def test_rendering(self):
start_time = datetime(2018, 1, 1, 0, 0, 0)
viewer = Viewer(start_time, 'adolescent#001')
for i in range(len(self.df)):
df_tmp = self.df.iloc[0:(i + 1), :]
viewer.render(df_tmp)
viewer.close()
class T1DSimEnv(object):
def __init__(self, patient, sensor, pump, scenario):
self.patient = patient
self.sensor = sensor
self.pump = pump
self.scenario = scenario
self._reset()
@property
def time(self):
return self.scenario.start_time + timedelta(minutes=self.patient.t)
def mini_step(self, action):
# current action
patient_action = self.scenario.get_action(self.time)
basal = self.pump.basal(action.basal)
bolus = self.pump.bolus(action.bolus)
insulin = basal + bolus
CHO = patient_action.meal
patient_mdl_act = Action(insulin=insulin, CHO=CHO)
# State update
self.patient.step(patient_mdl_act)
# next observation
BG = self.patient.observation.Gsub
CGM = self.sensor.measure(self.patient)
return CHO, insulin, BG, CGM
def step(self, action, reward_fun=risk_diff):
'''
action is a namedtuple with keys: basal, bolus
'''
CHO = 0.0
insulin = 0.0
BG = 0.0
CGM = 0.0
for _ in range(int(self.sample_time)):
# Compute moving average as the sample measurements
tmp_CHO, tmp_insulin, tmp_BG, tmp_CGM = self.mini_step(action)
CHO += tmp_CHO / self.sample_time
insulin += tmp_insulin / self.sample_time
BG += tmp_BG / self.sample_time
CGM += tmp_CGM / self.sample_time
# Compute risk index
horizon = 1
LBGI, HBGI, risk = risk_index([BG], horizon)
# Record current action
self.CHO_hist.append(CHO)
self.insulin_hist.append(insulin)
# Record next observation
self.time_hist.append(self.time)
self.BG_hist.append(BG)
self.CGM_hist.append(CGM)
self.risk_hist.append(risk)
self.LBGI_hist.append(LBGI)
self.HBGI_hist.append(HBGI)
# Compute reward, and decide whether game is over
window_size = int(60 / self.sample_time)
BG_last_hour = self.CGM_hist[-window_size:]
reward = reward_fun(BG_last_hour)
done = BG < 70 or BG > 350
obs = Observation(CGM=CGM)
return Step(observation=obs,
reward=reward,
done=done,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=CHO,
patient_state=self.patient.state,
time=self.time,
bg=BG,
lbgi=LBGI,
hbgi=HBGI,
risk=risk)
def _reset(self):
self.sample_time = self.sensor.sample_time
self.viewer = None
BG = self.patient.observation.Gsub
horizon = 1
LBGI, HBGI, risk = risk_index([BG], horizon)
CGM = self.sensor.measure(self.patient)
self.time_hist = [self.scenario.start_time]
self.BG_hist = [BG]
self.CGM_hist = [CGM]
self.risk_hist = [risk]
self.LBGI_hist = [LBGI]
self.HBGI_hist = [HBGI]
self.CHO_hist = []
self.insulin_hist = []
def reset(self):
self.patient.reset()
self.sensor.reset()
self.pump.reset()
self.scenario.reset()
self._reset()
CGM = self.sensor.measure(self.patient)
obs = Observation(CGM=CGM)
return Step(observation=obs,
reward=0,
done=False,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=0,
patient_state=self.patient.state,
time=self.time,
bg=self.BG_hist[0],
lbgi=self.LBGI_hist[0],
hbgi=self.HBGI_hist[0],
risk=self.risk_hist[0])
def render(self, close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
if self.viewer is None:
self.viewer = Viewer(self.scenario.start_time, self.patient.name)
self.viewer.render(self.show_history())
def show_history(self):
