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_baseline_with_temp(self):
b = Loadshape(self.get_kw_data_filepath(),
self.get_temp_data_filepath(),
timezone='America/Los_Angeles',
log_level=30)
prediction = b.baseline()
assert len(prediction.data()) > 0
def test_baseline_with_temp(self):
b = Loadshape(self.get_kw_data_filepath(),
self.get_temp_data_filepath(),
timezone='America/Los_Angeles',
log_level=30)
prediction = b.baseline()
assert len(prediction.data()) > 0
def test_one_step_output_time_series_generator(self):
start_at = 1379487600
end_at = 1379488500
l = Loadshape([], log_level=40, timezone='America/Los_Angeles')
s = l._build_output_time_series(start_at, end_at, step_count=1)
assert len(s.data()) == 2
assert s.start_at() == start_at
assert s.end_at() == end_at
def test_many_step_output_time_series_generator(self):
start_at = 1379487600
end_at = start_at + (900 * 5)
l = Loadshape([], log_level=40, timezone='America/Los_Angeles')
s = l._build_output_time_series(start_at, end_at, step_size=900)
assert len(s.data()) == 6
assert s.start_at() == start_at
assert s.end_at() == end_at
def test_many_step_output_time_series_generator(self):
start_at = 1379487600
end_at = start_at + (900 * 5)
l = Loadshape([], log_level=40, timezone='America/Los_Angeles')
s = l._build_output_time_series(start_at, end_at, step_size=900)
assert len(s.data()) == 6
assert s.start_at() == start_at
assert s.end_at() == end_at
def test_one_step_output_time_series_generator(self):
start_at = 1379487600
end_at = 1379488500
l = Loadshape([], log_level=40, timezone='America/Los_Angeles')
s = l._build_output_time_series(start_at, end_at, step_count=1)
assert len(s.data()) == 2
assert s.start_at() == start_at
assert s.end_at() == end_at
def test_cost(self):
l_data = [(1379487600, 5.0), (1379488500, 5.0), (1379489400, 5.0), (1379490300, 5.0), (1379491200, 5.0)]
cost = [(1379487600, 0.0), (1379488500, 0.17), (1379489400, 0.17), (1379490300, 0.17), (1379491200, 0.17)]
cumulative_cost = [(1379487600, 0.0), (1379488500, 0.17), (1379489400, 0.34), (1379490300, 0.52), (1379491200, 0.69)]
tariff = Tariff(tariff_file=self.get_test_tariff(), timezone='America/Los_Angeles')
ls = Loadshape(l_data, timezone='America/Los_Angeles', log_level=30, tariff=tariff)
cost_out, cumulative_cost_out = ls.cost()
assert cost_out.data() == cost
assert cumulative_cost_out.data() == cumulative_cost
def process_request(self, arg_set):
inst_args = ["load_data", "temp_data", "timezone", "temp_units", "sq_ft"]
inst_args = { a_name: arg_set[a_name] for a_name in inst_args if arg_set.get(a_name)}
event_args = ["start_at", "end_at"]
event_args = { a_name: arg_set[a_name] for a_name in event_args if arg_set.get(a_name)}
ls = Loadshape(**inst_args)
event_performance_data = ls.event_performance(**event_args)
return event_performance_data
def process_request(self, arg_set):
inst_args = ["load_data", "temp_data", "timezone", "temp_units", "sq_ft"]
inst_args = {a_name: arg_set[a_name] for a_name in inst_args if arg_set.get(a_name)}
baseline_args = ["start_at", "end_at", "weighting_days", "modeling_interval", "step_size"]
baseline_args = {a_name: arg_set[a_name] for a_name in baseline_args if arg_set.get(a_name)}
ls = Loadshape(**inst_args)
baseline_series = ls.baseline(**baseline_args)
response = {"baseline": baseline_series.data(), "error_stats": ls.error_stats}
return response
def process_request(self, arg_set):
inst_args = ["load_data", "temp_data", "timezone", "temp_units", "sq_ft"]
inst_args = { a_name: arg_set[a_name] for a_name in inst_args if arg_set.get(a_name)}
sum_args = ["start_at", "end_at", "step_size"]
sum_args = { a_name: arg_set[a_name] for a_name in sum_args if arg_set.get(a_name)}
ls = Loadshape(**inst_args)
sum_series = ls.cumulative_sum(**sum_args)
response = {
"cumulative_kwh_diff": sum_series.data(),
}
return response
def process_request(self, arg_set):
inst_args = ["load_data", "temp_data", "timezone", "temp_units", "sq_ft"]
inst_args = { a_name: arg_set[a_name] for a_name in inst_args if arg_set.get(a_name)}
baseline_args = ["start_at", "end_at", "weighting_days", "modeling_interval", "step_size"]
baseline_args = { a_name: arg_set[a_name] for a_name in baseline_args if arg_set.get(a_name)}
ls = Loadshape(**inst_args)
baseline_series = ls.baseline(**baseline_args)
response = {
"baseline": baseline_series.data(),
"error_stats": ls.error_stats
}
return response
def test_diff(self):
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)
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_cost(self):
