|*^c@stdZddlmZddlZddlmZddlmZdjddd gZ d efd YZ dS( sBase class for MultiGraph.i(tdeepcopyN(tGraph(t NetworkXErrors sAric Hagberg (hagberg@lanl.gov)sPieter Swart (swart@lanl.gov)sDan Schult(dschult@colgate.edu)t MultiGraphcBseZdZeZddZdddZddZddZ dZ ddZ de e ddZ de e ddZddd Zddd Zd Zd Zd Ze e ddZdddZdZRS(s An undirected graph class that can store multiedges. Multiedges are multiple edges between two nodes. Each edge can hold optional data or attributes. A MultiGraph holds undirected edges. Self loops are allowed. Nodes can be arbitrary (hashable) Python objects with optional key/value attributes. Edges are represented as links between nodes with optional key/value attributes. Parameters ---------- data : input graph Data to initialize graph. If data=None (default) an empty graph is created. The data can be an edge list, or any NetworkX graph object. If the corresponding optional Python packages are installed the data can also be a NumPy matrix or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph. attr : keyword arguments, optional (default= no attributes) Attributes to add to graph as key=value pairs. See Also -------- Graph DiGraph MultiDiGraph Examples -------- Create an empty graph structure (a "null graph") with no nodes and no edges. >>> G = nx.MultiGraph() G can be grown in several ways. **Nodes:** Add one node at a time: >>> G.add_node(1) Add the nodes from any container (a list, dict, set or even the lines from a file or the nodes from another graph). >>> G.add_nodes_from([2,3]) >>> G.add_nodes_from(range(100,110)) >>> H=nx.Graph() >>> H.add_path([0,1,2,3,4,5,6,7,8,9]) >>> G.add_nodes_from(H) In addition to strings and integers any hashable Python object (except None) can represent a node, e.g. a customized node object, or even another Graph. >>> G.add_node(H) **Edges:** G can also be grown by adding edges. Add one edge, >>> G.add_edge(1, 2) a list of edges, >>> G.add_edges_from([(1,2),(1,3)]) or a collection of edges, >>> G.add_edges_from(H.edges()) If some edges connect nodes not yet in the graph, the nodes are added automatically. If an edge already exists, an additional edge is created and stored using a key to identify the edge. By default the key is the lowest unused integer. >>> G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))]) >>> G[4] {3: {0: {}}, 5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}} **Attributes:** Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). By default these are empty, but can be added or changed using add_edge, add_node or direct manipulation of the attribute dictionaries named graph, node and edge respectively. >>> G = nx.MultiGraph(day="Friday") >>> G.graph {'day': 'Friday'} Add node attributes using add_node(), add_nodes_from() or G.node >>> G.add_node(1, time='5pm') >>> G.add_nodes_from([3], time='2pm') >>> G.node[1] {'time': '5pm'} >>> G.node[1]['room'] = 714 >>> del G.node[1]['room'] # remove attribute >>> G.nodes(data=True) [(1, {'time': '5pm'}), (3, {'time': '2pm'})] Warning: adding a node to G.node does not add it to the graph. Add edge attributes using add_edge(), add_edges_from(), subscript notation, or G.edge. >>> G.add_edge(1, 2, weight=4.7 ) >>> G.add_edges_from([(3,4),(4,5)], color='red') >>> G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})]) >>> G[1][2][0]['weight'] = 4.7 >>> G.edge[1][2][0]['weight'] = 4 **Shortcuts:** Many common graph features allow python syntax to speed reporting. >>> 1 in G # check if node in graph True >>> [n for n in G if n<3] # iterate through nodes [1, 2] >>> len(G) # number of nodes in graph 5 >>> G[1] # adjacency dict keyed by neighbor to edge attributes ... # Note: you should not change this