import os
from pathlib import Path
import shutil
from typing import Any, Iterable, Mapping, List, Union
import numpy as np
import pandas as pd
from citylearn.utilities import read_json, read_yaml
TOLERANCE = 0.0001
ZERO_DIVISION_PLACEHOLDER = 0.000001
DATA_DIRECTORY = os.path.join(os.path.dirname(__file__), 'data')
DATASETS_DIRECTORY = os.path.join(DATA_DIRECTORY, 'datasets')
MISC_DATA_DIRECTORY = os.path.join(DATA_DIRECTORY, 'misc')
MISC_DIRECTORY = os.path.join(os.path.dirname(__file__), 'misc')
QUERIES_DIRECTORY = os.path.join(MISC_DIRECTORY, 'queries')
SETTINGS_FILEPATH = os.path.join(MISC_DIRECTORY, 'settings.yaml')
BATTERY_CHOICES_FILEPATH = os.path.join(MISC_DATA_DIRECTORY, 'battery_choices.yaml')
PV_CHOICES_FILEPATH = os.path.join(MISC_DATA_DIRECTORY, 'lbl-tracking_the_sun-res-pv.csv')
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def get_settings():
directory = os.path.join(os.path.join(os.path.dirname(__file__), 'misc'))
filepath = os.path.join(directory, 'settings.yaml')
settings = read_yaml(filepath)
return settings
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class DataSet:
"""CityLearn input data set and schema class."""
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@staticmethod
def get_names() -> List[str]:
"""Returns list of internally stored CityLearn datasets that are `schema`
names and can be used to initialize `citylearn.citylearn.CityLearnEnv`.
Returns
-------
names: List[str]
schema names
"""
return sorted([
d for d in os.listdir(DATASETS_DIRECTORY)
if os.path.isdir(os.path.join(DATASETS_DIRECTORY, d))
])
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@staticmethod
def copy(name: str, destination_directory: Union[Path, str] = None):
"""Copies an internally stored CityLearn dataset to a location of choice.
Parameters
----------
destination_directory: Union[Path, str], optional
Target directory to copy data set files to. Copies to current directory if not specifed.
"""
source_directory = os.path.join(DATASETS_DIRECTORY,name)
destination_directory = '' if destination_directory is None else destination_directory
destination_directory = os.path.join(destination_directory,name)
os.makedirs(destination_directory,exist_ok=True)
for f in os.listdir(source_directory):
if f.endswith('.csv') or f.endswith('.json'):
source_filepath = os.path.join(source_directory,f)
destination_filepath = os.path.join(destination_directory,f)
shutil.copy(source_filepath,destination_filepath)
else:
continue
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@staticmethod
def get_schema(name: str) -> Mapping[str, Union[dict, float, str]]:
"""Returns a data set's schema.
Parameters
----------
name: str
Name of data set.
Returns
-------
schema: Mapping[str, Union[dict, float, str]]
Data set schema.
"""
root_directory = os.path.join(DATASETS_DIRECTORY,name)
filepath = os.path.join(root_directory,'schema.json')
schema = read_json(filepath)
schema['root_directory'] = root_directory
return schema
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class TimeSeriesData:
"""Generic time series data class.
Parameters
----------
variable: np.array, optional
A generic time series variable.
start_time_step: int, optional
Time step to start reading variables.
end_time_step: int, optional
Time step to end reading variables.
"""
def __init__(self, variable: Iterable = None, start_time_step: int = None, end_time_step: int = None):
self.variable = variable if variable is None else np.array(variable)
self.start_time_step = start_time_step
self.end_time_step = end_time_step
def __getattr__(self, name: str, start_time_step: int = None, end_time_step: int = None):
"""Returns values of the named variable within the specified time steps and
is useful for selecting episode-specific observation."""
# not the most elegant solution tbh
try:
variable = self.__dict__[f'_{name}']
except KeyError:
raise AttributeError(f'_{name}')
if isinstance(variable, Iterable):
start_time_step = self.start_time_step if start_time_step is None else start_time_step
start_index = 0 if start_time_step is None else start_time_step
end_time_step = self.end_time_step if end_time_step is None else end_time_step
end_index = len(variable) if end_time_step is None else end_time_step + 1
return variable[start_index:end_index]
else:
return variable
def __setattr__(self, name: str, value: Any):
"""Sets named variable.
Variables are named with a single underscore prefix.
