コード例 #1
0
ファイル: master_task.py プロジェクト: jhcepas/npr
 def store_data(self, fasta, phylip):
     # self.alg_fasta_file = db.add_task_data(self.taskid, DATATYPES.alg_fasta,
     #                                        fasta)
     # self.alg_phylip_file = db.add_task_data(self.taskid,
     #                                         DATATYPES.alg_phylip, phylip)
     db.add_task_data(self.taskid, DATATYPES.alg_fasta, fasta)
     db.add_task_data(self.taskid, DATATYPES.alg_phylip, phylip)
コード例 #2
0
ファイル: meta_aligner.py プロジェクト: jhcepas/npr
    def finish(self):
        # Once executed, alignment is converted into relaxed
        # interleaved phylip format.
        alg = SeqGroup(os.path.join(self.jobs[0].jobdir, "mcoffee.fasta"))
        fasta = alg.write(format="fasta")
        phylip = alg.write(format="iphylip_relaxed")

        alg_list_string = '\n'.join([pjoin(GLOBALS["input_dir"],
                                           aname) for aname in self.all_alg_files])
        db.add_task_data(self.taskid, DATATYPES.alg_list, alg_list_string)
        
        AlgTask.store_data(self, fasta, phylip)
コード例 #3
0
ファイル: supermatrix.py プロジェクト: jhcepas/npr
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
コード例 #4
0
ファイル: master_task.py プロジェクト: jhcepas/npr
 def store_data(self, cogs, cog_analysis):
     db.add_task_data(self.taskid, DATATYPES.cogs, cogs)
     db.add_task_data(self.taskid, DATATYPES.cog_analysis, cog_analysis)
     self.cogs = cogs
     self.cog_analysis = cog_analysis
コード例 #5
0
ファイル: master_task.py プロジェクト: jhcepas/npr
 def store_data(self, fasta, phylip, partitions):
     db.add_task_data(self.taskid, DATATYPES.model_partitions, partitions)
     db.add_task_data(self.taskid, DATATYPES.concat_alg_fasta, fasta)
     db.add_task_data(self.taskid, DATATYPES.concat_alg_phylip, phylip)
コード例 #6
0
ファイル: master_task.py プロジェクト: jhcepas/npr
 def store_data(self, newick, stats):
     db.add_task_data(self.taskid, DATATYPES.tree, newick)
     db.add_task_data(self.taskid, DATATYPES.tree_stats, stats)
     self.stats = stats
コード例 #7
0
ファイル: master_task.py プロジェクト: jhcepas/npr
 def store_data(self, best_model, ranking):
     db.add_task_data(self.taskid, DATATYPES.best_model, best_model)
     db.add_task_data(self.taskid, DATATYPES.model_ranking, ranking)
     self.best_model = best_model
     self.model_ranking[:] = []
     self.model_ranking.extend(ranking)
コード例 #8
0
ファイル: master_task.py プロジェクト: jhcepas/npr
 def store_data(self, fasta, phylip, kept_columns):
     db.add_task_data(self.taskid, DATATYPES.clean_alg_fasta, fasta)
     db.add_task_data(self.taskid, DATATYPES.clean_alg_phylip, phylip)
     db.add_task_data(self.taskid, DATATYPES.kept_alg_columns, kept_columns)
     self.kept_columns[:] = []  # security clear
     self.kept_columns.extend(kept_columns)
コード例 #9
0
ファイル: master_task.py プロジェクト: jhcepas/npr
 def store_data(self, msf):
     db.add_task_data(self.taskid, DATATYPES.msf, msf)
コード例 #10
0
ファイル: genetree.py プロジェクト: jhcepas/npr
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)