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date	Fri, 31 Jul 2020 00:32:28 -0400
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+#    Copyright (C) 2004-2019 by
+#    Aric Hagberg <hagberg@lanl.gov>
+#    Dan Schult <dschult@colgate.edu>
+#    Pieter Swart <swart@lanl.gov>
+#    All rights reserved.
+#    BSD license.
+#
+# Authors:   Aric Hagberg <hagberg@lanl.gov>
+#            Dan Schult <dschult@colgate.edu>
+#            Pieter Swart <swart@lanl.gov>
+"""Base class for MultiDiGraph."""
+from copy import deepcopy
+import networkx as nx
+from networkx.classes.graph import Graph  # for doctests
+from networkx.classes.digraph import DiGraph
+from networkx.classes.multigraph import MultiGraph
+from networkx.classes.coreviews import MultiAdjacencyView
+from networkx.classes.reportviews import OutMultiEdgeView, InMultiEdgeView, \
+DiMultiDegreeView, OutMultiDegreeView, InMultiDegreeView
+from networkx.exception import NetworkXError
+class MultiDiGraph(MultiGraph, DiGraph):
+"""A directed graph class that can store multiedges.
+Multiedges are multiple edges between two nodes.  Each edge
+can hold optional data or attributes.
+A MultiDiGraph holds directed edges.  Self loops are allowed.
+Nodes can be arbitrary (hashable) Python objects with optional
+key/value attributes. By convention `None` is not used as a node.
+Edges are represented as links between nodes with optional
+key/value attributes.
+Parameters
+----------
+incoming_graph_data : input graph (optional, default: None)
+Data to initialize graph. If None (default) an empty
+graph is created.  The data can be any format that is supported
+by the to_networkx_graph() function, currently including edge list,
+dict of dicts, dict of lists, NetworkX graph, NumPy matrix
+or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.
+attr : keyword arguments, optional (default= no attributes)
+Attributes to add to graph as key=value pairs.
+See Also
+--------
+Graph
+DiGraph
+MultiGraph
+OrderedMultiDiGraph
+Examples
+--------
+Create an empty graph structure (a "null graph") with no nodes and
+no edges.
+>>> G = nx.MultiDiGraph()
+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.path_graph(10)
+>>> 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,
+>>> key = G.add_edge(1, 2)
+a list of edges,
+>>> keys = G.add_edges_from([(1, 2), (1, 3)])
+or a collection of edges,
+>>> keys = 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.
+>>> keys = G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))])
+>>> G[4]
+AdjacencyView({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.MultiDiGraph(day="Friday")
+>>> G.graph
+{'day': 'Friday'}
+Add node attributes using add_node(), add_nodes_from() or G.nodes
+>>> G.add_node(1, time='5pm')
+>>> G.add_nodes_from([3], time='2pm')
+>>> G.nodes[1]
+{'time': '5pm'}
+>>> G.nodes[1]['room'] = 714
+>>> del G.nodes[1]['room'] # remove attribute
+>>> list(G.nodes(data=True))
+[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+Add edge attributes using add_edge(), add_edges_from(), subscript
+notation, or G.edges.
+>>> key = G.add_edge(1, 2, weight=4.7 )
+>>> keys = G.add_edges_from([(3, 4), (4, 5)], color='red')
+>>> keys = G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
+>>> G[1][2][0]['weight'] = 4.7
+>>> G.edges[1, 2, 0]['weight'] = 4
+Warning: we protect the graph data structure by making `G.edges[1, 2]` a
+read-only dict-like structure. However, you can assign to attributes
+in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
+data attributes: `G.edges[1, 2]['weight'] = 4`
+(For multigraphs: `MG.edges[u, v, key][name] = value`).
+**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-like view keyed by neighbor to edge attributes
+AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
+Often the best way to traverse all edges of a graph is via the neighbors.
+The neighbors are available as an adjacency-view `G.adj` object or via
+the method `G.adjacency()`.
