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RMS_function.py
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import pandas as pd
from datetime import datetime
from datetime import timedelta
from dateutil import parser
from datetime import date
from azure.storage.blob import BlockBlobService
from io import StringIO
day_now=0
day_before=1
def setflag(timestamp,day):
flag = date.today()- timedelta(day)
flag = flag.strftime('%Y-%m-%d')
flag = flag + ' '+ timestamp
return datetime.strptime(flag, "%Y-%m-%d %H:%M:%S")
def from_blob_load_data(account_name_,account_key_,container_name_,types):
blob_service = BlockBlobService(account_name=account_name_, account_key = account_key_)
blobs = [];blob_date = []
generator = blob_service.list_blobs(container_name_)
for blob in generator:
blobs.append(blob.name)
blob_date.append(blob.name[:10])
blob_table = pd.DataFrame()
blob_table['date'] = blob_date
blob_table['blobname'] = blobs
Today = date.today().strftime('%Y-%m-%d')
Yst = (date.today() - timedelta(1)).strftime('%Y-%m-%d')
blob_table = blob_table[(blob_table['date']==Today)|(blob_table['date']==Yst)]
if blob_table.shape[0]>0:
blob_df = pd.DataFrame()
for blobname in blob_table['blobname']:
blob_Class = blob_service.get_blob_to_text(container_name=container_name_, blob_name = blobname)
blob_String =blob_Class.content
blob_df1 = pd.read_csv(StringIO(blob_String),low_memory=False)
blob_df = blob_df.append(blob_df1)
blob_df.index = range(blob_df.shape[0])
#print(blob_df.shape[0])
#if types=='PIR':
#blob_df = blob_df#[blob_df['tasklocation']=='Bathroom']
else:
blob_df = pd.DataFrame()
return blob_df
def add_hubid2rmsdata(rms_raw):
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7ae74' ),'hubid' ] = 'SG-04-testingN'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af6a' ),'hubid' ] = 'SG-04-hub00016'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af1d' ),'hubid' ] = 'SG-04-develop4'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af1b' ),'hubid' ] = 'SG-04-testingQ'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af25' ),'hubid' ] = 'SG-04-inter001'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7adae' ),'hubid' ] = 'SG-04-testingK'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af42' ),'hubid' ] = 'SG-04-hub00001'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af7d' ),'hubid' ] = 'SG-04-hub00004'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af70' ),'hubid' ] = 'SG-04-hub00005'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af68' ),'hubid' ] = 'SG-04-hub00013'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af4a' ),'hubid' ] = 'SG-04-hub00006'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af7b' ),'hubid' ] = 'SG-04-hub00015'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af64' ),'hubid' ] = 'SG-04-hub00002'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af61' ),'hubid' ] = 'SG-04-hub00012'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af5b' ),'hubid' ] = 'SG-04-hub00008'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af2a' ),'hubid' ] = 'SG-04-hub00007'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af21' ),'hubid' ] = 'SG-04-hub00014'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af1f' ),'hubid' ] = 'SG-04-interdev'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af60' ),'hubid' ] = 'SG-04-starhub7'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af1e' ),'hubid' ] = 'SG-04-hub00019'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af6f' ),'hubid' ] = 'SG-04-hub00020'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af5e' ),'hubid' ] = 'SG-04-hub00021'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af47' ),'hubid' ] = 'SG-04-hub00017'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af5d' ),'hubid' ] = 'SG-04-hub00010'
rms_raw.loc[(rms_raw['gwId'] == 'e47fb2f7af4e' ),'hubid' ] = 'SG-04-hub00009'
return rms_raw
def del_neighbor(data,types):
data = data.drop_duplicates('time',keep='first')#.reset_index(drop=True)
data = data.sort_values(['time'], ascending=[1])
data.index = range(data.shape[0])
