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https://github.com/gsi-upm/soil
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TerroristNetworkModel to FSM
This commit is contained in:
Tasio Mendez
parent
1083112473
commit
a0613f8a58
@ -1,6 +1,6 @@
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import random
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import networkx as nx
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from soil.agents import BaseAgent
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from soil.agents import BaseAgent, FSM, state
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from scipy.spatial import cKDTree as KDTree
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global betweenness_centrality_global
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@ -9,7 +9,7 @@ global degree_centrality_global
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betweenness_centrality_global = None
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degree_centrality_global = None
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class TerroristSpreadModel(BaseAgent):
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class TerroristSpreadModel(FSM):
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"""
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Settings:
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information_spread_intensity
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@ -38,12 +38,14 @@ class TerroristSpreadModel(BaseAgent):
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self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
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self.prob_interaction = environment.environment_params['prob_interaction']
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if self.state['id'] == 0: # Civilian
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if self['id'] == self.civilian.id: # Civilian
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self.initial_belief = random.uniform(0.00, 0.5)
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elif self.state['id'] == 1: # Terrorist
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elif self['id'] == self.terrorist.id: # Terrorist
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self.initial_belief = random.uniform(0.8, 1.00)
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elif self.state['id'] == 2: # Leader
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elif self['id'] == self.leader.id: # Leader
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self.initial_belief = 1.00
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else:
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raise Exception('Invalid state id: {}'.format(self['id']))
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if 'min_vulnerability' in environment.environment_params:
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self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
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@ -72,15 +74,8 @@ class TerroristSpreadModel(BaseAgent):
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_list = super().get_agents(state_id, limit_neighbors=True)
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return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
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def step(self):
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if self.state['id'] == 0: # Civilian
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self.civilian_behaviour()
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elif self.state['id'] == 1: # Terrorist
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self.terrorist_behaviour()
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elif self.state['id'] == 2: # Leader
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self.leader_behaviour()
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def civilian_behaviour(self):
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@state
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def civilian(self):
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if self.count_neighboring_agents() > 0:
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neighbours = []
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for neighbour in self.get_neighboring_agents():
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@ -93,37 +88,33 @@ class TerroristSpreadModel(BaseAgent):
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self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
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if self.mean_belief >= 0.8:
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self.state['id'] = 1
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return self.terrorist
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# self.state['radicalism'] = self.mean_belief
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def leader_behaviour(self):
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@state
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def leader(self):
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self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
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if self.count_neighboring_agents(state_id=[1,2]) > 0:
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for neighbour in self.get_neighboring_agents(state_id=[1,2]):
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if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
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for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
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if neighbour.betweenness_centrality > self.betweenness_centrality:
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self.state['id'] = 1
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return self.terrorist
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# self.state['radicalism'] = self.mean_belief
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def terrorist_behaviour(self):
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if self.count_neighboring_agents(state_id=[1,2]) > 0:
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neighbours = self.get_neighboring_agents(state_id=[1,2])
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@state
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def terrorist(self):
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if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
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neighbours = self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id])
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influence = sum( neighbour.degree_centrality for neighbour in neighbours )
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mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
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self.initial_belief = self.mean_belief
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self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
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self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
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if self.count_neighboring_agents(state_id=2) == 0 and self.count_neighboring_agents(state_id=1) > 0:
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if self.count_neighboring_agents(state_id=self.leader.id) == 0 and self.count_neighboring_agents(state_id=self.terrorist.id) > 0:
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max_betweenness_centrality = self
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for neighbour in self.get_neighboring_agents(state_id=1):
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for neighbour in self.get_neighboring_agents(state_id=self.terrorist.id):
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if neighbour.betweenness_centrality > max_betweenness_centrality.betweenness_centrality:
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max_betweenness_centrality = neighbour
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if max_betweenness_centrality == self:
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self.state['id'] = 2
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# self.state['radicalism'] = self.mean_belief
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return self.leader
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def add_edge(self, G, source, target):
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G.add_edge(source.id, target.id, start=self.env._now)
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@ -189,7 +180,7 @@ class HavenModel(BaseAgent):
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civilian_haven = False
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if self.state['id'] == 0:
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for neighbour_agent in self.get_neighboring_agents():
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if isinstance(neighbour_agent, TerroristSpreadModel) and neighbour_agent.state['id'] == 0:
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if isinstance(neighbour_agent, TerroristSpreadModel) and neighbour_agent['id'] == neighbor_agent.civilian.id:
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civilian_haven = True
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if civilian_haven:
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@ -224,13 +215,18 @@ class TerroristNetworkModel(TerroristSpreadModel):
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self.weight_social_distance = environment.environment_params['weight_social_distance']
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self.weight_link_distance = environment.environment_params['weight_link_distance']
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def step(self):
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if self.state['id'] == 1 or self.state['id'] == 2:
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self.update_relationships()
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super().step()
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@state
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def terrorist(self):
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self.update_relationships()
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return super().terrorist()
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@state
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def leader(self):
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self.update_relationships()
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return super().leader()
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def update_relationships(self):
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if self.count_neighboring_agents(state_id=0) == 0:
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if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
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close_ups = self.link_search(self.global_topology, self.id, self.vision_range)
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step_neighbours = self.social_search(self.global_topology, self.id, self.sphere_influence)
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search = list(set(close_ups).union(step_neighbours))
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@ -240,7 +236,7 @@ class TerroristNetworkModel(TerroristSpreadModel):
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social_distance = 1 / self.shortest_path_length(self.global_topology, self.id, agent.id)
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spatial_proximity = ( 1 - self.get_distance(self.global_topology, self.id, agent.id) )
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prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
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if agent.state['id'] == 0 and random.random() < prob_new_interaction:
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if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
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self.add_edge(self.global_topology, self, agent)
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break
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@ -1,6 +1,6 @@
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name: TerroristNetworkModel_sim
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load_module: TerroristNetworkModel
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max_time: 200
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max_time: 100
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num_trials: 1
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network_params:
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generator: random_geometric_graph
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@ -12,19 +12,19 @@ network_agents:
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- agent_type: TerroristNetworkModel
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weight: 0.8
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state:
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id: 0 # Civilians
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id: civilian # Civilians
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- agent_type: TerroristNetworkModel
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weight: 0.1
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state:
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id: 2 # Leaders
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id: leader # Leaders
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- agent_type: TrainingAreaModel
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weight: 0.05
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state:
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id: 2 # Terrorism
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id: terrorist # Terrorism
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- agent_type: HavenModel
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weight: 0.05
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state:
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id: 0 # Civilian
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id: civilian # Civilian
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environment_params:
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# TerroristSpreadModel
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@ -51,11 +51,11 @@ visualization_params:
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HavenModel: home
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TerroristNetworkModel: person
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colors:
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- attr_id: 0
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- attr_id: civilian
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color: '#40de40'
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- attr_id: 1
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- attr_id: terrorist
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color: red
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- attr_id: 2
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- attr_id: leader
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color: '#c16a6a'
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background_image: 'map_4800x2860.jpg'
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background_opacity: '0.9'
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@ -60,6 +60,10 @@
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return false;
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}
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String.prototype.type = function() {
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return "string";
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}
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var lastFocusNode;
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var _helpers = {
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set_node: function(node, property, time) {
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