def test_zipcode_to_station(zipcode_station):
zipcode, station = zipcode_station
assert station == zipcode_to_station(zipcode)
def test_zipcode_to_station(zipcode_station):
zipcode, station = zipcode_station
assert station == zipcode_to_station(zipcode)
def get_single_thermostat(thermostat_id, zipcode, equipment_type, utc_offset, interval_data_filename):
""" Load a single thermostat directly from an interval data file.
Parameters
----------
thermostat_id : str
A unique identifier for the thermostat.
zipcode : str
The zipcode of the thermostat, e.g. `"01234"`.
equipment_type : str
The equipment type of the thermostat.
utc_offset : str
A string representing the UTC offset of the interval data, e.g. `"-0700"`.
Could also be `"Z"` (UTC), or just `"+7"` (equivalent to `"+0700"`),
or any other timezone format recognized by the library
method dateutil.parser.parse.
interval_data_filename : str
The path to the CSV in which the interval data is stored.
Returns
-------
thermostat : thermostat.Thermostat
The loaded thermostat object.
"""
df = pd.read_csv(interval_data_filename)
heating, cooling, aux_emerg = _get_equipment_type(equipment_type)
# load indices
dates = pd.to_datetime(df["date"])
daily_index = pd.DatetimeIndex(start=dates[0], periods = dates.shape[0], freq="D")
hourly_index = pd.DatetimeIndex(start=dates[0], periods = dates.shape[0] * 24, freq="H")
# raise an error if dates are not aligned
if not all(dates == daily_index):
message("Dates provided for thermostat_id={} may contain some "
"which are out of order, missing, or duplicated.".format(thermostat_id))
raise ValueError(message)
# load hourly time series values
temp_in = pd.Series(_get_hourly_block(df, "temp_in"), hourly_index)
if heating:
heating_setpoint = pd.Series(_get_hourly_block(df, "heating_setpoint"), hourly_index)
else:
heating_setpoint = None
if cooling:
cooling_setpoint = pd.Series(_get_hourly_block(df, "cooling_setpoint"), hourly_index)
else:
cooling_setpoint = None
if aux_emerg:
auxiliary_heat_runtime = pd.Series(_get_hourly_block(df, "auxiliary_heat_runtime"), hourly_index)
emergency_heat_runtime = pd.Series(_get_hourly_block(df, "emergency_heat_runtime"), hourly_index)
else:
auxiliary_heat_runtime = None
emergency_heat_runtime = None
# load outdoor temperatures
station = zipcode_to_station(zipcode)
if station is None:
message = "Could not locate a valid source of outdoor temperature " \
"data for ZIP code {}".format(zipcode)
raise ValueError(message)
ws_hourly = ISDWeatherSource(station, daily_index[0].year, daily_index[-1].year)
utc_offset = dateutil.parser.parse("2000-01-01T00:00:00" + utc_offset).tzinfo.utcoffset(None)
temp_out = pd.Series(_get_outdoor_temperatures(daily_index, ws_hourly, utc_offset), hourly_index)
# load daily time series values
if cooling:
cool_runtime = pd.Series(df["cool_runtime"].values, daily_index)
else:
cool_runtime = None
if heating:
heat_runtime = pd.Series(df["heat_runtime"].values, daily_index)
else:
heat_runtime = None
# create thermostat instance
thermostat = Thermostat(thermostat_id, equipment_type, zipcode, station,
temp_in, temp_out, cooling_setpoint, heating_setpoint,
cool_runtime, heat_runtime, auxiliary_heat_runtime,
emergency_heat_runtime)
return thermostat
parser.add_argument("data_config", type=str, help="Dataset configuration")
parser.add_argument("project_csv", type=str, help="Project CSV location")
parser.add_argument("consumption_csv",
type=str,
help="Consupmtion CSV location")
parser.add_argument('--show-plots', action='store_true')
args = parser.parse_args()
data_config = configparser.ConfigParser()
data_config.read(args.data_config)
for section_name in data_config.sections():
section = data_config[section_name]
zipcode = section["zipcode"]
station = zipcode_to_station(zipcode)
n_projects = int(section["n_projects"])
project_cost_k = float(section["project_cost_k"])
project_cost_theta = float(section["project_cost_theta"])
total_usage_pre_retrofit_k = float(
section["total_usage_pre_retrofit_k"])
total_usage_pre_retrofit_theta = float(
section["total_usage_pre_retrofit_theta"])
proportion_total_usage_pre_retrofit_gas_alpha = float(
section["proportion_total_usage_pre_retrofit_gas_alpha"])
proportion_total_usage_pre_retrofit_gas_beta = float(
section["proportion_total_usage_pre_retrofit_gas_beta"])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("data_config", type=str, help="Dataset configuration")
parser.add_argument("project_csv", type=str, help="Project CSV location")
parser.add_argument("consumption_csv", type=str, help="Consupmtion CSV location")
parser.add_argument('--show-plots', action='store_true')
args = parser.parse_args()
data_config = configparser.ConfigParser()
data_config.read(args.data_config)
for section_name in data_config.sections():
section = data_config[section_name]
zipcode = section["zipcode"]
station = zipcode_to_station(zipcode)
n_projects = int(section["n_projects"])
project_cost_k = float(section["project_cost_k"])
project_cost_theta = float(section["project_cost_theta"])
total_usage_pre_retrofit_k = float(section["total_usage_pre_retrofit_k"])
total_usage_pre_retrofit_theta = float(section["total_usage_pre_retrofit_theta"])
proportion_total_usage_pre_retrofit_gas_alpha = float(section["proportion_total_usage_pre_retrofit_gas_alpha"])
proportion_total_usage_pre_retrofit_gas_beta = float(section["proportion_total_usage_pre_retrofit_gas_beta"])
proportion_total_savings_gas_alpha = float(section["proportion_total_savings_gas_alpha"])
proportion_total_savings_gas_beta = float(section["proportion_total_savings_gas_beta"])
total_proportion_savings_mean = float(section["total_proportion_savings_mean"])