import os
import numpy as np
import pandas as pd
import psychrolib as psy
file_path = os.path.abspath(__file__)
PATH = os.path.split(os.path.dirname(file_path))[0]
# Set the unit system for psychrolib
psy.SetUnitSystem(psy.SI)
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class CoherentNoise:
"""Class to add coherent noise to the data.
Args:
base (List[float]): Base data
weight (float): Weight of the noise to be added
desired_std_dev (float, optional): Desired standard deviation. Defaults to 0.1.
scale (int, optional): Scale. Defaults to 1.
"""
def __init__(self, base, weight, desired_std_dev=0.1, scale=1):
"""Initialize CoherentNoise class
Args:
base (List[float]): Base data
weight (float): Weight of the noise to be added
desired_std_dev (float, optional): Desired standard deviation. Defaults to 0.1.
scale (int, optional): Scale. Defaults to 1.
"""
self.base = base
self.weight = weight
self.desired_std_dev = desired_std_dev
self.scale = scale
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def generate(self, n_steps):
"""
Generate coherent noise
Args:
n_steps (int): Length of the data to generate.
Returns:
numpy.ndarray: Array of generated coherent noise.
"""
steps = np.random.normal(loc=0, scale=self.scale, size=n_steps)
random_walk = np.cumsum(self.weight * steps)
random_walk_scaled = self.base + (random_walk / np.std(random_walk)) * self.desired_std_dev
return random_walk_scaled
# Function to normalize a value v given a minimum and a maximum
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def normalize(v, min_v, max_v):
"""Function to normalize values
Args:
v (float): Value to be normalized
min_v (float): Lower limit
max_v (float): Upper limit
Returns:
float: Normalized value
"""
return (v - min_v)/(max_v - min_v)
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def standarize(v):
"""Function to standarize a list of values
Args:
v (float): Values to be normalized
Returns:
float: Normalized values
"""
return (v - np.mean(v))/np.std(v)
# Function to generate cosine and sine values for a given hour and day
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def sc_obs(current_hour, current_day):
"""Generate sine and cosine of the hour and day
Args:
current_hour (int): Current hour of the day
current_day (int): Current day of the year
Returns:
List[float]: Sine and cosine of the hour and day
"""
# Normalize and round the current hour and day
two_pi = np.pi * 2
norm_hour = round(current_hour/24, 3) * two_pi
norm_day = round((current_day)/365, 3) * two_pi
# Calculate cosine and sine values for the current hour and day
cos_hour = np.cos(norm_hour)*0.5 + 0.5
sin_hour = np.sin(norm_hour)*0.5 + 0.5
cos_day = np.cos(norm_day)*0.5 + 0.5
sin_day = np.sin(norm_day)*0.5 + 0.5
return [cos_hour, sin_hour, cos_day, sin_day]
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class Time_Manager():
"""Class to manage the time dimenssion over an episode
Args:
init_day (int, optional): Day to start from. Defaults to 0.
days_per_episode (int, optional): Number of days that an episode would last. Defaults to 30.
timezone_shift (int, optional): Shift for the timezone. Defaults to 0.
"""
def __init__(self, init_day=0, days_per_episode=30, timezone_shift=0):
"""Initialize the Time_Manager class.
Args:
init_day (int, optional): Day to start from. Defaults to 0.
days_per_episode (int, optional): Number of days that an episode would last. Defaults to 30.
timezone_shift (int, optional): Shift for the timezone. Defaults to 0.
"""
self.init_day = init_day
self.timestep_per_hour = 4
self.days_per_episode = days_per_episode
self.timezone_shift = timezone_shift
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def reset(self, init_day=None, init_hour=None):
"""Reset time manager to a specific initial day and hour.
Args:
init_day (int, optional): Day to start from. If None, defaults to the initial day set during initialization.
init_hour (int, optional): Hour to start from. If None, defaults to the timezone shift set during initialization.
Returns:
List[float]: Sine and cosine of the current hour and day.
"""
self.day = init_day if init_day is not None else self.init_day
self.hour = init_hour if init_hour is not None else self.timezone_shift
return sc_obs(self.hour, self.day)
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def step(self):
"""Step function for the time maneger
Returns:
List[float]: Current hour and day in sine and cosine form.
bool: Signal if the episode has reach the end.
