#period-dim
j = pd.DataFrame(finalized)
j['period_type'] = 'half-hourly'
j.columns = ['date_time', 'period_type']
gg = list(range(48))
#repeating 48 period n times
j['period'] = pd.Series(np.tile(gg, len(j)))
except:
print("check the date_Time range_calculation_function")
try:
pr1 = fetch_.date_time_operation(df)
pr1['date_time'] = pr1['Date'] + " " + pr1['Time']
pr1.columns = [
'area_id', 'device_id', 'date', 'time', 'value', 'date_time'
]
Gigar = fetch_.data_loss_finder(pr1, client_id, areaId, j)
Gigar['client_id'] = np.repeat(client_id, len(Gigar))
Gigar_2 = Gigar[[
'client_id', 'area_id', 'device_id', 'date_time', 'date',
'period_type', 'period', 'value'
]]
Gigar_2.columns = [
'client_id', 'area_id', 'device_id', 'date_time', 'date',
'period_type', 'period', 'traffic_count'
]
Gigar_2['created_date'] = pd.to_datetime('now').replace(microsecond=0)
except:
print("Error : Check the data")
try:
zk = z[z.areaid == "" + areaId + ""]
data_ass = zk[['clientid', 'clinetname', 'areaid']]
'created_date'
]]
Gigar_3 = fetch_.optimize_smoothed_error_count_paper(Gigar_2)
Gigar_4 = fetch_.paper_towel_usage_calculation_optimize(Gigar_3, R)
#Gigar_5=fetch_.Final_clean(Gigar_3)
except:
print("Error : Check the data")
try:
Gigar_6 = Gigar_4[[
'client_id', 'area_id', 'device_id', 'date_time', 'date',
'raw_value', 'smoothed_value', 'usage', 'created_date'
]]
Gigar_6.date_time = Gigar_6.date_time.astype(str)
Gigar_6.device_id = Gigar_6.device_id.astype(str)
ghkl = fetch_.data_loss_finder(Gigar_6, client_id, areaId, j)
#ghkl_.reset_index(inplace=True)
result = fetch_.usage_gap_distribution(ghkl, ab)
result_ = result[result.device_id != 'nan']
result_.smoothed_value = np.where(result_.smoothed_value > 100, 0,
result_.smoothed_value)
result_['usage'] = np.where(
result_['date_time'] == '2019-05-01 00:00:00', 0, result_.usage)
except:
print("enter dedata")
try:
database_username = '******'
database_password = '******'
database_ip = '****'
database_name = '****'
database_connection = sqlalchemy.create_engine(
'period_type', 'period', 'traffic_count', 'created_date'
]]
Gigar_3 = fetch_.Final_clean_People_count(Gigar_2)
Gigar_3['traffic_count'] = Gigar_3['traffic_count'].astype(int)
Final = Gigar_3[[
'client_id', 'area_id', 'device_id', 'date_time', 'date',
'traffic_count', 'created_date'
]]
j['date_time'] = j['date_time'].astype(str)
grpd_traff = pd.DataFrame(
Final.groupby(['date_time'])['traffic_count'].sum())
grpd_traff.reset_index(inplace=True)
uniqs = Final.device_id.unique()
kj = ""
grpd_traff['device_id'] = kj.join(uniqs[0])
data_loss_traffic = fetch_.data_loss_finder(grpd_traff, client_id,
areaId, j)
Gigar_fin = data_loss_traffic[[
'client_id', 'area_id', 'device_id', 'date_time', 'date',
'period_type', 'period', 'traffic_count', 'created_date'
]]
print(j.shape, Gigar_fin.shape)
print(Gigar_fin.isnull().sum())
except:
print("Error : Check the data", areaId)
try:
zk = z[z.areaid == "" + areaId + ""]
data_ass = zk[['clientid', 'clinetname', 'areaid']]
data_ass['devicetype'] = 'PeopleCount'
data_ass['start_date'] = np.min(df['Date'])
data_ass['end_date'] = np.max(df['Date'])
data_ass['device_count'] = df.deviceName.nunique()