df = pd.DataFrame()
df['Time'] = pd.Series(self.time_hist)
df['BG'] = pd.Series(self.BG_hist)
df['CGM'] = pd.Series(self.CGM_hist)
df['CHO'] = pd.Series(self.CHO_hist)
df['insulin'] = pd.Series(self.insulin_hist)
df['LBGI'] = pd.Series(self.LBGI_hist)
df['HBGI'] = pd.Series(self.HBGI_hist)
df['Risk'] = pd.Series(self.risk_hist)
df = df.set_index('Time')
return df
class T1DSimEnv(object):
def __init__(self, patient, sensor, pump, scenario):
self.patient = patient
self.sensor = sensor
self.pump = pump
self.scenario = scenario
# self.observation_space = spaces.Box(20, 350, (1, 1))
# self.action_space = spaces.Box(0, 400, (2, 1))
self._reset()
# def seed(self, rand_seed):
# self.sensor.seed = rand_seed
@property
def time(self):
return self.scenario.start_time + timedelta(minutes=self.patient.t)
def mini_step(self, action):
# current action
action_offset = 15 # ASSUMPTION: max pump 30
patient_action = self.scenario.get_action(self.time)
basal = self.pump.basal(
action.basal[0]) # making sure not out fo bounds
# basal = self.pump.basal(max(min(action.basal, action_offset), -action_offset)) # making sure not out fo bounds
# basal = self.pump.basal(action[0]) # CHANGED
# bolus = self.pump.bolus(max(min(action.bolus, action_offset), -action_offset)) # making sure not out fo bounds
# bolus = self.pump.bolus(action[1]) # CHANGED
# insulin = basal + bolus + action_offset * 2 # CHANGED
insulin = basal
CHO = patient_action.meal
patient_mdl_act = Action(insulin=insulin, CHO=CHO)
# State update
self.patient.step(patient_mdl_act)
# next observation
BG = self.patient.observation.Gsub
CGM = self.sensor.measure(self.patient)
return CHO, insulin, BG, CGM
def step(self, action, reward_fun=risk_diff):
'''
action is a namedtuple with keys: basal, bolus
'''
CHO = 0.0
insulin = 0.0
BG = 0.0
CGM = 0.0
CHO_norm = 0.0
insulin_norm = 0.0
CGM_norm = 0.0
for _ in range(int(self.sample_time)):
# Compute moving average as the sample measurements
tmp_CHO, tmp_insulin, tmp_BG, tmp_CGM = self.mini_step(action)
if tmp_insulin < 0:
print(tmp_insulin)
print(self.patient.t)
CHO += tmp_CHO / self.sample_time
insulin += tmp_insulin / self.sample_time
BG += tmp_BG / self.sample_time
CGM += tmp_CGM / self.sample_time
# CHO_norm += normalize_cho(tmp_CHO / self.sample_time)
# CGM_norm += normalize_cgm(tmp_CGM / self.sample_time)
# insulin_norm += normalize_ins(tmp_insulin / self.sample_time)
# Compute risk index
horizon = 1
LBGI, HBGI, risk = risk_index([BG], horizon)
# Record current action
self.CHO_hist.append(CHO)
# self.CHO_hist.append(CHO_norm)
self.insulin_hist.append(insulin)
# self.insulin_hist.append(insulin_norm)
# Record next observation
self.time_hist.append(self.time)
self.BG_hist.append(BG)
self.CGM_hist.append(CGM)
# self.CGM_hist.append(CGM_norm)
self.risk_hist.append(risk)
self.LBGI_hist.append(LBGI)
self.HBGI_hist.append(HBGI)
# Compute reward, and decide whether game is over
window_size = int(60 / self.sample_time) # Horizon
BG_last_hour = self.CGM_hist[-window_size:]
reward = reward_fun(BG_last_hour)
# done = BG < 70 or BG > 350
done = self.patient.t == (24 *
60) / self.sample_time - 1 * self.sample_time
cgm_s = self.CGM_hist[-window_size:]
ins_s = self.insulin_hist[-window_size:]
cho_s = self.CHO_hist[-window_size:]
if min(len(self.CGM_hist), len(
self.insulin_hist)) < window_size: # Padding
pad_size_cgm = max(window_size - len(self.CGM_hist), 0)
pad_size_IN = max(window_size - len(self.insulin_hist), 0)
pad_size_CHO = max(window_size - len(self.CHO_hist), 0)
cgm_s = [
self.CGM_hist[0]
] * pad_size_cgm + self.CGM_hist # Blood Glucose Last Hour
ins_s = [self.insulin_hist[0]
] * pad_size_IN + self.insulin_hist # Insulin Last Hour
cho_s = [self.CHO_hist[0]
] * pad_size_CHO + self.CHO_hist # Insulin Last Hour
obs = Observation(CGM=cgm_s, INSULIN=ins_s, CHO=cho_s)
return Step(observation=obs,
reward=reward,
done=done,
sample_time=float(self.sample_time),
patient_name=self.patient.name,
meal=CHO,
patient_state=self.patient.state)
def _reset(self):
self.sample_time = self.sensor.sample_time
self.viewer = None
BG = self.patient.observation.Gsub
horizon = 1 # TODO understand
LBGI, HBGI, risk = risk_index([BG], horizon)
CGM = self.sensor.measure(self.patient)
self.time_hist = [self.scenario.start_time]
self.BG_hist = [BG]
self.CGM_hist = [CGM]
self.risk_hist = [risk]
self.LBGI_hist = [LBGI]
self.HBGI_hist = [HBGI]
self.CHO_hist = []
self.insulin_hist = []
self.CHO_hist = []
self.insulin_hist = []
def reset(self):
self.patient.reset()
self.sensor.reset()
self.pump.reset()
self.scenario.reset()
self._reset()
CGM = self.sensor.measure(self.patient)
obs = Observation(CGM=[normalize_cgm(CGM)] * 20,
INSULIN=[0] * 20,
CHO=[0] * 20)
return Step(observation=obs,
reward=0,
done=False,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=0,
patient_state=self.patient.state)
def render(self, close=True):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
if self.viewer is None:
self.viewer = Viewer(
self.scenario.start_time,
self.patient.name,
)
self.viewer.render(self.show_history())
def show_history(self):
df = pd.DataFrame()
df['Time'] = pd.Series(self.time_hist)
df['BG'] = pd.Series(self.BG_hist)
df['CGM'] = pd.Series(self.CGM_hist)
df['CHO'] = pd.Series(self.CHO_hist)
df['insulin'] = pd.Series(self.insulin_hist)
df['LBGI'] = pd.Series(self.LBGI_hist)
df['HBGI'] = pd.Series(self.HBGI_hist)
df['Risk'] = pd.Series(self.risk_hist)
df = df.set_index('Time')
return df
class T1DSimEnv(object):
def __init__(self, patient, sensor, pump, scenario):
self.patient = patient
self.sensor = sensor
self.pump = pump
self.scenario = scenario
self._reset()
@property
def time(self):
return self.scenario.start_time + timedelta(minutes=self.patient.t)
def mini_step(self, action):
# Note: Every mini_step runs 1 min in the patient's life
# current action
patient_action = self.scenario.get_action(
self.time) # Get food for current minute
basal = self.pump.basal(action.basal)
bolus = self.pump.bolus(action.bolus)
insulin = basal + bolus
CHO = patient_action.meal
patient_mdl_act = Action(insulin=insulin, CHO=CHO)
# State update
self.patient.step(patient_mdl_act)
# next observation
BG = self.patient.observation.Gsub
CGM = self.sensor.measure(self.patient)
return CHO, insulin, BG, CGM
def step(self, action, reward_fun=neg_risk):
'''
action is a namedtuple with keys: basal, bolus
'''
CHO = 0.0
insulin = 0.0
BG = 0.0
CGM = 0.0