"""
if self.hour >= 24:
self.hour = 0
self.day += 1
self.hour += 1/self.timestep_per_hour
return self.day, self.hour, sc_obs(self.hour, self.day), self.isterminal()
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def isterminal(self):
"""Function to identify terminal state
Returns:
bool: Signals if a state is terminal or not
"""
done = False
if self.day > self.init_day+self.days_per_episode - 1:
done = True
return done
# Class to manage CPU workload data
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class Workload_Manager():
def __init__(self, workload_filename='', init_day=0, future_steps=4, weight=0.01, desired_std_dev=0.025, timezone_shift=0):
"""Manager of the DC workload.
Args:
workload_filename (str, optional): Filename of the CPU data. Defaults to ''. Should be a .csv file containing the CPU hourly normalized workload data between 0 and 1. Should contain 'cpu_load' column.
init_day (int, optional): Initial day of the episode. Defaults to 0.
future_steps (int, optional): Number of steps of the workload forecast. Defaults to 4.
weight (float, optional): Weight value for coherent noise. Defaults to 0.01.
desired_std_dev (float, optional): Desired standard deviation for coherent noise. Defaults to 0.025.
timezone_shift (int, optional): Shift for the timezone. Defaults to 0.
"""
# Load CPU data from a CSV file
# One year data=24*365=8760
if workload_filename == '':
cpu_data_list = pd.read_csv(PATH+'/data/Workload/Alibaba_CPU_Data_Hourly_1.csv')['cpu_load'].values[:8760]
else:
cpu_data_list = pd.read_csv(PATH+f'/data/Workload/{workload_filename}')['cpu_load'].values[:8760]
assert len(cpu_data_list) == 8760, "The number of data points in the workload data is not one year data=24*365=8760."
cpu_data_list = cpu_data_list.astype(float)
self.time_step = 0
self.future_steps = future_steps
self.timestep_per_hour = 4
self.time_steps_day = self.timestep_per_hour*24
self.init_day = init_day
self.timezone_shift = timezone_shift
# Interpolate the CPU data to increase the number of data points
x = range(0, len(cpu_data_list))
xcpu_new = np.linspace(0, len(cpu_data_list), len(cpu_data_list)*self.timestep_per_hour)
self.cpu_smooth = np.interp(xcpu_new, x, cpu_data_list)
# Shift the data to match the timezone shift
self.cpu_smooth = np.roll(self.cpu_smooth, -1*self.timezone_shift*self.timestep_per_hour)
# Save a copy of the original data
self.original_data = self.cpu_smooth.copy()
# Initialize CoherentNoise process
self.coherent_noise = CoherentNoise(base=self.original_data[0], weight=weight, desired_std_dev=desired_std_dev)
# Function to return all workload data
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def get_total_wkl(self):
"""Get current workload
Returns:
List[float]: CPU data
"""
return np.array(self.cpu_smooth[self.time_step:])
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def scale_array(self, arr):
"""
Scales the input array so that approximately 90% of its values
fall within the range of 0.2 to 0.8, based on the 5th and 95th percentiles.
Parameters:
arr (np.array): The input numpy array to be scaled.
Returns:
np.array: The scaled numpy array.
"""
# Calculate the 5th and 95th percentiles of the array
p5 = np.percentile(arr, 5)
p95 = np.percentile(arr, 95)
# Scale the array based on the percentiles, without clipping
# This ensures values outside the 5th to 95th percentile range naturally
# fall outside the 0.2 to 0.8 range.
scaled_arr = 0.2 + ((arr - p5) * (0.8 - 0.2) / (p95 - p5))
# Clip values to be within 0 to 1
scaled_arr = np.clip(scaled_arr, 0, 1)
return scaled_arr
# Function to reset the time step and return the workload at the first time step
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def reset(self, init_day=None, init_hour=None):
"""Reset Workload_Manager to a specific initial day and hour.
Args:
init_day (int, optional): Day to start from. If None, defaults to the initial day set during initialization.
init_hour (int, optional): Hour to start from. If None, defaults to 0.
Returns:
float: CPU workload at current time step.
