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https://github.com/gsi-upm/soil
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Make py3 compatibility explicit
This commit is contained in:
J. Fernando Sánchez
parent
e8988015e2
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FROM python:3.5-onbuild
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ENTRYPOINT ["python", "-m", "soil"]
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596
models_org.py
596
models_org.py
@ -1,596 +0,0 @@
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from nxsim import BaseNetworkAgent
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import numpy as np
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import random
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import settings
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settings.init()
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##############################
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# Variables initialization #
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##############################
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def init():
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global networkStatus
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networkStatus = {} # Dict that will contain the status of every agent in the network
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sentimentCorrelationNodeArray=[]
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for x in range(0, settings.number_of_nodes):
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sentimentCorrelationNodeArray.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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####################
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# Available models #
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####################
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class BaseBehaviour(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._attrs = {}
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@property
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def attrs(self):
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now = self.env.now
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if now not in self._attrs:
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self._attrs[now] = {}
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return self._attrs[now]
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@attrs.setter
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def attrs(self, value):
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self._attrs[self.env.now] = value
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def run(self):
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while True:
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self.step(self.env.now)
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yield self.env.timeout(settings.timeout)
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def step(self, now):
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networkStatus['agent_%s'% self.id] = self.to_json()
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def to_json(self):
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final = {}
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for stamp, attrs in self._attrs.items():
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for a in attrs:
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if a not in final:
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final[a] = {}
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final[a][stamp] = attrs[a]
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return final
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class ControlModelM2(BaseBehaviour):
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#Init infected
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init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
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init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
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# Init beacons
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
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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.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
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self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
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self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
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self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
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self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
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self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
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def step(self, now):
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if self.state['id'] == 0: #Neutral
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self.neutral_behaviour()
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elif self.state['id'] == 1: #Infected
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self.infected_behaviour()
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elif self.state['id'] == 2: #Cured
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self.cured_behaviour()
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elif self.state['id'] == 3: #Vaccinated
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self.vaccinated_behaviour()
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elif self.state['id'] == 4: #Beacon-off
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self.beacon_off_behaviour()
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elif self.state['id'] == 5: #Beacon-on
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self.beacon_on_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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# Infected
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors)>0:
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if random.random() < self.prob_neutral_making_denier:
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self.state['id'] = 3 # Vaccinated making denier
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def infected_behaviour(self):
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# Neutral
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_infect:
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neighbor.state['id'] = 1 # Infected
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def cured_behaviour(self):
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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def vaccinated_behaviour(self):
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Generate anti-rumor
