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import json
import dash_bootstrap_components as dbc
import numpy as np
import pandas as pd
import plotly.express as px
from dash import dash_table as dt
from dash import dcc, html
class Components:
def __init__(self):
self.colorscale = [
"rgb(0, 68, 27)", # greens
"rgb(0, 109, 44)",
"rgb(35, 139, 69)",
"rgb(65, 171, 93)",
"rgb(116, 196, 118)",
"rgb(161, 217, 155)",
"rgb(199, 233, 192)",
"rgb(229, 245, 224)",
"rgb(240, 240, 240)", # greys
"rgb(217, 217, 217)",
"rgb(189, 189, 189)",
"rgb(253, 208, 162)", # oranges
"rgb(253, 174, 107)",
"rgb(253, 141, 60)",
] # px.colors.sequential.Greens_r,
@staticmethod
def get_header():
return html.Div(
dbc.Container(
[
html.H1("Carbon Footprint", style={"textAlign": "center"}),
html.P(
"Measure Compute Emissions",
style={"textAlign": "center", "paddingLeft": "0.5%"},
className="lead",
),
],
fluid=True,
className="py-3",
),
className="p-3 mb-5 bg-light rounded-5",
)
@staticmethod
def get_net_summary():
return html.Div(
[
html.H2("Across All Projects", style={"textAlign": "center"}),
html.H4(
[
"Net Power Consumption : ",
html.Strong(
id="net_power_consumption",
style={"fontWeight": "normal", "color": "green"},
),
],
style={"textAlign": "left", "float": "left"},
),
html.H4(
[
"Net Carbon Equivalent : ",
html.Strong(
id="net_carbon_equivalent",
style={"fontWeight": "normal", "color": "green"},
),
],
style={"textAlign": "right", "float": "right"},
),
],
style={"paddingLeft": "1.4%", "paddingRight": "1.4%"},
)
@staticmethod
def get_project_dropdown(df: pd.DataFrame):
projects = sorted(list(df["project_name"].unique()))
return html.Div(
dbc.Col(
[
html.Br(),
html.H3("Select a Project", style={"textAlign": "left"}),
dcc.Dropdown(
id="project_name",
options=[{"label": i, "value": i} for i in projects],
value=projects[0],
),
],
style={"display": "inline-block"},
)
)
@staticmethod
def get_project_details():
return html.Div(
[
html.Br(),
html.Div(
[
html.H3(
[
"Infrastructure Hosted at ",
html.Strong(
id="project_infrastructure_location",
style={"fontWeight": "normal", "color": "green"},
),
],
style={"float": "left"},
)
]
),
html.Br(),
html.Br(),
html.Div(
[
html.H4(
[
"Power Consumption Across All Experiments : ",
html.Strong(
id="project_power_consumption",
style={"fontWeight": "normal", "color": "green"},
),
],
style={"float": "left"},
),
html.H4(
[
"Last Run Power Consumption : ",
html.Strong(
id="last_run_power_consumption",
style={"fontWeight": "normal", "color": "green"},
),
],
style={"float": "right"},
),
]
),
html.Br(),
html.Div(
[
html.H4(
[
"Carbon Equivalent Across All Experiments : ",
html.Strong(
id="project_carbon_equivalent",
style={"color": "green", "fontWeight": "bold"},
),
],
style={"float": "left"},
),
html.H4(
[
"Last Run Carbon Equivalent : ",
html.Strong(
id="last_run_carbon_equivalent",
style={"fontWeight": "normal", "color": "green"},
),
],
style={"float": "right"},
),
]
),
],
style={"paddingLeft": "1.4%", "paddingRight": "1.4%"},
)
@staticmethod
def get_exemplary_equivalents():
return html.Div(
[
html.Br(),
html.Br(),
html.Br(),
html.Br(),
html.Div(
[
html.H2("Exemplary Equivalents", style={"textAlign": "center"}),
html.Br(),
html.P(
[
html.Div(
[
html.Img(
id="house_icon",
style={"height": "20%", "width": "50%"},
),
html.Div(
[
html.Strong(
id="household_fraction",
style={
"color": "green",
"fontSize": 20,
"paddingLeft": "4%",
},
