# -*- coding: utf-8 -*-
"""
Maximum flow (and minimum cut) algorithms on capacitated graphs.
"""
import networkx as nx

# Define the default flow function for computing maximum flow.
from .edmondskarp import edmonds_karp
from .fordfulkerson import ford_fulkerson
from .preflowpush import preflow_push
from .shortestaugmentingpath import shortest_augmenting_path
from .utils import build_flow_dict
default_flow_func = preflow_push

__all__ = ['maximum_flow',
           'maximum_flow_value',
           'minimum_cut',
           'minimum_cut_value']


def maximum_flow(G, s, t, capacity='capacity', flow_func=None, **kwargs):
    """Find a maximum single-commodity flow.

    Parameters
    ----------
    G : NetworkX graph
        Edges of the graph are expected to have an attribute called
        'capacity'. If this attribute is not present, the edge is
        considered to have infinite capacity.

    s : node
        Source node for the flow.

    t : node
        Sink node for the flow.

    capacity : string
        Edges of the graph G are expected to have an attribute capacity
        that indicates how much flow the edge can support. If this
        attribute is not present, the edge is considered to have
        infinite capacity. Default value: 'capacity'.

    flow_func : function
        A function for computing the maximum flow among a pair of nodes
        in a capacitated graph. The function has to accept at least three
        parameters: a Graph or Digraph, a source node, and a target node.
        And return a residual network that follows NetworkX conventions
        (see Notes). If flow_func is None, the default maximum
        flow function (:meth:`preflow_push`) is used. See below for
        alternative algorithms. The choice of the default function may change
        from version to version and should not be relied on. Default value:
        None.

    kwargs : Any other keyword parameter is passed to the function that
        computes the maximum flow.

    Returns
    -------
    flow_value : integer, float
        Value of the maximum flow, i.e., net outflow from the source.

    flow_dict : dict
        A dictionary containing the value of the flow that went through
        each edge.

    Raises
    ------
    NetworkXError
        The algorithm does not support MultiGraph and MultiDiGraph. If
        the input graph is an instance of one of these two classes, a
        NetworkXError is raised.

    NetworkXUnbounded
        If the graph has a path of infinite capacity, the value of a
        feasible flow on the graph is unbounded above and the function
        raises a NetworkXUnbounded.

    See also
    --------
    :meth:`maximum_flow_value`
    :meth:`minimum_cut`
    :meth:`minimum_cut_value`
    :meth:`edmonds_karp`
    :meth:`ford_fulkerson`
    :meth:`preflow_push`
    :meth:`shortest_augmenting_path`

    Notes
    -----
    The function used in the flow_func paramter has to return a residual
    network that follows NetworkX conventions:

    The residual network :samp:`R` from an input graph :samp:`G` has the
    same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
    of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
    self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
    in :samp:`G`.

    For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
    is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
    in :samp:`G` or zero otherwise. If the capacity is infinite,
    :samp:`R[u][v]['capacity']` will have a high arbitrary finite value
    that does not affect the solution of the problem. This value is stored in
    :samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
    :samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
    satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.

    The flow value, defined as the total flow into :samp:`t`, the sink, is
    stored in :samp:`R.graph['flow_value']`. Reachability to :samp:`t` using
    only edges :samp:`(u, v)` such that
    :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
    :samp:`s`-:samp:`t` cut.

    Specific algorithms may store extra data in :samp:`R`.

    The function should supports an optional boolean parameter value_only. When
    True, it can optionally terminate the algorithm as soon as the maximum flow
    value and the minimum cut can be determined.

    The legacy :meth:`ford_fulkerson` maximum flow implementation doesn't
    follow this conventions but it is supported as a valid flow_func.

    Examples
    --------
    >>> import networkx as nx
    >>> G = nx.DiGraph()
    >>> G.add_edge('x','a', capacity=3.0)
    >>> G.add_edge('x','b', capacity=1.0)
    >>> G.add_edge('a','c', capacity=3.0)
    >>> G.add_edge('b','c', capacity=5.0)
    >>> G.add_edge('b','d', capacity=4.0)
    >>> G.add_edge('d','e', capacity=2.0)
    >>> G.add_edge('c','y', capacity=2.0)
    >>> G.add_edge('e','y', capacity=3.0)

    maximum_flow returns both the value of the maximum flow and a
    dictionary with all flows.

    >>> flow_value, flow_dict = nx.maximum_flow(G, 'x', 'y')
    >>> flow_value
    3.0
    >>> print(flow_dict['x']['b'])
    1.0

    You can also use alternative algorithms for computing the
    maximum flow by using the flow_func parameter.

    >>> from networkx.algorithms.flow import shortest_augmenting_path
    >>> flow_value == nx.maximum_flow(G, 'x', 'y',
    ...                         flow_func=shortest_augmenting_path)[0]
    True

    """
    if flow_func is None:
        if kwargs:
            raise nx.NetworkXError("You have to explicitly set a flow_func if"
                                   " you need to pass parameters via kwargs.")
        flow_func = default_flow_func

    if not callable(flow_func):
        raise nx.NetworkXError("flow_func has to be callable.")

    if flow_func is ford_fulkerson:
        R = flow_func(G, s, t, capacity=capacity)
        flow_dict = R.graph['flow_dict']
    else:
        R = flow_func(G, s, t, capacity=capacity, value_only=False, **kwargs)
        flow_dict = build_flow_dict(G, R)

    return (R.graph['flow_value'], flow_dict)


def maximum_flow_value(G, s, t, capacity='capacity', flow_func=None, **kwargs):
    """Find the value of maximum single-commodity flow.

    Parameters
    ----------
    G : NetworkX graph
        Edges of the graph are expected to have an attribute called
        'capacity'. If this attribute is not present, the edge is
        considered to have infinite capacity.

    s : node
        Source node for the flow.

    t : node
        Sink node for the flow.

    capacity : string
        Edges of the graph G are expected to have an attribute capacity
        that indicates how much flow the edge can support. If this
        attribute is not present, the edge is considered to have
        infinite capacity. Default value: 'capacity'.

    flow_func : function
        A function for computing the maximum flow among a pair of nodes
        in a capacitated graph. The function has to accept at least three
        parameters: a Graph or Digraph, a source node, and a target node.
        And return a residual network that follows NetworkX conventions
        (see Notes). If flow_func is None, the default maximum
        flow function (:meth:`preflow_push`) is used. See below for
        alternative algorithms. The choice of the default function may change
        from version to version and should not be relied on. Default value:
        None.

    kwargs : Any other keyword parameter is passed to the function that
        computes the maximum flow.

    Returns
    -------
    flow_value : integer, float
        Value of the maximum flow, i.e., net outflow from the source.

    Raises
    ------
    NetworkXError
        The algorithm does not support MultiGraph and MultiDiGraph. If
        the input graph is an instance of one of these two classes, a
        NetworkXError is raised.

    NetworkXUnbounded
        If the graph has a path of infinite capacity, the value of a
        feasible flow on the graph is unbounded above and the function
        raises a NetworkXUnbounded.

    See also
    --------
    :meth:`maximum_flow`
    :meth:`minimum_cut`
    :meth:`minimum_cut_value`
    :meth:`edmonds_karp`
    :meth:`ford_fulkerson`
    :meth:`preflow_push`
    :meth:`shortest_augmenting_path`

    Notes
    -----
    The function used in the flow_func paramter has to return a residual
    network that follows NetworkX conventions:

    The residual network :samp:`R` from an input graph :samp:`G` has the
    same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
    of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
    self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
    in :samp:`G`.

    For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
    is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
    in :samp:`G` or zero otherwise. If the capacity is infinite,
    :samp:`R[u][v]['capacity']` will have a high arbitrary finite value
    that does not affect the solution of the problem. This value is stored in
    :samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
    :samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
    satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.

    The flow value, defined as the total flow into :samp:`t`, the sink, is
    stored in :samp:`R.graph['flow_value']`. Reachability to :samp:`t` using
    only edges :samp:`(u, v)` such that
    :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
    :samp:`s`-:samp:`t` cut.

    Specific algorithms may store extra data in :samp:`R`.

