LODUS/simplest_simulator.py at main · Virtual-Humans-Lab/LODUS · GitHub

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#encoding: utf-8
#python .\simplest_simulator.py --f .\DataInput\NT_13_Routines.json
import sys
sys.path.append('./Plugins/')

import argparse
import environment
from util import *
from data_parse_util import *
import population

from random_inst import FixedRandom

from ExamplePlugin import ExamplePlugin

from AgentBasedPlugin import AgentBasedPlugin
#from InfectionPlugin import InfectionPlugin
from SocialIsolationPlugin import SocialIsolationPlugin
from GatherPopulationPlugin import GatherPopulationPlugin
from ReverseSocialIsolationPlugin import ReverseSocialIsolationPlugin
from ReturnPopulationPlugin import ReturnPopulationPlugin
from LevyWalkPlugin import LevyWalkPlugin

from simulation_logger import SimulationLogger

from pathlib import Path

import time

arg_parser = argparse.ArgumentParser(description="Population Dynamics Simulation.")
arg_parser.add_argument('--f', metavar="F", type=str, default = '', help='Simulation file.')
arg_parser.add_argument('--n', metavar="N", type=str, default = None, help='Experiment Name.')
arg_parser.add_argument('--l', metavar="L", type=float, default = 0, help='L.')
arg_parser.add_argument('--s', metavar="S", type=int, default = 0, help='S')
arg_parser.add_argument('--r', metavar="R", type=float, default = 0, help='R')

args = vars(arg_parser.parse_args())

FixedRandom()
'''
Data Loading
'''
data_input_file_path = args['f']
env_graph = generate_EnvironmentGraph(data_input_file_path)

'''
Parameters
'''
#how many steps each day has
days = 3
day_duration = 24
env_graph.routine_day_length = day_duration

simulation_steps = days * day_duration

'''
Load Plugins Examples
'''
plug = ExamplePlugin(env_graph)
plug.example_parameter = 'bar'

env_graph.LoadPlugin(plug)


walk = LevyWalkPlugin(env_graph)
walk.distribution_scale = int(args['s'])
walk.levy_probability = float(args['l'])
env_graph.LoadPlugin(walk)

social_distance = ReverseSocialIsolationPlugin(env_graph, '', isolation_rate = float(args['r']))
social_distance.day_cycle = day_duration
social_distance.iso_mode = 'regular'
env_graph.LoadPlugin(social_distance)


return_plugin = ReturnPopulationPlugin(env_graph)
env_graph.LoadPlugin(return_plugin)


'''
Logging
'''

logger = SimulationLogger(f'{args["n"]}', day_duration)

#logger.set_to_record('global')
#logger.set_to_record('neighbourhood')
logger.set_default_data_to_record('neighbourhood_disserta')
logger.set_default_data_to_record('metrics')
#logger.set_to_record('nodes')
#logger.set_to_record('positions')

pop_temp = PopTemplate()
#pop_temp.set_property('age', 'adults')
logger.pop_template = pop_temp
# logger.foreign_only = True
# this option saves REALLY big files
# logger.set_to_record('graph')

'''
Simulation
'''

for i in range(simulation_steps):
    print(i, end='\r')
    # print(i)
    # Routine/Repeating Global Action Invoke example
    # Updates Node Routines and Repeating Global Actions
    # These are defined in the input environment descriptor
    env_graph.update_time_step(i % day_duration, i)

    # Direct Action Invoke example
    # if i == 50:
    #     dummy_action = TimeAction('push_population', {'region':'example1', 'node':'example2', 'quantity':50})
    #     env_graph.direct_action_invoke(dummy_action)

    # Next frame queue action example
    # if i == 60:
    #     dummy_action = TimeAction('push_population', {'region':'example1', 'node':'example2', 'quantity':50})
    #     env_graph.queue_next_frame_action(dummy_action)
    logger.record_frame(env_graph, i)