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from cPickle import load
from math import exp, sin
from collections import Counter
from csv import reader, writer
from data2 import parse
import sys
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
def get_fatals():
with open(sys.argv[1]) as fh:
fh.readline()
r = reader(fh)
d = {i + 1: parse(row[7]) for (i, row) in enumerate(r)}
d = {k: v for k, v in d.iteritems() if v}
return d.items()
def cause(lamb, alpha, mu):
G = nx.DiGraph()
roots, droots, infections = 0, 0, 0
fatal_droots, fatal_infections, fatal_roots = 0, 0, 0
fatals = get_fatals()
for ((n1, t1), s) in event_edges.iteritems():
G.add_node((n1, t1))
if not s:
droots += 1
if (n1, t1) in fatals:
fatal_droots += 1
continue
background_rate = lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36))
neighbors = sorted([(n2, t2, alpha / d * mu * exp(-mu * (t1 - t2)))
for (n2, t2, d) in s], reverse=True)
neighbor_rate = sum(e[2] for e in neighbors)
# if sum(e[2] for e in prl[:1]) > br:
# G.add_edge((n1, t1), tuple(prl[0][:2]))
if background_rate > neighbor_rate:
roots += 1
if (n1, t1) in fatals:
fatal_roots += 1
else:
G.add_edge((n1, t1), tuple(neighbors[0][:2]))
# l.append(prl[0][2] / br)
infections += 1
if (n1, t1) in fatals:
fatal_infections += 1
# l.sort(reverse=True)
# plt.plot(l)
# plt.show()
return (droots, roots, infections, fatal_droots,
fatal_roots, fatal_infections, G)
def analyze_graph(G):
counts = Counter(len(c) for c in nx.weakly_connected_components(G))
w = writer(open("components_dist.csv", "w"))
w.writerows(counts.most_common())
edges = ((n1, t1, n2, t2) for ((n1, t1), (n2, t2)) in G.edges_iter())
e = writer(open("edges.csv", "w"))
e.writerows(edges)
if __name__ == "__main__":
nodes, edges, events, event_edges = load(open("data2.pickle", "rb"))
lamb, alpha, mu = 1.1847510744e-05, 0.00316718040144, 0.00393069204339
# print len(event_edges), sum(len(e) for e in events.itervalues())
# print len(fatal())
(doors, roots, infections, fatal_droots,
fatal_roots, fatal_infections, G) = cause(lamb, alpha, mu)
analyze_graph(G)
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