feat: enable autoscaling for y-axis and x-axis in combined time series graph

app.py

@@ -593,14 +593,8 @@ def create_combined_time_series_graph(df):
     # Create Plotly figure in a clean state
     fig = go.Figure()
     
-    # FORCE FIXED Y-AXIS RANGE based on the known data range
-    # Set explicit fixed values that will show the data clearly
-    min_apr = -40  # Bottom of the range
-    max_apr = -10  # Top of the range
-    
-    logger.info(f"Using fixed y-axis range: [{min_apr}, {max_apr}]")
-    
-    logger.info(f"Setting y-axis range to [{min_apr:.2f}, {max_apr:.2f}] based on filtered data")
+    # Enable autoscaling instead of fixed ranges
+    logger.info("Using autoscaling for axes ranges")
     
     # Add background shapes for APR and Performance regions
     min_time = df['timestamp'].min()
@@ -611,7 +605,7 @@ def create_combined_time_series_graph(df):
         type="rect",
         fillcolor="rgba(230, 243, 255, 0.3)",
         line=dict(width=0),
-        y0=0, y1=max_apr,
+        y0=0, y1=100,  # Use a fixed positive value
         x0=min_time, x1=max_time,
         layer="below"
     )
@@ -621,7 +615,7 @@ def create_combined_time_series_graph(df):
         type="rect",
         fillcolor="rgba(255, 230, 230, 0.3)",
         line=dict(width=0),
-        y0=min_apr, y1=0,
+        y0=-100, y1=0,  # Use a fixed negative value
         x0=min_time, x1=max_time,
         layer="below"
     )
@@ -844,46 +838,28 @@ def create_combined_time_series_graph(df):
     # Use fixed tick spacing for clarity
     dtick = 5  # 5-unit ticks for better readability
     
+    # Update y-axis with autoscaling and improved appearance
     fig.update_yaxes(
         showgrid=True, 
         gridwidth=1, 
         gridcolor='rgba(0,0,0,0.1)',
-        range=[min_apr, max_apr],  # Updated range based on filtered data
-        tickmode='linear',
-        tick0=0,
-        dtick=dtick
+        # No fixed range - let Plotly autoscale
+        autorange=True,  # Explicitly enable autoscaling
+        tickformat=".2f",  # Format tick labels with 2 decimal places
+        tickfont=dict(size=12)  # Larger font for tick labels
     )
     
-    # Update x-axis with better formatting and focus on data points
-    # Get the actual data time range
-    if not apr_data.empty:
-        # Find the actual time range where we have data
-        data_min_time = apr_data['timestamp'].min()
-        data_max_time = apr_data['timestamp'].max()
-        
-        # Add some padding (10% on each side)
-        time_range = data_max_time - data_min_time
-        padding = time_range * 0.1
-        display_min_time = data_min_time - padding
-        display_max_time = data_max_time + padding
-        
-        # Update x-axis range to focus on the actual data
-        fig.update_xaxes(
-            showgrid=True, 
-            gridwidth=1, 
-            gridcolor='rgba(0,0,0,0.1)',
-            range=[display_min_time, display_max_time],
-            tickformat="%b %d, %H:%M",  # More detailed time format
-            tickangle=-30,  # Angle the labels for better readability
-            tickfont=dict(size=12)  # Larger font for tick labels
-        )
-    else:
-        # Default x-axis settings if no data
-        fig.update_xaxes(
-            showgrid=True, 
-            gridwidth=1, 
-            gridcolor='rgba(0,0,0,0.1)'
-        )
+    # Update x-axis with better formatting and autoscaling
+    fig.update_xaxes(
+        showgrid=True, 
+        gridwidth=1, 
+        gridcolor='rgba(0,0,0,0.1)',
+        # No fixed range - let Plotly autoscale
+        autorange=True,  # Explicitly enable autoscaling
+        tickformat="%b %d, %H:%M",  # More detailed time format
+        tickangle=-30,  # Angle the labels for better readability
+        tickfont=dict(size=12)  # Larger font for tick labels
+    )
     
     # SIMPLIFIED APPROACH: Do a direct plot without markers for comparison
     # This creates a simple, reliable fallback plot if the advanced one fails
@@ -1028,36 +1004,24 @@ def create_combined_time_series_graph(df):
             title="Average APR Values Across All Agents",
             xaxis_title="Time",
             yaxis_title="Value",
-            yaxis=dict(
-                range=[-40, -10],  # Fixed range to match the main graph
-                tickmode='linear',
-                tick0=0,
-                dtick=5
-            ),
+                    yaxis=dict(
+                        # No fixed range - let Plotly autoscale
+                        autorange=True,  # Explicitly enable autoscaling
+                        tickformat=".2f",  # Format tick labels with 2 decimal places
+                        tickfont=dict(size=12)  # Larger font for tick labels
+                    ),
             height=700,  # Increased height
             width=1400,  # Increased width
             template="plotly_white"  # Use a cleaner template
         )
         
-        # Apply the same x-axis improvements to the fallback graph
-        if not apr_data.empty:
-            # Find the actual time range where we have data
-            data_min_time = apr_data['timestamp'].min()
-            data_max_time = apr_data['timestamp'].max()
-            
-            # Add some padding (10% on each side)
-            time_range = data_max_time - data_min_time
-            padding = time_range * 0.1
-            display_min_time = data_min_time - padding
-            display_max_time = data_max_time + padding
-            
-            # Update x-axis range to focus on the actual data
-            simple_fig.update_xaxes(
-                range=[display_min_time, display_max_time],
-                tickformat="%b %d, %H:%M",
-                tickangle=-30,
-                tickfont=dict(size=12)
-            )
+        # Apply autoscaling to the x-axis for the fallback graph
+        simple_fig.update_xaxes(
+            autorange=True,  # Explicitly enable autoscaling
+            tickformat="%b %d, %H:%M",
+            tickangle=-30,
+            tickfont=dict(size=12)
+        )
         
         # Add a note about hidden agents if there are more than MAX_VISIBLE_AGENTS
         if len(unique_agents) > MAX_VISIBLE_AGENTS: