def processor(time_=None):
'''
Esta función detecta las transacciones en la tabla transactions.
y las ejecuta sobre el nbi.
Por ahora no se reintentan transacciones fallidas.
'''
logger.debug(f"time_ {time_}")
if not time_:
return
engine = get_engine()
session = get_session(engine=engine)
# detectar las transacciones a procesar
# trxs = session.query(Transaction).filter(Transaction.sent.is_(null()))
# trxs = session.query(Transaction).filter(
# or_(Transaction.sent.is_(null()),
# Transaction.oldtilt != Transaction.newtilt)).first()
trxs = session.query(Transaction).filter(
or_(Transaction.sent.is_(null()),
Transaction.oldtilt != Transaction.newtilt))
if ENV == 'sim':
for trx in trxs:
# logger.info(f"trx \n{trx}")
nbi_simulator(time_=time_, session_=session, trx_=trx)
if ENV == 'prod':
nbi_processor(time_=time_, session_=session, trxs_=trxs)
session.commit()
session.close()
def scheduler(time_=None):
logger.debug(f"time_ {time_}")
if not time_:
return
engine = get_engine()
db_connection = engine.connect()
# set más reciente de 'High' overshooters en terreno plano
query_ = '''
select o.cellname
from overshooters o, terrains t
where o.cellname = t.cellname
and o.overshooter and t.is_plain
and o.intensity = 'High'
and o.datetimeid = (select max(datetimeid) from overshooters)
and t.datetimeid = (select max(datetimeid) from terrains);
'''
candidates_df = pd.read_sql(query_, db_connection)
db_connection.close()
# entregar los candidatos a mid_term_evaluator()
mid_term_evaluator(time_=time_, candidates_df=candidates_df)
def load_rets(time_=None):
logger.info(f'ENV {ENV}')
if not time_:
return
list_ = [
{
'datetimeid': time_,
'node': 'MBTS-AIS_3G_003',
'cellname': 'AIS_4G_003_3',
'eci': 2816002,
'devicename': 'RET82',
'deviceno': 2,
'tilt': 30,
'subname': 'RET82',
'subunitno': 1,
'localcellid': 2,
},
{
'datetimeid': time_,
'node': 'MBTS-ARA_3G_013',
'cellname': 'ARA_4G_013_3',
'eci': 2304258,
'devicename': 'RET82R_S3',
'deviceno': 2,
'tilt': 40,
'subname': 'RET82R_S3',
'subunitno': 1,
'localcellid': 2,
},
{
'datetimeid': time_,
'node': 'MBTS-ARA_3G_013',
'cellname': 'ARA_4G_013_3',
'eci': 2304258,
'devicename': 'RET82L_S3',
'deviceno': 12,
'tilt': 40,
'subname': 'RET82L_S3',
'subunitno': 1,
'localcellid': 2,
},
]
if ENV == 'sim':
df = pd.DataFrame.from_dict(list_)
if ENV == 'prod':
df = rets_data(time_=time_)
engine = get_engine()
session = get_session(engine=engine)
df.to_sql('rets', con=engine, if_exists='append', index=False)
session.commit()
session.close()
def load_terrains(time_=None,
neighborhood_df=pd.DataFrame(),
cells_df=pd.DataFrame()):
logger.info(f'ENV {ENV}')
if not time_:
return
list_ = [
{
'datetimeid': time_,
'cellname': 'AIS_4G_003_3',
'is_plain': True,
'slope': 0,
},
{
'datetimeid': time_,
'cellname': 'ARA_4G_013_3',
'is_plain': True,
'slope': 0,
},
]
if ENV == 'sim':
terrain_df = pd.DataFrame.from_dict(list_)
if ENV == 'prod':
terrain_df, neighborhood_df, cells_df = check_terrain(
time_=time_, neighborhood_df=neighborhood_df, cells_df=cells_df)
logger.info(f'terrain_df.shape {terrain_df.shape}')
logger.info(f'terrain_df.columns {terrain_df.columns}')
terrains_dict = terrain_df.to_dict('index')
engine = get_engine()
session = get_session(engine=engine)
now_ = datetime.datetime.now()
for index, dict_ in terrains_dict.items():
is_plain_ = not dict_['HILL']
if is_plain_:
slope_ = 0
else:
slope_ = 1 if int(dict_['HEIGHT_DIFF']) > 0 else -1
obj_ = Terrain(datetimeid=now_,
cellname=dict_['CELLNAME'],
slope=slope_,
is_plain=is_plain_)
session.add(obj_)
session.commit()
session.close()
return neighborhood_df, cells_df
def enabler(cellnames=None):
logger.debug(f'ENV {ENV}')
engine = get_engine()
session = get_session(engine=engine)
for cellname in cellnames:
# logger.debug(f'cellname {cellname}')
antennas = session.query(Ret).filter(Ret.cellname == cellname, )
for antenna in antennas:
antenna.enabled = True
# logger.info(f'node {antenna.node} deviceno {antenna.deviceno}')
# session.commit()
session.commit()
session.close()
def trx_updater(commands=None, sent_=None):
'''
Esta función recibe una lista de diccionarios, con las respuestas
a los comandos de cambio de tilt ejecutados en el NBI.
Si el resultado es exitoso se actualizan las tablas rets y
transactions.