    The function should supports an optional boolean parameter value_only. When
    True, it can optionally terminate the algorithm as soon as the maximum flow
    value and the minimum cut can be determined.

    The legacy :meth:`ford_fulkerson` maximum flow implementation doesn't
    follow this conventions but it is supported as a valid flow_func.

    Examples
    --------
    >>> import networkx as nx
    >>> G = nx.DiGraph()
    >>> G.add_edge('x','a', capacity=3.0)
    >>> G.add_edge('x','b', capacity=1.0)
    >>> G.add_edge('a','c', capacity=3.0)
    >>> G.add_edge('b','c', capacity=5.0)
    >>> G.add_edge('b','d', capacity=4.0)
    >>> G.add_edge('d','e', capacity=2.0)
    >>> G.add_edge('c','y', capacity=2.0)
    >>> G.add_edge('e','y', capacity=3.0)

    maximum_flow_value computes only the value of the
    maximum flow:

    >>> flow_value = nx.maximum_flow_value(G, 'x', 'y')
    >>> flow_value
    3.0

    You can also use alternative algorithms for computing the
    maximum flow by using the flow_func parameter.

    >>> from networkx.algorithms.flow import shortest_augmenting_path
    >>> flow_value == nx.maximum_flow_value(G, 'x', 'y',
    ...                                     flow_func=shortest_augmenting_path)
    True

    """
    if flow_func is None:
        if kwargs:
            raise nx.NetworkXError("You have to explicitly set a flow_func if"
                                   " you need to pass parameters via kwargs.")
        flow_func = default_flow_func

    if not callable(flow_func):
        raise nx.NetworkXError("flow_func has to be callable.")

    if flow_func is ford_fulkerson:
        R = flow_func(G, s, t, capacity=capacity)
    else:
        R = flow_func(G, s, t, capacity=capacity, value_only=True, **kwargs)

    return R.graph['flow_value']


def minimum_cut(G, s, t, capacity='capacity', flow_func=None, **kwargs):
    """Compute the value and the node partition of a minimum (s, t)-cut.

    Use the max-flow min-cut theorem, i.e., the capacity of a minimum
    capacity cut is equal to the flow value of a maximum flow.

    Parameters
    ----------
    G : NetworkX graph
        Edges of the graph are expected to have an attribute called
        'capacity'. If this attribute is not present, the edge is
        considered to have infinite capacity.

    s : node
        Source node for the flow.

    t : node
        Sink node for the flow.

    capacity : string
        Edges of the graph G are expected to have an attribute capacity
        that indicates how much flow the edge can support. If this
        attribute is not present, the edge is considered to have
        infinite capacity. Default value: 'capacity'.

    flow_func : function
        A function for computing the maximum flow among a pair of nodes
        in a capacitated graph. The function has to accept at least three
        parameters: a Graph or Digraph, a source node, and a target node.
        And return a residual network that follows NetworkX conventions
        (see Notes). If flow_func is None, the default maximum
        flow function (:meth:`preflow_push`) is used. See below for
        alternative algorithms. The choice of the default function may change
        from version to version and should not be relied on. Default value:
        None.

    kwargs : Any other keyword parameter is passed to the function that
        computes the maximum flow.

    Returns
    -------
    cut_value : integer, float
        Value of the minimum cut.

    partition : pair of node sets
        A partitioning of the nodes that defines a minimum cut.

    Raises
    ------
    NetworkXUnbounded
        If the graph has a path of infinite capacity, all cuts have
        infinite capacity and the function raises a NetworkXError.

    See also
    --------
    :meth:`maximum_flow`
    :meth:`maximum_flow_value`
    :meth:`minimum_cut_value`
    :meth:`edmonds_karp`
    :meth:`ford_fulkerson`
    :meth:`preflow_push`
    :meth:`shortest_augmenting_path`

    Notes
    -----
    The function used in the flow_func paramter has to return a residual
    network that follows NetworkX conventions:

    The residual network :samp:`R` from an input graph :samp:`G` has the
    same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
    of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
    self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
    in :samp:`G`.

    For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
    is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
    in :samp:`G` or zero otherwise. If the capacity is infinite,
    :samp:`R[u][v]['capacity']` will have a high arbitrary finite value
    that does not affect the solution of the problem. This value is stored in
    :samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
    :samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
    satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.

