# -*- coding: utf-8 -*-
#    Copyright (C) 2017-2018 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
#
# Authors: Aric Hagberg <aric.hagberg@gmail.com>
#          Jordi Torrents <jtorrents@milnou.net>
"""One-mode (unipartite) projections of bipartite graphs."""
import networkx as nx
from networkx.utils import not_implemented_for

__all__ = ['project',
           'projected_graph',
           'weighted_projected_graph',
           'collaboration_weighted_projected_graph',
           'overlap_weighted_projected_graph',
           'generic_weighted_projected_graph']


def projected_graph(B, nodes, multigraph=False):
    r"""Returns the projection of B onto one of its node sets.

    Returns the graph G that is the projection of the bipartite graph B
    onto the specified nodes. They retain their attributes and are connected
    in G if they have a common neighbor in B.

    Parameters
    ----------
    B : NetworkX graph
      The input graph should be bipartite.

    nodes : list or iterable
      Nodes to project onto (the "bottom" nodes).

    multigraph: bool (default=False)
       If True return a multigraph where the multiple edges represent multiple
       shared neighbors.  They edge key in the multigraph is assigned to the
       label of the neighbor.

    Returns
    -------
    Graph : NetworkX graph or multigraph
       A graph that is the projection onto the given nodes.

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> B = nx.path_graph(4)
    >>> G = bipartite.projected_graph(B, [1, 3])
    >>> list(G)
    [1, 3]
    >>> list(G.edges())
    [(1, 3)]

    If nodes `a`, and `b` are connected through both nodes 1 and 2 then
    building a multigraph results in two edges in the projection onto
    [`a`, `b`]:

    >>> B = nx.Graph()
    >>> B.add_edges_from([('a', 1), ('b', 1), ('a', 2), ('b', 2)])
    >>> G = bipartite.projected_graph(B, ['a', 'b'], multigraph=True)
    >>> print([sorted((u, v)) for u, v in G.edges()])
    [['a', 'b'], ['a', 'b']]

    Notes
    -----
    No attempt is made to verify that the input graph B is bipartite.
    Returns a simple graph that is the projection of the bipartite graph B
    onto the set of nodes given in list nodes.  If multigraph=True then
    a multigraph is returned with an edge for every shared neighbor.

    Directed graphs are allowed as input.  The output will also then
    be a directed graph with edges if there is a directed path between
    the nodes.

    The graph and node properties are (shallow) copied to the projected graph.

    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    is_bipartite,
    is_bipartite_node_set,
    sets,
    weighted_projected_graph,
    collaboration_weighted_projected_graph,
    overlap_weighted_projected_graph,
    generic_weighted_projected_graph
    """
    if B.is_multigraph():
        raise nx.NetworkXError("not defined for multigraphs")
    if B.is_directed():
        directed = True
        if multigraph:
            G = nx.MultiDiGraph()
        else:
            G = nx.DiGraph()
    else:
        directed = False
        if multigraph:
            G = nx.MultiGraph()
        else:
            G = nx.Graph()
    G.graph.update(B.graph)
    G.add_nodes_from((n, B.nodes[n]) for n in nodes)
    for u in nodes:
        nbrs2 = set(v for nbr in B[u] for v in B[nbr] if v != u)
        if multigraph:
            for n in nbrs2:
                if directed:
                    links = set(B[u]) & set(B.pred[n])
                else:
                    links = set(B[u]) & set(B[n])
                for l in links:
                    if not G.has_edge(u, n, l):
                        G.add_edge(u, n, key=l)
        else:
            G.add_edges_from((u, n) for n in nbrs2)
    return G


@not_implemented_for('multigraph')
def weighted_projected_graph(B, nodes, ratio=False):
    r"""Returns a weighted projection of B onto one of its node sets.

