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https://github.com/gsi-upm/soil
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72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
#from clase_base import *
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from models import *
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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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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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import models
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import math
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import json
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settings.init() # Loads all the data from settings
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models.init() # Loads the models and network variables
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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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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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##############
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# Simulation #
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##############
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=BigMarketModel,
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max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
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sim.run_simulation()
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###########
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# Results #
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###########
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trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
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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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#################
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# Visualization #
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#################
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for x in range(0, settings.number_of_nodes):
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for empresa in models.networkStatus["agente_%s"%x]:
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emotionStatusAux=[]
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for tiempo in models.networkStatus["agente_%s"%x][empresa]:
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prec = 2
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output = math.floor(models.networkStatus["agente_%s"%x][empresa][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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emotionStatusAux.append((output,tiempo,None))
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attributes = {}
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attributes[empresa] = emotionStatusAux
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G.add_node(x, attributes)
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print("Done!")
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with open('data.txt', 'w') as outfile:
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json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
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nx.write_gexf(G,"test.gexf", version="1.2draft")
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