+>>> for n, nbrsdict in G.adjacency():
+...     for nbr, keydict in nbrsdict.items():
+...        for key, eattr in keydict.items():
+...            if 'weight' in eattr:
+...                # Do something useful with the edges
+...                pass
+But the edges() method is often more convenient:
+>>> for u, v, keys, weight in G.edges(data='weight', keys=True):
+...     if weight is not None:
+...         # Do something useful with the edges
+...         pass
+**Reporting:**
+Simple graph information is obtained using methods and object-attributes.
+Reporting usually provides views instead of containers to reduce memory
+usage. The views update as the graph is updated similarly to dict-views.
+The objects `nodes, `edges` and `adj` provide access to data attributes
+via lookup (e.g. `nodes[n], `edges[u, v]`, `adj[u][v]`) and iteration
+(e.g. `nodes.items()`, `nodes.data('color')`,
+`nodes.data('color', default='blue')` and similarly for `edges`)
+Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+For details on these and other miscellaneous methods, see below.
+**Subclasses (Advanced):**
+The MultiDiGraph class uses a dict-of-dict-of-dict-of-dict structure.
+The outer dict (node_dict) holds adjacency information keyed by node.
+The next dict (adjlist_dict) represents the adjacency information 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_dict) 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, node_attr_dict_factory, adjlist_inner_dict_factory,
+adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
+and graph_attr_dict_factory.
+node_dict_factory : function, (default: dict)
+Factory function to be used to create the dict containing node
+attributes, keyed by node id.
+It should require no arguments and return a dict-like object
+node_attr_dict_factory: function, (default: dict)
+Factory function to be used to create the node attribute
+dict which holds attribute values keyed by attribute name.
+It should require no arguments and return a dict-like object
+adjlist_outer_dict_factory : function, (default: dict)
+Factory function to be used to create the outer-most dict
+in the data structure that holds adjacency info keyed by node.
+It should require no arguments and return a dict-like object.
+adjlist_inner_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 attribute values keyed by attribute name.
+It should require no arguments and return a dict-like object.
+graph_attr_dict_factory : function, (default: dict)
+Factory function to be used to create the graph attribute
+dict which holds attribute values keyed by attribute name.
+It should require no arguments and return a dict-like object.
+Typically, if your extension doesn't impact the data structure all
+methods will inherited without issue except: `to_directed/to_undirected`.
+By default these methods create a DiGraph/Graph class and you probably
+want them to create your extension of a DiGraph/Graph. To facilitate
+this we define two class variables that you can set in your subclass.
+to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+Class to create a new graph structure in the `to_directed` method.
+If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+to_undirected_class : callable, (default: Graph or MultiGraph)
+Class to create a new graph structure in the `to_undirected` method.
+If `None`, a NetworkX class (Graph or MultiGraph) is used.
+Examples
+--------
+Please see :mod:`~networkx.classes.ordered` for examples of
+creating graph subclasses by overwriting the base class `dict` with
+a dictionary-like object.
+"""
+# node_dict_factory = dict    # already assigned in Graph
+# adjlist_outer_dict_factory = dict
+# adjlist_inner_dict_factory = dict
+edge_key_dict_factory = dict
+# edge_attr_dict_factory = dict
+def __init__(self, incoming_graph_data=None, **attr):
+"""Initialize a graph with edges, name, or graph attributes.
+Parameters
+----------
+incoming_graph_data : input graph
+Data to initialize graph.  If incoming_graph_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
+--------
+convert
+Examples
+--------
+>>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
+>>> G = nx.Graph(name='my graph')
+>>> e = [(1, 2), (2, 3), (3, 4)] # list of edges
+>>> G = nx.Graph(e)
+Arbitrary graph attribute pairs (key=value) may be assigned
+>>> G = nx.Graph(e, day="Friday")
+>>> G.graph
+{'day': 'Friday'}
+"""
+self.edge_key_dict_factory = self.edge_key_dict_factory
+DiGraph.__init__(self, incoming_graph_data, **attr)
+@property
+def adj(self):
+"""Graph adjacency object holding the neighbors of each node.