),
html.H5(
"of weekly",
style={"paddingLeft": "3.5%"},
),
html.H5(
"American",
style={"paddingLeft": "2.8%"},
),
html.H5("household"),
html.H5(
"emissions",
style={"paddingLeft": "1.4%"},
),
]
),
],
style={"float": "left", "width": "25%"},
),
html.Div(
[
html.Img(
id="car_icon",
style={
"height": "43%",
"width": "28.5%",
# "paddingLeft": "2%",
},
),
html.Div(
[
html.Strong(
id="car_miles",
style={
"color": "green",
"fontWeight": "bold",
"fontSize": 20,
},
),
html.H5(
"driven",
style={"paddingLeft": "5.5%"},
),
]
),
],
style={
"float": "left",
"width": "50%",
"paddingLeft": 90,
},
),
html.Div(
[
html.Img(
id="tv_icon",
style={
"height": "35%",
"width": "53%",
"paddingLeft": "5%",
},
),
html.Div(
[
html.Strong(
id="tv_time",
style={
"color": "green",
"fontSize": 20,
"paddingLeft": "8%",
},
),
html.H5(
"of 32-inch",
style={"paddingLeft": "5%"},
),
html.H5(
"LCD TV",
style={"paddingLeft": "10%"},
),
html.H5(
"watched",
style={"paddingLeft": "6.4%"},
),
]
),
],
style={"float": "right", "width": "25%"},
),
],
style={"paddingLeft": "20%", "paddingRight": "15%"},
),
],
style={"display": "inline-block"},
),
]
)
@staticmethod
def get_cloud_emissions_comparison():
return html.Div(
dbc.Col(
[
html.Br(),
html.Br(),
html.H2(
[
"Emissions Across ",
html.Strong(
id="cloud_provider_name",
style={"fontWeight": "normal", "color": "green"},
),
" Regions",
],
style={"textAlign": "center", "marginLeft": "12%"},
),
dcc.Graph(id="cloud_emissions_barchart"),
html.Br(),
html.Div(
id="cloud_recommendation",
style={"marginLeft": "12%", "textAlign": "center"},
),
],
id="cloud_emissions_comparison_component",
),
style={"marginLeft": "-12%"},
)
def get_cloud_emissions_barchart_figure(
self, cloud_emissions_barchart_data: pd.DataFrame
):
return (
px.bar(
cloud_emissions_barchart_data,
x="region",
y="emissions",
hover_data=["region", "country_name", "emissions"],
color="emissions",
labels={
"emissions": "Carbon Equivalent (KgCO2)",
"region": "Region",
"country_name": "Country",
},
color_continuous_scale=self.colorscale,
height=500,
width=1400,
)
.update_xaxes(tickangle=45)
.update_layout(plot_bgcolor="rgb(255,255,255)")
)
def get_cloud_recommendation(
self,
on_cloud: str,
cloud_provider_name: str,
cloud_emissions_barchart_data: pd.DataFrame,
):
if on_cloud == "N":
return html.H4()
cloud_emissions_project_region = cloud_emissions_barchart_data.iloc[0, :]
cloud_emissions_minimum_region = cloud_emissions_barchart_data.iloc[1, :]
if (
cloud_emissions_minimum_region.emissions
> cloud_emissions_project_region.emissions
):
return html.H4(
[
f"Already running on {cloud_provider_name}'s least emissions region ",
html.Strong(
f"{cloud_emissions_project_region.region}",
style={"fontWeight": "normal", "color": "green"},
),
]
)
return (
html.H4(
[
"Had this been run in ",
html.Strong(
f"{cloud_emissions_minimum_region.region}",
style={"fontWeight": "normal", "color": "green"},
),
" region, ",
]
),
html.H4(
[
"then the emitted carbon would have been ",
html.Strong(
f"{'{:.1f}'.format(cloud_emissions_minimum_region.emissions)} kg",
style={"fontWeight": "normal", "color": "green"},
),
]
),
html.H4(
[
"Reducing the current emissions by ",
html.Strong(
f"{'{:.1f}'.format(cloud_emissions_project_region.emissions - cloud_emissions_minimum_region.emissions)} kg",
style={"fontWeight": "normal", "color": "green"},
),
]
),
)
@staticmethod
def get_emissions_tab():
return dcc.Tab(
