def save_timing_performance(timing, filename):
full_timings = [3] * 8
full_timings[0] = timing[0]
full_timings[4] = timing[1]
traffic_light = PhaseModifier("node1")
controller = StaticTrafficLightController(controller=traffic_light,
sequence=list(range(8)),
timings=full_timings)
sim = Simulator()
sim.add_simulation_component(SimulationOutputParser)
sim.add_tickable(controller)
sim.run(sumocfg1, gui=False)
sim.save_results(filename)
def runSim(trials):
sim = Simulator(requests)
outFile = open("data/results/{0}_{1}_{2}_delays.results".format(week, day, trials), 'w')
# Report the results to the open file.
def reportResults(report):
report.printTSV(outFile)
if trials > 1:
runSim(trials - 1)
# Run the simulation.
tieBreakingPolicy = crisisThresholdsAndFlip(10 * 60, 40 * 60, 1.5)
sim.run(reportResults, tieBreakingPolicy, regular, senior)
def run_n_simulations(designpoint,
dp_name,
iterations,
outputs,
all_samples=False):
""" Evaluate the given design point by calculating the MTTF for a given amount of sample_budget.
This function is used for parallelising the Monte Carlo Simulation evaluation.
:param designpoint: DesignPoint object - this DesignPoint is evauluated.
:param dp_name: any - unique inidcator of this designpoint (e.g. integer)
:param iterations: number of sample_budget to run the MCS.
:param outputs: dictionary to write the MTTF output.
:return: None
"""
TTFs = []
consumptions = []
sim = Simulator(designpoint)
for i in range(iterations):
ttf, consum, size = sim.run()
TTFs.append(ttf)
consumptions.append(consum)
if not all_samples:
outputs[dp_name] = sum(TTFs) / len(TTFs), sum(consumptions) / len(
consumptions), size
else:
outputs[dp_name] = list(
zip(TTFs, consumptions, [size for _ in range(len(TTFs))]))
def evaluate_timing(timing):
traffic_light = PhaseModifier("node1")
full_timings = [3] * 8
full_timings[0] = timing[0]
full_timings[4] = timing[1]
controller = StaticTrafficLightController(controller=traffic_light,
sequence=list(range(8)),
timings=full_timings)
sim = Simulator()
sim.add_simulation_component(SimulationOutputParser)
sim.add_tickable(controller)
if not sim.run(sumocfg1, time_steps=2000, gui=False):
return sim.results
return False
from reward import RewardCalculator
from simulation import Simulator
import numpy as np
from stats.output_parser import SimulationOutputParser
from gradient_descent.static_controller import StaticTrafficLightController
from action import PhaseModifier
sim = Simulator()
sim.add_simulation_component(SimulationOutputParser)
t = [3]*8
t[0] = 50
t[4] = 50
print(t)
controller = StaticTrafficLightController(PhaseModifier("node1"),list(range(8)),t)
sim.add_tickable(controller)
sumocfg1 = "..\\test_environments\\single_intersection_random_trips\\newnet.sumocfg"
sumocfg2 = "..\\test_environments\\grid_map\\4by4.sumocfg"
sim.run(sumocfg1, gui=False)
print(sim.results["mean_speed"].var()**.5)
print(sim.results["duration"].var()**.5)
if __name__ == '__main__':
args = P.parse_args()
# If not given as a fraction, then assume percentage
if args.z > 1:
args.z /= 100
sim = Simulator(args.n, args.z, args.tm, args.bm)
print("\n >>>> Cleaning graphs directory ")
sim.remove_graphs()
print("\n >>>> Running simulation \n")
sim.run(args.until, args.q)
if args.network:
print("\n >>>> Dumping network graph ")
sim.dump_network()
print("\n >>>> Dumping blockchains of all nodes ")
sim.dump_node_chains()
print("\n >>>> Dumping blockchains of all nodes (pruned) ")
sim.prune_node_chains()
sim.dump_node_chains(pruned=True)
print("\n >>>> Rendering graphs to png images ")
sim.convert_graphs()
def __init__(self, reward_calc, state, controller, actions):
super().__init__(reward_calc, state, controller, actions)
import itertools
self._phases = itertools.cycle([0] * 30 + [4] * 30)
def _select_action(self, state):
return next(self._phases)
if __name__ == "__main__":
class X:
def __init__(self, s):
self.x = 0
self.s = s
def tick(self):
x = int(input("Enter a phase :"))
self.s.do_action(x)
sim = Simulator()
rc = RewardCalculator()
sim.add_tickable(rc)
c = Controller(PhaseModifier("node1"))
s = StateGenerator()
agent = SingleIntersectQAgent(rc, s, c, [0, 3, 4])
sim.add_tickable(agent)
sim.add_tickable(X(c))
sumocfg = "..\\..\\test_environments\\single_intersection_map\\newnet.sumocfg"
sim.run(sumocfg, time_steps=1000, gui=True)
