Python py2neo.rel函数代码示例

 def create(cls, name, *emails): 
     person_node, _ = graph_db.create(node(name=name), 
         rel(cls._root, "PERSON", 0)) 
     for email in emails: 
         graph_db.create(node(email=email), rel(cls._root, "EMAIL", 0),
             rel(person_node, "EMAIL", 0)) 
     return Person(person_node) 

    def create_operons(self):
        f = open(self.directory + 'Operons.txt', 'r')
        data = f.readlines()
        f.close()
        i = 0
        for line in data:
            if line[0] == '#':
                continue
            chunks = line.split('\t')

            ### testing
            if chunks[0] == '' or chunks[1] == '' or chunks[2] == 0:
                continue
            if chunks[3] == '':
                chunks[3] = 'unknown'

            operon, term, term_rel, org_rel = self.connection.\
                create(node({'name': chunks[0], 'start': int(chunks[1]),
                             'end': int(chunks[2]), 'strand': chunks[3],
                             'evidence': chunks[6], 'source': 'RegulonDB'}),
                       node({'text': chunks[0]}),
                       rel(0, 'HAS_NAME', 1),
                       rel(0, 'PART_OF', self.ecoli_node))
            operon.add_labels('Operon', 'BioEntity', 'DNA')
            i += 1
        logging.info('%d operons were created!' % i)

    def whipUp(self):
        objects = json.load(urlopen(self.api_url))["objects"]

        for object in objects:
            args = {}
            args["ID"]=object["id"]

            args["N"]=object["Name"]
            args["DR"]=object["DateReg"]
            #print args
            db = self.graph_db.cypher.begin()
            db.append(self.statement, args)
            db.commit()

            f = getFilia(args["ID"])
            adr = getAddress(id = int(object["Address"]["id"]), Street = object["Address"]["Street"])
            #per = getPerson(id = int(object["Person"]["id"]), Name = (object["Person"]["Surname"] + " " + object["Person"]["Name"]))
            per = getPerson(Name = (object["Person"]["Surname"] + " " + object["Person"]["Name"]))

            soc = getSocialFormation(id = int(object["SocialFormation"]["id"]))

            if adr != None:
                self.graph_db.create(rel(f.one, "HAVE_ADDRESS", adr.one))
            if per != None:
                self.graph_db.create(rel(f.one, "FILIA_HAVE_PERSON", per.one))
            if soc != None:
                self.graph_db.create(rel(f.one, "HAVE_SocialFormation", soc.one))

def create_project_graph():
    """Creates a project Graph and stashes it in Neo4j.

    Returns a tuple of (users, projects, relationships), where each item is
    a list of the created data.

    """
    # Create some Users
    user_nodes = [node(name=t[0], username=t[1]) for t in random_users()]
    users = db.create(*user_nodes)
    for u in users:
        # ...and label them as such.
        u.add_labels("user")

    # Create some Projects.
    project_nodes = [node(name=s) for s in random_projects()]
    projects = db.create(*project_nodes)

    rels = []
    for p in projects:
        # ...and label them as such.
        p.add_labels("project")

        # Set up some relationships.
        # 1. Give the project a single Owner
        rels.append(rel((p, "OWNED_BY", random.choice(users))))

        # 2. Give the project a random number of contributors.
        for u in random.sample(users, random.randrange(3, 50)):
            rels.append(rel((u, "CONTRIBUTES_TO", p)))

    # Save the relationships
    rels = db.create(*rels)
    return (users, projects, rels)

def processPlayerTeam(playerteam_dict,graph_db,root):
	teams = set({})
	# Add teams to database, connected to root node
	# (Currently, each team requires a separate HTTP request)
	team_node_dict = {}
	for pair in playerteam_dict.values():
		team = pair[0]
		teams.add(team)
	for team in teams:
		team_node,relation = graph_db.create({"name":team},rel(root,"TEAM",0))
		team_node.add_labels("team")
		team_node_dict[team] = team_node