"""
self.__dict__[f'_{name}'] = value
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class EnergySimulation(TimeSeriesData):
"""`Building` `energy_simulation` data class.
Parameters
----------
month : np.array
Month time series value ranging from 1 - 12.
hour : np.array
Hour time series value ranging from 1 - 24.
day_type : np.array
Numeric day of week time series ranging from 1 - 8 where 1 - 7 is Monday - Sunday and 8 is reserved for special days e.g. holiday.
indoor_dry_bulb_temperature : np.array
Average building dry bulb temperature time series in [C].
non_shiftable_load : np.array
Total building non-shiftable plug and equipment loads time series in [kWh].
dhw_demand : np.array
Total building domestic hot water demand time series in [kWh].
cooling_demand : np.array
Total building space cooling demand time series in [kWh].
heating_demand : np.array
Total building space heating demand time series in [kWh].
solar_generation : np.array
Inverter output per 1 kW of PV system time series in [W/kW].
daylight_savings_status : np.array, optional
Daylight saving status time series signal of 0 or 1 indicating inactive or active daylight saving respectively.
average_unmet_cooling_setpoint_difference : np.array, optional
Average difference between `indoor_dry_bulb_temperature` and cooling temperature setpoints time series in [C].
indoor_relative_humidity : np.array, optional
Average building relative humidity time series in [%].
occupant_count: np.array, optional
Building occupant count time series in [people].
indoor_dry_bulb_temperature_cooling_set_point: np.array
Average building dry bulb temperature cooling set point time series in [C].
indoor_dry_bulb_temperature_heating_set_point: np.array
Average building dry bulb temperature heating set point time series in [C].
hvac_mode: np.array, default: 1
Cooling and heating device availability. If 0, both HVAC devices are unavailable (off), if 1,
the cooling device is available for space cooling and if 2, the heating device is available
for space heating only. Automatic (auto) mode is 3 and allows for either cooling or heating
depending on the control action. The default is to set the mode to cooling at all times.
The HVAC devices are always available for cooling and heating storage charging irrespective
of the hvac mode.
power_outage np.array, default: 0
Signal for power outage. If 0, there is no outage and building can draw energy from grid.
If 1, there is a power outage and building can only use its energy resources to meet loads.
comfort_band np.array, default: 2
Occupant comfort band above the `indoor_dry_bulb_temperature_cooling_set_point` and below the `indoor_dry_bulb_temperature_heating_set_point` [C]. The value is added
to and subtracted from the set point to set the upper and lower bounds of comfort bound.
start_time_step: int, optional
Time step to start reading variables.
end_time_step: int, optional
Time step to end reading variables.
"""
DEFUALT_COMFORT_BAND = 2.0
def __init__(
self, month: Iterable[int], hour: Iterable[int], day_type: Iterable[int],
indoor_dry_bulb_temperature: Iterable[float],
non_shiftable_load: Iterable[float], dhw_demand: Iterable[float], cooling_demand: Iterable[float], heating_demand: Iterable[float], solar_generation: Iterable[float],
daylight_savings_status: Iterable[int] = None, average_unmet_cooling_setpoint_difference: Iterable[float] = None, indoor_relative_humidity: Iterable[float] = None, occupant_count: Iterable[int] = None, indoor_dry_bulb_temperature_cooling_set_point: Iterable[int] = None, indoor_dry_bulb_temperature_heating_set_point: Iterable[int] = None, hvac_mode: Iterable[int] = None, power_outage: Iterable[int] = None, comfort_band: Iterable[float] = None, start_time_step: int = None, end_time_step: int = None
):
super().__init__(start_time_step=start_time_step, end_time_step=end_time_step)
self.month = np.array(month, dtype='int32')
self.hour = np.array(hour, dtype='int32')
self.day_type = np.array(day_type, dtype='int32')
self.indoor_dry_bulb_temperature = np.array(indoor_dry_bulb_temperature, dtype='float32')
self.non_shiftable_load = np.array(non_shiftable_load, dtype = 'float32')
self.dhw_demand = np.array(dhw_demand, dtype = 'float32')
# set space demands and check there is not cooling and heating demand at same time step
self.cooling_demand = np.array(cooling_demand, dtype = 'float32')
self.heating_demand = np.array(heating_demand, dtype = 'float32')
assert (self.cooling_demand*self.heating_demand).sum() == 0, 'Cooling and heating in the same time step is not allowed.'