label="Emissions Equivalent",
value="emissions_tab",
id="global_emissions",
children=[
html.Div(
dbc.Col(
dcc.Graph(id="global_emissions_choropleth"),
style={"marginLeft": "-16%"},
)
)
],
)
@staticmethod
def get_energy_mix_tab():
return dcc.Tab(
label="Energy Mix",
value="energy_mix_tab",
id="global_energy_mix",
children=[
html.Div(
dbc.Col(
[
html.Br(),
html.H4("Select Energy Source"),
dcc.Dropdown(
id="energy_type",
options=[
{"label": "Fossil", "value": "fossil"},
{
"label": "Hydroelectricity",
"value": "hydroelectricity",
},
{"label": "Nuclear", "value": "nuclear"},
{"label": "Solar", "value": "solar"},
{"label": "Wind", "value": "wind"},
],
value="fossil",
),
dcc.Graph(
id="global_energy_mix_choropleth",
style={"marginLeft": "-16%"},
),
]
)
)
],
)
def get_global_comparison(self):
return html.Div(
[
html.Br(),
html.Br(),
html.H2(
"Global Benchmarks",
style={
"textAlign": "center",
"paddingLeft": "15%",
"marginLeft": "-15%",
},
),
html.Br(),
html.Br(),
dcc.Tabs(
id="global_benchmarks",
value="emissions_tab",
children=[self.get_emissions_tab(), self.get_energy_mix_tab()],
),
]
)
def get_global_emissions_choropleth_figure(self, choropleth_data):
return px.choropleth(
data_frame=choropleth_data,
locations="iso_code",
color="emissions",
hover_data=[
"country",
"emissions",
"carbon_intensity",
"fossil",
"hydroelectricity",
"nuclear",
"solar",
"wind",
],
labels={
"country": "Country",
"emissions": "Carbon Equivalent (KgCO2)",
"carbon_intensity": "Carbon Intensity (gCO2/kWh)",
"iso_code": "Country Code",
"fossil": "Fossil Energy(%)",
"hydroelectricity": "Hydroelectricity Energy (%)",
"nuclear": "Nuclear Energy (%)",
"solar": "Solar Energy (%)",
"wind": "Wind Energy (%)",
},
width=1400,
height=600,
color_continuous_scale=self.colorscale,
)
def get_global_energy_mix_choropleth_figure(self, energy_type, choropleth_data):
energy_labels = {
"fossil": "Fossil Energy(%)",
"hydroelectricity": "Hydroelectricity Energy (%)",
"nuclear": "Nuclear Energy (%)",
"solar": "Solar Energy (%)",
"wind": "Wind Energy (%)",
}
return px.choropleth(
data_frame=choropleth_data,
locations="iso_code",
color=energy_type,
hover_data=["country", "emissions", energy_type],
labels={
"country": "Country",
"emissions": "Carbon Equivalent (KgCO2)",
"iso_code": "Country Code",
energy_type: energy_labels[energy_type],
},
width=1400,
height=600,
color_continuous_scale=(
list(reversed(self.colorscale))
if energy_type == "low_carbon"
else self.colorscale
),
)
@staticmethod
def get_regional_emissions_comparison():
return html.Div(
dbc.Col(
[
html.Br(),
html.Br(),
html.H2(
[
"Emissions Across Regions in ",
html.Strong(
id="country_name",
style={"fontWeight": "normal", "color": "green"},
),
],
style={"textAlign": "center", "marginLeft": "12%"},
),
dcc.Graph(id="regional_emissions_comparison_choropleth"),
],
id="regional_emissions_comparison_component",
),
style={"marginLeft": "-12%"},
)
def get_regional_emissions_choropleth_figure(
self, choropleth_data, country_iso_code: str
):
# add location_modes and scopes for other country codes
location_modes = {"usa": "USA-states", "can": "geojson-id"}
scopes = {"usa": "usa", "can": "north america"}
locations = {"usa": "region_code", "can": "cartodb_id"}
# formatting required for geojson data (not required for USA regional map)
if country_iso_code.lower() == "can":
with open("codecarbon/data/canada_provinces.geojson", "r") as geo:
mp = json.load(geo)
