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"""continous-time independent cascade model"""

import numpy as np

import random

from graph_tool.all import shortest_distance

from utils import edges2graph

from feasibility import is_arborescence

def gen_cascade(g, scale=1.0, source=None, stop_fraction=0.5, return_tree=True):

rands = np.random.exponential(scale, g.num_edges())

delays = g.new_edge_property('float')

delays.set_2d_array(rands)

if source is None:

source = random.choice(np.arange(g.num_vertices()))

dist, pred = shortest_distance(g, source=source, weights=delays, pred_map=True)

q = stop_fraction * 100

percentile = np.percentile(dist.a, q)

infected_nodes = np.nonzero(dist.a <= percentile)[0]

uninfected_nodes = np.nonzero(dist.a > percentile)[0]

infection_times = np.array(dist.a)

infection_times[uninfected_nodes] = -1

rets = (source, infection_times)

if return_tree:

tree_edges = set()

for n in infected_nodes:

c = n

while pred[c] != c:

edge = ((pred[c], c))

if edge not in tree_edges:

tree_edges.add(edge)

else:

break

tree = edges2graph(g, tree_edges)

rets += (tree, )

return rets