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infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_2:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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def beacon_off_behaviour(self):
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors) > 0:
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self.state['id'] == 5 #Beacon on
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def beacon_on_behaviour(self):
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# Cure (M2 feature added)
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors_infected:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 3 # Vaccinated
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infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_infected:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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class SpreadModelM2(BaseBehaviour):
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init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
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init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
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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.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
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self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
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self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
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self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
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self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
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self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
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def step(self, now):
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if self.state['id'] == 0: #Neutral
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self.neutral_behaviour()
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elif self.state['id'] == 1: #Infected
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self.infected_behaviour()
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elif self.state['id'] == 2: #Cured
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self.cured_behaviour()
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elif self.state['id'] == 3: #Vaccinated
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self.vaccinated_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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# Infected
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors)>0:
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if random.random() < self.prob_neutral_making_denier:
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self.state['id'] = 3 # Vaccinated making denier
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def infected_behaviour(self):
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# Neutral
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_infect:
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neighbor.state['id'] = 1 # Infected
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def cured_behaviour(self):
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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def vaccinated_behaviour(self):
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Generate anti-rumor
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infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_2:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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class SISaModel(BaseBehaviour):
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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.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, settings.standard_variance)
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self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,settings.standard_variance)
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self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob,settings.standard_variance)
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self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,settings.standard_variance)
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self.discontent_neutral = np.random.normal(settings.discontent_neutral,settings.standard_variance)
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self.discontent_content = np.random.normal(settings.discontent_content,settings.variance_d_c)
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self.content_discontent = np.random.normal(settings.content_discontent,settings.variance_c_d)
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self.content_neutral = np.random.normal(settings.content_neutral,settings.standard_variance)
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def step(self, now):
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if self.state['id'] == 0:
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self.neutral_behaviour()
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if self.state['id'] == 1:
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self.discontent_behaviour()
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if self.state['id'] == 2:
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self.content_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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#Spontaneus effects
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if random.random() < self.neutral_discontent_spon_prob:
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self.state['id'] = 1
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if random.random() < self.neutral_content_spon_prob:
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self.state['id'] = 2
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#Infected
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discontent_neighbors = self.get_neighboring_agents(state_id=1)
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if random.random() < len(discontent_neighbors)*self.neutral_discontent_infected_prob:
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self.state['id'] = 1
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content_neighbors = self.get_neighboring_agents(state_id=2)
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if random.random() < len(content_neighbors)*self.neutral_content_infected_prob:
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self.state['id'] = 2
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def discontent_behaviour(self):
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#Healing
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if random.random() < self.discontent_neutral:
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self.state['id'] = 0
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#Superinfected
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content_neighbors = self.get_neighboring_agents(state_id=2)
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if random.random() < len(content_neighbors)*self.discontent_content:
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self.state['id'] = 2
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def content_behaviour(self):
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#Healing
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if random.random() < self.content_neutral:
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self.state['id'] = 0
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#Superinfected
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discontent_neighbors = self.get_neighboring_agents(state_id=1)
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if random.random() < len(discontent_neighbors)*self.content_discontent:
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self.state['id'] = 1
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class BigMarketModel(BaseBehaviour):
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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.enterprises = settings.enterprises
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self.type = ""
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self.number_of_enterprises = len(settings.enterprises)
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if self.id < self.number_of_enterprises: #Enterprises
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self.state['id']=self.id
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self.type="Enterprise"
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self.tweet_probability = settings.tweet_probability_enterprises[self.id]
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else: #normal users
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self.state['id']=self.number_of_enterprises
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self.type="User"
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self.tweet_probability = settings.tweet_probability_users
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self.tweet_relevant_probability = settings.tweet_relevant_probability
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self.tweet_probability_about = settings.tweet_probability_about #List
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self.sentiment_about = settings.sentiment_about #List
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def step(self, now):
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if(self.id < self.number_of_enterprises): # Ennterprise
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self.enterpriseBehaviour()
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else: # Usuario
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self.userBehaviour()
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for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
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self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
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super().step(now)
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def enterpriseBehaviour(self):
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if random.random()< self.tweet_probability: #Tweets
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users
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for x in aware_neighbors:
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if random.uniform(0,10) < 5:
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x.sentiment_about[self.id] += 0.1 #Increments for enterprise
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else:
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x.sentiment_about[self.id] -= 0.1 #Decrements for enterprise
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# Establecemos limites
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if x.sentiment_about[self.id] > 1:
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x.sentiment_about[self.id] = 1
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if x.sentiment_about[self.id]< -1:
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x.sentiment_about[self.id] = -1
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x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
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def userBehaviour(self):
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if random.random() < self.tweet_probability: #Tweets
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if random.random() < self.tweet_relevant_probability: #Tweets something relevant
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#Tweet probability per enterprise
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for i in range(self.number_of_enterprises):
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random_num = random.random()
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if random_num < self.tweet_probability_about[i]:
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#The condition is fulfilled, sentiments are evaluated towards that enterprise
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if self.sentiment_about[i] < 0:
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#NEGATIVO
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self.userTweets("negative",i)
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elif self.sentiment_about[i] == 0:
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#NEUTRO
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pass
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else:
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#POSITIVO
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self.userTweets("positive",i)
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def userTweets(self,sentiment,enterprise):
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbours users
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for x in aware_neighbors:
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if sentiment == "positive":
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x.sentiment_about[enterprise] +=0.003
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elif sentiment == "negative":
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x.sentiment_about[enterprise] -=0.003
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else:
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pass
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# Establecemos limites
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if x.sentiment_about[enterprise] > 1:
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x.sentiment_about[enterprise] = 1
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if x.sentiment_about[enterprise] < -1:
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x.sentiment_about[enterprise] = -1
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x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
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class SentimentCorrelationModel(BaseBehaviour):
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|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.outside_effects_prob = settings.outside_effects_prob
|
||||
self.anger_prob = settings.anger_prob
|
||||
self.joy_prob = settings.joy_prob
|
||||
self.sadness_prob = settings.sadness_prob
|
||||
self.disgust_prob = settings.disgust_prob
|
||||
self.time_awareness=[]
|
||||
for i in range(4): #In this model we have 4 sentiments
|
||||
self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
||||
|
||||
|
||||
def step(self, now):
|
||||
self.behaviour()
|
||||
super().step(now)
|
||||
|
||||
def behaviour(self):
|
||||
|
||||
angry_neighbors_1_time_step=[]
|
||||
joyful_neighbors_1_time_step=[]
|
||||
sad_neighbors_1_time_step=[]
|
||||
disgusted_neighbors_1_time_step=[]
|
||||
|
||||
|
||||
angry_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in angry_neighbors:
|
||||
if x.time_awareness[0] > (self.env.now-500):
|
||||
angry_neighbors_1_time_step.append(x)
|
||||
num_neighbors_angry = len(angry_neighbors_1_time_step)
|
||||
|
||||
|
||||
joyful_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
for x in joyful_neighbors:
|
||||
if x.time_awareness[1] > (self.env.now-500):
|
||||
joyful_neighbors_1_time_step.append(x)
|
||||
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
|
||||
|
||||
|
||||
sad_neighbors = self.get_neighboring_agents(state_id=3)
|
||||
for x in sad_neighbors:
|
||||
if x.time_awareness[2] > (self.env.now-500):
|
||||
sad_neighbors_1_time_step.append(x)
|
||||
num_neighbors_sad = len(sad_neighbors_1_time_step)
|
||||
|
||||
|
||||
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
|
||||
for x in disgusted_neighbors:
|
||||
if x.time_awareness[3] > (self.env.now-500):
|
||||
disgusted_neighbors_1_time_step.append(x)
|
||||
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
|
||||
|
||||
|
||||
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
|
||||
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
|
||||
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
|
||||
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
|
||||
outside_effects_prob= settings.outside_effects_prob
|
||||
|
||||
|
||||
num = random.random()
|
||||
|
||||
|
||||
if(num<outside_effects_prob):
|
||||
self.state['id'] = random.randint(1,4)
|
||||
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #It is stored when it has been infected for the dynamic network
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
self.attrs['sentiment'] = self.state['id']
|
||||
|
||||
|
||||
|
||||
if(num<anger_prob):
|
||||
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[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):
|
||||
|
||||
self.state['id'] = 2
|
||||
sentimentCorrelationNodeArray[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):
|
||||
|
||||
|
||||
self.state['id'] = 3
|
||||
sentimentCorrelationNodeArray[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):
|
||||
|
||||
|
||||
self.state['id'] = 4
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=4
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
|
||||
self.attrs['sentiment'] = self.state['id']
|
||||
|
||||
|
||||
class BassModel(BaseBehaviour):
|
||||
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
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
||||
|
||||
def step(self, now):
|
||||
self.behaviour()
|
||||
super().step(now)
|
||||
|
||||
def behaviour(self):
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
else:
|
||||
pass
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
return
|
||||
|
||||
#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):
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
|
||||
else:
|
||||
pass
|
||||
self.attrs['status'] = self.state['id']
|
||||
|
||||
|
||||
class IndependentCascadeModel(BaseBehaviour):
|
||||
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
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
||||
|
||||
def step(self,now):
|
||||
self.behaviour()
|
||||
super().step(now)
|
||||
|
||||
def behaviour(self):
|
||||
aware_neighbors_1_time_step=[]
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
self.time_awareness = self.env.now #To know when they have been infected
|
||||
else:
|
||||
pass
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
return
|
||||
|
||||
#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):
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
else:
|
||||
pass
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
return
|
||||
11
setup.py
11
setup.py
@ -28,7 +28,16 @@ setup(
|
||||
download_url='https://github.com/gsi-upm/soil/archive/{}.tar.gz'.format(
|
||||
__version__),
|
||||
keywords=['agent', 'social', 'simulator'],
|
||||
classifiers=[],
|
||||
classifiers=[
|
||||
'Development Status :: 5 - Production/Stable',
|
||||
'Environment :: Console',
|
||||
'Intended Audience :: End Users/Desktop',
|
||||
'Intended Audience :: Developers',
|
||||
'License :: OSI Approved :: Apache Software License',
|
||||
'Operating System :: MacOS :: MacOS X',
|
||||
'Operating System :: Microsoft :: Windows',
|
||||
'Operating System :: POSIX',
|
||||
'Programming Language :: Python :: 3'],
|
||||
install_requires=install_reqs,
|
||||
tests_require=test_reqs,
|
||||
setup_requires=['pytest-runner', ],
|
||||
|
||||
@ -6,7 +6,7 @@ network_params:
|
||||
generator: barabasi_albert_graph
|
||||
n: 100
|
||||
m: 2
|
||||
agent_distribution:
|
||||
network_agents:
|
||||
- agent_type: ControlModelM2
|
||||
weight: 0.1
|
||||
state:
|
||||
@ -30,11 +30,11 @@ max_time: 50
|
||||
num_trials: 2
|
||||
network_params:
|
||||
generator: erdos_renyi_graph
|
||||
n: 10000
|
||||
n: 1000
|
||||
p: 0.05
|
||||
#other_agents:
|
||||
# - agent_type: DrawingAgent
|
||||
agent_distribution:
|
||||
network_agents:
|
||||
- agent_type: SISaModel
|
||||
weight: 1
|
||||
state:
|
||||
|
||||
@ -2,7 +2,7 @@ import importlib
|
||||
import sys
|
||||
import os
|
||||
|
||||
__version__ = "0.9.4"
|
||||
__version__ = "0.9.6"
|
||||
|
||||
try:
|
||||
basestring
|
||||
|
||||
32
soil/__main__.py
Normal file
32
soil/__main__.py
Normal file
@ -0,0 +1,32 @@
|
||||
import importlib
|
||||
import sys
|
||||
import argparse
|
||||
from . import simulation
|
||||
|
||||
|
||||
def main():
|
||||
|
||||
parser = argparse.ArgumentParser(description='Run a SOIL simulation')
|
||||
parser.add_argument('file', type=str,
|
||||
nargs="?",
|
||||
default='simulation.yml',
|
||||
help='python module containing the simulation configuration.')
|
||||
parser.add_argument('--module', '-m', type=str,
|
||||
help='file containing the code of any custom agents.')
|
||||
parser.add_argument('--dry-run', '--dry', action='store_true',
|
||||
help='Do not store the results of the simulation.')