+This object is a read-only dict-like structure with node keys
+and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+the color of the edge `(3, 2, 0)` to `"blue"`.
+Iterating over G.adj behaves like a dict. Useful idioms include
+`for nbr, datadict in G.adj[n].items():`.
+The neighbor information is also provided by subscripting the graph.
+So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+For directed graphs, `G.adj` holds outgoing (successor) info.
+"""
+return MultiAdjacencyView(self._succ)
+@property
+def succ(self):
+"""Graph adjacency object holding the successors of each node.
+This object is a read-only dict-like structure with node keys
+and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+the color of the edge `(3, 2, 0)` to `"blue"`.
+Iterating over G.adj behaves like a dict. Useful idioms include
+`for nbr, datadict in G.adj[n].items():`.
+The neighbor information is also provided by subscripting the graph.
+So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+For directed graphs, `G.succ` is identical to `G.adj`.
+"""
+return MultiAdjacencyView(self._succ)
+@property
+def pred(self):
+"""Graph adjacency object holding the predecessors of each node.
+This object is a read-only dict-like structure with node keys
+and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+the color of the edge `(3, 2, 0)` to `"blue"`.
+Iterating over G.adj behaves like a dict. Useful idioms include
+`for nbr, datadict in G.adj[n].items():`.
+"""
+return MultiAdjacencyView(self._pred)
+def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
+"""Add an edge between u and v.
+The nodes u and v will be automatically added if they are
+not already in the graph.
+Edge attributes can be specified with keywords or by directly
+accessing the edge's attribute dictionary. See examples below.
+Parameters
+----------
+u_for_edge, v_for_edge : nodes
+Nodes can be, for example, strings or numbers.
+Nodes must be hashable (and not None) Python objects.
+key : hashable identifier, optional (default=lowest unused integer)
+Used to distinguish multiedges between a pair of nodes.
+attr_dict : dictionary, optional (default= no attributes)
+Dictionary of edge attributes.  Key/value pairs will
+update existing data associated with the edge.
+attr : keyword arguments, optional
+Edge data (or labels or objects) can be assigned using
+keyword arguments.
+Returns
+-------
+The edge key assigned to the edge.
+See Also
+--------
+add_edges_from : add a collection of edges
+Notes
+-----
+To replace/update edge data, use the optional key argument
+to identify a unique edge.  Otherwise a new edge will be created.
+NetworkX algorithms designed for weighted graphs cannot use
+multigraphs directly because it is not clear how to handle
+multiedge weights.  Convert to Graph using edge attribute
+'weight' to enable weighted graph algorithms.
+Default keys are generated using the method `new_edge_key()`.
+This method can be overridden by subclassing the base class and
+providing a custom `new_edge_key()` method.
+Examples
+--------
+The following all add the edge e=(1, 2) to graph G:
+>>> G = nx.MultiDiGraph()
+>>> e = (1, 2)
+>>> key = G.add_edge(1, 2)     # explicit two-node form
+>>> G.add_edge(*e)             # single edge as tuple of two nodes
+1
+>>> G.add_edges_from( [(1, 2)] ) # add edges from iterable container
+[2]
+Associate data to edges using keywords:
+>>> key = G.add_edge(1, 2, weight=3)
+>>> key = G.add_edge(1, 2, key=0, weight=4)  # update data for key=0
+>>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+For non-string attribute keys, use subscript notation.
