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from pandas import DataFrame
from plotly.subplots import make_subplots
from plotly.graph_objects import Figure, Pie, Bar, Scatter
def draw_report_figure(df: DataFrame) -> Figure:
figure_specs = [
[{"type": "xy"}, {"type": "xy"}],
[{"type": "domain"}, None],
]
fig = make_subplots(
rows=2,
cols=2,
specs=figure_specs,
subplot_titles=(
"Carbon Emission by Category",
"Cumulative Emission %",
"Emission Distribution",
),
)
# Pie chart settings
pie_pull = [0.15 if x == min(df["Value"]) else 0.0 for x in df["Value"]]
fig.add_trace(
Pie(
values=df["Value"],
labels=df["Category"],
hole=0.3,
pull=pie_pull,
name="Emission Distribution",
marker={"colors": ["#6DA34D", "#81C3D7", "#FFC857"]},
),
row=2,
col=1,
)
# Bar chart for emissions by category
fig.add_trace(
Bar(
x=df["Category"],
y=df["Value"],
name="Carbon Emission (kgCO2)",
marker_color=["#6DA34D", "#81C3D7", "#FFC857"],
),
row=1,
col=1,
)
# Annotation for highest emission
fig.add_annotation(
x=df["Category"][df["Value"].idxmax()],
y=df["Value"].max(),
text="Highest Emission",
showarrow=True,
arrowhead=1,
ax=0,
ay=-40,
row=1,
col=1,
)
# Cumulative line chart
cumulative_percentage = (df["Value"].cumsum() / df["Value"].sum()) * 100
fig.add_trace(
Scatter(
x=df["Category"],
y=cumulative_percentage,
name="Cumulative %",
mode="lines+markers",
line=dict(color="#333333", dash="dash"),
),
row=1,
col=2,
)
# Update layout for axes and overall layout
fig.update_layout(
title_text=f"Carbon Footprint of {df['Name'][0]}",
plot_bgcolor="white",
legend_title_text="Breakdown",
xaxis_title="Emission Category",
yaxis_title="Carbon Emission (kgCO2)",
yaxis=dict(
linecolor="black",
showline=True,
ticks="outside",
mirror=True,
gridcolor="lightgrey",
),
yaxis2=dict(
title="Cumulative Percentage",
side="right",
showgrid=False,
),
legend=dict(
x=1, # Horizontal position (1 for right)
y=0, # Vertical position (0 for bottom)
xanchor="right",
yanchor="bottom",
orientation="h", # Horizontal layout for compactness
),
)
fig.update_xaxes(
linecolor="black",
ticks="outside",
showline=True,
mirror=True,
)
fig.update_yaxes(
linecolor="black",
showline=True,
ticks="outside",
mirror=True,
gridcolor="lightgrey",
)
return fig
def draw_historic_figure(df: DataFrame) -> Figure:
# Create subplots with 2 rows and 2 columns
fig = make_subplots(
rows=2,
cols=2,
subplot_titles=(
"Energy Usage by Company",
"Waste Generated by Company",
"Business Travel by Company",
"Total Carbon Footprint by Company",
),
)
# Add gradient-filled area traces for each metric
fig.add_trace(
Scatter(
x=df["Name"],
y=df["Energy Usage"],
mode="lines",
fill="tozeroy",
line=dict(color="blue"),
fillcolor="rgba(31, 119, 180, 0.5)", # Gradient fill for blue
name="Energy Usage",
),
row=1,
col=1,
)
fig.add_trace(
Scatter(
x=df["Name"],
y=df["Waste Generated"],
mode="lines",
fill="tozeroy",
line=dict(color="orange"),
fillcolor="rgba(255, 127, 14, 0.5)", # Gradient fill for orange
name="Waste Generated",
),
row=1,
col=2,
)
fig.add_trace(
Scatter(
x=df["Name"],
y=df["Business Travel"],
mode="lines",
fill="tozeroy",
line=dict(color="green"),
fillcolor="rgba(44, 160, 44, 0.5)", # Gradient fill for green
name="Business Travel",
),
row=2,
col=1,
)
# Calculate each company's total carbon footprint as the sum of the three metrics
df["Carbon Footprint"] = (
df["Energy Usage"] + df["Waste Generated"] + df["Business Travel"]
)
# Add a line trace for the total sum of each metric
fig.add_trace(
Scatter(
x=df["Name"],
y=df["Carbon Footprint"],
mode="lines+markers",
line=dict(color="black", dash="dash"),
name="Total Carbon Footprint",
),
row=2,
col=2,
)
# Update layout for titles, legends, and aesthetics
fig.update_layout(
title="Company Metrics with Total Carbon Footprint",
barmode="group", # Group bars by category
template="plotly_white",
showlegend=True,
height=600,
width=1000,
legend=dict(x=1.05, y=1), # Adjust legend position outside plot for clarity
)
# Add axis labels to each subplot
fig.update_xaxes(title_text="Company", row=1, col=1)
fig.update_yaxes(title_text="Energy Usage", row=1, col=1)
fig.update_xaxes(title_text="Company", row=1, col=2)
fig.update_yaxes(title_text="Waste Generated", row=1, col=2)
fig.update_xaxes(title_text="Company", row=2, col=1)
fig.update_yaxes(title_text="Business Travel", row=2, col=1)
fig.update_xaxes(title_text="Company", row=2, col=2)
fig.update_yaxes(title_text="Carbon Footprint (total)", row=2, col=2)
return fig
def make_dataframe(
company_name: str,
avg_electric_bill: float,
avg_gas_bill: float,
avg_transport_bill: float,
monthly_waste_generated: float,
recycled_waste_percent: float,
annual_travel_kms: float,
fuel_efficiency: float,
) -> DataFrame:
energy_usage = (
(avg_electric_bill * 12 * 5e-4)
+ (avg_gas_bill * 12 * 5.3e-3)
+ (avg_transport_bill * 12 * 2.32)
)
waste_generated = monthly_waste_generated * 12 * 0.57 - recycled_waste_percent
business_travel = annual_travel_kms * 1 / fuel_efficiency * 2.31
return DataFrame(
{
"Name": company_name,
"Category": ["Energy Usage", "Waste Generated", "Business Travel"],
"Value": [energy_usage, waste_generated, business_travel],
}
)
def dataframe_to_dict(df: DataFrame) -> dict:
return {
"Name": df["Name"][0],
"Energy Usage": df["Value"][0],
"Waste Generated": df["Value"][1],
"Business Travel": df["Value"][2],
}
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