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__pycache__/models.cpython-34.pyc
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__pycache__/models.cpython-34.pyc
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__pycache__/settings.cpython-34.pyc
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__pycache__/settings.cpython-34.pyc
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sim_01/log.0.state.pickled
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sim_01/log.0.state.pickled
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168
soil.py
168
soil.py
@ -8,8 +8,11 @@ import numpy as np
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import networkx as nx
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import settings
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settings.init() # Loads all the data from settings
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settings.init()
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####################
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# Network creation #
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####################
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if settings.network_type == 0:
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G = nx.complete_graph(settings.number_of_nodes)
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@ -17,26 +20,28 @@ if settings.network_type == 1:
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G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
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if settings.network_type == 2:
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G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
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# More types of networks can be added here
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myList=[]
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networkStatus=[]
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##############################
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# Variables initializitation #
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##############################
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myList=[] # List just for debugging
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networkStatus=[] # This list will contain the status of every node of the network
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for x in range(0, settings.number_of_nodes):
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networkStatus.append({'id':x})
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# Initialize agent states. Let's assume everyone is normal.
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init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
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# Seed a zombie, just for zombie model
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#init_states[5] = {'id': 1}
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#init_states[3] = {'id': 1}
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# # Just like subclassing a process in SimPy
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# class MyAgent(BaseNetworkAgent):
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# def __init__(self, environment=None, agent_id=0, state=()): # Make sure to have these three keyword arguments
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# super().__init__(environment=environment, agent_id=agent_id, state=state)
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# # Add your own attributes here
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# def run(self):
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# # Add your behaviors here
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####################
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# Available models #
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####################
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class SentimentCorrelationModel(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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@ -47,16 +52,14 @@ class SentimentCorrelationModel(BaseNetworkAgent):
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self.sadness_prob = settings.sadness_prob
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self.disgust_prob = settings.disgust_prob
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self.time_awareness=[]
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for i in range(4):
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for i in range(4): #En este modelo tenemos 4 sentimientos
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self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
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networkStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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if self.env.now > 10:
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G.add_node(205)
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G.add_edge(205,0)
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angry_neighbors_1_time_step=[]
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joyful_neighbors_1_time_step=[]
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sad_neighbors_1_time_step=[]
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@ -90,67 +93,12 @@ class SentimentCorrelationModel(BaseNetworkAgent):
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disgusted_neighbors_1_time_step.append(x)
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num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
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# #Outside effects. Asignamos un estado aleatorio
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# if random.random() < settings.outside_effects_prob:
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# if self.state['id'] == 0:
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# self.state['id'] = random.randint(1,4)
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# myList.append(self.id)
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# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
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# self.time_awareness = self.env.now #Para saber cuando se han contagiado
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# yield self.env.timeout(settings.timeout)
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# else:
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Joy
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# if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
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# myList.append(self.id)
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# self.state['id'] = 2
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# networkStatus[self.id][self.env.now]=2
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Sadness
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# if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
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# myList.append(self.id)
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# self.state['id'] = 3
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# networkStatus[self.id][self.env.now]=3
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Disgust
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# if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
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# myList.append(self.id)
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# self.state['id'] = 4
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# networkStatus[self.id][self.env.now]=4
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Anger
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# if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
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# myList.append(self.id)
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# self.state['id'] = 1
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# networkStatus[self.id][self.env.now]=1
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# yield self.env.timeout(settings.timeout)
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# yield self.env.timeout(settings.timeout)