if types == 'wakeup':
data['gap'] = data['time'].diff()
data['gap'].ix[0] = timedelta(seconds=1000)
data = data[data['gap']>timedelta(seconds=300)]
if types == 'sleep':
test = data['time'].diff().tolist()
test.pop(0)
test.append(timedelta(seconds=300))
data['gap'] = test
data = data[data['gap']>timedelta(seconds=300)]
return data
def remove_outliers(table):
i=1 ;n = table.shape[0]-1
while i<n:
if table[i]>11:
table[i]=max(table[i-1],table[i+1])
i = i+1
return table
def get_agv_int(rms_data_input):
rms_data_input['eventtime'] = rms_data_input['eventtime'].apply(lambda x: datetime.strptime(x, "%Y-%m-%d %H:%M:%S"))
intensity = rms_data_input.set_index('eventtime').groupby(pd.TimeGrouper(freq='5Min')).size()
intensity = list(filter(lambda x: x != 0,intensity))
output = sum(intensity)/len(intensity)
return output
def time_picker(raw_wake_table,types,rms_data_input,room_acttime_input):
if types=='sleep':
flag3 = setflag("01:30:01",day_now); flag4 = setflag("20:59:59",day_before)
sleep = raw_wake_table[(raw_wake_table['time']<flag3)&(raw_wake_table['time']>flag4)]
#if sleep.shape[0]>3:
#sleep = sleep[sleep['gap']!=600]
if sleep.shape[0]==0:
#sleep = sleep
print("no sleep data")
if sleep.shape[0]==1:
sleep = sleep
if sleep.shape[0]==2:
if (sleep.at[sleep.index[1], 'time']-sleep.at[sleep.index[0], 'time']).seconds>=2400:
sleep.at[sleep.index[0], 'time'] = sleep.at[sleep.index[0], 'time']+timedelta(minutes=10)
#check_frame = room_acttime_input
check_frame = room_acttime_input[(room_acttime_input['time']>sleep.at[sleep.index[0], 'time'])&(room_acttime_input['time']<sleep.at[sleep.index[1], 'time'])]
if check_frame.shape[0]==0:
sleep = sleep.loc[[sleep.index[0]]]
if check_frame.shape[0]==1:
if ((check_frame.at[check_frame.index[0],'time']-sleep.at[sleep.index[0],'time']).seconds<=300)|\
((sleep.at[sleep.index[1],'time']-check_frame.at[check_frame.index[0],'time']).seconds<=300):
sleep = sleep.loc[[sleep.index[0]]]
else:
sleep = sleep.loc[[sleep.index[1]]]
if check_frame.shape[0]>1:
sleep = sleep.loc[[sleep.index[1]]]
if sleep.shape[0]>2:
if (sleep.at[sleep.index[2], 'time']-sleep.at[sleep.index[1], 'time']).seconds>=2400:
sleep.at[sleep.index[1], 'time'] = sleep.at[sleep.index[1], 'time']+timedelta(minutes=10)
check_frame = room_acttime_input
check_frame = room_acttime_input[(room_acttime_input['time']>sleep.at[sleep.index[1], 'time'])&(room_acttime_input['time']<sleep.at[sleep.index[2], 'time'])]
if check_frame.shape[0]==0:
sleep = sleep.loc[[sleep.index[1]]]
if check_frame.shape[0]==1:
if ((check_frame.at[check_frame.index[0],'time']-sleep.at[sleep.index[1],'time']).seconds<=300)|\
((sleep.at[sleep.index[2],'time']-check_frame.at[check_frame.index[0],'time']).seconds<=300):
sleep = sleep.loc[[sleep.index[1]]]
else:
sleep = sleep.loc[[sleep.index[2]]]
if check_frame.shape[0]>1:
sleep = sleep.loc[[sleep.index[2]]]
sleep = sleep.rename(index=str, columns={ 'time': 'sleep'})
return sleep
if types =='wakeup':
flag3 = setflag("05:00:00",day_now)
wakeup = raw_wake_table[raw_wake_table['time']>flag3]
# if wakeup.shape[0]>3:
# wakeup = wakeup[wakeup['gap']!=600]
if wakeup.shape[0]==0:
wakeup = wakeup; print("no wake up data")
if wakeup.shape[0]==1:
wakeup = wakeup
if wakeup.shape[0]==2:
wakeup.index = range(wakeup.shape[0])
rms_data_check = rms_data_input[(rms_data_input['time']>(wakeup.at[0,'time']+timedelta(minutes=5)))&(rms_data_input['time']<(wakeup.at[1,'time']-timedelta(minutes=5)))]
if rms_data_check.shape[0]>4:
agv_int = get_agv_int(rms_data_check)
if agv_int<=5:
wakeup = wakeup.loc[[1]]
if agv_int>5:
wakeup = wakeup.loc[[0]]
if rms_data_check.shape[0] <= 4:
wakeup = wakeup.loc[[0]]
if wakeup.shape[0]>2:
wakeup.index = range(wakeup.shape[0])
rms_data_check = rms_data_input[(rms_data_input['time']>(wakeup.at[0,'time']+timedelta(minutes=5)))&(rms_data_input['time']<(wakeup.at[1,'time']-timedelta(minutes=5)))]
if rms_data_check.shape[0]>4:
agv_int = get_agv_int(rms_data_check)
if agv_int<=5:
rms_data_check2 = rms_data_input[(rms_data_input['time']>(wakeup.at[1,'time']+timedelta(minutes=5)))&(rms_data_input['time']<(wakeup.at[2,'time']-timedelta(minutes=5)))]