    The weighted projected graph is the projection of the bipartite
    network B onto the specified nodes with weights representing the
    number of shared neighbors or the ratio between actual shared
    neighbors and possible shared neighbors if ``ratio is True`` [1]_.
    The nodes retain their attributes and are connected in the resulting
    graph if they have an edge to a common node in the original graph.

    Parameters
    ----------
    B : NetworkX graph
        The input graph should be bipartite.

    nodes : list or iterable
        Nodes to project onto (the "bottom" nodes).

    ratio: Bool (default=False)
        If True, edge weight is the ratio between actual shared neighbors
        and possible shared neighbors. If False, edges weight is the number
        of shared neighbors.

    Returns
    -------
    Graph : NetworkX graph
       A graph that is the projection onto the given nodes.

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> B = nx.path_graph(4)
    >>> G = bipartite.weighted_projected_graph(B, [1, 3])
    >>> list(G)
    [1, 3]
    >>> list(G.edges(data=True))
    [(1, 3, {'weight': 1})]
    >>> G = bipartite.weighted_projected_graph(B, [1, 3], ratio=True)
    >>> list(G.edges(data=True))
    [(1, 3, {'weight': 0.5})]

    Notes
    -----
    No attempt is made to verify that the input graph B is bipartite.
    The graph and node properties are (shallow) copied to the projected graph.

    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    is_bipartite,
    is_bipartite_node_set,
    sets,
    collaboration_weighted_projected_graph,
    overlap_weighted_projected_graph,
    generic_weighted_projected_graph
    projected_graph

    References
    ----------
    .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
        Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
        of Social Network Analysis. Sage Publications.
    """
    if B.is_directed():
        pred = B.pred
        G = nx.DiGraph()
    else:
        pred = B.adj
        G = nx.Graph()
    G.graph.update(B.graph)
    G.add_nodes_from((n, B.nodes[n]) for n in nodes)
    n_top = float(len(B) - len(nodes))
    for u in nodes:
        unbrs = set(B[u])
        nbrs2 = set((n for nbr in unbrs for n in B[nbr])) - set([u])
        for v in nbrs2:
            vnbrs = set(pred[v])
            common = unbrs & vnbrs
            if not ratio:
                weight = len(common)
            else:
                weight = len(common) / n_top
            G.add_edge(u, v, weight=weight)
    return G


@not_implemented_for('multigraph')
def collaboration_weighted_projected_graph(B, nodes):
    r"""Newman's weighted projection of B onto one of its node sets.

    The collaboration weighted projection is the projection of the
    bipartite network B onto the specified nodes with weights assigned
    using Newman's collaboration model [1]_:

    .. math::

        w_{u, v} = \sum_k \frac{\delta_{u}^{k} \delta_{v}^{k}}{d_k - 1}

    where `u` and `v` are nodes from the bottom bipartite node set,
    and `k` is a node of the top node set.
    The value `d_k` is the degree of node `k` in the bipartite
    network and `\delta_{u}^{k}` is 1 if node `u` is
    linked to node `k` in the original bipartite graph or 0 otherwise.

    The nodes retain their attributes and are connected in the resulting
    graph if have an edge to a common node in the original bipartite
    graph.

    Parameters
    ----------
    B : NetworkX graph
      The input graph should be bipartite.

    nodes : list or iterable
      Nodes to project onto (the "bottom" nodes).

    Returns
    -------
    Graph : NetworkX graph
       A graph that is the projection onto the given nodes.

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> B = nx.path_graph(5)
    >>> B.add_edge(1, 5)
    >>> G = bipartite.collaboration_weighted_projected_graph(B, [0, 2, 4, 5])
    >>> list(G)
    [0, 2, 4, 5]
    >>> for edge in G.edges(data=True): print(edge)
    ...
    (0, 2, {'weight': 0.5})
    (0, 5, {'weight': 0.5})
    (2, 4, {'weight': 1.0})
    (2, 5, {'weight': 0.5})

    Notes
    -----
    No attempt is made to verify that the input graph B is bipartite.
    The graph and node properties are (shallow) copied to the projected graph.