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###########################################
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anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
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print("anger_prob " + str(anger_prob))
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joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
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print("joy_prob " + str(joy_prob))
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sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
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print("sadness_prob "+ str(sadness_prob))
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disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
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print("disgust_prob " + str(disgust_prob))
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outside_effects_prob= settings.outside_effects_prob
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print("outside_effects_prob " + str(outside_effects_prob))
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num = random.random()
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@ -192,52 +140,6 @@ class SentimentCorrelationModel(BaseNetworkAgent):
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yield self.env.timeout(settings.timeout)
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# anger_propagation = settings.anger_prob*num_neighbors_angry/10
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# joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
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# sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
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# disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
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# outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
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# if (num<anger_propagation):
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# if(self.state['id'] !=0):
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# myList.append(self.id)
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# self.state['id'] = 1
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# networkStatus[self.id][self.env.now]=1
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# yield self.env.timeout(settings.timeout)
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# if (num<joy_propagation):
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# if(self.state['id'] !=0):
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# myList.append(self.id)
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# self.state['id'] = 2
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# networkStatus[self.id][self.env.now]=2
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# yield self.env.timeout(settings.timeout)
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# if(num<sadness_propagation):
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# if(self.state['id'] !=0):
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# myList.append(self.id)
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# self.state['id'] = 3
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# networkStatus[self.id][self.env.now]=3
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# yield self.env.timeout(settings.timeout)
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# # if(num<disgust_propagation):
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# # if(self.state['id'] !=0):
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# # myList.append(self.id)
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# # self.state['id'] = 4
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# # networkStatus[self.id][self.env.now]=4
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# # yield self.env.timeout(settings.timeout)
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# if(num <outside_effects_propagation):
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# if self.state['id'] == 0:
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# self.state['id'] = random.randint(1,4)
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# myList.append(self.id)
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# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
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# self.time_awareness = self.env.now #Para saber cuando se han contagiado
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# yield self.env.timeout(settings.timeout)
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# else:
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# yield self.env.timeout(settings.timeout)
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# else:
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# yield self.env.timeout(settings.timeout)
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class BassModel(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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@ -349,12 +251,9 @@ class ZombieOutbreak(BaseNetworkAgent):
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break
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# Initialize agent states. Let's assume everyone is normal.
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init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
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# Seed a zombie
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#init_states[5] = {'id': 1}
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#init_states[3] = {'id': 1}
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##############
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# Simulation #
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##############
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
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max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
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@ -362,16 +261,20 @@ sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorr
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sim.run_simulation()
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###########
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# Results #
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###########
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myList = sorted(myList, key=int)
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#print("Los zombies son: " + str(myList))
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trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
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zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
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status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
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#for x in range(len(myList)):
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# G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
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#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
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#################
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# Visualization #
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#################
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#lista = nx.nodes(G)
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#print('Nodos: ' + str(lista))
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@ -380,15 +283,16 @@ for x in range(0, settings.number_of_nodes):
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for tiempo in networkStatus[x]:
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if tiempo != 'id':
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networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
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G.add_node(x, zombie= networkStatusAux)
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G.add_node(x, status= networkStatusAux)
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#print(networkStatus)
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nx.write_gexf(G,"test.gexf", version="1.2draft")
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plt.plot(zombie_census)
