alg" or ttype == "acleaner":
if ttype == "alg":
nodeid2info[nodeid]["alg_path"] = task.alg_fasta_file
elif ttype == "acleaner":
nodeid2info[nodeid]["alg_clean_path"] = task.clean_alg_fasta_file
alg_fasta_file = getattr(task, "clean_alg_fasta_file",
task.alg_fasta_file)
alg_phylip_file = getattr(task, "clean_alg_phylip_file",
task.alg_phylip_file)
# Calculate alignment stats
# cons_mean, cons_std = get_trimal_conservation(task.alg_fasta_file,
# conf["app"]["trimal"])
#
# max_identity = get_trimal_identity(task.alg_fasta_file,
# conf["app"]["trimal"])
# log.info("Conservation: %0.2f +-%0.2f", cons_mean, cons_std)
# log.info("Max. Identity: %0.2f", max_identity)
#import time
#t1 = time.time()
#mx, mn, mean, std = get_identity(task.alg_fasta_file)
#print time.time()-t1
#log.log(26, "Identity: max=%0.2f min=%0.2f mean=%0.2f +- %0.2f",
# mx, mn, mean, std)
#t1 = time.time()
if seqtype == "aa" and npr_conf.switch_aa_similarity < 1:
try:
alg_stats = db.get_task_data(taskid, DATATYPES.alg_stats)
except Exception, e:
alg_stats = {}
if ttype == "alg":
algfile = pjoin(GLOBALS["input_dir"], task.alg_phylip_file)
dataid = DATATYPES.alg_phylip
elif ttype == "acleaner":
algfile = pjoin(GLOBALS["input_dir"], task.clean_alg_phylip_file)
dataid = DATATYPES.clean_alg_phylip
if "i_mean" not in alg_stats:
log.log(24, "Calculating alignment stats...")
# dump data if necesary
algfile = pjoin(GLOBALS["input_dir"], task.alg_phylip_file)
if not pexist(algfile):
# dump phylip alg
open(algfile, "w").write(db.get_data(db.get_dataid(taskid, dataid)))
mx, mn, mean, std = get_statal_identity(algfile,
conf["app"]["statal"])
alg_stats = {"i_max":mx, "i_mean":mean, "i_min":mn, "i_std":std}
db.add_task_data(taskid, DATATYPES.alg_stats, alg_stats)
log.log(22, "Alignment stats (sequence similarity):")
log.log(22, " max: %(i_max)0.2f, min:%(i_min)0.2f, avg:%(i_mean)0.2f+-%(i_std)0.2f" %
(alg_stats))
else:
alg_stats = {"i_max":-1, "i_mean":-1, "i_min":-1, "i_std":-1}
#print time.time()-t1
#log.log(24, "Identity: max=%0.2f min=%0.2f mean=%0.2f +- %0.2f",
# mx, mn, mean, std)
task.max_ident = alg_stats["i_max"]
task.min_ident = alg_stats["i_min"]
task.mean_ident = alg_stats["i_mean"]
task.std_ident = alg_stats["i_std"]
next_task = None
if ttype == "alg" and cleanerclass:
next_task = cleanerclass(nodeid, seqtype, alg_fasta_file,
alg_phylip_file,
conf, cleanerconf)
else:
# Converts aa alignment into nt if necessary
if seqtype == "aa" and \
"nt" in GLOBALS["seqtypes"] and \
task.mean_ident >= npr_conf.switch_aa_similarity:
log.log(28, "@@2:Switching to codon alignment!@@1: amino-acid sequence similarity: %0.2f >= %0.2f" %\
(task.mean_ident, npr_conf.switch_aa_similarity))
alg_fasta_file = "%s.%s" %(taskid, DATATYPES.alg_nt_fasta)
alg_phylip_file = "%s.%s" %(taskid, DATATYPES.alg_nt_phylip)
try:
alg_fasta_file = db.get_dataid(taskid, DATATYPES.alg_nt_fasta)
alg_fasta_file = db.get_dataid(taskid, DATATYPES.alg_nt_phylip)
except ValueError:
log.log(22, "Calculating codon alignment...")