'''
logger.debug(f"ENV {ENV}")
if not commands:
return
engine = get_engine()
session = get_session(engine=engine)
for command in commands:
result = command['data']['result']
logger.debug(f"result {result}")
executed_time_stamp_str = command['data']['executed_time_stamp']
executed_time_stamp = datetime.datetime.strptime(
executed_time_stamp_str, '%Y-%m-%d %H:%M:%S')
object_id = command['object_id']
trx = session.query(Transaction).filter(
Transaction.id == object_id).first()
if not trx:
session.commit()
session.close()
return
trx.sent = sent_
if result:
trx.oldtilt = trx.newtilt
trx.success = executed_time_stamp
ret_updater(node=trx.node,
deviceno=trx.deviceno,
tilt=trx.newtilt,
session=session)
else:
logger.info(f"result {result}")
trx.failure = executed_time_stamp
session.commit()
session.close()
def load_overshooters():
logger.info(f'load_overshooters:')
neighborhood_df, overshooters_df = overshooting()
overshooters_dict = overshooters_df.to_dict('index')
engine = get_engine()
session = get_session(engine=engine)
now_ = datetime.now()
for index, dict_ in overshooters_dict.items():
obj_ = Overshooter(
date_time = now_,
cell_name = dict_['CELLNAME'],
time_advanced = int(dict_['ta_']),
average_distance = int(dict_['distance_']),
is_overshooter = dict_['overshooter']
)
session.add(obj_)
session.commit()
session.close()
def load_overshooters(time_=None,
neighborhood_df=pd.DataFrame(),
cells_df=pd.DataFrame()):
logger.info(f'load_overshooters:')
if not time_:
return
list_ = [
{
'datetimeid': time_,
'cellname': 'AIS_4G_003_3',
'ta_calculated': 14.4,
'average_distance': 2.43982546537283,
'overshooter': True,
'intensity': 'High',
},
{
'datetimeid': time_,
'cellname': 'ARA_4G_013_3',
'ta_calculated': 14.4,
'average_distance': 6.14587256200947,
'overshooter': True,
'intensity': 'High',
},
]
if ENV == 'sim':
df = pd.DataFrame.from_dict(list_)
if ENV == 'prod':
df = overshooters(time_=time_,
neighborhood_df=neighborhood_df,
cells_df=cells_df)
engine = get_engine()
session = get_session(engine=engine)
df.to_sql('overshooters', con=engine, if_exists='append', index=False)
session.commit()
session.close()
def transactions(time_=None):
'''
Esta función detecta las transacciones en la tabla transactions.
y las ejecuta sobre el nbi.
Por ahora no se reintentan transacciones fallidas.
'''
logger.debug(f"time_ {time_}")
if not time_:
return
engine = get_engine()
session = get_session(engine=engine)
# detectar las transacciones a procesar
trxs = session.query(Transaction).filter(Transaction.sent.is_(null()))
for trx in trxs:
# logger.info(f"trx \n{trx}")
processor(time_=time_,session_=session,trx_=trx)
session.commit()
session.close()
def evaluator(time_=None, candidates_kpis_df=pd.DataFrame()):
'''
Esta función recibe todas las celdas candidatas y sus kpis promedio,
para el instante actual.
Dependiendo de si la celda existe en la tabla transactions,
hay comparaciones con kpis promedio iniciales.
En base a reglas pueden entrar transacciones a la tabla transactions.
'''
logger.debug(f"time_ {time_}")
if not time_:
return
if candidates_kpis_df.empty:
return
logger.debug(f"candidates_kpis_df \n{candidates_kpis_df}")
engine = get_engine()
session = get_session(engine=engine)
for idx in candidates_kpis_df.index: # overshooters plain terrain
node = candidates_kpis_df['eNodeB_Name'][idx]
user_avg = candidates_kpis_df['user_avg'][idx]
user_thrp_dl = candidates_kpis_df['user_thrp_dl'][idx]
traffic_dl = candidates_kpis_df['traffic_dl'][idx]
antennas = session.query(Ret).filter(Ret.node == node, )
for antenna in antennas:
if not antenna.enabled:
continue
logger.debug(f"node {antenna.node} deviceno {antenna.deviceno}")
trx = session.query(Transaction).filter(
and_(Transaction.node == antenna.node,
Transaction.deviceno == antenna.deviceno)).first()
if trx:
# si trx anterior no fue exitosa
if not trx.success:
logger.debug(f"continue: success {trx.success}")
continue
cond_ = delta_percentaje(
trx.user_thrp_dl_initial,
user_thrp_dl) > MAX_DELTA_USER_THRP_DL_PERCENTAJE
cond_ = cond_ or delta_percentaje(
trx.traffic_dl_initial,
traffic_dl) > MAX_DELTA_TRAFFIC_DL_PERCENTAJE
if cond_:
# rollback
logger.debug(f"rollback")
newtilt_ = trx.oldtilt
else:
newtilt_ = newtilt(trx.newtilt)
if trx.newtilt == newtilt_:
logger.debug(f"continue: newtilt_ {newtilt_}")
continue
# si nuevo tilt es distinto al último
trx.newtilt = newtilt_
trx.generated = datetime.now()
else:
if not (user_avg >= MIN_USER_AVG and user_avg <= MAX_USER_AVG):
logger.debug(f"continue: user_avg {user_avg}")
continue
if antenna.tilt == newtilt(antenna.tilt):
logger.debug(
f"continue: antenna.tilt == newtilt(antenna.tilt)")
continue
# se crea entrada en tabla transactions
trx = Transaction(
node=antenna.node,
cellname=antenna.cellname,
deviceno=antenna.deviceno,
subunitno=antenna.subunitno,
tilt_initial=antenna.tilt,
# oldtilt = tilt_initial,
oldtilt=antenna.tilt,
# originalmente
# user_thrp_dl_initial = user_thrp_dl,
# traffic_dl_initial = traffic_dl,
# para ver si pasa
user_thrp_dl_initial=float(user_thrp_dl),
traffic_dl_initial=float(traffic_dl),
newtilt=newtilt(antenna.tilt),
datetimeid=time_,
generated=datetime.now(),
)
logger.debug(f"trx \n{trx}")
session.add(trx)
session.commit()
session.commit()
session.close()