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r""" This module provides functions and operations for bipartite
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graphs.  Bipartite graphs `B = (U, V, E)` have two node sets `U,V` and edges in
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`E` that only connect nodes from opposite sets. It is common in the literature
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to use an spatial analogy referring to the two node sets as top and bottom nodes.
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The bipartite algorithms are not imported into the networkx namespace
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at the top level so the easiest way to use them is with:
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>>> import networkx as nx
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>>> from networkx.algorithms import bipartite
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NetworkX does not have a custom bipartite graph class but the Graph()
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or DiGraph() classes can be used to represent bipartite graphs. However,
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you have to keep track of which set each node belongs to, and make
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sure that there is no edge between nodes of the same set. The convention used
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in NetworkX is to use a node attribute named `bipartite` with values 0 or 1 to
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identify the sets each node belongs to. This convention is not enforced in
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the source code of bipartite functions, it's only a recommendation.
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For example:
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>>> B = nx.Graph()
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>>> # Add nodes with the node attribute "bipartite"
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>>> B.add_nodes_from([1, 2, 3, 4], bipartite=0)
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>>> B.add_nodes_from(['a', 'b', 'c'], bipartite=1)
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>>> # Add edges only between nodes of opposite node sets
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>>> B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (4, 'a')])
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Many algorithms of the bipartite module of NetworkX require, as an argument, a
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container with all the nodes that belong to one set, in addition to the bipartite
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graph `B`. The functions in the bipartite package do not check that the node set
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is actually correct nor that the input graph is actually bipartite.
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If `B` is connected, you can find the two node sets using a two-coloring 
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algorithm: 
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>>> nx.is_connected(B)
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True
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>>> bottom_nodes, top_nodes = bipartite.sets(B)
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However, if the input graph is not connected, there are more than one possible
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colorations. This is the reason why we require the user to pass a container
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with all nodes of one bipartite node set as an argument to most bipartite
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functions. In the face of ambiguity, we refuse the temptation to guess and
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raise an :exc:`AmbiguousSolution <networkx.AmbiguousSolution>`
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Exception if the input graph for
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:func:`bipartite.sets <networkx.algorithms.bipartite.basic.sets>`
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is disconnected.
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Using the `bipartite` node attribute, you can easily get the two node sets:
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>>> top_nodes = {n for n, d in B.nodes(data=True) if d['bipartite']==0}
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>>> bottom_nodes = set(B) - top_nodes
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So you can easily use the bipartite algorithms that require, as an argument, a
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container with all nodes that belong to one node set:
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>>> print(round(bipartite.density(B, bottom_nodes), 2))
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0.5
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>>> G = bipartite.projected_graph(B, top_nodes)
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All bipartite graph generators in NetworkX build bipartite graphs with the 
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`bipartite` node attribute. Thus, you can use the same approach:
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>>> RB = bipartite.random_graph(5, 7, 0.2)
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>>> RB_top = {n for n, d in RB.nodes(data=True) if d['bipartite']==0}
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>>> RB_bottom = set(RB) - RB_top
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>>> list(RB_top)
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[0, 1, 2, 3, 4]
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>>> list(RB_bottom)
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[5, 6, 7, 8, 9, 10, 11]
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For other bipartite graph generators see
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:mod:`Generators <networkx.algorithms.bipartite.generators>`.
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"""
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from networkx.algorithms.bipartite.basic import *
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from networkx.algorithms.bipartite.centrality import *
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from networkx.algorithms.bipartite.cluster import *
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from networkx.algorithms.bipartite.covering import *
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from networkx.algorithms.bipartite.edgelist import *
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from networkx.algorithms.bipartite.matching import *
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from networkx.algorithms.bipartite.matrix import *
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from networkx.algorithms.bipartite.projection import *
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from networkx.algorithms.bipartite.redundancy import *
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from networkx.algorithms.bipartite.spectral import *
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from networkx.algorithms.bipartite.generators import *