def populate_observations(cls, values, begin_date):
dates = list()
values = str(values)
for val in values:
dates.append(begin_date)
InvestmentCycleObservationFactory.create(as_of=begin_date,
cycle=int(val))
begin_date += datetime.timedelta(1)
return dates
def populate_cycle_obs(days, asof=now().date()):
# do not input less than 400 days, method might fail
cycle = int(np.random.uniform(0, 6))
prog = [0, 1, 2, 0, 3, 4]
dt = asof - timedelta(days=days)
while days:
run = min(int(np.random.uniform(20, 50)), days)
while run:
InvestmentCycleObservationFactory.create(as_of=dt,
cycle=prog[cycle])
dt += timedelta(days=1)
run -= 1
days -= 1
cycle += 1
cycle %= 6
def populate_observations(self):
# We need to have a full range of cycle plus at least one date behind.
InvestmentCycleObservationFactory.create(
as_of=date(2016, 1, 1),
cycle=InvestmentCycleObservation.Cycle.EQ_PIT.value)
InvestmentCycleObservationFactory.create(
as_of=self.last_cycle_start,
cycle=InvestmentCycleObservation.Cycle.PIT_EQ.value)
InvestmentCycleObservationFactory.create(
as_of=date(2016, 1, 7),
cycle=InvestmentCycleObservation.Cycle.EQ.value)
InvestmentCycleObservationFactory.create(
as_of=date(2016, 1, 11),
cycle=InvestmentCycleObservation.Cycle.EQ_PK.value)
InvestmentCycleObservationFactory.create(
as_of=date(2016, 1, 13),
cycle=InvestmentCycleObservation.Cycle.PK_EQ.value)
InvestmentCycleObservationFactory.create(
as_of=date(2016, 1, 15),
cycle=InvestmentCycleObservation.Cycle.EQ.value)
InvestmentCycleObservationFactory.create(
as_of=date(2016, 1, 18),
cycle=InvestmentCycleObservation.Cycle.EQ_PIT.value)
InvestmentCycleObservationFactory.create(
as_of=date(2016, 1, 20),
cycle=InvestmentCycleObservation.Cycle.PIT_EQ.value)