# This loop runs for n minutes in patients life
# n is determined by the sample time, which is in turn defined by
# the quality of sensor and the interval between each measurement
for _ in range(int(self.sample_time)):
# Compute moving average as the sample measurements
tmp_CHO, tmp_insulin, tmp_BG, tmp_CGM = self.mini_step(action)
CHO += tmp_CHO / self.sample_time
insulin += tmp_insulin / self.sample_time
BG += tmp_BG / self.sample_time
CGM += tmp_CGM / self.sample_time
# Compute risk index
horizon = 1
LBGI, HBGI, risk = risk_index([BG], horizon)
# Record current action
self.CHO_hist.append(CHO)
self.insulin_hist.append(insulin)
# Record next observation
self.time_hist.append(self.time)
self.BG_hist.append(BG)
self.CGM_hist.append(CGM)
self.risk_hist.append(risk)
self.LBGI_hist.append(LBGI)
self.HBGI_hist.append(HBGI)
# Compute reward, and decide whether game is over
window_size = int(60 / self.sample_time)
BG_last_hour = self.CGM_hist[-window_size:]
reward = reward_fun(BG_last_hour)
# Stopping criteria
# done = BG < 70 or BG > 350 # If the BG level go out of control
done = self.patient.t >= 1440 # At the end of the day (24hrs = 1440 mins)
obs = Observation(CGM=CGM)
return Step(observation=obs,
reward=reward,
done=done,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=CHO,
patient_state=self.patient.state)
def _reset(self):
self.sample_time = self.sensor.sample_time
self.viewer = None
BG = self.patient.observation.Gsub
horizon = 1
LBGI, HBGI, risk = risk_index([BG], horizon)
CGM = self.sensor.measure(self.patient)
self.time_hist = [self.scenario.start_time]
self.BG_hist = [BG]
self.CGM_hist = [CGM]
self.risk_hist = [risk]
self.LBGI_hist = [LBGI]
self.HBGI_hist = [HBGI]
self.CHO_hist = []
self.insulin_hist = []
def reset(self):
self.patient.reset()
self.sensor.reset()
self.pump.reset()
self.scenario.reset()
self._reset()
CGM = self.sensor.measure(self.patient)
obs = Observation(CGM=CGM)
return Step(observation=obs,
reward=0,
done=False,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=0,
patient_state=self.patient.state)
def render(self, close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
if self.viewer is None:
self.viewer = Viewer(self.scenario.start_time, self.patient.name)
self.viewer.render(self.show_history())
def show_history(self):
df = pd.DataFrame()
df['Time'] = pd.Series(self.time_hist)
df['BG'] = pd.Series(self.BG_hist)
df['CGM'] = pd.Series(self.CGM_hist)
df['CHO'] = pd.Series(self.CHO_hist)
df['insulin'] = pd.Series(self.insulin_hist)
df['LBGI'] = pd.Series(self.LBGI_hist)
df['HBGI'] = pd.Series(self.HBGI_hist)
df['Risk'] = pd.Series(self.risk_hist)
df = df.set_index('Time')
return df
class T1DSimEnv(object):
producer = KafkaProducer(
acks=0,
compression_type='gzip',
bootstrap_servers=["220.69.209.21%d:9092" % i for i in range(3)],
value_serializer=lambda x: dumps(x).encode('utf-8'))
start_comment = "------------------------------start CGM--------------------------------"
producer.send('BG', value=start_comment)
producer.flush()
def __init__(
self,
patient,
sensor,
pump,
scenario,
):
self.patient = patient
self.sensor = sensor
self.pump = pump
self.scenario = scenario
self._reset()
@property
def time(self):
return self.scenario.start_time + timedelta(minutes=self.patient.t)
#여기가 값내보내는 부분이다 꼭꼮꼬로꼮꼬ㅓㄱ꼭ㄱ꼭 잘 써보자꾸나.
