Spaces:
Sleeping
Sleeping
File size: 11,246 Bytes
deb7c43 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 |
from typing import Any, Dict, List, Tuple
import pandas as pd
import requests
from dash import dash_table as dt
from codecarbon.core.emissions import Emissions
from codecarbon.input import DataSource, DataSourceException
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: str) -> dt.DataTable:
project_df = df[df.project_name == project_name]
project_df = project_df.sort_values(by="timestamp")
project_data = project_df.to_dict("records")
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 f"{project_carbon_equivalent / 0.409:.0f}"
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 = f"{time_in_minutes:.0f} minutes"
if time_in_minutes >= 60:
time_in_hours = time_in_minutes / 60
formated_value = f"{time_in_hours:.0f} hours"
if time_in_hours >= 24:
time_in_days = time_in_hours / 24
formated_value = f"{time_in_days:.0f} 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 f"{project_carbon_equivalent / 160.58 * 100:.2f}"
def get_global_emissions_choropleth_data(
self, net_energy_consumed: float
) -> List[Dict]:
global_energy_mix = self._data_source.get_global_energy_mix_data()
choropleth_data = []
for country_iso_code in global_energy_mix.keys():
country_energy_mix = global_energy_mix[country_iso_code]
country_name = country_energy_mix["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
),
)
country_choropleth_data = self.get_country_choropleth_data(
country_energy_mix=country_energy_mix,
country_name=country_name,
country_iso_code=country_iso_code,
country_emissions=country_emissions,
)
choropleth_data.append(country_choropleth_data)
return choropleth_data
@staticmethod
def get_country_choropleth_data(
country_energy_mix: Dict,
country_name: str,
country_iso_code: str,
country_emissions: float,
) -> Dict[str, Any]:
def format_energy_percentage(energy_type: float, total: float) -> float:
return float(f"{energy_type / total * 100:.1f}")
total = country_energy_mix["total_TWh"]
return {
"iso_code": country_iso_code,
"emissions": country_emissions,
"country": country_name,
"carbon_intensity": country_energy_mix["carbon_intensity"],
"fossil": format_energy_percentage(country_energy_mix["fossil_TWh"], total),
"hydroelectricity": format_energy_percentage(
country_energy_mix["hydroelectricity_TWh"],
total,
),
"nuclear": format_energy_percentage(
country_energy_mix["nuclear_TWh"], total
),
"solar": format_energy_percentage(country_energy_mix["solar_TWh"], total),
"wind": format_energy_percentage(country_energy_mix["wind_TWh"], total),
}
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 + f"/experiments/{project['id']}/detailed_sums"
)
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 + f"/projects/team/{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
|