|
||||
parser.add_argument('--output', '-o', type=str,
|
||||
help='folder to write results to. It defaults to the current directory.')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.module:
|
||||
sys.path.append(os.getcwd())
|
||||
importlib.import_module(args.module)
|
||||
|
||||
print('Loading config file: {}'.format(args.file, args.output))
|
||||
simulation.run_from_config(args.file, dump=not args.dry_run, results_dir=args.output)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,123 +0,0 @@
|
||||
import nxsim
|
||||
from collections import OrderedDict
|
||||
from copy import deepcopy
|
||||
import json
|
||||
|
||||
from functools import wraps
|
||||
|
||||
|
||||
class BaseAgent(nxsim.BaseAgent):
|
||||
"""
|
||||
A special simpy BaseAgent that keeps track of its state history.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
self._history = OrderedDict()
|
||||
self._neighbors = None
|
||||
super().__init__(*args, **kwargs)
|
||||
self._history[None] = deepcopy(self.state)
|
||||
|
||||
@property
|
||||
def now(self):
|
||||
try:
|
||||
return self.env.now
|
||||
except AttributeError:
|
||||
# No environment
|
||||
return None
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
res = self.step()
|
||||
self._history[self.env.now] = deepcopy(self.state)
|
||||
yield res or self.env.timeout(self.env.interval)
|
||||
|
||||
def step(self):
|
||||
pass
|
||||
|
||||
def to_json(self):
|
||||
return json.dumps(self._history)
|
||||
|
||||
class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
|
||||
|
||||
def count_agents(self, state_id=None, limit_neighbors=False):
|
||||
if limit_neighbors:
|
||||
agents = self.global_topology.neighbors(self.id)
|
||||
else:
|
||||
agents = self.global_topology.nodes()
|
||||
count = 0
|
||||
for agent in agents:
|
||||
if state_id and state_id != self.global_topology.node[agent]['agent'].state['id']:
|
||||
continue
|
||||
count += 1
|
||||
return count
|
||||
|
||||
def count_neighboring_agents(self, state_id=None):
|
||||
return self.count_agents(state_id, limit_neighbors=True)
|
||||
|
||||
|
||||
def state(func):
|
||||
|
||||
@wraps(func)
|
||||
def func_wrapper(self):
|
||||
when = None
|
||||
next_state = func(self)
|
||||
try:
|
||||
next_state, when = next_state
|
||||
except TypeError:
|
||||
pass
|
||||
if next_state:
|
||||
try:
|
||||
self.state['id'] = next_state.id
|
||||
except AttributeError:
|
||||
raise NotImplemented('State id %s is not valid.' % next_state)
|
||||
return when
|
||||
|
||||
func_wrapper.id = func.__name__
|
||||
func_wrapper.is_default = False
|
||||
return func_wrapper
|
||||
|
||||
|
||||
def default_state(func):
|
||||
func.is_default = True
|
||||
return func
|
||||
|
||||
|
||||
class MetaFSM(type):
|
||||
def __init__(cls, name, bases, nmspc):
|
||||
super(MetaFSM, cls).__init__(name, bases, nmspc)
|
||||
states = {}
|
||||
# Re-use states from inherited classes
|
||||
default_state = None
|
||||
for i in bases:
|
||||
if isinstance(i, MetaFSM):
|
||||
for state_id, state in i.states.items():
|
||||
if state.is_default:
|
||||
default_state = state
|
||||
states[state_id] = state
|
||||
|
||||
# Add new states
|
||||
for name, func in nmspc.items():
|
||||
if hasattr(func, 'id'):
|
||||
if func.is_default:
|
||||
default_state = func
|
||||
states[func.id] = func
|
||||
cls.default_state = default_state
|
||||
cls.states = states
|
||||
|
||||
|
||||
class FSM(BaseAgent, metaclass=MetaFSM):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(FSM, self).__init__(*args, **kwargs)
|
||||
if 'id' not in self.state:
|
||||
self.state['id'] = self.default_state.id
|
||||
|
||||
def step(self):
|
||||
if 'id' in self.state:
|
||||
next_state = self.state['id']
|
||||
elif self.default_state:
|
||||
next_state = self.default_state.id
|
||||
else:
|
||||
raise Exception('{} has no valid state id or default state'.format(self))
|
||||
if next_state not in self.states:
|
||||
raise Exception('{} is not a valid id for {}'.format(next_state, self))
|
||||
self.states[next_state](self)
|
||||
@ -1,9 +1,9 @@
|
||||
import random
|
||||
import numpy as np
|
||||
from . import FSM, state
|
||||
from . import FSM, NetworkAgent, state
|
||||
|
||||
|
||||
class SISaModel(FSM):
|
||||
class SISaModel(FSM, NetworkAgent):
|
||||
"""
|
||||
Settings:
|
||||
neutral_discontent_spon_prob
|
||||
|
||||
Loading…
Reference in New Issue
Block a user