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import igraph
import random
import copy
import pylab as pl
import scipy
from scipy import random
import heapq
import NetGraphics
#model constructor
class simpleNetworkSIRModel():
def __init__(self, b , e , g , S , E , p , nei):
#parameters
self.b = b
self.e = e
self.g = g
self.t = 0
self.p = p
self.N = S + E
self.graph = igraph.Graph.Watts_Strogatz(1, self.N, nei=nei, p = p)
self.graph.simplify()
self.adjacencyList = []
for i in range(self.N):
self.adjacencyList.append([])
for edge in self.graph.es:
self.adjacencyList[edge.source].append(edge.target)
self.adjacencyList[edge.target].append(edge.source)
self.sAgentList = []
self.eAgentList = []
self.iAgentList = []
self.rAgentList = []
self.sList = []
self.eList = []
self.iList = []
self.rList = []
self.newIList = []
self.latencyTimesHeap = []
self.recoveryTimesHeap = []
allAgents = range(self.N)
random.shuffle(allAgents)
self.sAgentList = copy.copy(allAgents)
# infecter quelques agents à t = 0
self.indexCases = []
for i in xrange(E):
indexCase = self.sAgentList[0]
self.indexCases.append(indexCase)
self.latentAgent(indexCase)
self.eAgentList.append(indexCase)
def latentAgent(self,agent):
self.sAgentList.remove(agent)
latencyTime = self.t + scipy.random.exponential(1/self.e)
heapq.heappush(self.latencyTimesHeap, (latencyTime, agent))
def infectAgent(self,agent):
self.eAgentList.remove(agent)
recoveryTime = self.t + scipy.random.exponential(1/self.g)
heapq.heappush(self.recoveryTimesHeap, (recoveryTime, agent))
return 1
def recoverAgents(self):
recoverList = []
if len(self.recoveryTimesHeap) > 0:
while self.recoveryTimesHeap[0][0] <= self.t:
recoveryTuple = heapq.heappop(self.recoveryTimesHeap)
recoverList.append(recoveryTuple[1])
if len(self.recoveryTimesHeap) == 0:
break
return recoverList
print('r',self.rAgentList)
def run(self):
while len(self.eAgentList) > 0:
tempEAgentList = []
infectList = []
recoverList = []
newE = 0
for eAgent in self.eAgentList:
for agent in self.adjacencyList[eAgent]:
if agent in self.sAgentList:
if (random.random() < self.b):
newE += self.latentAgent(agent)
tempEAgentList.append(agent)
infectList = self.infectAgents()
for infectAgent in infectList:
self.eAgentList.remove(infectAgent)
self.iAgentList.append(infectAgent)
print('i',self.iAgentList)
recoverList = self.recoverAgents()
for recoverAgent in recoverList:
self.iAgentList.remove(recoverAgent)
self.rAgentList.append(recoverAgent)
print('r',self.rAgentList)
self.eAgentList.extend(tempEAgentList)
self.sList.append(len(self.sAgentList))
self.eList.append(len(self.eAgentList))
self.iList.append(len(self.iAgentList))
self.rList.append(len(self.sAgentList))
self.newEList.append(newE)
self.t += 1
print('t', self.t, 'numS', len(self.sAgentList),'numE', len(self.eAgentList), 'numI', len(self.iAgentList) )
random.shuffle(self.eAgentList)
return [self.sList, self.eList, self.iList, self.rList, self.newIList]
if __name__=='__main__':
#paramètres
b = .002
e = .01
g = .01
#condition initiale
S = 500
E = 2
#paramètres du réseau
p = .05
nei = 3
myNetworkModel = simpleNetworkSIRModel(b = b, e = e, g = g, S = S, E = E, p = p, nei = nei)
networkResults = myNetworkModel.run()
numNetworkCases = sum(networkResults[3])
pl.figure()
pl.plot(networkResults[1], label = 'Evolution d epidemie dans le reseau; ' + str(numNetworkCases) + ' cas')
pl.plot(allAgents)
pl.xlabel('temps')
pl.ylabel('Nbr Infectes') |
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