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plt.plot(status_census)
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plt.draw() # pyplot draw()
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plt.savefig("zombie.png")
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plt.savefig("status.png")
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#print(networkStatus)
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#nx.draw(G)
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#plt.show()
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#plt.savefig("path.png")
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394
soil.py~
Normal file
394
soil.py~
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@ -0,0 +1,394 @@
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from nxsim import NetworkSimulation
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from nxsim import BaseNetworkAgent
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from nxsim import BaseLoggingAgent
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from random import randint
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from matplotlib import pyplot as plt
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import random
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import numpy as np
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import networkx as nx
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import settings
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settings.init()
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if settings.network_type == 0:
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G = nx.complete_graph(settings.number_of_nodes)
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if settings.network_type == 1:
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G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
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if settings.network_type == 2:
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G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
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myList=[]
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networkStatus=[]
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for x in range(0, settings.number_of_nodes):
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networkStatus.append({'id':x})
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# # Just like subclassing a process in SimPy
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# class MyAgent(BaseNetworkAgent):
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# def __init__(self, environment=None, agent_id=0, state=()): # Make sure to have these three keyword arguments
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# super().__init__(environment=environment, agent_id=agent_id, state=state)
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# # Add your own attributes here
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# def run(self):
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# # Add your behaviors here
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class SentimentCorrelationModel(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.outside_effects_prob = settings.outside_effects_prob
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self.anger_prob = settings.anger_prob
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self.joy_prob = settings.joy_prob
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self.sadness_prob = settings.sadness_prob
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self.disgust_prob = settings.disgust_prob
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self.time_awareness=[]
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for i in range(4):
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self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
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networkStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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if self.env.now > 10:
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G.add_node(205)
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G.add_edge(205,0)
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angry_neighbors_1_time_step=[]
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joyful_neighbors_1_time_step=[]
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sad_neighbors_1_time_step=[]
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disgusted_neighbors_1_time_step=[]
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angry_neighbors = self.get_neighboring_agents(state_id=1)
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for x in angry_neighbors:
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if x.time_awareness[0] > (self.env.now-500):
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angry_neighbors_1_time_step.append(x)
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num_neighbors_angry = len(angry_neighbors_1_time_step)
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joyful_neighbors = self.get_neighboring_agents(state_id=2)
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for x in joyful_neighbors:
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if x.time_awareness[1] > (self.env.now-500):
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joyful_neighbors_1_time_step.append(x)
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num_neighbors_joyful = len(joyful_neighbors_1_time_step)
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sad_neighbors = self.get_neighboring_agents(state_id=3)
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for x in sad_neighbors:
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if x.time_awareness[2] > (self.env.now-500):
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sad_neighbors_1_time_step.append(x)
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num_neighbors_sad = len(sad_neighbors_1_time_step)
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disgusted_neighbors = self.get_neighboring_agents(state_id=4)
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for x in disgusted_neighbors:
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if x.time_awareness[3] > (self.env.now-500):
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disgusted_neighbors_1_time_step.append(x)
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num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
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# #Outside effects. Asignamos un estado aleatorio
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# if random.random() < settings.outside_effects_prob:
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# if self.state['id'] == 0:
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# self.state['id'] = random.randint(1,4)
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# myList.append(self.id)
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# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
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# self.time_awareness = self.env.now #Para saber cuando se han contagiado
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# yield self.env.timeout(settings.timeout)
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# else:
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Joy
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# if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
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# myList.append(self.id)
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# self.state['id'] = 2
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# networkStatus[self.id][self.env.now]=2
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Sadness
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# if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
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# myList.append(self.id)
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# self.state['id'] = 3
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# networkStatus[self.id][self.env.now]=3
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Disgust
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# if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
