def emissions_comparison(process_kwh, locations, year):
# TODO: Disambiguation of states such as Georgia, US and Georgia
intl_data = utils.get_data("data/json/energy-mix-intl_" + year + ".json")
us_data = utils.get_data("data/json/us-emissions_" + year + ".json")
emissions = [] # list of tuples w/ format (location, emission)
for location in locations:
if locate.in_US(location):
emission = convert.lbs_to_kgs(us_data[location] *
convert.to_MWh(process_kwh))
emissions.append((location, emission))
else:
c = intl_data[location]
total, breakdown = c['total'], [c['coal'], c['petroleum'], \
c['naturalGas'], c['lowCarbon']]
if isinstance(total, float) and float(total) > 0:
breakdown = list(map(lambda x: 100 * x / total, breakdown))
coal, petroleum, natural_gas, low_carbon = breakdown
breakdown = [
convert.coal_to_carbon(process_kwh * coal / 100),
convert.petroleum_to_carbon(process_kwh * petroleum / 100),
convert.natural_gas_to_carbon(process_kwh * natural_gas /
100), 0
]
emission = sum(breakdown)
emissions.append((location, emission))
if emissions != []:
utils.log('Emissions Comparison', emissions)
return emissions
def old_emissions_comparison(process_kwh, year, default_location,
printToScreen):
# Calculates emissions in different locations
intl_data = utils.get_data("data/json/energy-mix-intl_" + year + ".json")
global_emissions, europe_emissions, us_emissions = [], [], []
# Handling international
for country in intl_data:
c = intl_data[country]
total, breakdown = c['total'], [c['coal'], c['petroleum'], \
c['naturalGas'], c['lowCarbon']]
if isinstance(total, float) and float(total) > 0:
breakdown = list(map(lambda x: 100 * x / total, breakdown))
coal, petroleum, natural_gas, low_carbon = breakdown
breakdown = [
convert.coal_to_carbon(process_kwh * coal / 100),
convert.petroleum_to_carbon(process_kwh * petroleum / 100),
convert.natural_gas_to_carbon(process_kwh * natural_gas / 100),
0
]
emission = sum(breakdown)
if locate.in_Europe(country):
europe_emissions.append((country, emission))
else:
global_emissions.append((country, emission))
global_emissions.sort(key=lambda x: x[1])
europe_emissions.sort(key=lambda x: x[1])
# Handling US
us_data = utils.get_data("data/json/us-emissions_" + year + ".json")
for state in us_data:
if ((state != "United States") and state != "_units"):
if us_data[state] != "lbs/MWh":
emission = convert.lbs_to_kgs(us_data[state] *
convert.to_MWh(process_kwh))
us_emissions.append((state, emission))
us_emissions.sort(key=lambda x: x[1])
max_global, max_europe, max_us = global_emissions[len(global_emissions)-1], \
europe_emissions[len(europe_emissions)-1], us_emissions[len(us_emissions)-1]
median_global, median_europe, median_us = global_emissions[len(global_emissions)//2], \
europe_emissions[len(europe_emissions)//2], us_emissions[len(us_emissions)//2]
min_global, min_europe, min_us = global_emissions[0], europe_emissions[
0], us_emissions[0]
if default_location and printToScreen:
utils.log('Emissions Comparison default', max_global, median_global, min_global, max_europe, \
median_europe, min_europe, max_us, median_us, min_us)
default_emissions = [max_global, median_global, min_global, max_europe, \
median_europe, min_europe, max_us, median_us, min_us]
return default_emissions
def energy_mix(location, location_of_default):
""" Gets the energy mix information for a specific location
Parameters:
location (str): user's location
location_of_default (str): Specifies which average to use if
location cannot be determined
Returns:
breakdown (list): percentages of each energy type
"""
if location == "Unknown":
if location_of_default == "USAverage":
location = "United States"
elif location_of_default == "EuropeAverage":
location = "Europe"
else:
location = "World"
if locate.in_US(location):
# Default to U.S. average for unknown location
data = utils.get_data("data/json/energy-mix-us.json")
s = data[location]['mix'] # get state
coal, oil, gas = s['coal'], s['oil'], s['gas']
nuclear, hydro, biomass, wind, solar, geo, = \
s['nuclear'], s['hydro'], s['biomass'], s['wind'], \
s['solar'], s['geothermal']
low_carbon = sum([nuclear, hydro, biomass, wind, solar, geo])
breakdown = [coal, oil, gas, low_carbon]
return breakdown # list of % of each
else:
data = utils.get_data('data/json/energy-mix-intl.json')
c = data[location] # get country
total, breakdown = c['total'], [c['coal'], c['petroleum'], \
c['naturalGas'], c['lowCarbon']]
# Get percentages
breakdown = list(map(lambda x: 100 * x / total, breakdown))
return breakdown # list of % of each
def emissions(process_kwh, breakdown, location, year):
""" Calculates the CO2 emitted by the program based on the location
Parameters:
process_kwh (int): kWhs used by the process
breakdown (list): energy mix corresponding to user's location
location (str): location of user
Returns:
emission (float): kilograms of CO2 emitted
state_emission (float): lbs CO2 per MWh; 0 if international location
"""
if process_kwh < 0:
raise OSError(
"Process wattage lower than baseline wattage. Do not run other processes"
" during the evaluation, or try evaluating a more resource-intensive process."
)
utils.log("Energy Data", breakdown, location)
state_emission = 0
# Case 1: Unknown location, default to US data
# Case 2: United States location
if locate.in_US(location):
if location == "Unknown":
location = "United States"
# US Emissions data is in lbs/Mwh
data = utils.get_data("data/json/us-emissions_" + year + ".json")
state_emission = data[location]
emission = convert.lbs_to_kgs(state_emission *
convert.to_MWh(process_kwh))
# Case 3: International location
else:
# Breaking down energy mix
coal, petroleum, natural_gas, low_carbon = breakdown
breakdown = [
convert.coal_to_carbon(process_kwh * coal / 100),
convert.petroleum_to_carbon(process_kwh * petroleum / 100),
convert.natural_gas_to_carbon(process_kwh * natural_gas / 100), 0
]
emission = sum(breakdown)
utils.log("Emissions", emission)
return (emission, state_emission)