"""
self.time_step = (init_day if init_day is not None else self.init_day) * self.time_steps_day + (init_hour if init_hour is not None else 0)
self.init_time_step = self.time_step
baseline = np.random.random()*0.5 - 0.25
# Add noise to the workload data using the CoherentNoise
cpu_data = self.original_data * np.random.uniform(0.9, 1.1, len(self.original_data))
cpu_smooth = cpu_data * 0.7 + self.coherent_noise.generate(len(cpu_data)) * 0.3 + baseline
self.cpu_smooth = self.scale_array(cpu_smooth)
num_roll_weeks = np.random.randint(0, 52) # Random roll the workload because is independed on the month, so I am rolling across weeks (52 weeks in a year)
self.cpu_smooth = np.roll(self.cpu_smooth, num_roll_weeks*self.timestep_per_hour*24*7)
return self.cpu_smooth[self.time_step]
# Function to advance the time step and return the workload at the new time step
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def step(self):
"""Step function for the Workload_Manager
Returns:
float: CPU workload at current time step
float: Amount of daily flexible workload
"""
self.time_step += 1
# If it tries to read further, restart from the inital day
if self.time_step - 1 >= len(self.cpu_smooth):
self.time_step = self.init_time_step
# assert self.time_step < len(self.cpu_smooth), f'Episode length: {self.time_step} is longer than the provide cpu_smooth: {len(self.cpu_smooth)}'
return self.cpu_smooth[max(self.time_step - 1,0)] # to avoid logical error
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def get_current_workload(self):
return self.cpu_smooth[self.time_step]
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def set_current_workload(self, workload):
self.cpu_smooth[self.time_step] = workload
# Class to manage carbon intensity data
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class CI_Manager():
"""Manager of the carbon intensity data.
Args:
filename (str, optional): Filename of the carbon intensity data. Defaults to ''.
location (str, optional): Location identifier. Defaults to 'NYIS'.
init_day (int, optional): Initial day of the episode. Defaults to 0.
future_steps (int, optional): Number of steps of the CI forecast. Defaults to 4.
weight (float, optional): Weight value for coherent noise. Defaults to 0.1.
desired_std_dev (float, optional): Desired standard deviation for coherent noise. Defaults to 5.
timezone_shift (int, optional): Shift for the timezone. Defaults to 0.
"""
def __init__(self, filename='', location='NYIS', init_day=0, future_steps=4, weight=0.1, desired_std_dev=5, timezone_shift=0):
"""Initialize the CI_Manager class.
Args:
filename (str, optional): Filename of the carbon intensity data. Defaults to ''.
location (str, optional): Location identifier. Defaults to 'NYIS'.
init_day (int, optional): Initial day of the episode. Defaults to 0.
future_steps (int, optional): Number of steps of the CI forecast. Defaults to 4.
weight (float, optional): Weight value for coherent noise. Defaults to 0.1.
desired_std_dev (float, optional): Desired standard deviation for coherent noise. Defaults to 5.
timezone_shift (int, optional): Shift for the timezone. Defaults to 0.
"""
# Load carbon intensity data from a CSV file
# One year data=24*365=8760
if not location == '':
carbon_data_list = pd.read_csv(PATH+f"/data/CarbonIntensity/{location}_NG_&_avgCI.csv")['avg_CI'].values[:8760]
else:
carbon_data_list = pd.read_csv(PATH+f"/data/CarbonIntensity/{filename}")['avg_CI'].values[:8760]
assert len(carbon_data_list) == 8760, "The number of data points in the carbon intensity data is not one year data=24*365=8760."
carbon_data_list = carbon_data_list.astype(float)
self.init_day = init_day
self.timezone_shift = timezone_shift
self.timestep_per_hour = 4
self.time_steps_day = self.timestep_per_hour*24
# Handle nan values just in case. Replace with average value
if np.isnan(carbon_data_list).any():
avg_value = np.nanmean(carbon_data_list)
carbon_data_list = np.nan_to_num(carbon_data_list, nan=avg_value)
x = range(0, len(carbon_data_list))
xcarbon_new = np.linspace(0, len(carbon_data_list), len(carbon_data_list)*self.timestep_per_hour)
# Interpolate the carbon data to increase the number of data points
self.carbon_smooth = np.interp(xcarbon_new, x, carbon_data_list)
# Shift the data to match the timezone shift
self.carbon_smooth = np.roll(self.carbon_smooth, -1*self.timezone_shift*self.timestep_per_hour)
# Save a copy of the original data
self.original_data = self.carbon_smooth.copy()
self.time_step = 0
# Initialize CoherentNoise process
self.coherent_noise = CoherentNoise(base=self.original_data[0], weight=weight, desired_std_dev=desired_std_dev)
self.future_steps = future_steps
# Function to return all carbon intensity data
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def get_total_ci(self):
"""Function to obtain the total carbon intensity
Returns:
List[float]: Total carbon intesity
"""
return self.carbon_smooth[self.time_step:]
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def reset(self, init_day=None, init_hour=None):
"""Reset CI_Manager to a specific initial day and hour.
Args:
init_day (int, optional): Day to start from. If None, defaults to the initial day set during initialization.
init_hour (int, optional): Hour to start from. If None, defaults to 0.
Returns:
float: Carbon intensity at current time step.
float: Normalized carbon intensity at current time step and its forecast.
