def test_latest_date_before_should_return_input_if_start_equals_ub():
starting_date = pd.Timestamp("8 June 2016")
upper_bound = pd.Timestamp("8 June 2016")
time_step = pd.Timedelta("7D")
result = latest_date_before(starting_date, upper_bound, time_step)
assert result == starting_date
def test_latest_date_before_should_shift_forward_n_weeks_input_as_required():
starting_date = pd.Timestamp("10 June 2016")
expected_date = pd.Timestamp("27 January 2017")
upper_bound = pd.Timestamp("29 January 2017")
time_step = pd.Timedelta("7D")
result = latest_date_before(starting_date, upper_bound, time_step)
assert result == expected_date
def test_latest_date_before_should_shift_backward_ne_week_input_as_required():
starting_date = pd.Timestamp("10 June 2016")
expected_date = pd.Timestamp("3 June 2016")
upper_bound = pd.Timestamp("8 June 2016")
time_step = pd.Timedelta("7D")
result = latest_date_before(starting_date, upper_bound, time_step)
assert result == expected_date
def test_latest_date_before_should_shift_forward_until_upper_bound():
# here the upper bound IS the expected date => makes sure we go up to
# thsi ons
starting_date = pd.Timestamp("10 June 2016")
upper_bound = pd.Timestamp("24 June 2016")
time_step = pd.Timedelta("7D")
result = latest_date_before(starting_date, upper_bound, time_step)
assert result == upper_bound
def __init__(self, clock, seed, config):
"""
This should not be used, only child classes
:type clock: Clock
:param clock: the master clock driving this simulator
:type seed: int
:param seed: seed for random number generator, default None
:return: A new TimeProfiler is created
"""
DependentGenerator.__init__(self)
self._state = RandomState(seed)
self.config = config
self.clock = clock
# "macro" time shift: we shift the whole profile n times in the future
# or the past until it overlaps with the current clock date
init_date = latest_date_before(
starting_date=config.start_date,
upper_bound=clock.current_date,
time_step=pd.Timedelta(config.profile_time_steps) *
len(config.profile))
# Un-scaled weight profile. We artificially adds a nan to force the
# up-sclaling to multiply the last element
profile_idx = pd.date_range(start=init_date,
freq=config.profile_time_steps,
periods=len(config.profile) + 1)
profile_ser = pd.Series(data=config.profile + [np.nan],
index=profile_idx)
# scaled weight profile, s.t. one clock step == one profile value
profile_ser = profile_ser.resample(rule=clock.step_duration).pad()[:-1]
self.n_time_bin = profile_ser.shape[0]
profile_cdf = (profile_ser / profile_ser.sum()).cumsum()
self.profile = pd.DataFrame({
"cdf": profile_cdf,
# for debugging
"timeframe": np.arange(len(profile_cdf))
})
# "micro" time shift,: we step forward along the profile until it is
# align with the current date
while self.profile.index[0] < clock.current_date:
self.increment()
# makes sure we'll get notified when the clock goes forward
clock.register_increment_listener(self)