def test_batch_sim(self):
# specify start_time as the beginning of today
now = datetime.now()
start_time = datetime.combine(now.date(), datetime.min.time())
# --------- Create Random Scenario --------------
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
scenario = RandomScenario(start_time=start_time, seed=1)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s1 = SimObj(env, controller, timedelta(
days=2), animate=True, path=save_folder)
results1 = sim(s1)
# --------- Create Custom Scenario --------------
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
# custom scenario is a list of tuples (time, meal_size)
scen = [(7, 45), (12, 70), (16, 15), (18, 80), (23, 10)]
scenario = CustomScenario(start_time=start_time, scenario=scen)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s2 = SimObj(env, controller, timedelta(
days=2), animate=False, path=save_folder)
results2 = sim(s2)
# --------- batch simulation --------------
s1.reset()
s2.reset()
s1.animate = False
s = [s1, s2]
results_para = batch_sim(s, parallel=True)
s1.reset()
s2.reset()
s = [s1, s2]
results_serial = batch_sim(s, parallel=False)
assert_frame_equal(results_para[0], results1)
assert_frame_equal(results_para[1], results2)
for r1, r2 in zip(results_para, results_serial):
assert_frame_equal(r1, r2)
def run_sim_once(simtime, meals, controller, patients):
# times
run_time = timedelta(hours=simtime)
start_time = datetime(2020, 1, 1, 0,0,0)
scenario = CustomScenario(start_time = start_time, scenario=meals)
envs = build_envs(scenario, start_time, patients)
# must deepcopy controllers because they're dynamic
controllers = [copy.deepcopy(controller) for _ in range(len(envs))]
sim_instances = [SimObj(env, ctr, run_time, animate=False, path='./results') for (env, ctr) in zip(envs, controllers)]
# run simulations
results = batch_sim(sim_instances, parallel=False)
# create dataframe with results from 1 sim
return pd.concat(results, keys=[s.env.patient.name for s in sim_instances])
def simulate(sim_time=None,
scenario=None,
controller=None,
start_time=None,
save_path=None,
animate=None,
parallel=None):
'''
Main user interface.
----
Inputs:
sim_time - a datetime.timedelta object specifying the simulation time.
scenario - a simglucose.scenario.Scenario object. Use
simglucose.scenario_gen.RandomScenario or
simglucose.scenario.CustomScenario to create a scenario object.
controller - a simglucose.controller.Controller object.
start_time - a datetime.datetime object specifying the simulation start time.
save_path - a string representing the directory to save simulation results.
animate - switch for animation. True/False.
parallel - switch for parallel computing. True/False.
'''
if animate is None:
while True:
select = input('Show animation? (y/n) ')
if select == 'y':
animate = True
break
elif select == 'n':
animate = False
break
else:
continue
if parallel is None:
while True:
select = input('Use multiple processes? (y/n) ')
if select == 'y':
parallel = True
break
elif select == 'n':
parallel = False
break
else:
continue
if platform.system() == 'Darwin':
if animate is True and parallel is True:
raise ValueError(
"""animate and parallel cannot be turned on at the same time in macOS."""
)
if save_path is None:
save_path = pick_save_path()
sim_instances = create_sim_instance(sim_time=sim_time,
scenario=scenario,
controller=controller,
start_time=start_time,
save_path=save_path,
animate=animate)
results = batch_sim(sim_instances, parallel=parallel)
df = pd.concat(results, keys=[s.env.patient.name for s in sim_instances])
results, ri_per_hour, zone_stats, figs, axes = report(df, save_path)
return 0
def simulate(sim_time=None,
scenario=None,
controller=None,
patient_names=[],
cgm_name=None,
cgm_seed=None,
insulin_pump_name=None,
start_time=None,
save_path=None,
animate=None,
parallel=None):
'''
Main user interface.
----
Inputs:
sim_time - a datetime.timedelta object specifying the simulation time.
scenario - a simglucose.scenario.Scenario object. Use
simglucose.scenario_gen.RandomScenario or
simglucose.scenario.CustomScenario to create a scenario object.
controller - a simglucose.controller.Controller object.
start_time - a datetime.datetime object specifying the simulation start time.
save_path - a string representing the directory to save simulation results.
animate - switch for animation. True/False.
parallel - switch for parallel computing. True/False.
'''
if animate is None:
animate = pick_animate()
if parallel is None:
parallel = pick_parallel()
if platform.system() == 'Darwin' and (animate and parallel):
raise ValueError(
"""animate and parallel cannot be turned on at the same time in macOS."""
)
if save_path is None:
save_path = pick_save_path()
if sim_time is None:
sim_time = timedelta(
hours=float(input('Input simulation time (hr): ')))
if scenario is None:
scenario = pick_scenario(start_time=start_time)
if not patient_names:
patient_names = pick_patients()
if cgm_name is None:
cgm_name = pick_cgm_sensor()
if cgm_seed is None:
cgm_seed = pick_cgm_seed()
if insulin_pump_name is None:
insulin_pump_name = pick_insulin_pump()
if controller is None:
controller = pick_controller()
def local_build_env(pname):
patient = T1DPatient.withName(pname)
cgm_sensor = CGMSensor.withName(cgm_name, seed=cgm_seed)
insulin_pump = InsulinPump.withName(insulin_pump_name)
scen = copy.deepcopy(scenario)
env = T1DSimEnv(patient, cgm_sensor, insulin_pump, scen)
return env
envs = [local_build_env(p) for p in patient_names]
ctrllers = [copy.deepcopy(controller) for _ in range(len(envs))]
sim_instances = [
SimObj(e, c, sim_time, animate=animate, path=save_path)
for (e, c) in zip(envs, ctrllers)
]
results = batch_sim(sim_instances, parallel=parallel)
df = pd.concat(results, keys=[s.env.patient.name for s in sim_instances])
results, ri_per_hour, zone_stats, figs, axes = report(df, save_path)
return results
print(results1)
# --------- Create Custom Scenario --------------
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
# custom scenario is a list of tuples (time, meal_size)
scen = [(7, 45), (12, 70), (16, 15), (18, 80), (23, 10)]
scenario = CustomScenario(start_time=start_time, scenario=scen)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s2 = SimObj(env, controller, timedelta(days=1), animate=False, path=path)
results2 = sim(s2)
print(results2)
# --------- batch simulation --------------
# Re-initialize simulation objects
s1.reset()
s2.reset()
# create a list of SimObj, and call batch_sim
s = [s1, s2]
results = batch_sim(s, parallel=True)
print(results)
results1 = sim(s1)
print(results1)
# --------- Create Custom Scenario --------------
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Navigator', seed=1)
pump = InsulinPump.withName('Insulet')
# custom scenario is a list of tuples (time, meal_size)
scen = [(7, 45), (12, 70), (16, 15), (18, 80), (23, 10)]
scenario = CustomScenario(start_time=start_time, scenario=scen)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s2 = SimObj(env, controller, timedelta(days=1), animate=False, path=path)
results2 = sim(s2)
print(results2)
# --------- batch simulation --------------
# Re-initialize simulation objects
s1.reset()
s2.reset()
# create a list of SimObj, and call batch_sim
s = [s1, s2]
results = batch_sim(s, parallel=False)
print(results)