dict manually! {2: {0: {'weight': 4}, 1: {'color': 'blue'}}} The fastest way to traverse all edges of a graph is via adjacency_iter(), but the edges() method is often more convenient. >>> for n,nbrsdict in G.adjacency_iter(): ... for nbr,keydict in nbrsdict.items(): ... for key,eattr in keydict.items(): ... if 'weight' in eattr: ... (n,nbr,key,eattr['weight']) (1, 2, 0, 4) (2, 1, 0, 4) (2, 3, 0, 8) (3, 2, 0, 8) >>> G.edges(data='weight', keys=True) [(1, 2, 0, 4), (1, 2, 1, None), (2, 3, 0, 8), (3, 4, 0, None), (4, 5, 0, None)] **Reporting:** Simple graph information is obtained using methods. Iterator versions of many reporting methods exist for efficiency. Methods exist for reporting nodes(), edges(), neighbors() and degree() as well as the number of nodes and edges. For details on these and other miscellaneous methods, see below. **Subclasses (Advanced):** The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure. The outer dict (node_dict) holds adjacency lists keyed by node. The next dict (adjlist) represents the adjacency list and holds edge_key dicts keyed by neighbor. The edge_key dict holds each edge_attr dict keyed by edge key. The inner dict (edge_attr) represents the edge data and holds edge attribute values keyed by attribute names. Each of these four dicts in the dict-of-dict-of-dict-of-dict structure can be replaced by a user defined dict-like object. In general, the dict-like features should be maintained but extra features can be added. To replace one of the dicts create a new graph class by changing the class(!) variable holding the factory for that dict-like structure. The variable names are node_dict_factory, adjlist_dict_factory, edge_key_dict_factory and edge_attr_dict_factory. node_dict_factory : function, (default: dict) Factory function to be used to create the outer-most dict in the data structure that holds adjacency lists keyed by node. It should require no arguments and return a dict-like object. adjlist_dict_factory : function, (default: dict) Factory function to be used to create the adjacency list dict which holds multiedge key dicts keyed by neighbor. It should require no arguments and return a dict-like object. edge_key_dict_factory : function, (default: dict) Factory function to be used to create the edge key dict which holds edge data keyed by edge key. It should require no arguments and return a dict-like object. edge_attr_dict_factory : function, (default: dict) Factory function to be used to create the edge attribute dict which holds attrbute values keyed by attribute name. It should require no arguments and return a dict-like object. Examples -------- Create a multigraph object that tracks the order nodes are added. >>> from collections import OrderedDict >>> class OrderedGraph(nx.MultiGraph): ... node_dict_factory = OrderedDict >>> G = OrderedGraph() >>> G.add_nodes_from( (2,1) ) >>> G.nodes() [2, 1] >>> G.add_edges_from( ((2,2), (2,1), (2,1), (1,1)) ) >>> G.edges() [(2, 1), (2, 1), (2, 2), (1, 1)] Create a multgraph object that tracks the order nodes are added and for each node track the order that neighbors are added and for each neighbor tracks the order that multiedges are added. >>> class OrderedGraph(nx.MultiGraph): ... node_dict_factory = OrderedDict ... adjlist_dict_factory = OrderedDict ... edge_key_dict_factory = OrderedDict >>> G = OrderedGraph() >>> G.add_nodes_from( (2,1) ) >>> G.nodes() [2, 1] >>> G.add_edges_from( ((2,2), (2,1,2,{'weight':0.1}), (2,1,1,{'weight':0.2}), (1,1)) ) >>> G.edges(keys=True) [(2, 2, 0), (2, 1, 2), (2, 1, 1), (1, 1, 0)] cKs#|j|_tj|||dS(N(tedge_key_dict_factoryRt__init__(tselftdatatattr((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyRs cKs|dkr|}n1y|j|Wntk rEtdnX||jkrx|j|j|>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> e = (1,2) >>> G.add_edge(1, 2) # explicit two-node form >>> G.add_edge(*e) # single edge as tuple of two nodes >>> G.add_edges_from( [(1,2)] ) # add edges from iterable container Associate data to edges using keywords: >>> G.add_edge(1, 2, weight=3) >>> G.add_edge(1, 2, key=0, weight=4) # update data for key=0 >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7) s,The attr_dict argument must be a dictionary.iiN( tNonetupdatetAttributeErrorRtadjtadjlist_dict_factorytnodetlentgettedge_attr_dict_factoryR(Rtutvtkeyt attr_dictRtkeydicttdatadict((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pytadd_edges<6              c Ks|dkr|}n1y|j|Wntk rEtdnXx|D]}t|}|dkr|\}}}} n^|dkr|\}}} d}n:|dkr|\}}i} d}ntd|fi} | j|| j| |j|||| qMWdS(sAdd all the edges in ebunch. Parameters ---------- ebunch : container of edges Each edge given in the container will be added to the graph. The edges can be: - 2-tuples (u,v) or - 3-tuples (u,v,d) for an edge attribute dict d, or - 4-tuples (u,v,k,d) for an edge identified by key k attr_dict : dictionary, optional (default= no attributes) Dictionary of edge attributes. Key/value pairs will update existing data associated with each edge. attr : keyword arguments, optional Edge data (or labels or objects) can be assigned using keyword arguments. See Also -------- add_edge : add a single edge add_weighted_edges_from : convenient way to add weighted edges Notes ----- Adding the same edge twice has no effect but any edge data will be updated when each duplicate edge is added. Edge attributes specified in edges take precedence over attributes specified generally. Examples -------- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> G.add_edges_from([(0,1),(1,2)]) # using a list of edge tuples >>> e = zip(range(0,3),range(1,4)) >>> G.add_edges_from(e) # Add the path graph 0-1-2-3 Associate data to edges >>> G.add_edges_from([(1,2),(2,3)], weight=3) >>> G.add_edges_from([(3,4),(1,4)], label='WN2898') s,The attr_dict argument must be a dictionary.iiis4Edge tuple %s must be a 2-tuple, 3-tuple or 4-tuple.N(R R R RRR( RtebunchRRtetneRRRtddtddd((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pytadd_edges_from[s0/              cCsy|j||}Wn'tk r>td||fnX|dkrX|jn8y ||=Wn*tk rtd|||fnXt|dkr|j||=||kr|j||=qndS(sRemove an edge between u and v. Parameters ---------- u, v : nodes Remove an edge between nodes u and v. key : hashable identifier, optional (default=None) Used to distinguish multiple edges between a pair of nodes. If None remove a single (abritrary) edge between u and v. Raises ------ NetworkXError If there is not an edge between u and v, or if there is no edge with the specified key. See Also -------- remove_edges_from : remove a collection of edges Examples -------- >>> G = nx.MultiGraph() >>> G.add_path([0,1,2,3]) >>> G.remove_edge(0,1) >>> e = (1,2) >>> G.remove_edge(*e) # unpacks e from an edge tuple For multiple edges >>> G = nx.MultiGraph() # or MultiDiGraph, etc >>> G.add_edges_from([(1,2),(1,2),(1,2)]) >>> G.remove_edge(1,2) # remove a single (arbitrary) edge For edges with keys >>> G = nx.MultiGraph() # or MultiDiGraph, etc >>> G.add_edge(1,2,key='first') >>> G.add_edge(1,2,key='second') >>> G.remove_edge(1,2,key='second') s#The edge %s-%s is not in the graph.s/The edge %s-%s with key %s is not in the graph.iN(R tKeyErrorRR tpopitemR(RRRRtd((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyt remove_edges"+      cCs>x7|D]/}y|j|d Wqtk r5qXqWdS(sRemove all edges specified in ebunch. Parameters ---------- ebunch: list or container of edge tuples Each edge