	#Add players to database as a batch
	batch = neo4j.WriteBatch(graph_db)
	for player_id, player_tuple in playerteam_dict.iteritems():
		team = player_tuple[0]
		full_name = player_tuple[1]
		player_node=batch.create({"name":full_name,"player_id":int(player_id)})
		batch.create(rel(root,"PLAYER",player_node))
		batch.add_labels(player_node,"player")
		team_node = team_node_dict[team]
		batch.create(rel(player_node,"PLAYS FOR",team_node))

	results=batch.submit()

    def whipUp(self):
        objects = json.load(urlopen(self.api_url))["objects"]

        for object in objects:
            args = {}
            args["ID"]=object["id"]

            args["NS"]=object["NStatement"]
            args["DS"]=object["DateStatement"]
            args["D"]=object["Date"]
            args["AI"]=object["AddedInfo"]

            db = self.graph_db.cypher.begin()
            db.append(self.statement, args)
            db.commit()

            enc = getEncumbrance(id=int(args["ID"]))
            obj = getObject(id=int(object["Obj"]["id"]))
            docBase = getDocumentBase(id=int(object["DocBase"]["id"]))

            if obj != None:
                self.graph_db.create(rel(enc.one, "HAVE_OBJECT", obj.one))
            if docBase != None:
                self.graph_db.create(rel(enc.one, "HAVE_DOCUMENT", docBase.one))

            for sp in object["SPerson"]:
                s_p = getPerson(id=int(sp["id"]))
                self.graph_db.create(rel(enc.one, "HAVE_DEPTOR", s_p.one))

            for wp in object["WPerson"]:
                w_p = getPerson(id=int(wp["id"]))
                self.graph_db.create(rel(enc.one, "HAVE_WEIGHT", w_p.one))

    def create_update_promoters(self):
        f = open(self.directory + 'All Promoters.txt', 'r')
        data = f.readlines()
        f.close()
        created, updated = [0]*2

        for line in data:
            if line[0] == '#':
                continue
            regid, name, strand, tss, sigma, seq, evidence = line.split('\t')
            tss = int(tss)

            # skipping incomplete data
            if '' in [regid, name, strand, tss]:
                continue

            query = 'MATCH (ch:Chromosome {name: "%s"})<-[:PART_OF]-' \
                    '(p:Promoter {tss: %d})-[:PART_OF]->' \
                    '(o:Organism {name: "%s"}) ' \
                    'RETURN p' % (self.chro_name, tss,  self.ecoli_name)
            res = neo4j.CypherQuery(self.connection, query)
            res_nodes = res.execute()

            # creating promoter
            if not res_nodes:
                promoter, term, rel_org, rel_chr, rel_term = self.connection.create(
                    node({'name': name, 'start': tss,
                          'end': tss, 'strand': strand,
                          'tss': tss, 'seq': seq,
                          'evidence': evidence, 'Reg_id': regid,
                          'source': 'RegulonDB'}),
                    node({'text': name}),
                    rel(0, 'PART_OF', self.ecoli_node),
                    rel(0, 'PART_OF', self.chro_node),
                    rel(0, 'HAS_NAME', 1))
                promoter.add_labels('Promoter', 'Feature', 'BioEntity', 'DNA')
                term.add_labels('Term')
                created += 1
            else:
                # one promoter with the tss
                for record in res_nodes.data:
                    promoter = record.values[0]
                    promoter.update_properties({'seq': seq,
                                                'evidence': evidence,
                                                'Reg_id': regid})
                    update_source_property(promoter)
                    self.check_create_terms(promoter, name)
                    updated += 1

                # duplicates!
                if len(res_nodes.data) > 1:
                    logging.warning("There are %d nodes for a promoter with "
                                     "tss in the %d position! It was skipped!"
                                     % (len(res_nodes.data), tss))

        logging.info("%d promoters were updated!" % updated)
        logging.info("%d promoters were created!" % created)

def updateOrCreateTagged(memeDict):
    """ Description: Checks to see if the img, tag and relationship are in the db
                     if not, it adds them at each level and increments the 
                     weighted property to indicate a stronger correlation
        Params: memeDict, dictionary, keys are urls and values are list of tags
        Returns: None """