    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    is_bipartite,
    is_bipartite_node_set,
    sets,
    weighted_projected_graph,
    overlap_weighted_projected_graph,
    generic_weighted_projected_graph,
    projected_graph

    References
    ----------
    .. [1] Scientific collaboration networks: II.
        Shortest paths, weighted networks, and centrality,
        M. E. J. Newman, Phys. Rev. E 64, 016132 (2001).
    """
    if B.is_directed():
        pred = B.pred
        G = nx.DiGraph()
    else:
        pred = B.adj
        G = nx.Graph()
    G.graph.update(B.graph)
    G.add_nodes_from((n, B.nodes[n]) for n in nodes)
    for u in nodes:
        unbrs = set(B[u])
        nbrs2 = set(n for nbr in unbrs for n in B[nbr] if n != u)
        for v in nbrs2:
            vnbrs = set(pred[v])
            common_degree = (len(B[n]) for n in unbrs & vnbrs)
            weight = sum(1.0 / (deg - 1) for deg in common_degree if deg > 1)
            G.add_edge(u, v, weight=weight)
    return G


@not_implemented_for('multigraph')
def overlap_weighted_projected_graph(B, nodes, jaccard=True):
    r"""Overlap weighted projection of B onto one of its node sets.

    The overlap weighted projection is the projection of the bipartite
    network B onto the specified nodes with weights representing
    the Jaccard index between the neighborhoods of the two nodes in the
    original bipartite network [1]_:

    .. math::

        w_{v, u} = \frac{|N(u) \cap N(v)|}{|N(u) \cup N(v)|}

    or if the parameter 'jaccard' is False, the fraction of common
    neighbors by minimum of both nodes degree in the original
    bipartite graph [1]_:

    .. math::

        w_{v, u} = \frac{|N(u) \cap N(v)|}{min(|N(u)|, |N(v)|)}

    The nodes retain their attributes and are connected in the resulting
    graph if have an edge to a common node in the original bipartite graph.

    Parameters
    ----------
    B : NetworkX graph
        The input graph should be bipartite.

    nodes : list or iterable
        Nodes to project onto (the "bottom" nodes).

    jaccard: Bool (default=True)

    Returns
    -------
    Graph : NetworkX graph
       A graph that is the projection onto the given nodes.

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> B = nx.path_graph(5)
    >>> nodes = [0, 2, 4]
    >>> G = bipartite.overlap_weighted_projected_graph(B, nodes)
    >>> list(G)
    [0, 2, 4]
    >>> list(G.edges(data=True))
    [(0, 2, {'weight': 0.5}), (2, 4, {'weight': 0.5})]
    >>> G = bipartite.overlap_weighted_projected_graph(B, nodes, jaccard=False)
    >>> list(G.edges(data=True))
    [(0, 2, {'weight': 1.0}), (2, 4, {'weight': 1.0})]

    Notes
    -----
    No attempt is made to verify that the input graph B is bipartite.
    The graph and node properties are (shallow) copied to the projected graph.

    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    is_bipartite,
    is_bipartite_node_set,
    sets,
    weighted_projected_graph,
    collaboration_weighted_projected_graph,
    generic_weighted_projected_graph,
    projected_graph

    References
    ----------
    .. [1] Borgatti, S.P. and Halgin, D. In press. Analyzing Affiliation
        Networks. In Carrington, P. and Scott, J. (eds) The Sage Handbook
        of Social Network Analysis. Sage Publications.