given in the list or container will be removed from the graph. The edges can be: - 2-tuples (u,v) All edges between u and v are removed. - 3-tuples (u,v,key) The edge identified by key is removed. - 4-tuples (u,v,key,data) where data is ignored. See Also -------- remove_edge : remove a single edge Notes ----- Will fail silently if an edge in ebunch is not in the graph. Examples -------- >>> G = nx.MultiGraph() # or MultiDiGraph >>> G.add_path([0,1,2,3]) >>> ebunch=[(1,2),(2,3)] >>> G.remove_edges_from(ebunch) Removing multiple copies of edges >>> G = nx.MultiGraph() >>> G.add_edges_from([(1,2),(1,2),(1,2)]) >>> G.remove_edges_from([(1,2),(1,2)]) >>> G.edges() [(1, 2)] >>> G.remove_edges_from([(1,2),(1,2)]) # silently ignore extra copy >>> G.edges() # now empty graph [] iN(R"R(RRR((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pytremove_edges_froms '  cCsOy6|dkr ||j|kS||j||kSWntk rJtSXdS(sReturn True if the graph has an edge between nodes u and v. Parameters ---------- u, v : nodes Nodes can be, for example, strings or numbers. key : hashable identifier, optional (default=None) If specified return True only if the edge with key is found. Returns ------- edge_ind : bool True if edge is in the graph, False otherwise. Examples -------- Can be called either using two nodes u,v, an edge tuple (u,v), or an edge tuple (u,v,key). >>> G = nx.MultiGraph() # or MultiDiGraph >>> G.add_path([0,1,2,3]) >>> G.has_edge(0,1) # using two nodes True >>> e = (0,1) >>> G.has_edge(*e) # e is a 2-tuple (u,v) True >>> G.add_edge(0,1,key='a') >>> G.has_edge(0,1,key='a') # specify key True >>> e=(0,1,'a') >>> G.has_edge(*e) # e is a 3-tuple (u,v,'a') True The following syntax are equivalent: >>> G.has_edge(0,1) True >>> 1 in G[0] # though this gives KeyError if 0 not in G True N(R R RtFalse(RRRR((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pythas_edges .  cCst|j||||S(sReturn a list of edges. Edges are returned as tuples with optional data and keys in the order (node, neighbor, key, data). Parameters ---------- nbunch : iterable container, optional (default= all nodes) A container of nodes. The container will be iterated through once. data : bool, optional (default=False) Return two tuples (u,v) (False) or three-tuples (u,v,data) (True). keys : bool, optional (default=False) Return two tuples (u,v) (False) or three-tuples (u,v,key) (True). Returns -------- edge_list: list of edge tuples Edges that are adjacent to any node in nbunch, or a list of all edges if nbunch is not specified. See Also -------- edges_iter : return an iterator over the edges Notes ----- Nodes in nbunch that are not in the graph will be (quietly) ignored. For directed graphs this returns the out-edges. Examples -------- >>> G = nx.MultiGraph() # or MultiDiGraph >>> G.add_path([0,1,2]) >>> G.add_edge(2,3,weight=5) >>> G.edges() [(0, 1), (1, 2), (2, 3)] >>> G.edges(data=True) # default edge data is {} (empty dictionary) [(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})] >>> list(G.edges_iter(data='weight', default=1)) [(0, 1, 1), (1, 2, 1), (2, 3, 5)] >>> G.edges(keys=True) # default keys are integers [(0, 1, 0), (1, 2, 0), (2, 3, 0)] >>> G.edges(data=True,keys=True) # default keys are integers [(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})] >>> list(G.edges(data='weight',default=1,keys=True)) [(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)] >>> G.edges([0,3]) [(0, 1), (3, 2)] >>> G.edges(0) [(0, 1)] (tlistt edges_iter(RtnbunchRtkeystdefault((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pytedgesIs6c#s"i}|dkr$jj}nfdj|D}|tkrx|D]\}}xl|jD]^\} } | |kroxC| jD]2\} } |r|| | | fn || | fVqWqoqoWd||>> G = nx.MultiGraph() # or