self.solar_generation = np.array(solar_generation, dtype = 'float32')
# optional
self.daylight_savings_status = np.zeros(len(solar_generation), dtype='int32') if daylight_savings_status is None else np.array(daylight_savings_status, dtype='int32')
self.average_unmet_cooling_setpoint_difference = np.zeros(len(solar_generation), dtype='float32') if average_unmet_cooling_setpoint_difference is None else np.array(average_unmet_cooling_setpoint_difference, dtype='float32')
self.indoor_relative_humidity = np.zeros(len(solar_generation), dtype='float32') if indoor_relative_humidity is None else np.array(indoor_relative_humidity, dtype = 'float32')
self.occupant_count = np.zeros(len(solar_generation), dtype='float32') if occupant_count is None else np.array(occupant_count, dtype='float32')
self.indoor_dry_bulb_temperature_cooling_set_point = np.zeros(len(solar_generation), dtype='float32') if indoor_dry_bulb_temperature_cooling_set_point is None else np.array(indoor_dry_bulb_temperature_cooling_set_point, dtype='float32')
self.indoor_dry_bulb_temperature_heating_set_point = np.zeros(len(solar_generation), dtype='float32') if indoor_dry_bulb_temperature_heating_set_point is None else np.array(indoor_dry_bulb_temperature_heating_set_point, dtype='float32')
self.power_outage = np.zeros(len(solar_generation), dtype='float32') if power_outage is None else np.array(power_outage, dtype='float32')
self.comfort_band = np.zeros(len(solar_generation), dtype='float32') + self.DEFUALT_COMFORT_BAND if comfort_band is None else np.array(comfort_band, dtype='float32')
# set controlled variable defaults
self.indoor_dry_bulb_temperature_without_control = self.indoor_dry_bulb_temperature.copy()
self.cooling_demand_without_control = self.cooling_demand.copy()
self.heating_demand_without_control = self.heating_demand.copy()
self.dhw_demand_without_control = self.dhw_demand.copy()
self.non_shiftable_load_without_control = self.non_shiftable_load.copy()
self.indoor_relative_humidity_without_control = self.indoor_relative_humidity.copy()
self.indoor_dry_bulb_temperature_cooling_set_point_without_control = self.indoor_dry_bulb_temperature_cooling_set_point.copy()
self.indoor_dry_bulb_temperature_heating_set_point_without_control = self.indoor_dry_bulb_temperature_heating_set_point.copy()
if hvac_mode is None:
hvac_mode = np.zeros(len(solar_generation), dtype='int32') + 1
else:
unique = list(set(hvac_mode))
for i in range(4):
try:
unique.remove(i)
except ValueError:
pass
assert len(unique) == 0, f'Invalid hvac_mode values were found: {unique}. '\
'Valid values are 0, 1, 2, 3 to indicate off, cooling mode, heating mode, and automatic mode.'
self.hvac_mode = np.array(hvac_mode, dtype='int32')
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@staticmethod
def get_pv_sizing_data() -> pd.DataFrame:
"""Reads and returns NREL's Tracking The Sun dataset that has been prefilered for completeness.
Returns
-------
data: pd.DataFrame
Notes
-----
Data source: https://github.com/intelligent-environments-lab/CityLearn/tree/master/citylearn/data/misc/lbl-tracking_the_sun_res-pv.csv.
"""
filepath = PV_CHOICES_FILEPATH
data = pd.read_csv(filepath, low_memory=False)
return data
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@staticmethod
def get_battery_sizing_data() -> Mapping[str, Union[float, str]]:
"""Reads and returns internally defined real world manufacturer models.
Returns
-------
data: Mapping[str, Union[float, str]]
Notes
-----
Data source: https://github.com/intelligent-environments-lab/CityLearn/tree/master/citylearn/data/misc/battery_choices.yaml.
"""
filepath = BATTERY_CHOICES_FILEPATH
data = read_yaml(filepath)
data = pd.DataFrame([{'model': k, **v['attributes']} for k, v in data.items()])
data = data.set_index('model')
return data
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class LogisticRegressionOccupantParameters(TimeSeriesData):
def __init__(self, a_increase: Iterable[float], b_increase: Iterable[float], a_decrease: Iterable[float], b_decrease: Iterable[float], start_time_step: int = None, end_time_step: int = None):
super().__init__(start_time_step=start_time_step, end_time_step=end_time_step)
self.a_increase = np.array(a_increase, dtype='float32')
self.b_increase = np.array(b_increase, dtype='float32')
self.a_decrease = np.array(a_decrease, dtype='float32')
self.b_decrease = np.array(b_decrease, dtype='float32')
self.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta = np.zeros(len(self.a_increase), dtype='float32')
self.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta_without_control = np.zeros(len(self.a_increase), dtype='float32')
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class Weather(TimeSeriesData):
"""`Building` `weather` data class.
