def test_diff(self):
from loadshape.utils import get_timezone
l_data = [(1379487600, 5.0), (1379488500, 5.0), (1379489400, 5.0),
(1379490300, 5.0), (1379491200, 5.0)]
b_data = [(1379487600, 4.0), (1379488500, 4.0), (1379489400, 4.0),
(1379490300, 4.0), (1379491200, 4.0)]
expected_kw_diff = [(1379488500, 1), (1379489400, 1), (1379490300, 1),
(1379491200, 1)]
expected_cumulative_kwh_diff = [(1379487600, 0), (1379488500, 0.25),
(1379489400, 0.5), (1379490300, 0.75),
(1379491200, 1.0)]
expected_cumulative_kwh_base = [(1379487600, 0), (1379488500, 1.0),
(1379489400, 2.0), (1379490300, 3.0),
(1379491200, 4.0)]
b = Loadshape(l_data, timezone='America/Los_Angeles', log_level=30)
b.baseline_series = Series(b_data, get_timezone('America/Los_Angeles'))
kw_diff, kw_base, cumulative_kwh_diff, cumulative_kwh_base = b.diff()
assert kw_diff.data() == expected_kw_diff
assert cumulative_kwh_diff.data() == expected_cumulative_kwh_diff
assert kw_base.data() == b_data[1:]
assert cumulative_kwh_base.data() == expected_cumulative_kwh_base
def test_diff(self):
from loadshape.utils import get_timezone
l_data = [(1379487600, 5.0), (1379488500, 5.0), (1379489400, 5.0), (1379490300, 5.0), (1379491200, 5.0)]
b_data = [(1379487600, 4.0), (1379488500, 4.0), (1379489400, 4.0), (1379490300, 4.0), (1379491200, 4.0)]
expected_kw_diff = [(1379488500, 1), (1379489400, 1), (1379490300, 1), (1379491200, 1)]
expected_cumulative_kwh_diff = [(1379487600, 0), (1379488500, 0.25), (1379489400, 0.5), (1379490300, 0.75), (1379491200, 1.0)]
expected_cumulative_kwh_base = [(1379487600, 0), (1379488500, 1.0), (1379489400, 2.0), (1379490300, 3.0), (1379491200, 4.0)]
b = Loadshape(l_data, timezone='America/Los_Angeles', log_level=30)
b.baseline_series = Series(b_data, get_timezone('America/Los_Angeles'))
kw_diff, kw_base, cumulative_kwh_diff, cumulative_kwh_base = b.diff()
assert kw_diff.data() == expected_kw_diff
assert cumulative_kwh_diff.data() == expected_cumulative_kwh_diff
assert kw_base.data() == b_data[1:]
assert cumulative_kwh_base.data() == expected_cumulative_kwh_base
# tariff_schedule=tariff_schedule
timezone='America/Los_Angeles',
temp_units="F", sq_ft=BUILDING_SQ_FT)
# ----- add exclusions as necessary ----- #
my_load_shape.add_exclusion("2013-09-23 00:00:00", "2013-09-24 00:00:00")
my_load_shape.add_exclusion("2013-09-27 00:00:00", "2013-09-28 00:00:00")
my_load_shape.add_named_exclusion("US_HOLIDAYS")
# ----- generate the appropriate baseline ----- #
baseline = my_load_shape.baseline(weighting_days=CUMULATIVE_SUM_WEIGHTING_DAYS,
modeling_interval=900,
step_size=900)
# ----- set up the cumulative sum dates ----- #
tz = utils.get_timezone('America/Los_Angeles')
start_at = utils.str_to_datetime(CUMULATIVE_SUM_START, tz)
end_at = utils.int_to_datetime(my_load_shape.baseline_series.end_at(), tz)
# ----- calculate long term event performance and cumulative sum series ----- #
event_performance = my_load_shape.event_performance(start_at, end_at)
cumulative_sum_series = my_load_shape.cumulative_sum(start_at, end_at)
# ----- assemble a payload summarizng the cumulative sum ----- #
out = {}
out["building"] = BUILDING_NAME
out["event_name"] = CUMULATIVE_SUM_NAME
out["sum_start_at"] = start_at.strftime("%Y-%m-%d %H:%M:%S")
out["sum_end_at"] = end_at.strftime("%Y-%m-%d %H:%M:%S")
out["event_performance"] = event_performance
out["cumulative_sum_data"] = cumulative_sum_series.data()
timezone='America/Los_Angeles',
temp_units="F",
sq_ft=BUILDING_SQ_FT)
# ----- add exclusions as necessary ----- #
my_load_shape.add_exclusion("2013-09-23 00:00:00", "2013-09-24 00:00:00")
my_load_shape.add_exclusion("2013-09-27 00:00:00", "2013-09-28 00:00:00")
my_load_shape.add_named_exclusion("US_HOLIDAYS")
# ----- generate the appropriate baseline ----- #
baseline = my_load_shape.baseline(weighting_days=CUMULATIVE_SUM_WEIGHTING_DAYS,
modeling_interval=900,
step_size=900)
# ----- set up the cumulative sum dates ----- #
tz = utils.get_timezone('America/Los_Angeles')
start_at = utils.str_to_datetime(CUMULATIVE_SUM_START, tz)
end_at = utils.int_to_datetime(my_load_shape.baseline_series.end_at(), tz)
# ----- calculate long term event performance and cumulative sum series ----- #
event_performance = my_load_shape.event_performance(start_at, end_at)
cumulative_sum_series = my_load_shape.cumulative_sum(start_at, end_at)
# ----- assemble a payload summarizng the cumulative sum ----- #
out = {}
out["building"] = BUILDING_NAME
out["event_name"] = CUMULATIVE_SUM_NAME
out["sum_start_at"] = start_at.strftime("%Y-%m-%d %H:%M:%S")
out["sum_end_at"] = end_at.strftime("%Y-%m-%d %H:%M:%S")
out["event_performance"] = event_performance
out["cumulative_sum_data"] = cumulative_sum_series.data()