if rms_data_check2.shape[0]>5:
agv_int2 = get_agv_int(rms_data_check2)
if agv_int2<=5:
wakeup = wakeup.loc[[2]]
if agv_int2 >5:
wakeup = wakeup.loc[[1]]
if rms_data_check2.shape[0]<=5:
wakeup = wakeup.loc[[1]]
if agv_int>5:
wakeup = wakeup.loc[[0]]
if rms_data_check.shape[0] <= 4:
wakeup = wakeup.loc[[0]]
wakeup = wakeup.rename(index=str, columns={ 'time': 'wakeup'})
return wakeup
def get_grouped(rawdata,types):
rawdata = rawdata.set_index(rawdata['eventtime'].map(parser.parse))
if types=='bathroom':
room_list = rawdata['tasklocation'].unique().tolist()
room_whole = pd.DataFrame()
for room in room_list:
room_frame = rawdata[rawdata['tasklocation']==room]
room_frame = room_frame.groupby(pd.TimeGrouper('300s')).max()
room_frame = room_frame[['value','tasklocation']]
room_whole = room_whole.append(room_frame)
room_whole = room_whole.dropna(subset=['value'], how='all')
room_whole['time'] = room_whole.index
room_whole.index = range(room_whole.shape[0])
return room_whole
if types=='bedroom':
if rawdata.shape[0]!=0:
one_time_table = rawdata.groupby(pd.TimeGrouper('60s')).size()
five_time_table = rawdata.groupby(pd.TimeGrouper('300s')).size()
one_time_table = remove_outliers(one_time_table)
time_table = one_time_table.to_frame()
time_table = time_table.rename(index=str, columns={ 0: "1min"})
time_table['sum'] = pd.rolling_sum(time_table['1min'],5)
time_table.loc[(time_table['sum']<12),'sum']= 0
time_table.loc[(time_table['sum']>12),'sum']= time_table['sum']
#time_table = time_table.rename(index=str, columns={ 2: "5min"})
time_table = time_table.set_index(time_table.index.map(parser.parse))
awake_table = time_table.groupby(pd.TimeGrouper('300s')).max()
awake_table['5min'] = five_time_table
awake_table['time'] = awake_table.index
awake_table['time'] = awake_table['time'].apply(lambda x: x.to_datetime())
awake_table['gap'] = awake_table['5min'].diff()
awake_table = awake_table[(awake_table['sum']>12)|(awake_table['gap']<-5)]
del awake_table['1min']; del awake_table['5min']
awake_table.index = range(awake_table.shape[0])
else:
awake_table = pd.DataFrame()
return awake_table
def time_normer(rawdata):
rawdata = rawdata.dropna(subset=['starttime'],how='all')
rawdata['time'] = rawdata['starttime'].apply(lambda x: str(x))
rawdata['time'] = rawdata['time'].apply(lambda x: x[:19])
rawdata['time'] = rawdata['time'].apply(lambda x: datetime.strptime(x,'%Y-%m-%dT%H:%M:%S')+timedelta(hours=8))
rawdata['eventtime'] = rawdata['time'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S'))
#flag3 = setflag("09:00:01",day_now); flag4 = setflag("19:59:59",day_before)
return rawdata[(rawdata['time']<setflag("09:00:01",day_now))&(rawdata['time']>setflag("19:59:59",day_before))]
def get_check(raw_rms_dara,wakeup_input):
'''
This function is going to generate the checking frame of other rooms
'''
raw_rms_dara = raw_rms_dara.set_index(raw_rms_dara['eventtime'].map(parser.parse))
five_time_table = raw_rms_dara.groupby(pd.TimeGrouper('300s')).size()
five_time_table = five_time_table.to_frame()
five_time_table = five_time_table.rename(index=str, columns={ 0: "sum"})
five_time_table['time'] = five_time_table.index
five_time_table['time'] = five_time_table['time'].apply(lambda x: datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))
five_time_table = five_time_table[(five_time_table['time']>wakeup_input.at[0, 'time'])&(five_time_table['time']<wakeup_input.at[1, 'time'])]
five_time_table = five_time_table[five_time_table['sum']==0]
five_time_table['gap'] = 0
five_time_table = five_time_table.append(wakeup_input[:2])
five_time_table = five_time_table.sort_values('time',ascending = 1)
five_time_table['gap'] = five_time_table['time'].diff()
five_time_table['gap'].ix[0] = timedelta(seconds=300)
five_time_table['gap'] = five_time_table['gap'].apply(lambda x: x.seconds)
return five_time_table
def check_awake_table(awake_table_input,raw_rms_input):
'''
This function is going to check whether the awake table is picking up all the morning and
evening movement timestamp.
