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network.py
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network.py
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#!/usr/bin/env python
# ! ## File: network.py
# ! ## Network class and read edges file.
# ! ## See README.md for more information and use
# !-----------------------------------------------------------------------------
# ! SIS epidemic model algorithm based on the article
# ! Computer Physics Communications 219C (2017) pp. 303-312
# ! "Optimized Gillespie algorithms for the simulation of
# ! Markovian epidemic processes on large and heterogeneous networks"
# ! Copyright (C) 2017 Wesley Cota, Silvio C. Ferreira
# !
# ! Please cite the above cited paper (available at <http://dx.doi.org/10.1016/j.cpc.2017.06.007> )
# ! as reference to our code.
# !
# ! This program is free software: you can redistribute it and/or modify
# ! it under the terms of the GNU General Public License as published by
# ! the Free Software Foundation, either version 3 of the License, or
# ! (at your option) any later version.
# !
# ! This program is distributed in the hope that it will be useful,
# ! but WITHOUT ANY WARRANTY; without even the implied warranty of
# ! MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# ! GNU General Public License for more details.
# !
# ! You should have received a copy of the GNU General Public License
# ! along with this program. If not, see <http://www.gnu.org/licenses/>.
# !-----------------------------------------------------------------------------
# ! Author : Wesley Cota
# ! Email : wesley@wcota.me
# ! Date : 27 Mar 2017
# ! Version : 1.0
# !-----------------------------------------------------------------------------
# ! See README.md for more details
# ! This code is available at <https://github.com/wcota/dynSIS-py>
# ! For performance, see <https://github.com/wcota/dynSIS> (Fortran implementation)
# ! For NetworkX library, see <https://github.com/wcota/dynSIS-networkx> (NetworkX implementation)
import numpy as np
from tools import *
# Defining a network object
class network(object):
def __init__(self):
self.size = 0
self.skk = 0
self.con = None
self.ini = None
self.k = None
def __str__(self):
return str(self.__class__) + ": " + str(self.__dict__)
# Read edges from file
def readEdges(fname):
netw = network()
tmp_con = []
print_info('Reading file...')
with open(fname, 'rt') as f:
for i in f:
li = list(map(int , i.strip().split(','))) # change ',' if another type
if li[0] == li[1]:
print_error('Self-connection found! Verify your data.')
if li[0] < 1 or li[1] < 1:
print_error('Vertex id MUST be >= 1. Verify your data.')
netw.size = max(netw.size, li[0], li[1])
tmp_con.append(li)
netw.skk += 2
# Now let's build the adjacency list. Numpy is used to use less memory.
print_info('Building the adjacency list...')
netw.k = np.zeros(netw.size, np.int)
netw.ini = np.zeros(netw.size, np.int)
netw.con = np.zeros(netw.skk, np.int)
pos = 0
print_info('Calculating degrees...')
for x, y in tmp_con:
netw.k[x-1] += 1
netw.k[y-1] += 1
for i in range(0,netw.size):
netw.ini[i] = pos
pos = pos + netw.k[i]
# Now we read again the tmp_con and save data (reindex! Nodes begin at zero)
print_info('Connecting edges...')
tmp_pos = np.copy(netw.ini)
for x,y in tmp_con:
netw.con[tmp_pos[x-1]] = y - 1
tmp_pos[x-1] += 1
netw.con[tmp_pos[y-1]] = x - 1
tmp_pos[y-1] += 1
return netw