+>>> ekey = G.add_edge(1, 2)
+>>> G[1][2][0].update({0: 5})
+>>> G.edges[1, 2, 0].update({0: 5})
+"""
+u, v = u_for_edge, v_for_edge
+# add nodes
+if u not in self._succ:
+self._succ[u] = self.adjlist_inner_dict_factory()
+self._pred[u] = self.adjlist_inner_dict_factory()
+self._node[u] = self.node_attr_dict_factory()
+if v not in self._succ:
+self._succ[v] = self.adjlist_inner_dict_factory()
+self._pred[v] = self.adjlist_inner_dict_factory()
+self._node[v] = self.node_attr_dict_factory()
+if key is None:
+key = self.new_edge_key(u, v)
+if v in self._succ[u]:
+keydict = self._adj[u][v]
+datadict = keydict.get(key, self.edge_key_dict_factory())
+datadict.update(attr)
+keydict[key] = datadict
+else:
+# selfloops work this way without special treatment
+datadict = self.edge_attr_dict_factory()
+datadict.update(attr)
+keydict = self.edge_key_dict_factory()
+keydict[key] = datadict
+self._succ[u][v] = keydict
+self._pred[v][u] = keydict
+return key
+def remove_edge(self, u, v, key=None):
+"""Remove 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 (arbitrary) 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.MultiDiGraph()
+>>> nx.add_path(G, [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.MultiDiGraph()
+>>> G.add_edges_from([(1, 2), (1, 2), (1, 2)])  # key_list returned
+[0, 1, 2]
+>>> G.remove_edge(1, 2) # remove a single (arbitrary) edge
+For edges with keys
+>>> G = nx.MultiDiGraph()
+>>> G.add_edge(1, 2, key='first')
+'first'
+>>> G.add_edge(1, 2, key='second')
+'second'
+>>> G.remove_edge(1, 2, key='second')
+"""
+try:
+d = self._adj[u][v]
+except KeyError:
+raise NetworkXError(
+"The edge %s-%s is not in the graph." % (u, v))
+# remove the edge with specified data
+if key is None:
+d.popitem()
+else:
+try:
+del d[key]
+except KeyError:
+msg = "The edge %s-%s with key %s is not in the graph."
+raise NetworkXError(msg % (u, v, key))
+if len(d) == 0:
+# remove the key entries if last edge
+del self._succ[u][v]
+del self._pred[v][u]
+@property
+def edges(self):
+"""An OutMultiEdgeView of the Graph as G.edges or G.edges().
+edges(self, nbunch=None, data=False, keys=False, default=None)
+The OutMultiEdgeView provides set-like operations on the edge-tuples
+as well as edge attribute lookup. When called, it also provides
+an EdgeDataView object which allows control of access to edge
+attributes (but does not provide set-like operations).
+Hence, `G.edges[u, v]['color']` provides the value of the color
+attribute for edge `(u, v)` while
+`for (u, v, c) in G.edges(data='color', default='red'):`
+iterates through all the edges yielding the color attribute
+with default `'red'` if no color attribute exists.
+Edges are returned as tuples with optional data and keys
+in the order (node, neighbor, key, data).
+Parameters
+----------
+nbunch : single node, container, or all nodes (default= all nodes)
+The view will only report edges incident to these nodes.
+data : string or bool, optional (default=False)
+The edge attribute returned in 3-tuple (u, v, ddict[data]).
+If True, return edge attribute dict in 3-tuple (u, v, ddict).
+If False, return 2-tuple (u, v).
+keys : bool, optional (default=False)
+If True, return edge keys with each edge.
+default : value, optional (default=None)
+Value used for edges that don't have the requested attribute.
+Only relevant if data is not True or False.
+Returns
+-------
+edges : EdgeView
+A view of edge attributes, usually it iterates over (u, v)
+(u, v, k) or (u, v, k, d) tuples of edges, but can also be
+used for attribute lookup as `edges[u, v, k]['foo']`.