if not, it will return timestamp following by the lone period blank
'''
flag2 = setflag("05:00:00",day_now)
awake_table_test1 = awake_table_input[awake_table_input['time']>flag2]
flag1=setflag("02:00:00",day_now)
awake_table_test2 = awake_table_input[awake_table_input['time']<flag1]
rawdata = raw_rms_input.set_index(raw_rms_input['eventtime'].map(parser.parse))
rawdata['eventtime'] = rawdata['eventtime'].apply(lambda x: datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))
if (awake_table_test1.shape[0]==0)&(awake_table_test2.shape[0]!=0):#wake up have not been capture
rawdata = rawdata[rawdata['eventtime']>flag2]
if (awake_table_test1.shape[0]!=0)&(awake_table_test2.shape[0]==0):#sleep have not been capture
rawdata = rawdata[rawdata['eventtime']<flag1]
if (awake_table_test1.shape[0]!=0)&(awake_table_test2.shape[0]!=0):#both have not been capture
rawdata = pd.DataFrame()
if rawdata.shape[0]!=0:
five_time_table = rawdata.groupby(pd.TimeGrouper('300s')).size().to_frame()
five_time_table = five_time_table.rename(index=str, columns={ 0: 'sum'})
five_time_table['time'] = five_time_table.index
five_time_table['time'] = five_time_table['time'].apply(lambda x: datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))
five_time_table.index = range(five_time_table.shape[0])
first_line = five_time_table[:1]
blank_time = five_time_table[five_time_table['sum']==0]
blank_time['gap'] = blank_time['time'].diff()
blank_time.index = range(blank_time.shape[0])
last_line = five_time_table[-1:]
if blank_time.shape[0]>2:
blank_time['gap'].ix[0] = blank_time['time'][0]-first_line['time'][0]
blank_time['gap'] = blank_time['gap'].apply(lambda x: x.seconds)
i=0; n=blank_time.shape[0]-1
add_frame = pd.DataFrame()
while i<n:
if (blank_time['gap'][i]==300)&(blank_time['gap'][i+1]==300):
add_line = blank_time.loc[[i]]
add_frame = add_frame.append(add_line)
i=i+1
#add_frame['time'] = add_frame['time'].apply(lambda x: x-timedelta(seconds=300))
add_frame = add_frame.append(last_line)
if blank_time.shape[0]==2:
add_frame = blank_time.loc[[1]]
add_frame['time'] = add_frame['time'].apply(lambda x: x-timedelta(seconds=300))
add_frame = add_frame.append(last_line)
if blank_time.shape[0]==1:
add_frame = blank_time.loc[[0]]
add_frame['time'] = add_frame['time'].apply(lambda x: x-timedelta(seconds=300))
add_frame = add_frame.append(last_line)
if blank_time.shape[0]==0:
add_frame = five_time_table[-1:]
add_frame['gap'] = 20;add_frame['sum'] = 20
awake_table_input = awake_table_input.append(add_frame)
if rawdata.shape[0]==0:
awake_table_input = awake_table_input
return awake_table_input
def time_picker_single(raw_wake_table, types,rms_data_input):
if types=='sleep':
flag3 = setflag("02:00:01",day_now); flag4 = setflag("22:59:59",day_before)
test = raw_wake_table[(raw_wake_table['time']<flag3)&(raw_wake_table['time']>flag4)]
sleep = pd.DataFrame()
if test.shape[0]>0:
flag3 = setflag("02:00:01",day_now); flag4 = setflag("22:59:59",day_before)
sleep = raw_wake_table[(raw_wake_table['time']<flag3)&(raw_wake_table['time']>flag4)]
sleep.index = range(sleep.shape[0])
if sleep.shape[0]!=0:
sleep = sleep.loc[[0]]
if sleep.shape[0]==0:
flag3 = setflag("23:00:00",day_before); flag4 = setflag("19:59:59",day_before)
sleep = raw_wake_table[(raw_wake_table['time']<flag3)&(raw_wake_table['time']>flag4)]