    The flow value, defined as the total flow into :samp:`t`, the sink, is
    stored in :samp:`R.graph['flow_value']`. Reachability to :samp:`t` using
    only edges :samp:`(u, v)` such that
    :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
    :samp:`s`-:samp:`t` cut.

    Specific algorithms may store extra data in :samp:`R`.

    The function should supports an optional boolean parameter value_only. When
    True, it can optionally terminate the algorithm as soon as the maximum flow
    value and the minimum cut can be determined.

    Examples
    --------
    >>> import networkx as nx
    >>> G = nx.DiGraph()
    >>> G.add_edge('x','a', capacity = 3.0)
    >>> G.add_edge('x','b', capacity = 1.0)
    >>> G.add_edge('a','c', capacity = 3.0)
    >>> G.add_edge('b','c', capacity = 5.0)
    >>> G.add_edge('b','d', capacity = 4.0)
    >>> G.add_edge('d','e', capacity = 2.0)
    >>> G.add_edge('c','y', capacity = 2.0)
    >>> G.add_edge('e','y', capacity = 3.0)

    minimum_cut computes both the value of the
    minimum cut and the node partition:

    >>> cut_value, partition = nx.minimum_cut(G, 'x', 'y')
    >>> reachable, non_reachable = partition

    'partition' here is a tuple with the two sets of nodes that define
    the minimum cut. You can compute the cut set of edges that induce
    the minimum cut as follows:

    >>> cutset = set()
    >>> for u, nbrs in ((n, G[n]) for n in reachable):
    ...     cutset.update((u, v) for v in nbrs if v in non_reachable)
    >>> print(sorted(cutset))
    [('c', 'y'), ('x', 'b')]
    >>> cut_value == sum(G.edge[u][v]['capacity'] for (u, v) in cutset)
    True

    You can also use alternative algorithms for computing the
    minimum cut by using the flow_func parameter.

    >>> from networkx.algorithms.flow import shortest_augmenting_path
    >>> cut_value == nx.minimum_cut(G, 'x', 'y',
    ...                             flow_func=shortest_augmenting_path)[0]
    True

    """
    if flow_func is None:
        if kwargs:
            raise nx.NetworkXError("You have to explicitly set a flow_func if"
                                   " you need to pass parameters via kwargs.")
        flow_func = default_flow_func

    if not callable(flow_func):
        raise nx.NetworkXError("flow_func has to be callable.")

    if (kwargs.get('cutoff') is not None and
        flow_func in (edmonds_karp, ford_fulkerson, preflow_push,
                      shortest_augmenting_path)):
        raise nx.NetworkXError("cutoff should not be specified.")

    if flow_func is ford_fulkerson:
        R = flow_func(G, s, t, capacity=capacity)
        # legacy always removes saturated edges
        cutset = None 
    else:
        R = flow_func(G, s, t, capacity=capacity, value_only=True, **kwargs)
        # Remove saturated edges from the residual network 
        cutset = [(u, v, d) for u, v, d in R.edges(data=True) 
                     if d['flow'] == d['capacity']]
        R.remove_edges_from(cutset)

    # Then, reachable and non reachable nodes from source in the
    # residual network form the node partition that defines
    # the minimum cut.
    non_reachable = set(nx.shortest_path_length(R, target=t))
    partition = (set(G) - non_reachable, non_reachable)
    # Finaly add again cutset edges to the residual network to make
    # sure that it is reusable.
    if cutset is not None:
        R.add_edges_from(cutset)
    return (R.graph['flow_value'], partition)


def minimum_cut_value(G, s, t, capacity='capacity', flow_func=None, **kwargs):
    """Compute the value of a minimum (s, t)-cut.

    Use the max-flow min-cut theorem, i.e., the capacity of a minimum
    capacity cut is equal to the flow value of a maximum flow.

