class Data:
def __init__(self):
self._data_source = DataSource()
self._emissions = Emissions(self._data_source)
@staticmethod
def get_project_data(df: pd.DataFrame, project_name) -> dt.DataTable:
project_df = df[df.project_name == project_name]
project_df = project_df.sort_values(by="timestamp")
project_data = project_df.to_dict("rows")
columns = [{
"name": column,
"id": column
} for column in project_df.columns]
return dt.DataTable(data=project_data, columns=columns)
@staticmethod
def get_project_summary(project_data: List[Dict]):
last_run = project_data[-1]
project_summary = {
"last_run": {
"timestamp": last_run["timestamp"],
"duration": last_run["duration"],
"emissions": round(last_run["emissions"], 1),
"energy_consumed": round((last_run["energy_consumed"]), 1),
},
"total": {
"duration":
sum(
map(lambda experiment: experiment["duration"],
project_data)),
"emissions":
round(
sum(
map(lambda experiment: experiment["emissions"],
project_data)),
1,
),
"energy_consumed":
round(
sum(
map(
lambda experiment: experiment["energy_consumed"],
project_data,
)),
1,
),
},
"country_name": last_run["country_name"],
"country_iso_code": last_run["country_iso_code"],
"region": last_run["region"],
"on_cloud": last_run["on_cloud"],
"cloud_provider": last_run["cloud_provider"],
"cloud_region": last_run["cloud_region"],
}
return project_summary
def get_car_miles(self, project_carbon_equivalent: float):
"""
8.89 × 10-3 metric tons CO2/gallon gasoline ×
1/22.0 miles per gallon car/truck average ×
1 CO2, CH4, and N2O/0.988 CO2
= 4.09 x 10-4 metric tons CO2E/mile
= 0.409 kg CO2E/mile
Source: EPA
:param project_carbon_equivalent: total project emissions in kg CO2E
:return: number of miles driven by avg car
"""
return "{:.0f}".format(project_carbon_equivalent / 0.409)
def get_tv_time(self, project_carbon_equivalent: float):
"""
Gives the amount of time
a 32-inch LCD flat screen TV will emit
an equivalent amount of carbon
Ratio is 0.097 kg CO2 / 1 hour tv
:param project_carbon_equivalent: total project emissions in kg CO2E
:return: equivalent TV time
"""
time_in_minutes = project_carbon_equivalent * (1 / 0.097) * 60
formated_value = "{:.0f} minutes".format(time_in_minutes)
if time_in_minutes >= 60:
time_in_hours = time_in_minutes / 60
formated_value = "{:.0f} hours".format(time_in_hours)
if time_in_hours >= 24:
time_in_days = time_in_hours / 24
formated_value = "{:.0f} days".format(time_in_days)
return formated_value
def get_household_fraction(self, project_carbon_equivalent: float):
"""
Total CO2 emissions for energy use per home: 5.734 metric tons CO2 for electricity
+ 2.06 metric tons CO2 for natural gas + 0.26 metric tons CO2 for liquid petroleum gas
+ 0.30 metric tons CO2 for fuel oil = 8.35 metric tons CO2 per home per year / 52 weeks
= 160.58 kg CO2/week on average
Source: EPA
:param project_carbon_equivalent: total project emissions in kg CO2E
:return: % of weekly emissions re: an average American household
"""
return "{:.2f}".format((project_carbon_equivalent / 160.58) * 100)
def get_global_emissions_choropleth_data(
self, net_energy_consumed: float) -> List[Dict]:
def formatted_energy_percentage(energy_type: float,
total: float) -> float:
return float("{:.1f}".format((energy_type / total) * 100))
global_energy_mix = self._data_source.get_global_energy_mix_data()
choropleth_data = []
for country_iso_code in global_energy_mix.keys():
country_name = global_energy_mix[country_iso_code]["countryName"]
if country_iso_code not in ["_define", "ATA"]:
from codecarbon.core.units import Energy
energy_consumed = Energy.from_energy(kwh=net_energy_consumed)
from codecarbon.external.geography import GeoMetadata
country_emissions = self._emissions.get_country_emissions(
energy_consumed,
GeoMetadata(country_name=country_name,
country_iso_code=country_iso_code),
)
total = global_energy_mix[country_iso_code]["total"]
choropleth_data.append({
"iso_code":
country_iso_code,
"emissions":
country_emissions,
"country":
country_name,
"coal":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["coal"], total),
"petroleum":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["petroleum"],
total),
"natural_gas":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["naturalGas"],
total),
"low_carbon":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["lowCarbon"],
total),
})
return choropleth_data
def get_regional_emissions_choropleth_data(
self, net_energy_consumed: float,
country_iso_code: str) -> List[Dict]:
# add country codes here to render for different countries
if country_iso_code.upper() not in ["USA"]:
return [{"region_code": "", "region_name": "", "emissions": ""}]
region_emissions = self._data_source.get_country_emissions_data(
country_iso_code.lower())
choropleth_data = []
for region_name in region_emissions.keys():
