def get_balances_over_time_for_user(cls, user):
rows = DBSession.query(cls.amount,
cls.type,
cls.to_account_virt_id,
cls.fr_account_virt_id,
event.Event.timestamp)\
.join(event.Event)\
.filter(or_(
cls.type=='purchase',
cls.type=='cashdeposit',
cls.type=='ccdeposit',
cls.type=='btcdeposit',
cls.type=='adjustment'
))\
.filter(or_(
cls.to_account_virt_id == user.id,
cls.fr_account_virt_id == user.id,
))\
.order_by(event.Event.timestamp)\
.all()
# We can re-use the global balance calculation code because the query
# filtered it down to only this user, only now the "global" total
# positive values (r[2]) and total debt (r[1]) are just this user's
# balance, so we pull out the right column at each point in time.
rows = utility.timeseries_balance_total_daily(rows)
rows = [(r[0],r[2]/100 if r[1]==0 else -r[1]/100) for r in rows]
return rows
def get_balances_over_time_for_user(cls, user):
rows = DBSession.query(cls.amount,
cls.type,
cls.to_account_virt_id,
cls.fr_account_virt_id,
event.Event.timestamp)\
.join(event.Event)\
.filter(or_(
cls.type=='purchase',
cls.type=='cashdeposit',
cls.type=='ccdeposit',
cls.type=='btcdeposit',
cls.type=='adjustment'
))\
.filter(or_(
cls.to_account_virt_id == user.id,
cls.fr_account_virt_id == user.id,
))\
.order_by(event.Event.timestamp)\
.all()
# We can re-use the global balance calculation code because the query
# filtered it down to only this user, only now the "global" total
# positive values (r[2]) and total debt (r[1]) are just this user's
# balance, so we pull out the right column at each point in time.
rows = utility.timeseries_balance_total_daily(rows)
rows = [(r[0],r[2]/100 if r[1]==0 else -r[1]/100) for r in rows]
return rows
def get_balances_over_time_for_user(cls, user):
rows = cls.get_transactions_over_time_for_user(user)
# We can re-use the global balance calculation code because the query
# filtered it down to only this user, only now the "global" total
# positive values (r[2]) and total debt (r[1]) are just this user's
# balance, so we pull out the right column at each point in time.
rows = utility.timeseries_balance_total_daily(rows)
rows = [(r[0], r[2] / 100 if r[1] == 0 else -r[1] / 100) for r in rows]
return rows
def get_balances_over_time_for_user(cls, user):
rows = cls.get_transactions_over_time_for_user(user)
# We can re-use the global balance calculation code because the query
# filtered it down to only this user, only now the "global" total
# positive values (r[2]) and total debt (r[1]) are just this user's
# balance, so we pull out the right column at each point in time.
rows = utility.timeseries_balance_total_daily(rows)
rows = [(r[0],r[2]/100 if r[1]==0 else -r[1]/100) for r in rows]
return rows
def get_balance_total_daily(cls):
rows = DBSession.query(cls.amount,
cls.type,
cls.to_account_virt_id,
cls.fr_account_virt_id,
event.Event.timestamp)\
.join(event.Event)\
.filter(or_(
cls.type=='purchase',
cls.type=='cashdeposit',
cls.type=='ccdeposit',
cls.type=='btcdeposit',
cls.type=='adjustment'
))\
.order_by(event.Event.timestamp)\
.all()
return utility.timeseries_balance_total_daily(rows)
def get_balance_total_daily(cls):
rows = DBSession.query(cls.amount,
cls.type,
cls.to_account_virt_id,
cls.fr_account_virt_id,
event.Event.timestamp)\
.join(event.Event)\
.filter(or_(
cls.type=='purchase',
cls.type=='cashdeposit',
cls.type=='ccdeposit',
cls.type=='btcdeposit',
cls.type=='adjustment'
))\
.order_by(event.Event.timestamp)\
.all()
return utility.timeseries_balance_total_daily(rows)