Unfold_Accessibility.py

#!/usr/bin/env python
"""Unfold Accessibility ecercises."""
#
import sys
sys.path.append('./src')

import numpy as np
from AdjacencyMatrixSequence import AdjMatrixSequence
from TemporalNetworkEdgeList import TemporalEdgeList
import Tools

# import an edgelist as sequence of adjacency matrices
# the_file = 'edgelists/Test.dat'
the_file = "edgelists/sexual_contacts.dat"
# the_file = "edgelists/livestock_trade.csv"
At = AdjMatrixSequence(the_file, directed=True, write_label_file=False)
c = At.si_model(p=.8)
for i in c:
    print(i)

# compute accessibility
# c = At.unfold_accessibility(return_accessibility_matrix=False)

# derivative of accessibility profile
# h = np.gradient(c)

# write the results to files

# Tools.dict2file(c, "shortest_path_durations_cumulative.txt")
# Tools.dict2file(h, "shortest_path_durations_histogram.txt")

# tracing
# f = At.trace_forward_multiple_sources([3, 1])
# f = At.unfold_accessibility_multi_nodes([3, 1])
# print(f)


# Causal fidelity
# causal_paths = c[-1]
# static_paths = At.static_path_density()
# print("---> Causal fidelity is ", float(causal_paths)/float(static_paths))

# ALTERNATIVELY: read a temporal edge list and randomize it
# Details about randomization techniques are in Supplementary Material of
# [1] Lentz et al, Phys. Rev. Lett. 110, 118701 (2013)
#

# import a temporal network edgelist for randomization
# E = TemporalEdgeList(the_file, directed=True)
# E.RE(5)
# E.write("Randomized_RE.txt")