region_code = region_emissions[region_name]["regionCode"]
if region_name not in ["_unit"]:
from codecarbon.core.units import Energy
energy_consumed = Energy.from_energy(kwh=net_energy_consumed)
from codecarbon.external.geography import GeoMetadata
emissions = self._emissions.get_region_emissions(
energy_consumed,
GeoMetadata(country_iso_code=country_iso_code,
region=region_name),
)
choropleth_data.append({
"region_code": region_code,
"region_name": region_name.upper(),
"emissions": emissions,
})
return choropleth_data
def get_cloud_emissions_barchart_data(
self,
net_energy_consumed: float,
on_cloud: str,
cloud_provider: str,
cloud_region: str,
) -> Tuple[str, pd.DataFrame]:
if on_cloud == "N":
return (
"",
pd.DataFrame(data={
"region": [],
"emissions": [],
"countryName": []
}),
)
cloud_emissions = self._data_source.get_cloud_emissions_data()
cloud_emissions = cloud_emissions[[
"provider", "providerName", "region", "impact", "countryName"
]]
from codecarbon.core.units import EmissionsPerKwh
cloud_emissions["emissions"] = cloud_emissions.apply(
lambda row: EmissionsPerKwh.from_g_per_kwh(row.impact).kgs_per_kwh
* net_energy_consumed,
axis=1,
)
cloud_emissions_project_region = cloud_emissions[cloud_emissions.region
== cloud_region]
cloud_emissions = cloud_emissions[
(cloud_emissions.provider == cloud_provider)
& (cloud_emissions.region != cloud_region)].sort_values(
by="emissions")
return (
cloud_emissions_project_region.iloc[0, :].providerName,
pd.concat([cloud_emissions_project_region, cloud_emissions]),
)
class Data:
def __init__(self):
self._data_source = DataSource()
self._emissions = Emissions(self._data_source)
@staticmethod
def get_project_data(df: pd.DataFrame, project_name) -> dt.DataTable:
project_df = df[df.project_name == project_name]
project_df = project_df.sort_values(by="timestamp")
project_data = project_df.to_dict("rows")
columns = [{
"name": column,
"id": column
} for column in project_df.columns]
return dt.DataTable(data=project_data, columns=columns)
@staticmethod
def get_project_summary(project_data: List[Dict]):
last_run = project_data[-1]
project_summary = {
"last_run": {
"timestamp": last_run["timestamp"],
"duration": last_run["duration"],
"emissions": round(last_run["emissions"], 1),
"energy_consumed": round((last_run["energy_consumed"]), 1),
},
"total": {
"duration":
sum(
map(lambda experiment: experiment["duration"],
project_data)),
"emissions":
sum(
map(lambda experiment: experiment["emissions"],
project_data)),
"energy_consumed":
sum(
map(lambda experiment: experiment["energy_consumed"],
project_data)),
},
"country_name": last_run["country_name"],
"country_iso_code": last_run["country_iso_code"],
"region": last_run["region"],
"on_cloud": last_run["on_cloud"],
"cloud_provider": last_run["cloud_provider"],
"cloud_region": last_run["cloud_region"],
}
return project_summary
def get_car_miles(self, project_carbon_equivalent: float):
"""
8.89 × 10-3 metric tons CO2/gallon gasoline ×
1/22.0 miles per gallon car/truck average ×
1 CO2, CH4, and N2O/0.988 CO2
= 4.09 x 10-4 metric tons CO2E/mile
= 0.409 kg CO2E/mile
Source: EPA
:param project_carbon_equivalent: total project emissions in kg CO2E
:return: number of miles driven by avg car
"""
return "{:.0f}".format(project_carbon_equivalent / 0.409)
def get_tv_time(self, project_carbon_equivalent: float):
"""
Gives the amount of time
a 32-inch LCD flat screen TV will emit
an equivalent amount of carbon
Ratio is 0.097 kg CO2 / 1 hour tv
:param project_carbon_equivalent: total project emissions in kg CO2E
:return: equivalent TV time
"""
time_in_minutes = project_carbon_equivalent * (1 / 0.097) * 60
formated_value = "{:.0f} minutes".format(time_in_minutes)
if time_in_minutes >= 60:
time_in_hours = time_in_minutes / 60
formated_value = "{:.0f} hours".format(time_in_hours)
if time_in_hours >= 24:
time_in_days = time_in_hours / 24
formated_value = "{:.0f} days".format(time_in_days)
return formated_value
def get_household_fraction(self, project_carbon_equivalent: float):
"""
Total CO2 emissions for energy use per home: 5.734 metric tons CO2 for electricity
+ 2.06 metric tons CO2 for natural gas + 0.26 metric tons CO2 for liquid petroleum gas
+ 0.30 metric tons CO2 for fuel oil = 8.35 metric tons CO2 per home per year / 52 weeks
= 160.58 kg CO2/week on average
Source: EPA
:param project_carbon_equivalent: total project emissions in kg CO2E
:return: % of weekly emissions re: an average American household
"""
return "{:.2f}".format((project_carbon_equivalent / 160.58) * 100)
def get_global_emissions_choropleth_data(
self, net_energy_consumed: float) -> List[Dict]:
def formatted_energy_percentage(energy_type: float,
total: float) -> float:
return float("{:.1f}".format((energy_type / total) * 100))