Parameters
----------
outdoor_dry_bulb_temperature : np.array
Outdoor dry bulb temperature time series in [C].
outdoor_relative_humidity : np.array
Outdoor relative humidity time series in [%].
diffuse_solar_irradiance : np.array
Diffuse solar irradiance time series in [W/m^2].
direct_solar_irradiance : np.array
Direct solar irradiance time series in [W/m^2].
outdoor_dry_bulb_temperature_predicted_1 : np.array
Outdoor dry bulb temperature `n` hours ahead prediction time series in [C]. `n` can be any number of hours and is typically 6 hours in existing datasets.
outdoor_dry_bulb_temperature_predicted_2 : np.array
Outdoor dry bulb temperature `n` hours ahead prediction time series in [C]. `n` can be any number of hours and is typically 12 hours in existing datasets.
outdoor_dry_bulb_temperature_predicted_3 : np.array
Outdoor dry bulb temperature `n` hours ahead prediction time series in [C]. `n` can be any number of hours and is typically 24 hours in existing datasets.
outdoor_relative_humidity_predicted_1 : np.array
Outdoor relative humidity `n` hours ahead prediction time series in [%]. `n` can be any number of hours and is typically 6 hours in existing datasets.
outdoor_relative_humidity_predicted_2 : np.array
Outdoor relative humidity `n` hours ahead prediction time series in [%]. `n` can be any number of hours and is typically 12 hours in existing datasets.
outdoor_relative_humidity_predicted_3 : np.array
Outdoor relative humidity `n` hours ahead prediction time series in [%]. `n` can be any number of hours and is typically 24 hours in existing datasets.
diffuse_solar_irradiance_predicted_1 : np.array
Diffuse solar irradiance `n` hours ahead prediction time series in [W/m^2]. `n` can be any number of hours and is typically 6 hours in existing datasets.
diffuse_solar_irradiance_predicted_2 : np.array
Diffuse solar irradiance `n` hours ahead prediction time series in [W/m^2]. `n` can be any number of hours and is typically 12 hours in existing datasets.
diffuse_solar_irradiance_predicted_3 : np.array
Diffuse solar irradiance `n` hours ahead prediction time series in [W/m^2]. `n` can be any number of hours and is typically 24 hours in existing datasets.
direct_solar_irradiance_predicted_1 : np.array
Direct solar irradiance `n` hours ahead prediction time series in [W/m^2]. `n` can be any number of hours and is typically 6 hours in existing datasets.
direct_solar_irradiance_predicted_2 : np.array
Direct solar irradiance `n` hours ahead prediction time series in [W/m^2]. `n` can be any number of hours and is typically 12 hours in existing datasets.
direct_solar_irradiance_predicted_3 : np.array
Direct solar irradiance `n` hours ahead prediction time series in [W/m^2]. `n` can be any number of hours and is typically 24 hours in existing datasets.
start_time_step: int, optional
Time step to start reading variables.
end_time_step: int, optional
Time step to end reading variables.
