def create_sim_instance(sim_time=None,
scenario=None,
controller=None,
start_time=None,
save_path=None,
animate=True):
if sim_time is None:
#입력받은 시간을 시간값으로 할당
sim_time = timedelta(
hours=float(input('Input simulation time (hr): ')))
if scenario is None:
scenario = pick_scenario()
envs = build_envs(scenario, start_time)
if controller is None:
controller = pick_controller()
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)
]
return sim_instances
def run_sim_PID_once(pname, runtime, meals, controller_params):
'''
Run the simulation a single time on a single patient with the PID controller.
Parameters
----------
pname: str
patient name
runtime: int
simulation time, in hours.
meals: (timedelta, int)
a tuple containing the time of meal (as referenced from simulation start) and the meal size, in grams.
targetBG: int
the target blood glucose for the controller, in mg/dl
lowBG: int
the pump suspension glucose for the controller, in mg/dl
Returns
-------
A pandas dataframe containing the simulation results.
axis=0: time, type datetime.datetime
axis=1: data category, type str
'''
sensor = CGMSensor.withName('Dexcom')
pump = InsulinPump.withName('Insulet')
scenario = CustomScenario(start_time = datetime(2020, 1, 1, 0,0,0), scenario=meals)
obj = SimObj(T1DSimEnv(T1DPatient.withName(pname),
sensor,
pump,
scenario),
controller.PIDController(controller_params, pname),
timedelta(hours=runtime),
animate=False,
path=None)
return sim(obj)
def test_results_consistency(self):
# Test data
results_exp = pd.read_csv(TESTDATA_FILENAME, index_col=0)
results_exp.index = pd.to_datetime(results_exp.index)
# specify start_time as the beginning of today
start_time = datetime(2018, 1, 1, 0, 0, 0)
# --------- 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
s = SimObj(env,
controller,
timedelta(days=2),
animate=False,
path=save_folder)
results = sim(s)
assert_frame_equal(results, results_exp)
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 run_sim_PID(no_runs, patients, runtime, meals, controller_params):
'''
Run the simulation a single time on a list of patients with the PID controller.
Parameters
----------
no_runs: int
the number of separate simulation runs.
patients: list of str
a list of patient name strings. Patient name strings can be found in the params/Quest.csv file inside simGlucose.
runtime: int
simulation time, in hours.
meals: (timedelta, int)
a tuple containing the time of meal (as referenced from simulation start) and the meal size, in grams.
targetBG: int
the target blood glucose for the controller, in mg/dl
lowBG: int
the pump suspension glucose for the controller, in mg/dl
Returns
-------
A pandas dataframe containing the simulation results.
axis=0: time, type datetime.datetime
axis=1: MultiIndex
level 0: data category, type str
level 1: patient id, type str
level 2: run number, type int (starts at 1)
'''
sensor = CGMSensor.withName('Dexcom')
pump = InsulinPump.withName('Insulet')
scenario = CustomScenario(start_time = datetime(2020, 1, 1, 0,0,0), scenario=meals)
sim_objs = []
keys = []
for run in range(0, no_runs):
for pname in patients:
sim_objs.append(SimObj(T1DSimEnv(T1DPatient.withName(pname),
sensor,
pump,
copy.deepcopy(scenario)), # because random numbers.
controller.PIDController(controller_params, pname),
timedelta(hours=runtime),
animate=False,
path=None))
keys.append((run + 1, pname))
p_start = time.time()
print('Running batch simulation of {} items...'.format(len(patients * no_runs)))
p = pathos.pools.ProcessPool()
results = p.map(sim, sim_objs)
print('Simulation took {} seconds.'.format(time.time() - p_start))
return pd.concat(results, axis=1, keys=keys)
def pidsim():
for idx,patient in enumerate(patients):
patient = T1DPatient.withName(patient)
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
p,i,d = pidparams[idx]
for seed in range (10,20):
scenario = RandomScenario(start_time=start_time, seed=randint(10, 99999))
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = FoxPIDController(112.517,kp=p, ki=i, kd=d)
# Put them together to create a simulation object
s1 = SimObj(env, controller, timedelta(days=10), animate=False, path=path+str(seed))
results1 = sim(s1)
print('Complete:',patient.name,'-',seed)
print('All done!')
def bbsim():
for patient in patients:
patient = T1DPatient.withName(patient)
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
for seed in range(10, 20):
scenario = RandomScenario(start_time=start_time,
seed=randint(10, 99999))
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=10),
animate=False,
path=path + str(seed))
results1 = sim(s1)
print('Complete:', patient.name, '-', seed)
print('All done!')
filename = 'dfs/' + 'p_' + str(PIDparams[0]) + ' i_' + str(PIDparams[1]) + ' d_' + str(PIDparams[2]) + ' target_' + str(PIDparams[3]) + '.bz2'
dfs.to_pickle(filename)
=======
pname = "adult#001"
t = 9
meals = [(timedelta(hours=2), 50)]
sensor = CGMSensor.withName('Dexcom')
pump = InsulinPump.withName('Insulet')
scenario = CustomScenario(start_time = datetime(2020, 1, 1, 0,0,0), scenario=meals)
keys = []
# forward horizon
horizon = 50
controller_params = (140, 80, horizon)
obj= SimObj(T1DSimEnv(T1DPatient.withName(pname),
sensor,
pump,
copy.deepcopy(scenario)), # because random numbers.
controller.MPCNaive(controller_params, pname),
timedelta(hours=t),
animate=False,
path=None)
keys.append((1, pname))
p_start = time.time()
results = sim(obj)
print('Simulation took {} seconds.'.format(time.time() - p_start))
dfs = results
filename = 'mpc_test.bz2'
dfs.to_pickle(filename)
>>>>>>> MPC-exploration
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
# --------- Create Random Scenario --------------
# Specify results saving path
path = './results'
# 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=1), animate=False, path=path)
results1 = sim(s1)
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()
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)
# --------- Create Random Scenario --------------
# Specify results saving path
path = './results'
# 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=1), animate=False, path=path)
results1 = sim(s1)
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
self.state = init_state
def policy(self, observation, reward, done, **info):
self.state = observation
action = Action(basal=.0, bolus=0)
return action
def reset(self):
self.state = self.init_state
# patient setup
patientID = 12
patient = T1DPatient.withID(12)
sim_sensor = CGMSensor.withName('Dexcom')
sim_pump = InsulinPump.withName('Insulet')
# env setup
RANDOM_SEED = 25
sim_start_time = datetime.now()
sim_run_time = timedelta(hours=24)
sim_scenario = RandomScenario(start_time = sim_start_time, seed = RANDOM_SEED)
environment = T1DSimEnv(patient, sim_sensor, sim_pump, sim_scenario)
controller = blankController(0)
# script saves csv(s) into this path
results_path = './results/'
simulator = SimObj(
environment,
controller,
sim_run_time,
animate=False,
path = results_path
sim(simulator)