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 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 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!')
# 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()
analysis_path = P.join(current_summary_path, 'Analysis')
# init simulation
patient_names = get_patients([patient_number])
pump_name = get_insulin_pump(selection=2)
cgm_seed = 5
cgm_sensor_name = get_cgm_sensor(selection=1)
sim_time = datetime.timedelta(hours=8) # datetime.time(23)
control = input("Choose controller:\n[1]DDPG\n[2]BB\n")
if control == '2':
# Original Controller
controller = BBController()
envs = our_build_envs(scenario, start_time, patient_names, cgm_sensor_name, cgm_seed, pump_name,
controller_name='BB Controller', results_path=analysis_path + '_BB')
env_BB = envs[0]
sim1 = SimObj(env_BB, controller, sim_time, animate=animate, path=analysis_path + '_BB')
results1 = sim(sim1)
report(results1, analysis_path + '_BB')
env_BB.render()
env_BB.viewer.close()
# Our Controller
else:
state_dim = gym_env.observation_space.shape[0]
action_dim = gym_env.action_space.shape[0]
action_bound = gym_env.action_space.high
actor = ActorNetwork(sess, state_dim, action_dim, action_bound,
float(args['actor_lr']), float(args['tau']),
int(args['minibatch_size']))
sess.run(tf.global_variables_initializer())
actor.restore(sess, P.join(args['Load_models_path'][0], f"actor_{args['Load_models_path'][1]}"))
our_controller = DDPG_Controller(actor=actor)
# 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 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)
scenario = RandomScenario(start_time=start_time, seed=1)
env = T1DSimEnv(patient, sensor, pump, scenario)
# tr_model = PPO2.load("sim-adl-02")
# in_state = env.reset()
# ctrller = MyController(0, model = tr_model) #BBController() #
controller = BBController()
sim_i = [
SimObj(env, controller, timedelta(days=1), animate=False, path=path)
]
# sim_instances.simulate()
# sim_instances.save_results()
# result = sim(sim_instances[0])
# print('Simulation Completed!')
# # df = pd.concat(result, keys=[s.env.patient.name for s in sim_instances])
# results, ri_per_hour, zone_stats, figs, axes = report(path, df=result)
results1 = sim(sim_i[0])
# print(results1)
pd_result = pd.DataFrame(results1)
bg = list(pd_result['BG'])
print(type(bg), bg[0], type(bg[0]))
percent_time.append(
(len([I for I in bg if 70 <= I <= 180]) / len(bg)) * 100)
final_result = pd.DataFrame(list(zip(person_options, percent_time)),
columns=['Paient_ID', 'Percent_Time'])
final_result.to_csv(path + '/Final Result.csv', index=False)
# simulate(controller=ctrller)
# env setup
RANDOM_SEED = 5550
RUN_TIME = 48
sim_start_time = datetime(2020, 1, 1, 0, 0, 0)
sim_run_time = timedelta(hours=RUN_TIME)
# random scenario
random_scen = RandomScenario(start_time=sim_start_time, seed=RANDOM_SEED)
# custom scenario
# meals is a list of tuples, each tuple containing a timedelta (time after start of sim) and a meal size, in g CHO.
meals = [(timedelta(hours=12), 100)]
# generate the custom scenario with the list of meals
custom_scen = CustomScenario(start_time=sim_start_time, scenario=meals)
# choose scenario
environment = T1DSimEnv(patient, sim_sensor, sim_pump, custom_scen)
# choose controller
controller = PController(gain=0.04, dweight=.5, pweight=1, target=120)
# script saves csv(s) into this path
results_path = './results/'
simulator = SimObj(environment,
controller,
sim_run_time,
animate=False,
path=results_path)
results = sim(simulator)
plotBG.group_plot(results, savedir='./results')