"""
def __init__(
self, outdoor_dry_bulb_temperature: Iterable[float], outdoor_relative_humidity: Iterable[float], diffuse_solar_irradiance: Iterable[float], direct_solar_irradiance: Iterable[float],
outdoor_dry_bulb_temperature_predicted_1: Iterable[float], outdoor_dry_bulb_temperature_predicted_2: Iterable[float], outdoor_dry_bulb_temperature_predicted_3: Iterable[float],
outdoor_relative_humidity_predicted_1: Iterable[float], outdoor_relative_humidity_predicted_2: Iterable[float], outdoor_relative_humidity_predicted_3: Iterable[float],
diffuse_solar_irradiance_predicted_1: Iterable[float], diffuse_solar_irradiance_predicted_2: Iterable[float], diffuse_solar_irradiance_predicted_3: Iterable[float],
direct_solar_irradiance_predicted_1: Iterable[float], direct_solar_irradiance_predicted_2: Iterable[float], direct_solar_irradiance_predicted_3: Iterable[float], start_time_step: int = None, end_time_step: int = None
):
super().__init__(start_time_step=start_time_step, end_time_step=end_time_step)
self.outdoor_dry_bulb_temperature = np.array(outdoor_dry_bulb_temperature, dtype='float32')
self.outdoor_relative_humidity = np.array(outdoor_relative_humidity, dtype='float32')
self.diffuse_solar_irradiance = np.array(diffuse_solar_irradiance, dtype='float32')
self.direct_solar_irradiance = np.array(direct_solar_irradiance, dtype='float32')
self.outdoor_dry_bulb_temperature_predicted_1 = np.array(outdoor_dry_bulb_temperature_predicted_1, dtype='float32')
self.outdoor_dry_bulb_temperature_predicted_2 = np.array(outdoor_dry_bulb_temperature_predicted_2, dtype='float32')
self.outdoor_dry_bulb_temperature_predicted_3 = np.array(outdoor_dry_bulb_temperature_predicted_3, dtype='float32')
self.outdoor_relative_humidity_predicted_1 = np.array(outdoor_relative_humidity_predicted_1, dtype='float32')
self.outdoor_relative_humidity_predicted_2 = np.array(outdoor_relative_humidity_predicted_2, dtype='float32')
self.outdoor_relative_humidity_predicted_3 = np.array(outdoor_relative_humidity_predicted_3, dtype='float32')
self.diffuse_solar_irradiance_predicted_1 = np.array(diffuse_solar_irradiance_predicted_1, dtype='float32')
self.diffuse_solar_irradiance_predicted_2 = np.array(diffuse_solar_irradiance_predicted_2, dtype='float32')
self.diffuse_solar_irradiance_predicted_3 = np.array(diffuse_solar_irradiance_predicted_3, dtype='float32')
self.direct_solar_irradiance_predicted_1 = np.array(direct_solar_irradiance_predicted_1, dtype='float32')
self.direct_solar_irradiance_predicted_2 = np.array(direct_solar_irradiance_predicted_2, dtype='float32')
self.direct_solar_irradiance_predicted_3 = np.array(direct_solar_irradiance_predicted_3, dtype='float32')
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class Pricing(TimeSeriesData):
"""`Building` `pricing` data class.
Parameters
----------
electricity_pricing : np.array
Electricity pricing time series in [$/kWh].
electricity_pricing_predicted_1 : np.array
Electricity pricing `n` hours ahead prediction time series in [$/kWh]. `n` can be any number of hours and is typically 1 or 6 hours in existing datasets.
electricity_pricing_predicted_2 : np.array
Electricity pricing `n` hours ahead prediction time series in [$/kWh]. `n` can be any number of hours and is typically 2 or 12 hours in existing datasets.
electricity_pricing_predicted_3 : np.array
Electricity pricing `n` hours ahead prediction time series in [$/kWh]. `n` can be any number of hours and is typically 3 or 24 hours in existing datasets.
start_time_step: int, optional
Time step to start reading variables.
end_time_step: int, optional
Time step to end reading variables.
"""
def __init__(
self, electricity_pricing: Iterable[float], electricity_pricing_predicted_1: Iterable[float], electricity_pricing_predicted_2: Iterable[float],
electricity_pricing_predicted_3: Iterable[float], start_time_step: int = None, end_time_step: int = None
):
super().__init__(start_time_step=start_time_step, end_time_step=end_time_step)
self.electricity_pricing = np.array(electricity_pricing, dtype='float32')
self.electricity_pricing_predicted_1 = np.array(electricity_pricing_predicted_1, dtype='float32')
self.electricity_pricing_predicted_2 = np.array(electricity_pricing_predicted_2, dtype='float32')
self.electricity_pricing_predicted_3 = np.array(electricity_pricing_predicted_3, dtype='float32')
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class CarbonIntensity(TimeSeriesData):
"""`Building` `carbon_intensity` data class.
Parameters
----------
carbon_intensity : np.array
Grid carbon emission rate time series in [kg_co2/kWh].
start_time_step: int, optional
Time step to start reading variables.
end_time_step: int, optional
Time step to end reading variables.
"""
def __init__(self, carbon_intensity: Iterable[float], start_time_step: int = None, end_time_step: int = None):
super().__init__(start_time_step=start_time_step, end_time_step=end_time_step)
self.carbon_intensity = np.array(carbon_intensity, dtype='float32')