flag4 = setflag("05:00:00",day_now)
sleep = sleep[sleep['time']<flag4]
if sleep.shape[0]!=0:
sleep = sleep.sort('time',ascending = 0)
sleep.index = range(sleep.shape[0])
sleep = sleep.loc[[0]]
if sleep.shape[0]==0:
flag3 = setflag("03:00:00",day_now); flag4 = setflag("02:00:00",day_now)
sleep = raw_wake_table[(raw_wake_table['time']<flag3)&(raw_wake_table['time']>flag4)]
if sleep.shape[0]==0:
print("no sleeping data")
sleep = pd.DataFrame()
if sleep.shape[0]!=0:
sleep.index = range(sleep.shape[0])
sleep = sleep.loc[[0]]
sleep = sleep.rename(index=str, columns={ 'time': 'sleep'})
if test.shape[0]==0:
flag3 = setflag("02:00:01",day_now)
raw_wake_table = raw_wake_table[raw_wake_table['time']<flag3]
raw_wake_table.index = range(raw_wake_table.shape[0])
sleep = raw_wake_table[-1:]
sleep.index = range(sleep.shape[0])
sleep = sleep.rename(index=str, columns={ 'time': 'sleep'})
return sleep
'''
if types =='wakeup':
flag3 = setflag("05:00:00",day_now)
wakeup = raw_wake_table[raw_wake_table['time']>flag3]
if wakeup.shape[0]>1:
wakeup.index = range(wakeup.shape[0])
check_frame = get_check(rms_data_input,wakeup)
check_frame = check_frame[check_frame['gap']==300]
if wakeup.shape[0]==2:
if check_frame.shape[0]>2:
wakeup = wakeup.iloc[[0]]
if check_frame.shape[0]<3:
wakeup = wakeup.iloc[[1]]
if wakeup.shape[0]>2:
check_frame = get_check(rms_data_input,wakeup)
check_frame = check_frame[check_frame['gap']==300]
if check_frame.shape[0]>2:
wakeup = wakeup.iloc[[0]]
if check_frame.shape[0]<3:
wakeup.drop(wakeup.index[0], inplace=True)
wakeup.index = range(wakeup.shape[0])
check_frame = get_check(rms_data_input,wakeup)
check_frame = check_frame[check_frame['gap']==300]
if check_frame.shape[0]>2:
wakeup = wakeup.iloc[[0]]
if check_frame.shape[0]<3:
wakeup = wakeup.iloc[[1]]
if wakeup.shape[0]==1:
print(wakeup)
if wakeup.shape[0]==0:
print("hello, no data lah")
wakeup = wakeup.rename(index=str, columns={ 'time': 'wakeup'})
return wakeup
'''
def bathroon_checker(bathroom_input):
# This function is going to eleminate the sunddently appear value 1 bathroom time stamp
bathroom_input['gap'] = bathroom_input['time'].diff()
bathroom_input['gap'][0] = timedelta(minutes = 5)
bathroom_input = bathroom_input[(bathroom_input['sum']!=1)|(bathroom_input['gap']==timedelta(minutes = 5))]
return bathroom_input
def bathroom_table_prep(room_acttime_input):
'''
This function is going to get the bathroom raw data, which has not been filtered by the awake table
NOTE: here delete 5 min for each tampstam so that when checking all room blank not getting confused
'''
bathroom_time = room_acttime_input[room_acttime_input['tasklocation']=='Bathroom']
del bathroom_time['tasklocation']
bathroom_time = bathroom_time.rename(index=str, columns={'value': "sum"})
bathroom_time['gap'] = 0
bathroom_time['time'] = bathroom_time['time'].apply(lambda x: x-timedelta(minutes = 5))
if bathroom_time.shape[0]>0:
bathroom_time = bathroon_checker(bathroom_time)
return bathroom_time
def final_generator(hub_id,types,rms_whole_input,blob_df_whole_input):
rms_data = rms_whole_input[rms_whole_input['hubid']==hub_id]
blob_df = blob_df_whole_input[blob_df_whole_input['deviceid']==hub_id]
awake_table = get_grouped(rms_data,'bedroom')