global_energy_mix = self._data_source.get_global_energy_mix_data()
choropleth_data = []
for country_iso_code in global_energy_mix.keys():
country_name = global_energy_mix[country_iso_code]["country_name"]
if country_iso_code not in ["_define", "ATA"]:
from codecarbon.core.units import Energy
energy_consumed = Energy.from_energy(kWh=net_energy_consumed)
from codecarbon.external.geography import GeoMetadata
country_emissions = self._emissions.get_country_emissions(
energy_consumed,
GeoMetadata(country_name=country_name,
country_iso_code=country_iso_code),
)
total = global_energy_mix[country_iso_code]["total_TWh"]
choropleth_data.append({
"iso_code":
country_iso_code,
"emissions":
country_emissions,
"country":
country_name,
"fossil":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["fossil_TWh"],
total),
"geothermal":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["geothermal_TWh"],
total),
"hydroelectricity":
formatted_energy_percentage(
global_energy_mix[country_iso_code]
["hydroelectricity_TWh"],
total,
),
"nuclear":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["nuclear_TWh"],
total),
"solar":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["solar_TWh"],
total),
"wind":
formatted_energy_percentage(
global_energy_mix[country_iso_code]["wind_TWh"],
total),
})
return choropleth_data
def get_regional_emissions_choropleth_data(
self, net_energy_consumed: float,
country_iso_code: str) -> List[Dict]:
# add country codes here to render for different countries
if country_iso_code.upper() not in ["USA", "CAN"]:
return [{"region_code": "", "region_name": "", "emissions": ""}]
try:
region_emissions = self._data_source.get_country_emissions_data(
country_iso_code.lower())
except DataSourceException: # This country has regional data at the energy mix level, not the emissions level
country_energy_mix = self._data_source.get_country_energy_mix_data(
country_iso_code.lower())
region_emissions = {
region: {
"regionCode": region
}
for region, energy_mix in country_energy_mix.items()
}
choropleth_data = []
for region_name in region_emissions.keys():
region_code = region_emissions[region_name]["regionCode"]
if region_name not in ["_unit"]:
from codecarbon.core.units import Energy
energy_consumed = Energy.from_energy(kWh=net_energy_consumed)
from codecarbon.external.geography import GeoMetadata
emissions = self._emissions.get_region_emissions(
energy_consumed,
GeoMetadata(country_iso_code=country_iso_code,
region=region_name),
)
choropleth_data.append({
"region_code": region_code,
"region_name": region_name.upper(),
"emissions": emissions,
})
return choropleth_data
def get_cloud_emissions_barchart_data(
self,
net_energy_consumed: float,
on_cloud: str,
cloud_provider: str,
cloud_region: str,
) -> Tuple[str, pd.DataFrame]:
if on_cloud == "N":
return (
"",
pd.DataFrame(data={
"region": [],
"emissions": [],
"country_name": []
}),
)
cloud_emissions = self._data_source.get_cloud_emissions_data()
cloud_emissions = cloud_emissions[[
"provider", "providerName", "region", "impact", "country_name"
]]
from codecarbon.core.units import EmissionsPerKWh
cloud_emissions["emissions"] = cloud_emissions.apply(
lambda row: EmissionsPerKWh.from_g_per_kWh(row.impact).kgs_per_kWh
* net_energy_consumed,
axis=1,
)
cloud_emissions_project_region = cloud_emissions[cloud_emissions.region
== cloud_region]
cloud_emissions = cloud_emissions[
(cloud_emissions.provider == cloud_provider)
& (cloud_emissions.region != cloud_region)].sort_values(
by="emissions")
return (
cloud_emissions_project_region.iloc[0, :].providerName,
pd.concat([cloud_emissions_project_region, cloud_emissions]),
)
@staticmethod
def get_data_from_api(host):
transformed_projects = []
project_list = Data.list_projects(host)
for project in project_list:
project_sum_by_experiments_url = (
host + "/experiments/{project_id}/detailed_sums".format(
project_id=project["id"]))
project_name = project["name"]
sums = requests.get(project_sum_by_experiments_url).json()
for experiment in sums:
experiment["project_name"] = project_name
# experiment["emission_rate"] = 0
# if experiment["emissions_count"] > 0:
# experiment["emission_rate"] = (
# experiment["emissions_rate"] / experiment["emissions_count"]
# )
transformed_projects.append(experiment)
df_projects = pd.DataFrame(transformed_projects)
return df_projects
@staticmethod
def list_projects(host):
projects = []
teams_url = host + "/teams"
teams = requests.get(teams_url).json()
for team in teams:
projets_url = host + "/projects/team/{team_id}".format(
team_id=team["id"])
team_projects = requests.get(projets_url).json()
if team_projects:
projects.append(
list(
map(
lambda x: {
"id": x["id"],
"name": x["name"]
},
iter(team_projects),
)))
project_list = sum(projects, [])
return project_list