"""
Checking for connected components in a graph.
"""
__author__ = "Sergio J. Rey <srey@asu.edu>"


__all__ = ["check_contiguity"]

from operator import lt

def is_component(w, ids):
    """Check if the set of ids form a single connected component

    Parameters
    ----------

    w   : spatial weights boject

    ids : list
          identifiers of units that are tested to be a single connected
          component


    Returns
    -------

    True    : if the list of ids represents a single connected component

    False   : if the list of ids forms more than a single connected component

    """

    components = 0
    marks = dict([(node, 0) for node in ids])
    q = []
    for node in ids:
        if marks[node] == 0:
            components += 1
            q.append(node)
            if components > 1:
                return False
        while q:
            node = q.pop()
            marks[node] = components
            others = [neighbor for neighbor in w.neighbors[node]
                      if neighbor in ids]
            for other in others:
                if marks[other] == 0 and other not in q:
                    q.append(other)
    return True


def check_contiguity(w, neighbors, leaver):
    """Check if contiguity is maintained if leaver is removed from neighbors


    Parameters
    ----------

    w           : spatial weights object
                  simple contiguity based weights
    neighbors   : list
                  nodes that are to be checked if they form a single \
                          connected component
    leaver      : id
                  a member of neighbors to check for removal


    Returns
    -------

    True        : if removing leaver from neighbors does not break contiguity
                  of remaining set
                  in neighbors
    False       : if removing leaver from neighbors breaks contiguity

    Example
    -------

    Setup imports and a 25x25 spatial weights matrix on a 5x5 square region.

    >>> import pysal.lib.api as lps
    >>> w = lps.lat2W(5, 5)

    Test removing various areas from a subset of the region's areas.  In the
    first case the subset is defined as observations 0, 1, 2, 3 and 4. The
    test shows that observations 0, 1, 2 and 3 remain connected even if
    observation 4 is removed.

    >>> check_contiguity(w,[0,1,2,3,4],4)
    True
    >>> check_contiguity(w,[0,1,2,3,4],3)
    False
    >>> check_contiguity(w,[0,1,2,3,4],0)
    True
    >>> check_contiguity(w,[0,1,2,3,4],1)
    False
    >>>
    """

    ids = neighbors[:]
    ids.remove(leaver)
    return is_component(w, ids)


class Graph(object):
    def __init__(self, undirected=True):
        self.nodes = set()
        self.edges = {}
        self.cluster_lookup = {}
        self.no_link = {}
        self.undirected = undirected

    def add_edge(self, n1, n2, w):
        self.nodes.add(n1)
        self.nodes.add(n2)
        self.edges.setdefault(n1, {}).update({n2: w})
        if self.undirected:
            self.edges.setdefault(n2, {}).update({n1: w})

    def connected_components(self, threshold=0.9, op=lt):
        if not self.undirected:
            warn = "Warning, connected _components not "
            warn += "defined for a directed graph"
            print(warn)
            return None
        else:
            nodes = set(self.nodes)
            components, visited = [], set()
            while len(nodes) > 0:
                connected, visited = self.dfs(
                    nodes.pop(), visited, threshold, op)
                connected = set(connected)
                for node in connected:
                    if node in nodes:
                        nodes.remove(node)
                subgraph = Graph()
                subgraph.nodes = connected
                subgraph.no_link = self.no_link
                for s in subgraph.nodes:
                    for k, v in list(self.edges.get(s, {}).items()):
                        if k in subgraph.nodes:
                            subgraph.edges.setdefault(s, {}).update({k: v})
                    if s in self.cluster_lookup:
                        subgraph.cluster_lookup[s] = self.cluster_lookup[s]
                components.append(subgraph)
            return components

    def dfs(self, v, visited, threshold, op=lt, first=None):
        aux = [v]
        visited.add(v)
        if first is None:
            first = v
        for i in (n for n, w in list(self.edges.get(v, {}).items())
                  if op(w, threshold) and n not in visited):
            x, y = self.dfs(i, visited, threshold, op, first)
            aux.extend(x)
            visited = visited.union(y)
        return aux, visited