if awake_table.shape[0]!=0:
awake_table = check_awake_table(awake_table,rms_data)
if blob_df.shape[0]!=0:
room_acttime = get_grouped(blob_df,'bathroom')
print("multiroom user")
if types=='wakeup':
bathroom_time = bathroom_table_prep(room_acttime)
if awake_table.shape[0]>1:
flag1 = awake_table['time'][0]#;flag.index = range(flag.shape[0])
bathroom_time = bathroom_time[bathroom_time['time']>flag1]
if awake_table.shape[0]==1:
flag1 = awake_table['time'][0];flag2=setflag("05:00:00",day_now)
if flag1<flag2:
bathroom_time = bathroom_time[bathroom_time['time']>flag1]
if flag1>flag2:
flag3 = setflag("20:59:59",day_before)
bathroom_time = bathroom_time[bathroom_time['time']>flag3]
if (awake_table.shape[0]==1)|(awake_table.shape[0]>1):
awake_table = awake_table.append(bathroom_time)
awake_table = del_neighbor(awake_table,'wakeup')
awake_table['time'] = awake_table['time'].apply(lambda x: x.to_datetime())
wakeup = time_picker(awake_table,'wakeup',rms_data,room_acttime)
if awake_table.shape[0]==0:
if bathroom_time.shape[0]!=0:
bathroom_time = del_neighbor(bathroom_time,'wakeup')
wakeup = time_picker(bathroom_time,'wakeup',rms_data,room_acttime)
if bathroom_time.shape[0]==0:
wakeup = pd.DataFrame()#; wakeup['wakeup'] = 'nan'
if types=='sleep':
bathroom_time = bathroom_table_prep(room_acttime)
if awake_table.shape[0]!=0:
awake_table = awake_table[awake_table['sum']>10]
if awake_table.shape[0]>0:
awake_table = awake_table.append(bathroom_time)
awake_table = del_neighbor(awake_table,'sleep')
awake_table['time'] = awake_table['time'].apply(lambda x: x.to_datetime())
sleep = time_picker(awake_table,'sleep',rms_data,room_acttime)
if awake_table.shape[0]==0:
if bathroom_time.shape[0]!=0:
bathroom_time = del_neighbor(bathroom_time,'sleep')
sleep = time_picker(bathroom_time,'sleep',rms_data,room_acttime)
if bathroom_time.shape[0]==0:
sleep = pd.DataFrame()#; sleep['wakeup'] = 'nan'
if blob_df.shape[0]==0:
print("RMS only")
bathroom_time = pd.DataFrame()
if awake_table.shape[0]>1:
#awake_table['time'] = awake_table.index
awake_table['time'] = awake_table['time'].apply(lambda x: x.to_datetime())
if types=='wakeup':
awake_table = del_neighbor(awake_table,'wakeup')
wakeup = time_picker(awake_table,'wakeup',rms_data,room_acttime_input = pd.DataFrame())
if types=='sleep':
#awake_table = awake_table[awake_table['sum']>12]
awake_table = del_neighbor(awake_table,'sleep')
sleep = time_picker_single(awake_table,'sleep',rms_data )
else:
sleep = pd.DataFrame(); sleep['sleep'] = 'nan'
wakeup = pd.DataFrame(); wakeup['wakeup'] = 'nan'
if types=='sleep':
if sleep.shape[0]!=0:
return sleep.ix[0]['sleep']
if sleep.shape[0]==0:
return ['nan']
if types=='wakeup':
if wakeup.shape[0]!=0:
return wakeup.ix[0]['wakeup']
if wakeup.shape[0]==0:
return ['nan']
'''
def up2blob(account_name_input,account_key_input,container_name_input = 'rmsoutput',uploadfile):
#uploadfile should be a dataframe
append_blob_service = AppendBlobService(account_name=account_name_input, account_key=account_key_input)
append_blob_service.create_container(container_name_input)
sleep_text = uploadfile.to_csv()
Today = date.today(); Today = Today.strftime('%Y-%m-%d')
append_blob_service.create_blob(container_name, Today)
append_blob_service.append_blob_from_text(container_name, Today, sleep_text)
'''