    Parameters
    ----------
    G : NetworkX graph
        Edges of the graph are expected to have an attribute called
        'capacity'. If this attribute is not present, the edge is
        considered to have infinite capacity.

    s : node
        Source node for the flow.

    t : node
        Sink node for the flow.

    capacity : string
        Edges of the graph G are expected to have an attribute capacity
        that indicates how much flow the edge can support. If this
        attribute is not present, the edge is considered to have
        infinite capacity. Default value: 'capacity'.

    flow_func : function
        A function for computing the maximum flow among a pair of nodes
        in a capacitated graph. The function has to accept at least three
        parameters: a Graph or Digraph, a source node, and a target node.
        And return a residual network that follows NetworkX conventions
        (see Notes). If flow_func is None, the default maximum
        flow function (:meth:`preflow_push`) is used. See below for
        alternative algorithms. The choice of the default function may change
        from version to version and should not be relied on. Default value:
        None.

    kwargs : Any other keyword parameter is passed to the function that
        computes the maximum flow.

    Returns
    -------
    cut_value : integer, float
        Value of the minimum cut.

    Raises
    ------
    NetworkXUnbounded
        If the graph has a path of infinite capacity, all cuts have
        infinite capacity and the function raises a NetworkXError.

    See also
    --------
    :meth:`maximum_flow`
    :meth:`maximum_flow_value`
    :meth:`minimum_cut`
    :meth:`edmonds_karp`
    :meth:`ford_fulkerson`
    :meth:`preflow_push`
    :meth:`shortest_augmenting_path`

    Notes
    -----
    The function used in the flow_func paramter has to return a residual
    network that follows NetworkX conventions:

    The residual network :samp:`R` from an input graph :samp:`G` has the
    same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
    of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
    self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
    in :samp:`G`.

    For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
    is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
    in :samp:`G` or zero otherwise. If the capacity is infinite,
    :samp:`R[u][v]['capacity']` will have a high arbitrary finite value
    that does not affect the solution of the problem. This value is stored in
    :samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
    :samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
    satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.

    The flow value, defined as the total flow into :samp:`t`, the sink, is
    stored in :samp:`R.graph['flow_value']`. Reachability to :samp:`t` using
    only edges :samp:`(u, v)` such that
    :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
    :samp:`s`-:samp:`t` cut.

    Specific algorithms may store extra data in :samp:`R`.

    The function should supports an optional boolean parameter value_only. When
    True, it can optionally terminate the algorithm as soon as the maximum flow
    value and the minimum cut can be determined.

    Examples
    --------
    >>> import networkx as nx
    >>> G = nx.DiGraph()
    >>> G.add_edge('x','a', capacity = 3.0)
    >>> G.add_edge('x','b', capacity = 1.0)
    >>> G.add_edge('a','c', capacity = 3.0)
    >>> G.add_edge('b','c', capacity = 5.0)
    >>> G.add_edge('b','d', capacity = 4.0)
    >>> G.add_edge('d','e', capacity = 2.0)
    >>> G.add_edge('c','y', capacity = 2.0)
    >>> G.add_edge('e','y', capacity = 3.0)

    minimum_cut_value computes only the value of the
    minimum cut:

    >>> cut_value = nx.minimum_cut_value(G, 'x', 'y')
    >>> cut_value
    3.0

    You can also use alternative algorithms for computing the
    minimum cut by using the flow_func parameter.

    >>> from networkx.algorithms.flow import shortest_augmenting_path
    >>> cut_value == nx.minimum_cut_value(G, 'x', 'y',
    ...                                   flow_func=shortest_augmenting_path)
    True

    """
    if flow_func is None:
        if kwargs:
            raise nx.NetworkXError("You have to explicitly set a flow_func if"
                                   " you need to pass parameters via kwargs.")
        flow_func = default_flow_func

    if not callable(flow_func):
        raise nx.NetworkXError("flow_func has to be callable.")

    if (kwargs.get('cutoff') is not None and
        flow_func in (edmonds_karp, ford_fulkerson, preflow_push,
                      shortest_augmenting_path)):
        raise nx.NetworkXError("cutoff should not be specified.")

    if flow_func is ford_fulkerson:
        R = flow_func(G, s, t, capacity=capacity)
    else:
        R = flow_func(G, s, t, capacity=capacity, value_only=True, **kwargs)

    return R.graph['flow_value']