+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.MultiDiGraph()
+>>> nx.add_path(G, [0, 1, 2])
+>>> key = G.add_edge(2, 3, weight=5)
+>>> [e for e in G.edges()]
+[(0, 1), (1, 2), (2, 3)]
+>>> list(G.edges(data=True)) # default data is {} (empty dict)
+[(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]
+>>> list(G.edges(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))
+[(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([0, 2]))
+[(0, 1), (2, 3)]
+>>> list(G.edges(0))
+[(0, 1)]
+See Also
+--------
+in_edges, out_edges
+"""
+return OutMultiEdgeView(self)
+# alias out_edges to edges
+out_edges = edges
+@property
+def in_edges(self):
+"""An InMultiEdgeView of the Graph as G.in_edges or G.in_edges().
+in_edges(self, nbunch=None, data=False, keys=False, default=None)
+Parameters
+----------
+nbunch : single node, container, or all nodes (default= all nodes)
+The view will only report edges incident to these nodes.
+data : string or bool, optional (default=False)
+The edge attribute returned in 3-tuple (u, v, ddict[data]).
+If True, return edge attribute dict in 3-tuple (u, v, ddict).
+If False, return 2-tuple (u, v).
+keys : bool, optional (default=False)
+If True, return edge keys with each edge.
+default : value, optional (default=None)
+Value used for edges that don't have the requested attribute.
+Only relevant if data is not True or False.
+Returns
+-------
+in_edges : InMultiEdgeView
+A view of edge attributes, usually it iterates over (u, v)
+or (u, v, k) or (u, v, k, d) tuples of edges, but can also be
+used for attribute lookup as `edges[u, v, k]['foo']`.
+See Also
+--------
+edges
+"""
+return InMultiEdgeView(self)
+@property
+def degree(self):
+"""A DegreeView for the Graph as G.degree or G.degree().
+The node degree is the number of edges adjacent to the node.
+The weighted node degree is the sum of the edge weights for
+edges incident to that node.
+This object provides an iterator for (node, degree) as well as
+lookup for the degree for a single node.
+Parameters
+----------
+nbunch : single node, container, or all nodes (default= all nodes)
+The view will only report edges incident to these nodes.
+weight : string or None, optional (default=None)
+The name of an 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
+-------
+If a single nodes is requested
+deg : int
+Degree of the node
+OR if multiple nodes are requested
+nd_iter : iterator
+The iterator returns two-tuples of (node, degree).
+See Also
+--------
+out_degree, in_degree
+Examples
+--------
+>>> G = nx.MultiDiGraph()
+>>> nx.add_path(G, [0, 1, 2, 3])
+>>> G.degree(0) # node 0 with degree 1
+1
+>>> list(G.degree([0, 1, 2]))
+[(0, 1), (1, 2), (2, 2)]
+"""
+return DiMultiDegreeView(self)
+@property
+def in_degree(self):
+"""A DegreeView for (node, in_degree) or in_degree for single node.
+The node in-degree is the number of edges pointing in to the node.
+The weighted node degree is the sum of the edge weights for
+edges incident to that node.
+This object provides an iterator for (node, degree) as well as
+lookup for the degree for a single node.
+Parameters
+----------
+nbunch : single node, container, or all nodes (default= all nodes)
+The view will only report edges incident to these nodes.
+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
+-------
+If a single node is requested
+deg : int
+Degree of the node
+OR if multiple nodes are requested
+nd_iter : iterator
+The iterator returns two-tuples of (node, in-degree).
+See Also
+--------
+degree, out_degree
+Examples
+--------
+>>> G = nx.MultiDiGraph()
+>>> nx.add_path(G, [0, 1, 2, 3])
+>>> G.in_degree(0) # node 0 with degree 0
+0
+>>> list(G.in_degree([0, 1, 2]))
+[(0, 0), (1, 1), (2, 1)]
+"""
+return InMultiDegreeView(self)
+@property
+def out_degree(self):
+"""Returns an iterator for (node, out-degree) or out-degree for single node.
+out_degree(self, nbunch=None, weight=None)
+The node out-degree is the number of edges pointing out of the node.
+This function returns the out-degree for a single node or an iterator
+for a bunch of nodes or if nothing is passed as argument.
