52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import pandas as pd
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from tabulate import tabulate
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from engine.configProcessor import generate_config
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from engine.mechanismExecutor import Executor
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from utils.engine import flatten
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from utils.ui import create_tensor_field
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from engine.multiproc import parallelize_simulations
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# from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
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import ui.config1 as conf1
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import ui.config2 as conf2
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def main():
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states_list1 = [conf1.state_dict]
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states_list2 = [conf2.state_dict]
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states_lists = [states_list1,states_list2]
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T = conf1.sim_config['T']
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N = conf2.sim_config['N']
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ep1 = list(conf1.exogenous_states.values())
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ep2 = list(conf2.exogenous_states.values())
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eps = [ep1, ep2]
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config1 = generate_config(conf1.state_dict, conf1.mechanisms, ep1)
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config2 = generate_config(conf2.state_dict, conf2.mechanisms, ep2)
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configs = [config1, config2]
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env_processes = [conf1.env_processes, conf2.env_processes]
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# Dimensions: N x r x mechs
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simulation1 = Executor(conf1.behavior_ops).simulation
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simulation2 = Executor(conf2.behavior_ops).simulation
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simulation_execs = [simulation1,simulation2]
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if len(configs) > 1:
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simulations = parallelize_simulations(simulation_execs, states_lists, configs, env_processes, T, N)
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# else:
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# simulations = [simulation(states_list1, configs[0], env_processes, T, N)]
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# behavior_ops, states_list, configs, env_processes, time_seq, runs
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# result = simulation(states_list1, config1, conf1.env_processes, T, N)
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# return pd.DataFrame(flatten(result))
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mechanisms = [conf1.mechanisms, conf2.mechanisms]
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for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
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print(tabulate(create_tensor_field(mechanism, ep), headers='keys', tablefmt='psql'))
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print(tabulate(pd.DataFrame(flatten(result)), headers='keys', tablefmt='psql')) |