updated title of plots

make_plot.py

@@ -21,37 +21,41 @@ def plot_train_test(df1: pd.DataFrame, df2: pd.DataFrame) -> go.Figure:
     fig = go.Figure()
 
     # Add the first scatter plot with steelblue color
-    fig.add_trace(go.Scatter(
+    fig.add_trace(
+        go.Scatter(
             x=df1.index,
             y=df1.iloc[:, 0],
-            mode='lines',
-            name='Training Data',
-            line=dict(color='steelblue'),
-            marker=dict(color='steelblue')
-            ))
+            mode="lines",
+            name="Training Data",
+            line=dict(color="steelblue"),
+            marker=dict(color="steelblue"),
+        )
+    )
 
     # Add the second scatter plot with yellow color
-    fig.add_trace(go.Scatter(
+    fig.add_trace(
+        go.Scatter(
             x=df2.index,
             y=df2.iloc[:, 0],
-            mode='lines',
-            name='Test Data',
-            line=dict(color='gold'),
-            marker=dict(color='gold')
-            ))
+            mode="lines",
+            name="Test Data",
+            line=dict(color="gold"),
+            marker=dict(color="gold"),
+        )
+    )
 
     # Customize the layout
     fig.update_layout(
-            title='Univariate Time Series',
-            xaxis=dict(title='Date'),
-            yaxis=dict(title='Value'),
-            showlegend=True,
-            template='plotly_white'
-            )
+        title="Univariate Time Series",
+        xaxis=dict(title="Date"),
+        yaxis=dict(title="Value"),
+        showlegend=True,
+        template="plotly_white",
+    )
     return fig
 
 
-def plot_forecast(df: pd.DataFrame, forecasts: List[pd.DataFrame]) -> go.Figure:
+def plot_forecast(df: pd.DataFrame, forecasts: List[pd.DataFrame]):
     """
     Plot the true values and forecasts using Plotly.
 
@@ -67,48 +71,53 @@ def plot_forecast(df: pd.DataFrame, forecasts: List[pd.DataFrame]) -> go.Figure:
     fig = go.Figure()
 
     # Add the true values trace
-    fig.add_trace(go.Scatter(
+    fig.add_trace(
+        go.Scatter(
             x=pd.to_datetime(df.index),
             y=df.iloc[:, 0],
-            mode='lines',
-            name='True values',
-            line=dict(color='black')
-            ))
+            mode="lines",
+            name="True values",
+            line=dict(color="black"),
+        )
+    )
 
     # Add the forecast traces
     colors = ["green", "blue", "purple"]
     for i, forecast in enumerate(forecasts):
         color = colors[i]
         for sample in forecast.samples:
-            fig.add_trace(go.Scatter(
+            fig.add_trace(
+                go.Scatter(
                     x=forecast.index.to_timestamp(),
                     y=sample,
-                    mode='lines',
+                    mode="lines",
                     opacity=0.15,  # Adjust opacity to control visibility of individual samples
-                    name=f'Forecast {i + 1}',
+                    name=f"Forecast {i + 1}",
                     showlegend=False,  # Hide the individual forecast series from the legend
-                    hoverinfo='none',  # Disable hover information for the forecast series
-                    line=dict(color=color)
-                    ))
+                    hoverinfo="none",  # Disable hover information for the forecast series
+                    line=dict(color=color),
+                )
+            )
         # Add the average
         mean_forecast = np.mean(forecast.samples, axis=0)
-        fig.add_trace(go.Scatter(
+        fig.add_trace(
+            go.Scatter(
                 x=forecast.index.to_timestamp(),
                 y=mean_forecast,
-                mode='lines',
-                name=f'Mean Forecast',
-                line=dict(color='red', dash='dash')
-                ))
+                mode="lines",
+                name="Mean Forecast",
+                line=dict(color="red", dash="dash"),
+            )
+        )
 
     # Customize the layout
     fig.update_layout(
-            title='Passenger Forecast',
-            xaxis=dict(title='Index'),
-            yaxis=dict(title='Passenger Count'),
-            showlegend=True,
-            legend=dict(x=0, y=1, font=dict(size=16)),
-            hovermode='x'  # Enable x-axis hover for better interactivity
-            )
+        title=f"{df.columns[0]} Forecast",
+        yaxis=dict(title=df.columns[0]),
+        showlegend=True,
+        legend=dict(x=0, y=1, font=dict(size=16)),
+        hovermode="x",  # Enable x-axis hover for better interactivity
+    )
 
     # Return the figure
     return fig