source_alg = pjoin(GLOBALS["input_dir"], task.alg_fasta_file)
if ttype == "alg":
kept_columns = []
elif ttype == "acleaner":
# if original alignment was trimmed, use it as reference
# but make the nt alignment only on the kept columns
kept_columns = db.get_task_data(taskid, DATATYPES.kept_alg_columns)
if not pexist(source_alg):
open(source_alg, "w").write(db.get_task_data(taskid, DATATYPES.alg_fasta))
nt_alg = switch_to_codon(source_alg, kept_columns=kept_columns)
db.add_task_data(taskid, DATATYPES.alg_nt_fasta, nt_alg.write())
db.add_task_data(taskid, DATATYPES.alg_nt_phylip, nt_alg.write(format='iphylip_relaxed'))
npr_conf = IterConfig(conf, wkname, task.size, "nt")
seqtype = "nt"
if mtesterclass:
next_task = mtesterclass(nodeid, alg_fasta_file,
alg_phylip_file,
constrain_id,
conf, mtesterconf)
elif treebuilderclass:
next_task = treebuilderclass(nodeid, alg_phylip_file,
constrain_id,
None, seqtype,
conf, treebuilderconf)
if next_task:
next_task.size = task.size
new_tasks.append(next_task)
def schedule(workflow_task_processor, pending_tasks, schedule_time, execution, debug, norender):
# Adjust debug mode
if debug == "all":
log.setLevel(10)
pending_tasks = set(pending_tasks)
## ===================================
## INITIALIZE BASIC VARS
execution, run_detached = execution
thread2tasks = defaultdict(list)
for task in pending_tasks:
thread2tasks[task.configid].append(task)
expected_threads = set(thread2tasks.keys())
past_threads = {}
thread_errors = defaultdict(list)
## END OF VARS AND SHORTCUTS
## ===================================
cores_total = GLOBALS["_max_cores"]
if cores_total > 0:
job_queue = Queue()
back_launcher = Process(target=background_job_launcher,
args=(job_queue, run_detached,
GLOBALS["launch_time"], cores_total))
back_launcher.start()
else:
job_queue = None
back_launcher = None
GLOBALS["_background_scheduler"] = back_launcher
GLOBALS["_job_queue"] = job_queue
# Captures Ctrl-C for debuging DEBUG
#signal.signal(signal.SIGINT, control_c)
last_report_time = None
BUG = set()
try:
# Enters into task scheduling
while pending_tasks:
wtime = schedule_time
# ask SGE for running jobs
if execution == "sge":
sgeid2jobs = db.get_sge_tasks()
qstat_jobs = sge.qstat()
else:
qstat_jobs = None
# Show summary of pending tasks per thread
thread2tasks = defaultdict(list)
for task in pending_tasks:
thread2tasks[task.configid].append(task)
set_logindent(0)
log.log(28, "@@13: Updating tasks status:@@1: (%s)" % (ctime()))
info_lines = []
for tid, tlist in thread2tasks.iteritems():
threadname = GLOBALS[tid]["_name"]
sizelist = ["%s" %getattr(_ts, "size", "?") for _ts in tlist]
info = "Thread @@13:%s@@1:: pending tasks: @@8:%s@@1: of sizes: %s" %(
threadname, len(tlist), ', '.join(sizelist))
info_lines.append(info)
for line in info_lines:
log.log(28, line)
if GLOBALS["email"] and last_report_time is None:
last_report_time = time()
send_mail(GLOBALS["email"], "Your NPR process has started", '\n'.join(info_lines))
## ================================
## CHECK AND UPDATE CURRENT TASKS
checked_tasks = set()
check_start_time = time()
to_add_tasks = set()
GLOBALS["cached_status"] = {}
for task in sorted(pending_tasks, sort_tasks):
# Avoids endless periods without new job submissions
elapsed_time = time() - check_start_time
#if not back_launcher and pending_tasks and \
# elapsed_time > schedule_time * 2:
# log.log(26, "@@8:Interrupting task checks to schedule new jobs@@1:")
# db.commit()
# wtime = launch_jobs(sorted(pending_tasks, sort_tasks),
# execution, run_detached)
# check_start_time = time()
# Enter debuging mode if necessary
if debug and log.level > 10 and task.taskid.startswith(debug):
log.setLevel(10)
log.debug("ENTERING IN DEBUGGING MODE")
thread2tasks[task.configid].append(task)
# Update tasks and job statuses
if task.taskid not in checked_tasks:
try:
show_task_info(task)
task.status = task.get_status(qstat_jobs)
db.dataconn.commit()
if back_launcher and task.status not in set("DE"):
for j, cmd in task.iter_waiting_jobs():
j.status = "Q"
GLOBALS["cached_status"][j.jobid] = "Q"
if j.jobid not in BUG:
if not os.path.exists(j.jobdir):
os.makedirs(j.jobdir)
for ifile, outpath in j.input_files.iteritems():
try:
_tid, _did = ifile.split(".")
_did = int(_did)
except (IndexError, ValueError):
dataid = ifile
else:
dataid = db.get_dataid(_tid, _did)
if not outpath:
outfile = pjoin(GLOBALS["input_dir"], ifile)
else:
outfile = pjoin(outpath, ifile)
if not os.path.exists(outfile):
open(outfile, "w").write(db.get_data(dataid))
log.log(24, " @@8:Queueing @@1: %s from %s" %(j, task))
job_queue.put([j.jobid, j.cores, cmd, j.status_file])
BUG.add(j.jobid)
update_task_states_recursively(task)
db.commit()
checked_tasks.add(task.taskid)
except TaskError, e:
log.error("Errors found in %s" %task)
import traceback
traceback.print_exc()
if GLOBALS["email"]:
threadname = GLOBALS[task.configid]["_name"]
send_mail(GLOBALS["email"], "Errors found in %s!" %threadname,
'\n'.join(map(str, [task, e.value, e.msg])))
pending_tasks.discard(task)
thread_errors[task.configid].append([task, e.value, e.msg])
continue
else:
# Set temporary Queued state to avoids launching
# jobs from clones
task.status = "Q"
if log.level < 24:
show_task_info(task)
if task.status == "D":
#db.commit()
show_task_info(task)
logindent(3)
# Log commands of every task
if 'cmd_log_file' not in GLOBALS[task.configid]:
GLOBALS[task.configid]['cmd_log_file'] = pjoin(GLOBALS[task.configid]["_outpath"], "cmd.log")
O = open(GLOBALS[task.configid]['cmd_log_file'], "w")
O.close()
cmd_lines = get_cmd_log(task)
CMD_LOG = open(GLOBALS[task.configid]['cmd_log_file'], "a")
print >>CMD_LOG, task
for c in cmd_lines:
print >>CMD_LOG, ' '+'\t'.join(map(str, c))
CMD_LOG.close()
#
try:
#wkname = GLOBALS[task.configid]['_name']
create_tasks = workflow_task_processor(task, task.target_wkname)
except TaskError, e:
log.error("Errors found in %s" %task)
pending_tasks.discard(task)
thread_errors[task.configid].append([task, e.value, e.msg])
continue
else:
logindent(-3)
to_add_tasks.update(create_tasks)
pending_tasks.discard(task)
elif task.status == "E":
log.error("task contains errors: %s " %task)
log.error("Errors found in %s")
pending_tasks.discard(task)
thread_errors[task.configid].append([task, None, "Found (E) task status"])