def mini_step(self, action):
# current action
patient_action = self.scenario.get_action(self.time)
basal = self.pump.basal(action.basal)
bolus = self.pump.bolus(action.bolus)
insulin = basal + bolus
CHO = patient_action.meal
patient_mdl_act = Action(insulin=insulin, CHO=CHO)
# State update
self.patient.step(patient_mdl_act)
# next observation
BG = self.patient.observation.Gsub
CGM = self.sensor.measure(self.patient)
print("혈당:", BG, " 인슐린:", insulin)
#time.sleep(0.95)
return CHO, insulin, BG, CGM
def step(self, action, reward_fun=risk_diff):
'''
action is a namedtuple with keys: basal, bolus
'''
CHO = 0.0
insulin = 0.0
BG = 0.0
CGM = 0.0
for _ in range(int(self.sample_time)):
# Compute moving average as the sample measurements
tmp_CHO, tmp_insulin, tmp_BG, tmp_CGM = self.mini_step(action)
CHO += tmp_CHO / self.sample_time
insulin += tmp_insulin / self.sample_time
BG += tmp_BG / self.sample_time
CGM += tmp_CGM / self.sample_time
kafBG = round(BG, 2)
#카프카 데이터 전송
producer = KafkaProducer(
acks=0,
compression_type='gzip',
bootstrap_servers=[
"220.69.209.21%d:9092" % i for i in range(3)
],
value_serializer=lambda x: dumps(x).encode('utf-8'))
data = {'Glucose': str(kafBG)}
producer.send('BG', value=data)
producer.flush()
# Compute risk index
horizon = 1
LBGI, HBGI, risk = risk_index([BG], horizon)
# Record current action
self.CHO_hist.append(CHO)
self.insulin_hist.append(insulin)
# Record next observation
self.time_hist.append(self.time)
self.BG_hist.append(BG)
self.CGM_hist.append(CGM)
self.risk_hist.append(risk)
self.LBGI_hist.append(LBGI)
self.HBGI_hist.append(HBGI)
# Compute reward, and decide whether game is over
window_size = int(60 / self.sample_time)
BG_last_hour = self.CGM_hist[-window_size:]
reward = reward_fun(BG_last_hour)
done = BG < 70 or BG > 350
obs = Observation(CGM=CGM)
return Step(observation=obs,
reward=reward,
done=done,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=CHO,
patient_state=self.patient.state)
def _reset(self):
self.sample_time = self.sensor.sample_time
self.viewer = None
BG = self.patient.observation.Gsub
horizon = 1
LBGI, HBGI, risk = risk_index([BG], horizon)
CGM = self.sensor.measure(self.patient)
self.time_hist = [self.scenario.start_time]
self.BG_hist = [BG]
self.CGM_hist = [CGM]
self.risk_hist = [risk]
self.LBGI_hist = [LBGI]
self.HBGI_hist = [HBGI]
self.CHO_hist = []
self.insulin_hist = []
def reset(self):
self.patient.reset()
self.sensor.reset()
self.pump.reset()
self.scenario.reset()
self._reset()
CGM = self.sensor.measure(self.patient)
obs = Observation(CGM=CGM)
return Step(observation=obs,
reward=0,
done=False,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=0,
patient_state=self.patient.state)
def render(self, close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
if self.viewer is None:
self.viewer = Viewer(self.scenario.start_time, self.patient.name)
self.viewer.render(self.show_history())
def show_history(self):
df = pd.DataFrame()
df['Time'] = pd.Series(self.time_hist)
df['BG'] = pd.Series(self.BG_hist)
df['CGM'] = pd.Series(self.CGM_hist)
df['CHO'] = pd.Series(self.CHO_hist)
df['insulin'] = pd.Series(self.insulin_hist)
df['LBGI'] = pd.Series(self.LBGI_hist)
df['HBGI'] = pd.Series(self.HBGI_hist)
df['Risk'] = pd.Series(self.risk_hist)
df = df.set_index('Time')
return df
class T1DSimEnv(object):
def __init__(self,
patient,
sensor,
pump,
scenario,
controller_name='',
results_path=None):
self.patient = patient
self.controller_name = controller_name
self.sensor = sensor
self.pump = pump
self.scenario = scenario
self.results_path = results_path
self._reset()
@property
def time(self):
return self.scenario.start_time + timedelta(minutes=self.patient.t)
def mini_step(self, action):
patient_action = self.scenario.get_action(self.time)
try:
basal = self.pump.basal(action.basal[0])