    # check to see if the image exists
    urls = memeDict.keys()
    for url in urls:
        pImg = graph_db.get_indexed_node("Img", "imgSrc", url)
        print "This should be an indexed img node: ", pImg
        # this is working right now
        tags = memeDict[url]
        if str(type(pImg)) != "<type 'NoneType'>":
            # if so check to see if the tag exists

            print "This should be a list", tags
            for tag in memeDict[url]:
                tagName = tagsExist([tag])

                if len(tagName) > 0:
                    pTagNode = graph_db.get_indexed_node("Tags", "tagName", tagName[0])
                    print "This should be an existing tag: ", pTagNode
                    # if so check to see if there's a relationship
                    pRel = graph_db.match_one(start_node=pImg, rel_type="TAGGED", end_node=pTagNode)
                    print "This should find the relationship or return none: ", pRel
                    # if so increment rel aweight
                    if str(type(pRel)) != "<type 'NoneType'>":
                        getOldWeight = pRel.get_properties()
                        oldWeight = getOldWeight.get("aWeight")
                        newWeight = oldWeight + 1
                        relProp = pRel.update_properties({"aWeight": newWeight})
                        print "This should be the old weight: ", oldWeight
                    # else
                    else:
                        # create a relationship
                        newRel = graph_db.create(rel(pImg, ("TAGGED", {"aWeight": 1}), pTagNode))
                        print "Hopefully this is a relationship: ", newRel
                        # and add the default weight
                # else
                else:
                    # create tag node
                    makeNewTagNode = getTagNode(tag)
                    newTagNode = graph_db.get_indexed_node("Tags", "tagName", tag)
                    # and then create a relationship
                    newRel = graph_db.create(rel(pImg, ("TAGGED", {"aWeight": 1}), newTagNode))
            # else
        else:
            # create image node
            newImgNode = createImgNode({url: tags})
            print "this should be a new img node:", newImgNode

 def co_appearance(self, wid_a, wid_b):
   """two words appearing in the same document. create edge if not any, increment one otherwise"""
   n_a = self.node_for_word(wid_a)
   n_b = self.node_for_word(wid_b)
   r = self.graph_db.match(start_node=n_a, end_node=n_b, bidirectional=True)
   if len(r) == 0:
     r0, r1 = self.graph_db.create(
               rel(n_a, "COAPPEARS", n_b, count=0),
               rel(n_b, "COAPPEARS", n_a, count=0))
     r = [r0, r1]
     
   r[0]["count"] += 1
   r[1]["count"] += 1

def processClusters(cluster_dict,graph_db,root):

	for cluster_number in cluster_dict:
		batch = neo4j.WriteBatch(graph_db)
		name = "Cluster " + str(cluster_number)
		cluster_node = batch.create({"name":name,"cluster_number":cluster_number})
		batch.create(rel(root,"CLUSTER",cluster_node))
		batch.add_labels(cluster_node,"cluster")
		for pitcher_id in cluster_dict[cluster_number]:
			for player_node in graph_db.find("player",property_key="player_id",property_value=int(pitcher_id)):
				batch.create(rel(player_node, "BELONGS TO",cluster_node))
		
		batch.submit()

    def create_RBSs(self):
        f = open(self.directory + 'RBSs.txt', 'r')
        data = f.readlines()
        f.close()
        created = 0

        for line in data:
            if line[0] == '#':
                continue

            regid, gene, start, end, strand, center, seq, \
            evidence = line.split('\t')

