def pickle_model(self, model):
"""
Creates and serializes the pickleLight object based on previously
defined properties.
Parameters
----------
model : Model
An instance of the model on which the current simulation is based.
"""
start = time.time()
model_lite = Model(5)
if self.pickle_schedule:
model_lite.schedule.agents = model.schedule.agents.union(
model.schedule.agents_to_schedule)
for a in model_lite.schedule.agents:
if a.__class__.__name__ == "CancerCell":
# Can't pickle functions
a.reactToDrug_ = None
model_lite.schedule.helpers = [h for h in model.schedule.helpers if
h.__class__.__name__ not in
"ExitConditionWatcher"]
if self.pickle_envs:
model_lite.environments = copy.deepcopy(model.environments)
env_start = time.time()
for k, v in model_lite.environments.iteritems():
v.grid = dict(v.grid)
env_end = time.time()
print("Cloning environments took %s seconds" % str(
env_end - env_start))
model_lite.output = model.output
model_lite.properties = model.properties
# Can't pickle functions
model_lite.properties["agents"]["cancerCells"][
"drugReactFunction"] = None
model_lite.current_epoch = model.current_epoch
if self.prefix is None:
target = "%s/epoch_%s.pickle" % (self.out_dir, model.current_epoch)
else:
target = "%s/%s_epoch_%s.pickle" % (
self.out_dir, self.prefix, model.current_epoch)
with open(target, "wb") as output_file:
pickle.dump(model_lite, output_file)
end = time.time()
print("Pickler lite took %s seconds" % str(end - start))
epochs = 50
model = Model(epochs)
xsize = ysize = 500
ObjectGrid2D("agent_env", xsize, ysize, model)
target_position = (12, 12)
model.properties = {
"best_fitness": 0, # setting global best to 0
"best_position": (0, 0),
"target_position": target_position
}
model.output = {"best_fitness_in_epochs": []}
num_agents = 20
for _ in range(num_agents):
agent_position = (randint(0, xsize), randint(0, ysize))
agent = PSOAgent(agent_position)
model.schedule.agents.add(agent)
agent.add_agent_to_grid("agent_env", agent_position, model)
model.schedule.helpers.append(FitnessTrackerHelper())
model.run()
plt.figure()
plt.scatter(range(model.current_epoch + 1),