def allocate_output_memory(
self, t_start, t_end, t_step, data_spec, categories_dict
):
"""preallocate output memory as numpy arrays to speed up simulation"""
# reset output memory
self.output = {}
self.fmu_output = {}
self.output = {
STATES.SIMULATION_TIME: np.arange(
t_start, t_end + t_step, t_step, dtype="int64"
)
}
n_s = len(self.output[STATES.SIMULATION_TIME])
# add output state variables
for state in self.output_states:
if data_spec.full.spec[state]["dtype"] == "category":
self.output[state] = pd.Series(
pd.Categorical(
pd.Series(index=np.arange(n_s)),
categories=categories_dict[state],
)
)
else:
(
np_default_value,
np_dtype,
) = Conversions.numpy_down_cast_default_value_dtype(
data_spec.full.spec[state]["dtype"]
)
self.output[state] = np.full(
n_s,
np_default_value,
dtype=np_dtype,
)
# add fmu state variables
self.fmu_output[STATES.STEP_STATUS] = np.full(n_s, False, dtype="bool")
self.fmu_output[STATES.SIMULATION_TIME] = self.output[STATES.SIMULATION_TIME]
for k, v in self.idf.output_spec.items():
(
np_default_value,
np_dtype,
) = Conversions.numpy_down_cast_default_value_dtype(v["dtype"])
self.fmu_output[k] = np.full(n_s, np_default_value, dtype=np_dtype)
# set current time
self.current_time = t_start
self.current_t_idx = 0
def allocate_output_memory(self, t_start, t_end, t_step, data_spec,
categories_dict):
"""preallocate output memory to speed up simulation"""
# reset output
self.output = {}
self.output = {
STATES.SIMULATION_TIME:
np.arange(t_start, t_end + t_step, t_step, dtype="int64")
}
n_s = len(self.output[STATES.SIMULATION_TIME])
# add state variables
for state in self.output_states:
if data_spec.full.spec[state]["dtype"] == "category":
self.output[state] = pd.Series(
pd.Categorical(
pd.Series(index=np.arange(n_s)),
categories=categories_dict[state],
))
else:
(
np_default_value,
np_dtype,
) = Conversions.numpy_down_cast_default_value_dtype(
data_spec.full.spec[state]["dtype"])
self.output[state] = np.full(
n_s,
np_default_value,
dtype=np_dtype,
)
self.output[STATES.STEP_STATUS] = np.full(n_s, 0, dtype="int8")
def generate_dummy_data(
sim_config,
spec,
outdoor_weather=None,
schedule_chg_pts=None,
comfort_chg_pts=None,
hvac_mode_chg_pts=None,
):
if isinstance(spec, Internal):
raise ValueError(
f"Supplied Spec {spec} is internal spec." +
" Data of this spec should not be stored in data files")
for _idx, sim in sim_config.iterrows():
# _df = pd.DataFrame(columns=spec.full.spec.keys())
_df = pd.DataFrame(index=pd.date_range(
start=sim.start_utc,
end=sim.end_utc,
freq=f"{spec.data_period_seconds}S",
))
if not schedule_chg_pts:
# set default ecobee schedule
schedule_chg_pts = {
sim.start_utc: [
{
"name":
"Home",
"minute_of_day":
390,
"on_day_of_week": [
True,
True,
True,
True,
True,
True,
True,
],
},
{
"name":
"Sleep",
"minute_of_day":
1410,
"on_day_of_week": [
True,
True,
True,
True,
True,
True,
True,
],
},
]
}
if not comfort_chg_pts:
# set default ecobee comfort setpoints
if isinstance(spec, FlatFilesSpec):
home_stp_cool = Conversions.C2Fx10(23.5)
home_stp_heat = Conversions.C2Fx10(21.0)
sleep_stp_cool = Conversions.C2Fx10(28.0)
sleep_stp_heat = Conversions.C2Fx10(16.5)
elif isinstance(spec, DonateYourDataSpec):
home_stp_cool = Conversions.C2F(23.5)
home_stp_heat = Conversions.C2F(21.0)
sleep_stp_cool = Conversions.C2F(28.0)
sleep_stp_heat = Conversions.C2F(16.5)
else:
home_stp_cool = 23.5
home_stp_heat = 21.0
sleep_stp_cool = 28.0
sleep_stp_heat = 16.5
comfort_chg_pts = {
sim.start_utc: {
"Home": {
STATES.TEMPERATURE_STP_COOL: home_stp_cool,
STATES.TEMPERATURE_STP_HEAT: home_stp_heat,
},
"Sleep": {
STATES.TEMPERATURE_STP_COOL: sleep_stp_cool,
STATES.TEMPERATURE_STP_HEAT: sleep_stp_heat,
},
}
}
if not hvac_mode_chg_pts:
# set default ecobee comfort setpoints
hvac_mode_chg_pts = {sim.start_utc: "heat"}
# enforce ascending sorting of dict keys
hvac_mode_chg_pts = dict(sorted(hvac_mode_chg_pts.items()))
comfort_chg_pts = dict(sorted(comfort_chg_pts.items()))
schedule_chg_pts = dict(sorted(schedule_chg_pts.items()))
# check for errors in settings
if len(hvac_mode_chg_pts) <= 0:
raise ValueError(
f"Invalid hvac_mode_chg_pts={hvac_mode_chg_pts}.")
