a œç1bµ)ã@sTdZddlZgd¢Zddd„Zdd„Zd d „Zd d „Zd d„Zdd„Z ddd„Z dS)zA Operations on graphs including union, intersection, difference. éN)ÚunionÚcomposeÚdisjoint_unionÚ intersectionÚ differenceÚsymmetric_differenceÚ full_join©NNcCs0|dur ddl}|jdtddt ||g|¡S)a~Return the union of graphs G and H. Graphs G and H must be disjoint after the renaming takes place, otherwise an exception is raised. Parameters ---------- G,H : graph A NetworkX graph rename : tuple , default=(None, None) Node names of G and H can be changed by specifying the tuple rename=('G-','H-') (for example). Node "u" in G is then renamed "G-u" and "v" in H is renamed "H-v". name : string Specify the name for the union graph .. deprecated:: 2.7 This is deprecated and will be removed in version v3.0. Returns ------- U : A union graph with the same type as G. Notes ----- To force a disjoint union with node relabeling, use disjoint_union(G,H) or convert_node_labels_to integers(). Graph, edge, and node attributes are propagated from G and H to the union graph. If a graph attribute is present in both G and H the value from H is used. Examples -------- >>> G = nx.Graph([(0, 1), (0, 2), (1, 2)]) >>> H = nx.Graph([(0, 1), (0, 3), (1, 3), (1, 2)]) >>> U = nx.union(G, H, rename=("G", "H")) >>> U.nodes NodeView(('G0', 'G1', 'G2', 'H0', 'H1', 'H3', 'H2')) >>> U.edges EdgeView([('G0', 'G1'), ('G0', 'G2'), ('G1', 'G2'), ('H0', 'H1'), ('H0', 'H3'), ('H1', 'H3'), ('H1', 'H2')]) See Also -------- disjoint_union Nrz?name parameter is deprecated and will be removed in version 3.0é)Ú stacklevel)ÚwarningsÚwarnÚDeprecationWarningÚnxZ union_all)ÚGÚHÚrenameÚnamer ©rúClib/python3.9/site-packages/networkx/algorithms/operators/binary.pyrs1ýrcCst ||g¡S)aIReturn the disjoint union of graphs G and H. This algorithm forces distinct integer node labels. Parameters ---------- G,H : graph A NetworkX graph Returns ------- U : A union graph with the same type as G. Notes ----- A new graph is created, of the same class as G. It is recommended that G and H be either both directed or both undirected. The nodes of G are relabeled 0 to len(G)-1, and the nodes of H are relabeled len(G) to len(G)+len(H)-1. Graph, edge, and node attributes are propagated from G and H to the union graph. If a graph attribute is present in both G and H the value from H is used. Examples -------- >>> G = nx.Graph([(0, 1), (0, 2), (1, 2)]) >>> H = nx.Graph([(0, 3), (1, 2), (2, 3)]) >>> G.nodes[0]["key1"] = 5 >>> H.nodes[0]["key2"] = 10 >>> U = nx.disjoint_union(G, H) >>> U.nodes(data=True) NodeDataView({0: {'key1': 5}, 1: {}, 2: {}, 3: {'key2': 10}, 4: {}, 5: {}, 6: {}}) >>> U.edges EdgeView([(0, 1), (0, 2), (1, 2), (3, 4), (4, 6), (5, 6)]) )rZdisjoint_union_all©rrrrrrNs&rcCst ||g¡S)a^Returns a new graph that contains only the nodes and the edges that exist in both G and H. Parameters ---------- G,H : graph A NetworkX graph. G and H can have different node sets but must be both graphs or both multigraphs. Raises ------ NetworkXError If one is a MultiGraph and the other one is a graph. Returns ------- GH : A new graph with the same type as G. Notes ----- Attributes from the graph, nodes, and edges are not copied to the new graph. If you want a new graph of the intersection of G and H with the attributes (including edge data) from G use remove_nodes_from() as follows >>> G = nx.path_graph(3) >>> H = nx.path_graph(5) >>> R = G.copy() >>> R.remove_nodes_from(n for n in G if n not in H) >>> R.remove_edges_from(e for e in G.edges if e not in H.edges) Examples -------- >>> G = nx.Graph([(0, 1), (0, 2), (1, 2)]) >>> H = nx.Graph([(0, 3), (1, 2), (2, 3)]) >>> R = nx.intersection(G, H) >>> R.nodes NodeView((0, 1, 2)) >>> R.edges EdgeView([(1, 2)]) )rZintersection_allrrrrrws)rcCs~| ¡| ¡kst d¡‚t |¡}t|ƒt|ƒkr>t d¡‚| ¡rT|jdd}n| ¡}|D]}|j|Žs`|j|Žq`|S)a®Returns a new graph that contains the edges that exist in G but not in H. The node sets of H and G must be the same. Parameters ---------- G,H : graph A NetworkX graph. G and H must have the same node sets. Returns ------- D : A new graph with the same type as G. Notes ----- Attributes from the graph, nodes, and edges are not copied to the new graph. If you want a new graph of the difference of G and H with the attributes (including edge data) from G use remove_nodes_from() as follows: >>> G = nx.path_graph(3) >>> H = nx.path_graph(5) >>> R = G.copy() >>> R.remove_nodes_from(n for n in G if n in H) Examples -------- >>> G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> H = nx.Graph([(0, 1), (1, 2), (0, 3)]) >>> R = nx.difference(G, H) >>> R.nodes NodeView((0, 1, 2, 3)) >>> R.edges EdgeView([(0, 2), (1, 3)]) ú+G and H must both be graphs or multigraphs.úNode sets of graphs not equalT©Úkeys)Ú is_multigraphrÚ NetworkXErrorÚcreate_empty_copyÚsetÚedgesÚhas_edgeÚadd_edge)rrÚRrÚerrrr£s%     rcCsÞ| ¡| ¡kst d¡‚t |¡}t|ƒt|ƒkr>t d¡‚t|ƒ}t|ƒ}| |¡}| |¡| ¡rx|jdd}n| ¡}|D]}|j|Žs„|j |Žq„| ¡r´|jdd}n| ¡}|D]}|j|ŽsÀ|j |ŽqÀ|S)a§Returns new graph with edges that exist in either G or H but not both. The node sets of H and G must be the same. Parameters ---------- G,H : graph A NetworkX graph. G and H must have the same node sets. Returns ------- D : A new graph with the same type as G. Notes ----- Attributes from the graph, nodes, and edges are not copied to the new graph. Examples -------- >>> G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3)]) >>> H = nx.Graph([(0, 1), (1, 2), (0, 3)]) >>> R = nx.symmetric_difference(G, H) >>> R.nodes NodeView((0, 1, 2, 3)) >>> R.edges EdgeView([(0, 2), (0, 3), (1, 3)]) rrTr) rrrrrrZadd_nodes_fromrr r!)rrr"ZgnodesZhnodesZnodesrr#rrrrÙs,         rcCst ||g¡S)a¨Returns a new graph of G composed with H. Composition is the simple union of the node sets and edge sets. The node sets of G and H do not need to be disjoint. Parameters ---------- G, H : graph A NetworkX graph Returns ------- C: A new graph with the same type as G Notes ----- It is recommended that G and H be either both directed or both undirected. Attributes from H take precedent over attributes from G. For MultiGraphs, the edges are identified by incident nodes AND edge-key. This can cause surprises (i.e., edge `(1, 2)` may or may not be the same in two graphs) if you use MultiGraph without keeping track of edge keys. Examples -------- >>> G = nx.Graph([(0, 1), (0, 2)]) >>> H = nx.Graph([(0, 1), (1, 2)]) >>> R = nx.compose(G, H) >>> R.nodes NodeView((0, 1, 2)) >>> R.edges EdgeView([(0, 1), (0, 2), (1, 2)]) )rZ compose_allrrrrrs"rcCs|t|||ƒ}dd„}|||dƒ}|||dƒ}|D]}|D]}| ||¡q>> G = nx.Graph([(0, 1), (0, 2)]) >>> H = nx.Graph([(3, 4)]) >>> R = nx.full_join(G, H, rename=("G", "H")) >>> R.nodes NodeView(('G0', 'G1', 'G2', 'H3', 'H4')) >>> R.edges EdgeView([('G0', 'G1'), ('G0', 'G2'), ('G0', 'H3'), ('G0', 'H4'), ('G1', 'H3'), ('G1', 'H4'), ('G2', 'H3'), ('G2', 'H4'), ('H3', 'H4')]) See Also -------- union disjoint_union cs$ˆdur |S‡fdd„}t ||¡S)Ncs$t|tƒrˆ|}n ˆt|ƒ}|S)N)Ú isinstanceÚstrÚrepr)Úxr©ÚprefixrrÚlabelws   z,full_join..add_prefix..label)rZ relabel_nodes)Zgraphr)r*rr(rÚ add_prefixss zfull_join..add_prefixré)rr!Z is_directed)rrrr"r+ÚiÚjrrrr<s5  r)r N)r ) Ú__doc__ZnetworkxrÚ__all__rrrrrrrrrrrÚs =),6>%