    """
    if B.is_directed():
        pred = B.pred
        G = nx.DiGraph()
    else:
        pred = B.adj
        G = nx.Graph()
    G.graph.update(B.graph)
    G.add_nodes_from((n, B.nodes[n]) for n in nodes)
    for u in nodes:
        unbrs = set(B[u])
        nbrs2 = set((n for nbr in unbrs for n in B[nbr])) - set([u])
        for v in nbrs2:
            vnbrs = set(pred[v])
            if jaccard:
                wt = float(len(unbrs & vnbrs)) / len(unbrs | vnbrs)
            else:
                wt = float(len(unbrs & vnbrs)) / min(len(unbrs), len(vnbrs))
            G.add_edge(u, v, weight=wt)
    return G


@not_implemented_for('multigraph')
def generic_weighted_projected_graph(B, nodes, weight_function=None):
    r"""Weighted projection of B with a user-specified weight function.

    The bipartite network B is projected on to the specified nodes
    with weights computed by a user-specified function.  This function
    must accept as a parameter the neighborhood sets of two nodes and
    return an integer or a float.

    The nodes retain their attributes and are connected in the resulting graph
    if they have an edge to a common node in the original graph.

    Parameters
    ----------
    B : NetworkX graph
        The input graph should be bipartite.

    nodes : list or iterable
        Nodes to project onto (the "bottom" nodes).

    weight_function : function
        This function must accept as parameters the same input graph
        that this function, and two nodes; and return an integer or a float.
        The default function computes the number of shared neighbors.

    Returns
    -------
    Graph : NetworkX graph
       A graph that is the projection onto the given nodes.

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> # Define some custom weight functions
    >>> def jaccard(G, u, v):
    ...     unbrs = set(G[u])
    ...     vnbrs = set(G[v])
    ...     return float(len(unbrs & vnbrs)) / len(unbrs | vnbrs)
    ...
    >>> def my_weight(G, u, v, weight='weight'):
    ...     w = 0
    ...     for nbr in set(G[u]) & set(G[v]):
    ...         w += G[u][nbr].get(weight, 1) + G[v][nbr].get(weight, 1)
    ...     return w
    ...
    >>> # A complete bipartite graph with 4 nodes and 4 edges
    >>> B = nx.complete_bipartite_graph(2, 2)
    >>> # Add some arbitrary weight to the edges
    >>> for i,(u,v) in enumerate(B.edges()):
    ...     B.edges[u, v]['weight'] = i + 1
    ... 
    >>> for edge in B.edges(data=True):
    ...     print(edge)
    ...
    (0, 2, {'weight': 1})
    (0, 3, {'weight': 2})
    (1, 2, {'weight': 3})
    (1, 3, {'weight': 4})
    >>> # By default, the weight is the number of shared neighbors
    >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1])
    >>> print(list(G.edges(data=True)))
    [(0, 1, {'weight': 2})]
    >>> # To specify a custom weight function use the weight_function parameter
    >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1], weight_function=jaccard)
    >>> print(list(G.edges(data=True)))
    [(0, 1, {'weight': 1.0})]
    >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1], weight_function=my_weight)
    >>> print(list(G.edges(data=True)))
    [(0, 1, {'weight': 10})]

    Notes
    -----
    No attempt is made to verify that the input graph B is bipartite.
    The graph and node properties are (shallow) copied to the projected graph.

    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    is_bipartite,
    is_bipartite_node_set,
    sets,
    weighted_projected_graph,
    collaboration_weighted_projected_graph,
    overlap_weighted_projected_graph,
    projected_graph

    """
    if B.is_directed():
        pred = B.pred
        G = nx.DiGraph()
    else:
        pred = B.adj
        G = nx.Graph()
    if weight_function is None:
        def weight_function(G, u, v):
            # Notice that we use set(pred[v]) for handling the directed case.
            return len(set(G[u]) & set(pred[v]))
    G.graph.update(B.graph)
    G.add_nodes_from((n, B.nodes[n]) for n in nodes)
    for u in nodes:
        nbrs2 = set((n for nbr in set(B[u]) for n in B[nbr])) - set([u])
        for v in nbrs2:
            weight = weight_function(B, u, v)
            G.add_edge(u, v, weight=weight)
    return G


def project(B, nodes, create_using=None):
    return projected_graph(B, nodes)