MultiDiGraph >>> G.add_path([0,1,2]) >>> G.add_edge(2,3,weight=5) >>> [e for e in G.edges_iter()] [(0, 1), (1, 2), (2, 3)] >>> list(G.edges_iter(data=True)) # default data is {} (empty dict) [(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})] >>> list(G.edges_iter(data='weight', default=1)) [(0, 1, 1), (1, 2, 1), (2, 3, 5)] >>> list(G.edges(keys=True)) # default keys are integers [(0, 1, 0), (1, 2, 0), (2, 3, 0)] >>> list(G.edges(data=True,keys=True)) # default keys are integers [(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})] >>> list(G.edges(data='weight',default=1,keys=True)) [(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)] >>> list(G.edges_iter([0,3])) [(0, 1), (3, 2)] >>> list(G.edges_iter(0)) [(0, 1)] c3s"|]}|j|fVqdS(N(R (t.0tn(R(so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pys siN(R R titemst nbunch_itertTrueR$(RR(RR)R*tseent nodes_nbrsR-tnbrstnbrRRtddictR!((Rso/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyR's4:   1  1  +cCsKy2|dkr|j||S|j|||SWntk rF|SXdS(sReturn the attribute dictionary associated with edge (u,v). Parameters ---------- u, v : nodes default : any Python object (default=None) Value to return if the edge (u,v) is not found. key : hashable identifier, optional (default=None) Return data only for the edge with specified key. Returns ------- edge_dict : dictionary The edge attribute dictionary. Notes ----- It is faster to use G[u][v][key]. >>> G = nx.MultiGraph() # or MultiDiGraph >>> G.add_edge(0,1,key='a',weight=7) >>> G[0][1]['a'] # key='a' {'weight': 7} Warning: Assigning G[u][v][key] corrupts the graph data structure. But it is safe to assign attributes to that dictionary, >>> G[0][1]['a']['weight'] = 10 >>> G[0][1]['a']['weight'] 10 >>> G[1][0]['a']['weight'] 10 Examples -------- >>> G = nx.MultiGraph() # or MultiDiGraph >>> G.add_path([0,1,2,3]) >>> G.get_edge_data(0,1) {0: {}} >>> e = (0,1) >>> G.get_edge_data(*e) # tuple form {0: {}} >>> G.get_edge_data('a','b',default=0) # edge not in graph, return 0 0 N(R R R(RRRRR*((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyt get_edge_datas 0  c #sk|dkrjj}nfdj|D}|dkrx|D]\\}}tg|jD]}t|^ql}||||kot||fVqPWnx|D]\}}tg|jD]+}|jD]}|j|d^qq}||krX|tg||jD]\} }|j|d^q-7}n||fVqWdS(s Return an iterator for (node, degree). The node degree is the number of edges adjacent to the node. Parameters ---------- nbunch : iterable container, optional (default=all nodes) A container of nodes. The container will be iterated through once. weight : string or None, optional (default=None) The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node. Returns ------- nd_iter : an iterator The iterator returns two-tuples of (node, degree). See Also -------- degree Examples -------- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> G.add_path([0,1,2,3]) >>> list(G.degree_iter(0)) # node 0 with degree 1 [(0, 1)] >>> list(G.degree_iter([0,1])) [(0, 1), (1, 2)] c3s"|]}|j|fVqdS(N(R (R,R-(R(so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pys 6siN(R R R.R/tsumtvaluesRR( RR(tweightR2R-R3RtdegR!R((Rso/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyt degree_iters#  +,.  9cCstS(s6Return True if graph is a multigraph, False otherwise.(R0(R((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyt is_multigraphGscCstS(s2Return True if graph is directed, False otherwise.