if len(comfort_chg_pts) <= 0:
raise ValueError(f"Invalid comfort_chg_pts={comfort_chg_pts}.")
if len(schedule_chg_pts) <= 0:
raise ValueError(
f"Invalid schedule_chg_pts={schedule_chg_pts}.")
for k, v in spec.full.spec.items():
_default_value, _ = Conversions.numpy_down_cast_default_value_dtype(
v["dtype"])
if v["channel"] == CHANNELS.THERMOSTAT_SETTING:
# settings channels set with default values first
# they are set below after full df columns have been filled
_df[k] = _default_value
elif v["channel"] == CHANNELS.WEATHER:
# default: set no values for outdoor_weather=None
# will default to using TMY3 data for the provided location
if outdoor_weather:
# outdoor_weather can be set with internal states as keys
if v["internal_state"] in outdoor_weather.keys():
_df[k] = outdoor_weather[v["internal_state"]]
elif v["channel"] == CHANNELS.THERMOSTAT_SENSOR:
# sensor data unused for dummy data
# set default
_df[k] = _default_value
elif v["channel"] == CHANNELS.EQUIPMENT:
# equipment data unused for dummy data
# set default
_df[k] = _default_value
# settings is always in spec add in specific order
# 1. add HVAC_MODE
k_hvac_mode = [
k for k, v in spec.full.spec.items()
if v["internal_state"] == STATES.HVAC_MODE
][0]
# assuming sorted ascending by timestamp
# each change point sets all future hvac modes
for _ts, _hvac_mode in hvac_mode_chg_pts.items():
_df.loc[_df.index >= _ts, k_hvac_mode] = _hvac_mode
# 2. add SCHEDULE
k_schedule = [
k for k, v in spec.full.spec.items()
if v["internal_state"] == STATES.SCHEDULE
][0]
# assuming sorted ascending by timestamp
# each change point sets all future schedules
for _ts, _schedule in schedule_chg_pts.items():
for _dow in range(7):
_dow_schedule = [
_s for _s in _schedule if _s["on_day_of_week"][_dow]
]
_dow_schedule = sorted(_dow_schedule,
key=lambda k: k["minute_of_day"])
_prev_dow_schedule = [
_s for _s in _schedule
if _s["on_day_of_week"][(_dow - 1) % 7]
]
_prev_dow_schedule = sorted(
_prev_dow_schedule, key=lambda k: k["minute_of_day"])
# first period is defined from previous day of week last schedule
_prev_s = _prev_dow_schedule[-1]
_s = _dow_schedule[0]
_df.loc[(_df.index >= _ts)
& (_df.index.day_of_week == _dow)
& (_df.index.hour * 60 +
_df.index.minute < _s["minute_of_day"]),
k_schedule, ] = _prev_s["name"]
for _s in _dow_schedule:
_df.loc[(_df.index >= _ts)
& (_df.index.day_of_week == _dow)
& (_df.index.hour * 60 +
_df.index.minute >= _s["minute_of_day"]),
k_schedule, ] = _s["name"]
# 3. add SCHEDULE
k_stp_cool = [
k for k, v in spec.full.spec.items()
if v["internal_state"] == STATES.TEMPERATURE_STP_COOL
][0]
k_stp_heat = [
k for k, v in spec.full.spec.items()
if v["internal_state"] == STATES.TEMPERATURE_STP_HEAT
][0]
# assuming sorted ascending by timestamp
# each change point sets all future comfort set points
for _ts, _comfort in comfort_chg_pts.items():
for _schedule_name, _setpoints in _comfort.items():
_df.loc[(_df.index >= _ts) &
(_df[k_schedule] == _schedule_name),
k_stp_cool, ] = _setpoints[
STATES.TEMPERATURE_STP_COOL]
_df.loc[(_df.index >= _ts) &
(_df[k_schedule] == _schedule_name),
k_stp_heat, ] = _setpoints[
STATES.TEMPERATURE_STP_HEAT]
_df = _df.reset_index().rename(
columns={"index": spec.datetime_column})
return _df