            ### testing
            if '' in [regid, strand, start, end] or 0 in [start, end]:
                continue

            start, end, center = [int(start), int(end), float(center)]

            query = 'MATCH (o:Organism {name: "%s"})<-[:PART_OF]-' \
                    '(g:Gene {strand: "%s"})-[:HAS_NAME]-(t:Term {text: "%s"}) ' \
                    'RETURN g' % (self.ecoli_name, strand, gene)
            res = neo4j.CypherQuery(self.connection, query)
            res_nodes = res.execute()

            if not res_nodes:
                continue
            elif len(res_nodes.data) == 1:
                g = res_nodes.data[0].values[0]
            else:
                # if there are many genes with the same name, we will
                # choose the closest by location gene
                genes = [min(g.values[0]['start'] + center,
                             g.values[0]['end'] + center)
                         for g in res_nodes.data]
                i = genes.index(min(genes))
                g = res_nodes.data[i].values[0]

            rbs, rel_chr, rel_gene = self.connection.create(
                node({'evidence': evidence, 'Reg_id': regid,
                      'source': 'RegulonDB', 'start': start,
                      'end': end, 'strand': strand,
                      'seq': seq, 'center_from_tss': center}),
                rel(0, 'PART_OF', self.chro_node),
                rel(g, 'CONTAINS', 0))
            rbs.add_labels('RBS', 'Feature')
            created += 1

        logging.info('%d RBSs were created!' % created)

def set_configuration(graph_db, node_lst):
	graph_db.create(
	rel(node_lst[0], "CONF_TO", node_lst[1], {'flow_id': 1, 'flow_size': 1}),
	rel(node_lst[1], "CONF_TO", node_lst[2], {'flow_id': 1, 'flow_size': 1}),
	rel(node_lst[0], "CONF_TO", node_lst[3], {'flow_id': 2, 'flow_size': 1}),
	rel(node_lst[3], "CONF_TO", node_lst[4], {'flow_id': 2, 'flow_size': 1}),
	rel(node_lst[4], "CONF_TO", node_lst[5], {'flow_id': 2, 'flow_size': 1}),
	rel(node_lst[0], "CONF_TO", node_lst[3], {'flow_id': 3, 'flow_size': 1}),
	rel(node_lst[3], "CONF_TO", node_lst[5], {'flow_id': 3, 'flow_size': 1}),
	rel(node_lst[2], "CONF_TO", node_lst[4], {'flow_id': 4, 'flow_size': 1}),
	rel(node_lst[4], "CONF_TO", node_lst[5], {'flow_id': 4, 'flow_size': 1}),
	)

def load(csvfile,verbose = True):	
	graph_db = database()
	if verbose:
		print 'started new graph database'
	# get the graph database server going.
	
	#if you want to delete the database!
	# cd /usr/local/Cellar/neo4j/1.9.4/libexec/data
	# os.command(rm -R graph_db)

	# this will store in usr/local/Cellar/neo4j/community-1.9.2-unix/libexec/data
	#make sure graph DB initialized 
	print 'Graph Version: ' + str(graph_db.neo4j_version)
	csvfile = open(csvfile)
	reader = csv.reader(csvfile,delimiter=',')
	nodes = {} # keep track of nodes already in graph_db.
	def get_or_create_node(graph_db, name):
	    if name not in nodes:
	        nodes[name], = graph_db.create(node(name=name)) #make the node if it doesn't exist 
	    return nodes[name] #return the node
	print 'Loading graph into database...'
	for row in reader:
	    parent = get_or_create_node(graph_db, row[0])
	    child = get_or_create_node(graph_db, row[1])
	    parent_child, = graph_db.create(rel(parent, "--", child)) 
	print 'Loaded graph into database'
	pickle.dump(nodes, open("nodes.p", "wb" ) )

def createRelationships():
    global relationships
    graph = Graph('http://localhost:7474/db/data')
    for r in relationships:
        NodeA = graph.find_one(r["start"]["collection"],property_key = "_id", property_value = str(r["start"]["_id"]))
        NodeB = graph.find_one(r["end"]["collection"],property_key = "_id", property_value = str(r["end"]["_id"]))
        graph.create(rel(NodeA,r["name"],NodeB))

def test_can_cast_3_tuple():
    casted = rel(("Alice", "KNOWS", "Bob"))
    assert isinstance(casted, neo4j.Relationship)
    assert not casted.bound
    assert casted.start_node == neo4j.Node("Alice")
    assert casted.type == "KNOWS"
    assert casted.end_node == neo4j.Node("Bob")