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# myList.append(self.id)
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# self.state['id'] = 4
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# networkStatus[self.id][self.env.now]=4
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# yield self.env.timeout(settings.timeout)
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# #Imitation effects-Anger
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# if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
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# myList.append(self.id)
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# self.state['id'] = 1
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# networkStatus[self.id][self.env.now]=1
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# yield self.env.timeout(settings.timeout)
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# yield self.env.timeout(settings.timeout)
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###########################################
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anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
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print("anger_prob " + str(anger_prob))
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joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
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print("joy_prob " + str(joy_prob))
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sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
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print("sadness_prob "+ str(sadness_prob))
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disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
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print("disgust_prob " + str(disgust_prob))
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outside_effects_prob= settings.outside_effects_prob
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||||
print("outside_effects_prob " + str(outside_effects_prob))
|
||||
|
||||
|
||||
num = random.random()
|
||||
|
||||
|
||||
if(num<outside_effects_prob):
|
||||
self.state['id'] = random.randint(1,4)
|
||||
myList.append(self.id)
|
||||
networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
if(num<anger_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 1
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<joy_prob+anger_prob and num>anger_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 2
|
||||
networkStatus[self.id][self.env.now]=2
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 3
|
||||
networkStatus[self.id][self.env.now]=3
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 4
|
||||
networkStatus[self.id][self.env.now]=4
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
# anger_propagation = settings.anger_prob*num_neighbors_angry/10
|
||||
# joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
|
||||
# sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
|
||||
# disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
|
||||
# outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
|
||||
|
||||
# if (num<anger_propagation):
|
||||
# if(self.state['id'] !=0):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 1
|
||||
# networkStatus[self.id][self.env.now]=1
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# if (num<joy_propagation):
|
||||
# if(self.state['id'] !=0):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 2
|
||||
# networkStatus[self.id][self.env.now]=2
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# if(num<sadness_propagation):
|
||||
# if(self.state['id'] !=0):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 3
|
||||
# networkStatus[self.id][self.env.now]=3
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# # if(num<disgust_propagation):
|
||||
# # if(self.state['id'] !=0):
|
||||
# # myList.append(self.id)
|
||||
# # self.state['id'] = 4
|
||||
# # networkStatus[self.id][self.env.now]=4
|
||||
# # yield self.env.timeout(settings.timeout)
|
||||
# if(num <outside_effects_propagation):
|
||||
# if self.state['id'] == 0:
|
||||
# self.state['id'] = random.randint(1,4)
|
||||
# myList.append(self.id)
|
||||
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
|
||||
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# else:
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# else:
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class BassModel(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = settings.innovation_prob
|
||||
self.imitation_prob = settings.imitation_prob
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
|
||||
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
myList.append(self.id)
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
#Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
num_neighbors_aware = len(aware_neighbors)
|
||||
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 1
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
class IndependentCascadeModel(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = settings.innovation_prob
|
||||
self.imitation_prob = settings.imitation_prob
|
||||
self.time_awareness = 0
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
aware_neighbors_1_time_step=[]
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
myList.append(self.id)
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
self.time_awareness = self.env.now #Para saber cuando se han contagiado
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
#Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in aware_neighbors:
|
||||
if x.time_awareness == (self.env.now-1):
|
||||
aware_neighbors_1_time_step.append(x)
|
||||
num_neighbors_aware = len(aware_neighbors_1_time_step)
|
||||
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 1
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
class ZombieOutbreak(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.bite_prob = settings.bite_prob
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
if random.random() < settings.heal_prob:
|
||||
if self.state['id'] == 1:
|
||||
self.zombify()
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
if self.state['id'] == 1:
|
||||
print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
if self.id in myList:
|
||||
myList.remove(self.id)
|
||||
self.state['id'] = 0
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
def zombify(self):
|
||||
normal_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in normal_neighbors:
|
||||
if random.random() < self.bite_prob:
|
||||
print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
|
||||
neighbor.state['id'] = 1 # zombie
|
||||
myList.append(neighbor.id)
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
networkStatus[neighbor.id][self.env.now]=1
|
||||
print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
|
||||
break
|
||||
|
||||
|
||||
# Initialize agent states. Let's assume everyone is normal.
|
||||
init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
|
||||
|
||||
# Seed a zombie
|
||||
#init_states[5] = {'id': 1}
|
||||
#init_states[3] = {'id': 1}
|
||||
|
||||
sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
|
||||
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
|
||||
|
||||
|
||||
sim.run_simulation()
|
||||
|
||||
myList = sorted(myList, key=int)
|
||||
#print("Los zombies son: " + str(myList))
|
||||
|
||||
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
|
||||
zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
|
||||
|
||||
#for x in range(len(myList)):
|
||||
# G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
|
||||
|
||||
#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
|
||||
|
||||
#lista = nx.nodes(G)
|
||||
#print('Nodos: ' + str(lista))
|
||||
for x in range(0, settings.number_of_nodes):
|
||||
networkStatusAux=[]
|
||||
for tiempo in networkStatus[x]:
|
||||
if tiempo != 'id':
|
||||
networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
|
||||
G.add_node(x, zombie= networkStatusAux)
|
||||
#print(networkStatus)
|
||||
|
||||
|
||||
nx.write_gexf(G,"test.gexf", version="1.2draft")
|
||||
plt.plot(zombie_census)
|
||||
plt.draw() # pyplot draw()
|
||||
plt.savefig("zombie.png")
|
||||
#print(networkStatus)
|
||||
#nx.draw(G)
|
||||
#plt.show()
|
||||
#plt.savefig("path.png")
|
||||
BIN
status.png
Normal file
BIN
status.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
Loading…
Reference in New Issue
Block a user