"""
self.time_step = (init_day if init_day is not None else self.init_day) * self.time_steps_day + (init_hour if init_hour is not None else 0)
# Add noise to the carbon data using the CoherentNoise
self.carbon_smooth = self.original_data + self.coherent_noise.generate(len(self.original_data))
self.carbon_smooth = np.clip(self.carbon_smooth, 0, None)
num_roll_days = np.random.randint(0, 14) # Random roll the workload some days. I can roll the carbon intensity up to 14 days.
self.carbon_smooth = np.roll(self.carbon_smooth, num_roll_days*self.timestep_per_hour*24)
self.min_ci = min(self.carbon_smooth)
self.max_ci = max(self.carbon_smooth)
self.norm_carbon = normalize(self.carbon_smooth, self.min_ci, self.max_ci)
# self.norm_carbon = standarize(self.carbon_smooth)
# self.norm_carbon = (np.clip(self.norm_carbon, -1, 1) + 1) * 0.5
return self.carbon_smooth[self.time_step], self.norm_carbon[self.time_step:self.time_step+self.future_steps]
# Function to advance the time step and return the carbon intensity at the new time step
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def step(self):
"""Step CI_Manager
Returns:
float: Carbon intensity at current time step
float: Normalized carbon intensity at current time step and it's forecast
"""
self.time_step +=1
# If it tries to read further, restart from the initial index
if self.time_step - 1 >= len(self.carbon_smooth):
self.time_step = self.init_day*self.time_steps_day
# assert self.time_step < len(self.carbon_smooth), 'Eposide length is longer than the provide CI_data'
if self.time_step - 1 + self.future_steps > len(self.carbon_smooth):
data = self.norm_carbon[self.time_step-1]*np.ones(shape=(self.future_steps))
else:
data = self.norm_carbon[(self.time_step-1):self.time_step-1+self.future_steps]
return self.carbon_smooth[self.time_step-1], data
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def get_current_ci(self):
return self.carbon_smooth[self.time_step]
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def get_forecast_ci(self, steps=4):
if self.time_step + steps > len(self.carbon_smooth):
data = self.norm_carbon[self.time_step]*np.ones(shape=(steps))
else:
data = self.norm_carbon[self.time_step:self.time_step+steps]
return data
# Class to manage weather data
# Where to obtain other weather files:
# https://climate.onebuilding.org/
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class Weather_Manager():
"""Manager of the weather data.
Where to obtain other weather files:
https://climate.onebuilding.org/
Args:
filename (str, optional): Filename of the weather data. Defaults to ''.
location (str, optional): Location identifier. Defaults to 'NY'.
init_day (int, optional): Initial day of the year. Defaults to 0.
weight (float, optional): Weight value for coherent noise. Defaults to 0.02.
desired_std_dev (float, optional): Desired standard deviation for coherent noise. Defaults to 0.75.
temp_column (int, optional): Column that contains the temperature data. Defaults to 6.
rh_column (int, optional): Column that contains the relative humidity data. Defaults to 8.
pres_column (int, optional): Column that contains the pressure data. Defaults to 9.
timezone_shift (int, optional): Shift for the timezone. Defaults to 0.
"""
def __init__(self, filename='', location='NY', init_day=0, weight=0.02, desired_std_dev=0.75, temp_column=6, rh_column=8, pres_column=9, timezone_shift=0):
"""Initialize the Weather_Manager class.
Args:
filename (str, optional): Filename of the weather data. Defaults to ''.
location (str, optional): Location identifier. Defaults to 'NY'.
init_day (int, optional): Initial day of the year. Defaults to 0.
weight (float, optional): Weight value for coherent noise. Defaults to 0.02.
desired_std_dev (float, optional): Desired standard deviation for coherent noise. Defaults to 0.75.
temp_column (int, optional): Column that contains the temperature data. Defaults to 6.
rh_column (int, optional): Column that contains the relative humidity data. Defaults to 8.
pres_column (int, optional): Column that contains the pressure data. Defaults to 9.
timezone_shift (int, optional): Shift for the timezone. Defaults to 0.