l_data = [(1379487600, 5.0), (1379488500, 5.0), (1379489400, 5.0),
(1379490300, 5.0), (1379491200, 5.0)]
cost = [(1379487600, 0.0), (1379488500, 0.17), (1379489400, 0.17),
(1379490300, 0.17), (1379491200, 0.17)]
cumulative_cost = [(1379487600, 0.0), (1379488500, 0.17),
(1379489400, 0.34), (1379490300, 0.52),
(1379491200, 0.69)]
tariff = Tariff(tariff_file=self.get_test_tariff(),
timezone='America/Los_Angeles')
ls = Loadshape(l_data,
timezone='America/Los_Angeles',
log_level=30,
tariff=tariff)
cost_out, cumulative_cost_out = ls.cost()
assert cost_out.data() == cost
assert cumulative_cost_out.data() == cumulative_cost
def process_request(self, arg_set):
inst_args = [
"load_data", "temp_data", "timezone", "temp_units", "sq_ft"
]
inst_args = {
a_name: arg_set[a_name]
for a_name in inst_args if arg_set.get(a_name)
}
event_args = ["start_at", "end_at"]
event_args = {
a_name: arg_set[a_name]
for a_name in event_args if arg_set.get(a_name)
}
ls = Loadshape(**inst_args)
event_performance_data = ls.event_performance(**event_args)
return event_performance_data
def process_request(self, arg_set):
inst_args = [
"load_data", "temp_data", "timezone", "temp_units", "sq_ft"
]
inst_args = {
a_name: arg_set[a_name]
for a_name in inst_args if arg_set.get(a_name)
}
sum_args = ["start_at", "end_at", "step_size"]
sum_args = {
a_name: arg_set[a_name]
for a_name in sum_args if arg_set.get(a_name)
}
ls = Loadshape(**inst_args)
sum_series = ls.cumulative_sum(**sum_args)
response = {
"cumulative_kwh_diff": sum_series.data(),
}
return response
WEEK_END = "2013-09-29"
# ----- write JSON output file ----- #
def write_json(data, file_name='output.json'):
print "writing file: %s" % file_name
with open(file_name, 'w') as outfile:
json.dump(data, outfile)
outfile.close()
# ----- build loadshape object ----- #
my_load_shape = Loadshape(
load_data=LOAD_DATA,
temp_data=TEMP_DATA,
# 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 a 7 day baseline ----- #
seven_day_baseline = my_load_shape.baseline(start_at=WEEK_START,
end_at=WEEK_END,
weighting_days=14,
modeling_interval=900,
step_size=900)
WEEK_START = "2013-09-22"
WEEK_END = "2013-09-29"
# ----- write JSON output file ----- #
def write_json(data, file_name="output.json"):
print "writing file: %s" % file_name
with open(file_name, "w") as outfile:
json.dump(data, outfile)
outfile.close()
# ----- build loadshape object ----- #
my_load_shape = Loadshape(
load_data=LOAD_DATA,
temp_data=TEMP_DATA,
# 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 a 7 day baseline ----- #
seven_day_baseline = my_load_shape.baseline(
start_at=WEEK_START, end_at=WEEK_END, weighting_days=14, modeling_interval=900, step_size=900
)
# ----- assemble a payload summarizng the seven day baseline ----- #
DR_EVENT_NAME = "DR Event 1"
DR_EVENT_START = "2013-09-27 14:00:00"
DR_EVENT_END = "2013-09-27 16:15:00"
DR_EVENT_DAY_START = "2013-09-27 00:00:00"
DR_EVENT_DAY_END = "2013-09-28 00:00:00"
# ----- write JSON output file ----- #
def write_json(data, file_name='output.json'):
print "writing file: %s" % file_name
with open(file_name, 'w') as outfile:
json.dump(data, outfile)
outfile.close()
# ----- build loadshape object ----- #
my_load_shape = Loadshape(load_data=LOAD_DATA, temp_data=TEMP_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")
# ----- add tariff to enable cost calculations ----- #
tariff = Tariff(tariff_file=TARIFF, timezone='America/Los_Angeles')
tariff.add_dr_period("2013-09-23 14:00:00", "2013-09-23 16:00:00")
tariff.add_dr_period("2013-09-27 14:00:00", "2013-09-27 16:15:00")
my_load_shape.set_tariff(tariff)
# ----- build the baseline to use as a reference for performance ----- #
TARIFF = path.join(EXAMPLES_DIR, "data", "tariff.json")
CUMULATIVE_SUM_NAME = "Cumulative Sum Test 1"
CUMULATIVE_SUM_START = "2013-09-30"
CUMULATIVE_SUM_WEIGHTING_DAYS = 14
# ----- write JSON output file ----- #
def write_json(data, file_name='output.json'):
print "writing file: %s" % file_name
with open(file_name, 'w') as outfile:
json.dump(data, outfile)
outfile.close()
# ----- build loadshape object ----- #
my_load_shape = Loadshape(load_data=LOAD_DATA, temp_data=TEMP_DATA,
# 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)
CUMULATIVE_SUM_WEIGHTING_DAYS = 14
# ----- write JSON output file ----- #
def write_json(data, file_name='output.json'):
print "writing file: %s" % file_name
with open(file_name, 'w') as outfile:
json.dump(data, outfile)
outfile.close()
# ----- build loadshape object ----- #
my_load_shape = Loadshape(
load_data=LOAD_DATA,
temp_data=TEMP_DATA,
# 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 ----- #