except:
basal = self.pump.basal(action.basal)
insulin = basal
CHO = patient_action.meal
patient_mdl_act = Action(insulin=insulin, CHO=CHO)
# State update
self.patient.step(patient_mdl_act)
# next observation
BG = self.patient.observation.Gsub
CGM = self.sensor.measure(self.patient)
return CHO, insulin, BG, CGM
def step(self, action, reward_fun=risk_diff):
'''
action is a namedtuple with keys: basal, bolus
'''
CHO = 0.0
insulin = 0.0
BG = 0.0
CGM = 0.0
for _ in range(int(self.sample_time)):
# Compute moving average as the sample measurements
tmp_CHO, tmp_insulin, tmp_BG, tmp_CGM = self.mini_step(action)
if tmp_insulin < 0:
print(tmp_insulin)
print(self.patient.t)
CHO += tmp_CHO / self.sample_time
insulin += tmp_insulin / self.sample_time
BG += tmp_BG / self.sample_time
CGM += tmp_CGM / self.sample_time
# Compute risk index
horizon = 1
LBGI, HBGI, risk = risk_index2([BG], horizon)
# Record current action
self.CHO_hist.append(CHO)
self.insulin_hist.append(insulin)
# Record next observation
self.time_hist.append(self.time)
self.BG_hist.append(BG)
self.CGM_hist.append(CGM)
self.risk_hist.append(risk)
self.LBGI_hist.append(LBGI)
self.HBGI_hist.append(HBGI)
# Compute reward, and decide whether game is over
window_size = int(60 / self.sample_time) # Horizon
BG_last_hour = self.CGM_hist[-window_size:]
reward = reward_fun(BG_last_hour)
# print (BG)
done = BG < 70 or BG > 350
# done = False
done = self.patient.t == (24 *
60) / self.sample_time - 1 * self.sample_time
cgm_s = self.CGM_hist[-window_size:]
ins_s = self.insulin_hist[-window_size:]
cho_s = self.CHO_hist[-window_size:]
if min(len(self.CGM_hist), len(
self.insulin_hist)) < window_size: # Padding
pad_size_cgm = max(window_size - len(self.CGM_hist), 0)
pad_size_IN = max(window_size - len(self.insulin_hist), 0)
pad_size_CHO = max(window_size - len(self.CHO_hist), 0)
cgm_s = [
self.CGM_hist[0]
] * pad_size_cgm + self.CGM_hist # Blood Glucose Last Hour
ins_s = [self.insulin_hist[0]
] * pad_size_IN + self.insulin_hist # Insulin Last Hour
cho_s = [self.CHO_hist[0]
] * pad_size_CHO + self.CHO_hist # Insulin Last Hour
obs = Observation(CGM=cgm_s, INSULIN=ins_s, CHO=cho_s)
return Step(observation=obs,
reward=reward,
done=done,
sample_time=float(self.sample_time),
patient_name=self.patient.name,
meal=CHO,
patient_state=self.patient.state)
def _reset(self):
self.sample_time = self.sensor.sample_time
self.viewer = None
BG = self.patient.observation.Gsub
horizon = 1 # TODO understand
LBGI, HBGI, risk = risk_index2([BG], horizon)
CGM = self.sensor.measure(self.patient)
self.time_hist = [self.scenario.start_time]
self.BG_hist = [BG]
self.CGM_hist = [CGM]
self.risk_hist = [risk]
self.LBGI_hist = [LBGI]
self.HBGI_hist = [HBGI]
self.CHO_hist = []
self.insulin_hist = []
def reset(self):
self.patient.reset()
self.sensor.reset()
self.pump.reset()
self.scenario.reset()
self._reset()
CGM = self.sensor.measure(self.patient)
obs = Observation(CGM=[CGM] * 20, INSULIN=[0] * 20, CHO=[0] * 20)
return Step(observation=obs,
reward=0,
done=False,
sample_time=self.sample_time,
patient_name=self.patient.name,
meal=0,
patient_state=self.patient.state)
def render(self, close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
if self.viewer is None:
self.viewer = Viewer(self.scenario.start_time,
self.patient.name,
self.controller_name,
save_path=self.results_path)
self.viewer.render(self.show_history())
def show_history(self):
df = pd.DataFrame()
df['Time'] = pd.Series(self.time_hist)
df['BG'] = pd.Series(self.BG_hist)
df['CGM'] = pd.Series(self.CGM_hist)
df['CHO'] = pd.Series(self.CHO_hist)
df['insulin'] = pd.Series(self.insulin_hist)
df['LBGI'] = pd.Series(self.LBGI_hist)
df['HBGI'] = pd.Series(self.HBGI_hist)
df['Risk'] = pd.Series(self.risk_hist)
df = df.set_index('Time')
return df