def populate_usergroups():
session = DBSession()
with transaction.manager:
admins = [(1, 1)] # admin user should be 1
ulist = admins
for gid, uid in ulist:
row = UserGroup(gid, uid)
session.add(row)
def populate_groups():
session = DBSession()
groups = ['admin', 'editor', 'manager']
for gname in groups:
try:
with transaction.manager:
group = Group(gname)
session.add(group)
except IntegrityError:
pass
def populate_users(admin_username):
from trumpet.security import encrypt_password
session = DBSession()
with transaction.manager:
users = [admin_username]
# Using id_count to presume
# the user's id, which should work
# when filling an empty database.
id_count = 0
for uname in users:
id_count += 1
user = User(uname)
password = encrypt_password(uname)
session.add(user)
pw = Password(id_count, password)
session.add(pw)
def __init__(self, user_id=None):
self.session = DBSession()
self.user_id = user_id
"""
Module for executing calculation about parameters
Learning Algorithm - 2015 - Subena
"""
from compiler.ast import flatten
import itertools
from sqlalchemy.sql.functions import func
from base import DBSession, logging
import numpy as np
from package.model.tables import Criterion, Value, Stats,Alerts
session = DBSession()
criteria = session.query(Criterion).all()
def execute():
logging.info( "\nStart learning parameters..." )
#calcul all proba dependencies according to proba and their parenthood
getProbaForAllMeasures()
def getAlertsFromStats():
"""
Get alerts from calculated stats (see getProbaForAllMeasures)
for each stat if a pattern does not correspond then create an alert
"""
stats = None