+Parameters
+----------
+nbunch : single node, container, or all nodes (default= all nodes)
+The view will only report edges incident to these nodes.
+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.
+Returns
+-------
+If a single node is requested
+deg : int
+Degree of the node
+OR if multiple nodes are requested
+nd_iter : iterator
+The iterator returns two-tuples of (node, out-degree).
+See Also
+--------
+degree, in_degree
+Examples
+--------
+>>> G = nx.MultiDiGraph()
+>>> nx.add_path(G, [0, 1, 2, 3])
+>>> G.out_degree(0) # node 0 with degree 1
+1
+>>> list(G.out_degree([0, 1, 2]))
+[(0, 1), (1, 1), (2, 1)]
+"""
+return OutMultiDegreeView(self)
+def is_multigraph(self):
+"""Returns True if graph is a multigraph, False otherwise."""
+return True
+def is_directed(self):
+"""Returns True if graph is directed, False otherwise."""
+return True
+def to_undirected(self, reciprocal=False, as_view=False):
+"""Returns an undirected representation of the digraph.
+Parameters
+----------
+reciprocal : bool (optional)
+If True only keep edges that appear in both directions
+in the original digraph.
+as_view : bool (optional, default=False)
+If True return an undirected view of the original directed graph.
+Returns
+-------
+G : MultiGraph
+An undirected graph with the same name and nodes and
+with edge (u, v, data) if either (u, v, data) or (v, u, data)
+is in the digraph.  If both edges exist in digraph and
+their edge data is different, only one edge is created
+with an arbitrary choice of which edge data to use.
+You must check and correct for this manually if desired.
+See Also
+--------
+MultiGraph, copy, add_edge, add_edges_from
+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=MultiiGraph(G) which
+returns a shallow copy of the data.
+See the Python copy module for more information on shallow
+and deep copies, https://docs.python.org/2/library/copy.html.
+Warning: If you have subclassed MultiDiGraph to use dict-like
+objects in the data structure, those changes do not transfer
+to the MultiGraph created by this method.
+Examples
+--------
+>>> G = nx.path_graph(2)   # or MultiGraph, etc
+>>> H = G.to_directed()
+>>> list(H.edges)
+[(0, 1), (1, 0)]
+>>> G2 = H.to_undirected()
+>>> list(G2.edges)
+[(0, 1)]
+"""
+graph_class = self.to_undirected_class()
+if as_view is True:
+return nx.graphviews.generic_graph_view(self, graph_class)
+# deepcopy when not a view
+G = graph_class()
+G.graph.update(deepcopy(self.graph))
+G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+if reciprocal is True:
+G.add_edges_from((u, v, key, deepcopy(data))
+for u, nbrs in self._adj.items()
+for v, keydict in nbrs.items()
+for key, data in keydict.items()
+if v in self._pred[u] and key in self._pred[u][v])
+else:
+G.add_edges_from((u, v, key, deepcopy(data))
+for u, nbrs in self._adj.items()
+for v, keydict in nbrs.items()
+for key, data in keydict.items())
+return G
+def reverse(self, copy=True):
+"""Returns the reverse of the graph.
+The reverse is a graph with the same nodes and edges
+but with the directions of the edges reversed.
+Parameters
+----------
+copy : bool optional (default=True)
+If True, return a new DiGraph holding the reversed edges.
+If False, the reverse graph is created using a view of
+the original graph.
+"""
+if copy:
+H = self.__class__()
+H.graph.update(deepcopy(self.graph))
+H.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+H.add_edges_from((v, u, k, deepcopy(d)) for u, v, k, d
+in self.edges(keys=True, data=True))
+return H
+return nx.graphviews.reverse_view(self)

Mercurial > repos > guerler > springsuite

comparison planemo/lib/python3.7/site-packages/networkx/classes/multidigraph.py @ 1:56ad4e20f292 draft