"""
Provides functions for finding and testing for locally `(k, l)`-connected
graphs.

"""
import copy
import networkx as nx

__all__ = ["kl_connected_subgraph", "is_kl_connected"]


def kl_connected_subgraph(G, k, l, low_memory=False, same_as_graph=False):
    """Returns the maximum locally `(k, l)`-connected subgraph of `G`.

    A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the
    graph there are at least `l` edge-disjoint paths of length at most `k`
    joining `u` to `v`.

    Parameters
    ----------
    G : NetworkX graph
        The graph in which to find a maximum locally `(k, l)`-connected
        subgraph.

    k : integer
        The maximum length of paths to consider. A higher number means a looser
        connectivity requirement.

    l : integer
        The number of edge-disjoint paths. A higher number means a stricter
        connectivity requirement.

    low_memory : bool
        If this is True, this function uses an algorithm that uses slightly
        more time but less memory.

    same_as_graph : bool
        If True then return a tuple of the form `(H, is_same)`,
        where `H` is the maximum locally `(k, l)`-connected subgraph and
        `is_same` is a Boolean representing whether `G` is locally `(k,
        l)`-connected (and hence, whether `H` is simply a copy of the input
        graph `G`).

    Returns
    -------
    NetworkX graph or two-tuple
        If `same_as_graph` is True, then this function returns a
        two-tuple as described above. Otherwise, it returns only the maximum
        locally `(k, l)`-connected subgraph.

    See also
    --------
    is_kl_connected

    References
    ----------
    .. [1]: Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid
            Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg,
            2004. 89--104.

    """
    H = copy.deepcopy(G)  # subgraph we construct by removing from G

    graphOK = True
    deleted_some = True  # hack to start off the while loop
    while deleted_some:
        deleted_some = False
        # We use `for edge in list(H.edges()):` instead of
        # `for edge in H.edges():` because we edit the graph `H` in
        # the loop. Hence using an iterator will result in
        # `RuntimeError: dictionary changed size during iteration`
        for edge in list(H.edges()):
            (u, v) = edge
            # Get copy of graph needed for this search
            if low_memory:
                verts = {u, v}
                for i in range(k):
                    for w in verts.copy():
                        verts.update(G[w])
                G2 = G.subgraph(verts).copy()
            else:
                G2 = copy.deepcopy(G)
            ###
            path = [u, v]
            cnt = 0
            accept = 0
            while path:
                cnt += 1  # Found a path
                if cnt >= l:
                    accept = 1
                    break
                # record edges along this graph
                prev = u
                for w in path:
                    if prev != w:
                        G2.remove_edge(prev, w)
                        prev = w
                #                path = shortest_path(G2, u, v, k) # ??? should "Cutoff" be k+1?
                try:
                    path = nx.shortest_path(G2, u, v)  # ??? should "Cutoff" be k+1?
                except nx.NetworkXNoPath:
                    path = False
            # No Other Paths
            if accept == 0:
                H.remove_edge(u, v)
                deleted_some = True
                if graphOK:
                    graphOK = False
    # We looked through all edges and removed none of them.
    # So, H is the maximal (k,l)-connected subgraph of G
    if same_as_graph:
        return (H, graphOK)
    return H


def is_kl_connected(G, k, l, low_memory=False):
    """Returns True if and only if `G` is locally `(k, l)`-connected.

    A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the
    graph there are at least `l` edge-disjoint paths of length at most `k`
    joining `u` to `v`.

    Parameters
    ----------
    G : NetworkX graph
        The graph to test for local `(k, l)`-connectedness.

    k : integer
        The maximum length of paths to consider. A higher number means a looser
        connectivity requirement.

    l : integer
        The number of edge-disjoint paths. A higher number means a stricter
        connectivity requirement.

    low_memory : bool
        If this is True, this function uses an algorithm that uses slightly
        more time but less memory.

    Returns
    -------
    bool
        Whether the graph is locally `(k, l)`-connected subgraph.

    See also
    --------
    kl_connected_subgraph

    References
    ----------
    .. [1]: Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid
            Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg,
            2004. 89--104.

    """
    graphOK = True
    for edge in G.edges():
        (u, v) = edge
        # Get copy of graph needed for this search
        if low_memory:
            verts = {u, v}
            for i in range(k):
                [verts.update(G.neighbors(w)) for w in verts.copy()]
            G2 = G.subgraph(verts)
        else:
            G2 = copy.deepcopy(G)
        ###
        path = [u, v]
        cnt = 0
        accept = 0
        while path:
            cnt += 1  # Found a path
            if cnt >= l:
                accept = 1
                break
            # record edges along this graph
            prev = u
            for w in path:
                if w != prev:
                    G2.remove_edge(prev, w)
                    prev = w
            #            path = shortest_path(G2, u, v, k) # ??? should "Cutoff" be k+1?
            try:
                path = nx.shortest_path(G2, u, v)  # ??? should "Cutoff" be k+1?
            except nx.NetworkXNoPath:
                path = False
        # No Other Paths
        if accept == 0:
            graphOK = False
            break
    # return status
    return graphOK