Update src/emission_calculator/calculator.py

src/emission_calculator/calculator.py

@@ -1,13 +1,15 @@
 from pandas import DataFrame
 
 from plotly.subplots import make_subplots
-from plotly.graph_objects import Figure, Pie, Bar, Scatter
+from plotly.graph_objects import Figure, Pie, Bar, Scatter, Scatterpolar
 
 
-def draw_report_figure(df: DataFrame) -> Figure:
+def draw_report_figure(
+    df: DataFrame, threshold: tuple[float] = [30_000, 5_000, 15_000]
+) -> Figure:
     figure_specs = [
+        [{"type": "xy"}, {"type": "domain"}],
         [{"type": "xy"}, {"type": "xy"}],
-        [{"type": "domain"}, None],
     ]
 
     fig = make_subplots(
@@ -16,7 +18,6 @@ def draw_report_figure(df: DataFrame) -> Figure:
         specs=figure_specs,
         subplot_titles=(
             "Carbon Emission by Category",
-            "Cumulative Emission %",
             "Emission Distribution",
         ),
     )
@@ -32,8 +33,8 @@ def draw_report_figure(df: DataFrame) -> Figure:
             name="Emission Distribution",
             marker={"colors": ["#6DA34D", "#81C3D7", "#FFC857"]},
         ),
-        row=2,
-        col=1,
+        row=1,
+        col=2,
     )
 
     # Bar chart for emissions by category
@@ -61,20 +62,6 @@ def draw_report_figure(df: DataFrame) -> Figure:
         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]}",
@@ -89,17 +76,12 @@ def draw_report_figure(df: DataFrame) -> Figure:
             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)
+            x=1,
+            y=0,
             xanchor="right",
             yanchor="bottom",
-            orientation="h",  # Horizontal layout for compactness
+            orientation="h",
         ),
     )
 
@@ -117,106 +99,132 @@ def draw_report_figure(df: DataFrame) -> Figure:
         gridcolor="lightgrey",
     )
 
+    # Add a single general recommendation text box
+    e, w, b = df["Value"]
+    threshold_values = [e >= threshold[0], w >= threshold[1], b >= threshold[2]]
+    recommendations = []
+    texts = [
+        "- Reduce energy usage by adopting energy-efficient practices.\n",
+        "- Minimize waste by recycling and using sustainable materials.\n",
+        "- Limit business travel and opt for virtual meetings where possible."
+    ]
+    if any(threshold_values):
+        fig.add_annotation(
+            text=("Recommendations to reduce carbon footprint:\n"),
+            xref="paper",
+            yref="paper",
+            x=0,
+            y=0.2,  # Positioning inside the plot area, just below center
+            showarrow=False,
+            font=dict(size=12),
+            align="center",
+            bordercolor="black",
+            borderwidth=1,
+            borderpad=10,
+            bgcolor="lightyellow",
+        )
+        if threshold_values[0]:
+            recommendations.append(texts[0]) 
+        if threshold_values[1]:
+            recommendations.append(texts[1])
+        if threshold_values[2]:
+            recommendations.append(texts[2]) 
+
+    for i, text in enumerate(recommendations):
+        fig.add_annotation(
+            text=(text),
+            xref="paper",
+            yref="paper",
+            x=0,
+            y=(i + 1) * 0.05,  # Positioning inside the plot area, just below center
+            showarrow=False,
+            font=dict(size=12),
+            align="center",
+            bordercolor="black",
+            borderwidth=1,
+            borderpad=10,
+            bgcolor="lightyellow",
+        )
+
     return fig
 
 
 def draw_historic_figure(df: DataFrame) -> Figure:
-    # Create subplots with 2 rows and 2 columns
+    # Create subplots with 2 rows and 1 column
     fig = make_subplots(
         rows=2,
-        cols=2,
+        cols=1,
+        specs=[[{"type": "xy"}], [{"type": "polar"}]],
         subplot_titles=(
-            "Energy Usage by Company",
-            "Waste Generated by Company",
-            "Business Travel by Company",
             "Total Carbon Footprint by Company",
+            "Company Metrics Radar Chart",
         ),
+        row_heights=[0.6, 0.4],
     )
 
-    # 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,
+    # 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 gradient-filled area trace for the Carbon Footprint
     fig.add_trace(
         Scatter(
             x=df["Name"],
-            y=df["Waste Generated"],
+            y=df["Carbon Footprint"],
             mode="lines",
             fill="tozeroy",
-            line=dict(color="orange"),
-            fillcolor="rgba(255, 127, 14, 0.5)",  # Gradient fill for orange
-            name="Waste Generated",
+            line=dict(color="blue"),
+            fillcolor="rgba(31, 119, 180, 0.5)",  # Gradient fill for blue
+            name="Carbon Footprint",
         ),
         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,
-    )
+    # Prepare data for radar chart (normalizing values for better comparability)
+    categories = ["Energy Usage", "Waste Generated", "Business Travel"]
+    for _, company in df.iterrows():
+        fig.add_trace(
+            Scatterpolar(
+                r=[
+                    company["Energy Usage"],
+                    company["Waste Generated"],
+                    company["Business Travel"],
+                    company["Energy Usage"],  # Closing the loop
+                ],
+                theta=categories + [categories[0]],  # Circular radar plot
+                fill="toself",
+                name=company["Name"],
+            ),
+            row=2,
+            col=1,
+        )
 
-    # Update layout for titles, legends, and aesthetics
+    # Update layout for the figure
     fig.update_layout(
-        title="Company Metrics with Total Carbon Footprint",
-        barmode="group",  # Group bars by category
+        title="Company Metrics Visualization",
         template="plotly_white",
-        showlegend=True,
-        height=600,
+        height=800,
         width=1000,
-        legend=dict(x=1.05, y=1),  # Adjust legend position outside plot for clarity
+        showlegend=True,
+        legend=dict(x=1.05, y=1),  # Adjust legend position
     )
 
-    # Add axis labels to each subplot
+    # Update x and y-axis titles for the first plot
     fig.update_xaxes(title_text="Company", row=1, col=1)
-    fig.update_yaxes(title_text="Energy Usage", row=1, col=1)
+    fig.update_yaxes(title_text="Carbon Footprint (total)", 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)
+    # Customize polar (radar) chart layout
+    fig.update_polars(
+        radialaxis=dict(
+            visible=True,
+            range=[
+                df[categories].values.min(),
+                df[categories].values.max(),
+            ],
+        )
+    )
 
     return fig