"""
# Load weather data from a CSV file
if not location == '':
weather_data = pd.read_csv(PATH+f'/data/Weather/{location}', skiprows=8, header=None).values
else:
weather_data = pd.read_csv(PATH+f'/data/Weather/{filename}', skiprows=8, header=None).values
temperature_data = weather_data[:,temp_column].astype(float)
relative_humidity_data = weather_data[:,rh_column].astype(float) # Added for relative humidity
pressure_data = weather_data[:,pres_column].astype(float) # Added for atmospheric pressure
self.wet_bulb_data = [psy.GetTWetBulbFromRelHum(t, rh / 100, p) for t, rh, p in zip(temperature_data, relative_humidity_data, pressure_data)]
# Normalize wet bulb temperature data
self.min_wb_temp = 0
self.max_wb_temp = 40
self.init_day = init_day
# One year data=24*365=8760
x = range(0, len(temperature_data))
self.timestep_per_hour = 4
xtemperature_new = np.linspace(0, len(temperature_data), len(temperature_data)*self.timestep_per_hour )
self.min_temp = 0
self.max_temp = 40
# Interpolate the data to increase the number of data points
self.wet_bulb_data = np.interp(xtemperature_new, x, self.wet_bulb_data)
self.norm_wet_bulb_data = normalize(self.wet_bulb_data, self.min_wb_temp, self.max_wb_temp)
self.temperature_data = np.interp(xtemperature_new, x, temperature_data)
self.norm_temp_data = normalize(self.temperature_data, self.min_temp, self.max_temp)
self.time_step = 0
self.timezone_shift = timezone_shift
# Shift the data to match the timezone shift
self.temperature_data = np.roll(self.temperature_data, -1*self.timezone_shift*self.timestep_per_hour)
self.wet_bulb_data = np.roll(self.wet_bulb_data, -1*self.timezone_shift*self.timestep_per_hour)
# Save a copy of the original data
self.original_temp_data = self.temperature_data.copy()
self.original_wb_data = self.wet_bulb_data.copy()
# Initialize CoherentNoise process
self.coherent_noise = CoherentNoise(base=0, weight=weight, desired_std_dev=desired_std_dev)
self.time_steps_day = self.timestep_per_hour*24
# Function to return all weather data
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def get_total_weather(self):
"""Obtain the weather data in a List form
Returns:
List[form]: Total temperature data
"""
return self.temperature_data[self.time_step:]
# Function to reset the time step and return the weather at the first time step
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def reset(self, init_day=None, init_hour=None):
"""Reset Weather_Manager to a specific initial day and hour.
Args:
init_day (int, optional): Day to start from. If None, defaults to the initial day set during initialization.
init_hour (int, optional): Hour to start from. If None, defaults to 0.
Returns:
tuple: Temperature at current step, normalized temperature at current step, wet bulb temperature at current step, normalized wet bulb temperature at current step.
"""
self.time_step = (init_day if init_day is not None else self.init_day) * self.time_steps_day + (init_hour if init_hour is not None else 0)
# Add noise to the temperature data using the CoherentNoise
coh_noise = self.coherent_noise.generate(len(self.original_temp_data))
self.temperature_data = self.original_temp_data + coh_noise
self.wet_bulb_data = self.original_wb_data + coh_noise
num_roll_days = np.random.randint(0, 14) # Random roll the workload some days. I can roll the carbon intensity up to 14 days.
self.temperature_data = np.roll(self.temperature_data, num_roll_days*self.timestep_per_hour*24)
self.wet_bulb_data = np.roll(self.wet_bulb_data, num_roll_days*self.timestep_per_hour*24)
self.temperature_data = np.clip(self.temperature_data, self.min_temp, self.max_temp)
self.norm_temp_data = normalize(self.temperature_data, self.min_temp, self.max_temp)
self.wet_bulb_data = np.clip(self.wet_bulb_data, self.min_wb_temp, self.max_wb_temp)
self.norm_wet_bulb_data = normalize(self.wet_bulb_data, self.min_wb_temp, self.max_wb_temp)
# return self.temperature_data[self.time_step], self.norm_temp_data[self.time_step]
return (self.temperature_data[self.time_step], self.norm_temp_data[self.time_step],
self.wet_bulb_data[self.time_step], self.norm_wet_bulb_data[self.time_step]) # Added wet bulb temp
# Function to advance the time step and return the weather at the new time step
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def step(self):
"""Step on the Weather_Manager
Returns:
float: Temperature a current step
float: Normalized temperature a current step
"""
self.time_step += 1
# If it tries to read further, restart from the initial index
if self.time_step - 1 >= len(self.temperature_data):
self.time_step = self.init_day*self.time_steps_day
# assert self.time_step < len(self.temperature_data), 'Episode length is longer than the provide Temperature_data'
# return self.temperature_data[self.time_step], self.norm_temp_data[self.time_step]
return (self.temperature_data[self.time_step - 1], self.norm_temp_data[self.time_step - 1],
self.wet_bulb_data[self.time_step - 1], self.norm_wet_bulb_data[self.time_step - 1]) # Added wet bulb temp
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def get_current_weather(self):
return self.temperature_data[self.time_step]