import networkx as nx
import numpy as np
import collections

from itertools import izip

class InfluenceGraph(nx.Graph):
    """
    networkX graph with mat and logmat attributes
    """
    def __init__(self, max_proba, *args, **kwargs):
        self.max_proba = max_proba
        super(InfluenceGraph, self).__init__(*args, **kwargs)

    def erdos_init(self, n, p):
        G = nx.erdos_renyi_graph(n, p, directed=True)
        self.add_edges_from(G.edges())

    def import_from_file(self, file_name):
        """
        Takes a file from the Stanford collection of networks
        """
        with open(file_name, 'r') as f:
            for edge in f:
                if "#" not in edge:
                    u, v = [int(node) for node in edge.split()]
                    self.add_edge(u, v)

    @property
    def mat(self):
        if not hasattr(self, '_mat'):
            self._mat = (self.max_proba * np.random.rand(len(self), len(self))
                         * np.asarray(nx.adjacency_matrix(self)))
        return self._mat

    @property
    def logmat(self):
        if not hasattr(self, '_logmat'):
            self._logmat = np.log(1 - self.mat)
        return self._logmat


class Cascade(list):
    """
    Cascade object: list with attributes
    """
    def __init__(self, *args, **kwargs):
        super(Cascade, self).__init__(*args, **kwargs)

    def infection_time(self, node):
        """
        Returns lists of infections times for node i in cascade
        """
        infected_times = []
        for t, infected_set in izip(xrange(len(self)), self):
            if infected_set[node]:
                infected_times.append(t)
        return infected_times

    def candidate_infectors(self, node):
        """
        Returns Counter of nodes infected just before node i was
        """
        candidate_infectors = collections.Counter()
        for t in self.infection_time(node):
            if t > 0:
                candidate_infectors.update(np.where(self[t-1])[0])
        return candidate_infectors


def icc_cascade(G, p_init):
    """
    Returns boolean vectors for one cascade
    True means node was active at that time step
    p_init: proba that node in seed set
    """
    susceptible = np.ones(G.number_of_nodes(), dtype=bool)
    active = np.random.rand(G.number_of_nodes()) < p_init
    susceptible = susceptible - active
    cascade = Cascade()
    while active.any() and susceptible.any():
        cascade.append(active)
        active = np.exp(np.dot(G.logmat, active)) \
                    < np.random.rand(G.number_of_nodes())
        active = active & susceptible
        susceptible = susceptible - active
    return cascade


def generate_cascades(G, p_init, n_cascades):
    """
    returns list of cascades
    """""
    return [icc_cascade(G,p_init) for i in xrange(n_cascades)]


def test():
    """
    unit test
    """
    G = InfluenceGraph(max_proba = .3)
    G.erdos_init(n = 100, p = 1)
    import time
    t0 = time.time()
    print len(icc_cascade(G, p_init=.1))
    t1 = time.time()
    print t1 - t0

if __name__ == "__main__":
    test()