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))
def generate_model(properties, numEpochs):
"""
Generates and sets up a model object for warburg investigation. Includes
assigning properties, instantiating agents and environments, etc.
Parameters
----------
properties : dict
A dictionary of model properties, as generated by the
generate_properties function
numEpochs : number
Number of epochs the model should run for
Returns
-------
Model
A configured instance of the model object.
"""
model = Model(numEpochs)
model.properties = properties
xsize = ysize = zsize = model.properties["envSize"]
# Adding environments
ObjectGrid3D(model.properties["envNames"]["agentEnvName"], xsize, ysize,
zsize, model)
NumericalGrid3D(model.properties["envNames"]["oxygenEnvName"], xsize,
ysize, zsize, model)
NumericalGrid3D(model.properties["envNames"]["vegfEnvName"], xsize, ysize,
zsize, model)
NumericalGrid3D(model.properties["envNames"]["glucoseEnvName"], xsize,
ysize, zsize, model)
NumericalGrid3D(model.properties["envNames"]["drugEnvName"], xsize, ysize,
zsize, model)
hc = 0
tc = 0
tic = 0
# Adding agents
for x in range(xsize):
for y in range(ysize):
for z in range(zsize):
if x % 2 == z % 2:
agent = HealthyCell(model)
hc += 1
agent.add_agent_to_grid(
model.properties["envNames"]["agentEnvName"],
(x, y, z), model)
model.schedule.agents.add(agent)
else:
for _ in range(model.properties["initialAgentSetup"]
["numEndothelialCells"]):
agent = TipCell(model)
tic += 1
agent.add_agent_to_grid(
model.properties["envNames"]["agentEnvName"],
(x, y, z), model)
model.schedule.agents.add(agent)
print("hc %s tc %s tic %s" % (hc, tc, tic))
for _ in range(model.properties["initialAgentSetup"]["numCancerCells"]):
for x in range(8, 12):
for y in range(8, 12):
for z in range(8, 12):
c = CancerCell(model)
state = ["G1", "S", "G2", "M"][randint(0, 3)]
state_length = \
model.properties["agents"]["baseCellCycleLength"][
state]
progress_in_state = randint(0, state_length - 1)
c.current_state = state
c.progress_in_state = progress_in_state
c.add_agent_to_grid(
model.properties["envNames"]["agentEnvName"],
(x, y, z), model)
model.schedule.agents.add(c)
# Adding helpers
model.schedule.helpers.append(HeartbeatHelper())
model.schedule.helpers.append(GlucoseDiffusionHelper(model))
odh = OxygenDiffusionHelper(model)
model.schedule.helpers.append(odh)
model.schedule.helpers.append(VegfDiffusionHelper(model))
snapshot_interval = 10
model.schedule.helpers.append(AgentCounter(model))
model.schedule.helpers.append(
CancerCellWatcher(model, distributionInterval=snapshot_interval))
model.schedule.helpers.append(VegfStimulusWatcher(model))
model.schedule.helpers.append(TumourVolumeWatcher(model))
model.schedule.helpers.append(
OxygenConcentrationWatcher(model, interval=snapshot_interval))
model.schedule.helpers.append(
GlucoseConcentrationWatcher(model, interval=snapshot_interval))
model.schedule.helpers.append(
DeathCauseWatcher(model, interval=snapshot_interval))
model.schedule.helpers.append(
ModelPicklerLite(model.properties["outDir"], pickle_every=400))
def num_agents_exit_condition(model):
return len([
a for a in model.schedule.agents
if a.__class__.__name__ == "CancerCell"
]) > 400000
def no_cancer_cells(model):
return len([
c for c in model.schedule.agents
if c.__class__.__name__ == "CancerCell" and not c.dead
]) == 0
model.schedule.helpers.append(
ExitConditionWatcher([num_agents_exit_condition, no_cancer_cells]))
return model
def step_epilogue(self, model):
model.output["best_fitness_in_epochs"] \
.append(model.properties["best_fitness"])
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())