for choropleth in choropleth_data:
region_name = choropleth["region_code"]
choropleth["cartodb_id"] = [
m["properties"]["cartodb_id"]
for m in mp["features"]
if m["properties"]["name"].lower() == region_name
][0]
else:
mp = None
# Get parameters, default to a blank world map if not in keys
location_mode = location_modes.get(country_iso_code.lower(), None)
scope = scopes.get(country_iso_code.lower(), "world")
location = locations.get(country_iso_code.lower(), None)
fig = px.choropleth(
data_frame=choropleth_data,
locations=location,
geojson=mp,
featureidkey="properties.cartodb_id",
locationmode=location_mode,
scope=scope,
color="emissions",
hover_data=["region_name", "emissions", "region_code"],
labels={
"region_name": "Region",
"emissions": "Carbon Equivalent (KgCO2)",
"region_code": "Region Code",
},
width=1400,
height=600,
color_continuous_scale=self.colorscale,
)
if country_iso_code.lower() != "usa":
fig.update_geos(fitbounds="locations", visible=True)
return fig
@staticmethod
def get_project_time_series():
return html.Div(
dbc.Col(
[
html.Br(),
html.Br(),
html.H2("Emissions Timeline", style={"textAlign": "center"}),
dcc.Graph(id="project_time_series"),
]
),
style={"paddingLeft": "3%"},
)
@staticmethod
def get_project_emissions_bar_chart():
return html.Div(
dbc.Col(
[
html.Br(),
html.Br(),
html.H2("Emissions Detail", style={"textAlign": "center"}),
dcc.Graph(id="project_emissions_bar_chart"),
]
),
style={"paddingLeft": "3%"},
)
@staticmethod
def get_project_time_series_figure(project_data: dt.DataTable):
return (
px.line(
project_data,
x="timestamp",
y="emissions",
hover_data=["emissions"],
labels={
"emissions": "Carbon Equivalent (KgCO2eq)",
"timestamp": "Timestamp",
},
)
.update_traces(line_color="green")
.update_layout(plot_bgcolor="rgb(255,255,255)")
)
@staticmethod
def get_project_emissions_bar_chart_figure(project_data: dt.DataTable):
# Note: necessary to both convert to pandas and replace null values for hover value
project_data = pd.DataFrame(project_data)
project_data = project_data.replace(np.nan, "", regex=True)
hover_data = {c: True for c in project_data.columns}
bar = (
px.bar(
project_data,
y="emissions",
hover_data=hover_data,
labels={
"emissions": "Carbon Equivalent (KgCO2eq)",
"energy_consumed": "Energy Consumed (kWh)",
"timestamp": "Timestamp",
"project_name": "Project Name",
"duration": "Duration",
"emissions_detail": "Emissions Detail",
"country_name": "Country Name",
"country_iso_code": "Country ISO Code",
"region": "Region",
"cloud_provider": "Cloud Provider",
"cloud_region": "Cloud Region",
},
)
.update_traces(marker_color="green")
.update_layout(plot_bgcolor="rgb(255,255,255)")
)
return bar
@staticmethod
def get_hidden_project_data():
return html.Div(id="hidden_project_data", style={"display": "none"})
@staticmethod
def get_hidden_project_summary():
return html.Div(
dcc.Store(id="hidden_project_summary"), style={"display": "none"}
)
@staticmethod
def get_references():
return html.Div(
[
html.Br(),
html.Br(),
html.H2("References "),
html.Ul(
[
html.Li(
html.A(
"Energy Usage Reports: Environmental awareness as part of algorithmic accountability",
href="https://arxiv.org/pdf/1911.08354.pdf",
)
),
html.Li(
html.A(
"Quantifying the Carbon Emissions of Machine Learning",
href="https://arxiv.org/pdf/1910.09700.pdf",
)
),
]
),
html.Br(),
html.Br(),
]
)
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