(R$(R((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyt is_directedKscCskddlm}|}|j||jd|jDt|j|_t|j|_|S(sReturn a directed representation of the graph. Returns ------- G : MultiDiGraph A directed graph with the same name, same nodes, and with each edge (u,v,data) replaced by two directed edges (u,v,data) and (v,u,data). Notes ----- This returns a "deepcopy" of the edge, node, and graph attributes which attempts to completely copy all of the data and references. This is in contrast to the similar D=DiGraph(G) which returns a shallow copy of the data. See the Python copy module for more information on shallow and deep copies, http://docs.python.org/library/copy.html. Warning: If you have subclassed MultiGraph to use dict-like objects in the data structure, those changes do not transfer to the MultiDiGraph created by this method. Examples -------- >>> G = nx.Graph() # or MultiGraph, etc >>> G.add_path([0,1]) >>> H = G.to_directed() >>> H.edges() [(0, 1), (1, 0)] If already directed, return a (deep) copy >>> G = nx.DiGraph() # or MultiDiGraph, etc >>> G.add_path([0,1]) >>> H = G.to_directed() >>> H.edges() [(0, 1)] i(t MultiDiGraphcss_|]U\}}|jD]<\}}|jD]#\}}|||t|fVq/qqdS(N(R.R(R,RR3RRRR((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pys |s (tnetworkx.classes.multidigraphR>tadd_nodes_fromRtadjacency_iterRtgraphR(RR>tG((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyt to_directedOs*   c CsG|tkr|rmg|jjD]G\}}||kr"||jD]\}}||||f^qHq"Sg|jjD]>\}}||kr}||jD]}|||f^qq}Sn|tk r|r;g|jjD]S\}}||kr||jD]*\}}||||j||f^q qSg|jjD]J\}}||krK||jD]!}|||j||f^qqqKSn|rg|jjD]>\}}||kr||jD]}|||f^qqSg|jjD];\}}||kr||jD]}||f^q*qSdS(sReturn a list of selfloop edges. A selfloop edge has the same node at both ends. Parameters ---------- data : bool, optional (default=False) Return selfloop edges as two tuples (u,v) (data=False) or three-tuples (u,v,datadict) (data=True) or three-tuples (u,v,datavalue) (data='attrname') default : value, optional (default=None) Value used for edges that dont have the requested attribute. Only relevant if data is not True or False. keys : bool, optional (default=False) If True, return edge keys with each edge. Returns ------- edgelist : list of edge tuples A list of all selfloop edges. See Also -------- nodes_with_selfloops, number_of_selfloops Examples -------- >>> G = nx.MultiGraph() # or MultiDiGraph >>> G.add_edge(1,1) >>> G.add_edge(1,2) >>> G.selfloop_edges() [(1, 1)] >>> G.selfloop_edges(data=True) [(1, 1, {})] >>> G.selfloop_edges(keys=True) [(1, 1, 0)] >>> G.selfloop_edges(keys=True, data=True) [(1, 1, 0, {})] N(R0R R.R8R$RR)(RRR)R*R-R3tkR!((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pytselfloop_edgess.( ?9 KE6cCsJ|dkr|jSy|j||}Wntk r?dSXt|S(s5Return the number of edges between two nodes. Parameters ---------- u, v : nodes, optional (default=all edges) If u and v are specified, return the number of edges between u and v. Otherwise return the total number of all edges. Returns ------- nedges : int The number of edges in the graph. If nodes u and v are specified return the number of edges between those nodes. See Also -------- size Examples -------- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> G.add_path([0,1,2,3]) >>> G.number_of_edges() 3 >>> G.number_of_edges(0,1) 1 >>> e = (0,1) >>> G.number_of_edges(*e) 1 iN(R tsizeR RR(RRRtedgedata((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pytnumber_of_edgess  c Cs|j|}|j}x"|D]}|j||j|>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> G.add_path([0,1,2,3]) >>> H = G.subgraph([0,1,2]) >>> H.edges() [(0, 1), (1, 2)] (R/t __class__RR R R.tcopyRB( RR(tbunchtHR-tH_adjtself_adjtHnbrsR4tedgedictted((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pytsubgraphs '           N(t__name__t __module__t__doc__tdictRR RRRR"R#R%R$R+R'R6R;R<R=RDRFRIRS(((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyRs$ [ K @ - 68W87   5D&( RVRKRtnetworkxtnxtnetworkx.classes.graphRRtjoint __author__R(((so/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/classes/multigraph.pyts