feat: enhance combined time series graph with fixed y-axis range and improved marker handling

app.py

@@ -531,16 +531,17 @@ def create_combined_time_series_graph(df):
     
     logger.info("Generated detailed graph data report")
     
-    # Create Plotly figure
+    # ENSURE THERE ARE NO CONFLICTING AXES OR TRACES
+    # Create Plotly figure in a clean state
     fig = go.Figure()
     
     # Get unique agents
     unique_agents = df['agent_id'].unique()
     colors = px.colors.qualitative.Plotly[:len(unique_agents)]
     
-    # IMPORTANT: Calculate min/max values for proper axis scaling
-    min_apr = min(df['apr'].min(), -100)  # Ensure we see at least down to -100
-    max_apr = max(df['apr'].max(), 100)   # Ensure we see at least up to 100
+    # IMPORTANT: Fixed y-axis range that always includes -100
+    min_apr = -110  # Fixed minimum to ensure -100 is visible
+    max_apr = 110   # Fixed maximum to ensure data is visible
     
     # Add background shapes for APR and Performance regions
     min_time = df['timestamp'].min()
@@ -574,6 +575,7 @@ def create_combined_time_series_graph(df):
         x0=min_time, x1=max_time
     )
     
+    # MODIFIED: Changed order of trace addition - add markers first, then lines
     # Add data for each agent
     for i, agent_id in enumerate(unique_agents):
         agent_data = df[df['agent_id'] == agent_id].copy()
@@ -588,7 +590,67 @@ def create_combined_time_series_graph(df):
         for idx, row in agent_data.iterrows():
             logger.info(f"  Point {idx}: timestamp={row['timestamp']}, apr={row['apr']}, type={row['metric_type']}")
         
-        # Add the combined line for both APR and Performance
+        # First add scatter points for Performance values
+        perf_data = agent_data[agent_data['metric_type'] == 'Performance']
+        if not perf_data.empty:
+            logger.info(f"  Adding {len(perf_data)} Performance markers for {agent_name}")
+            for idx, row in perf_data.iterrows():
+                logger.info(f"    Performance marker: timestamp={row['timestamp']}, apr={row['apr']}")
+            
+                # Use explicit Python boolean for showlegend
+                is_first_point = bool(idx == perf_data.index[0])
+                fig.add_trace(
+                    go.Scatter(
+                        x=[row['timestamp']],
+                        y=[row['apr']],
+                        mode='markers',
+                        marker=dict(
+                            color='red',  # Force consistent color
+                            symbol='square',
+                            size=16,      # Make markers larger
+                            line=dict(
+                                width=2,
+                                color='black'
+                            )
+                        ),
+                        name=f'{agent_name} Perf',
+                        legendgroup=agent_name,
+                        showlegend=is_first_point,  # Use native Python boolean
+                        hovertemplate='Time: %{x}<br>Performance: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
+                    )
+                )
+        
+        # Now add scatter points for APR values
+        apr_data = agent_data[agent_data['metric_type'] == 'APR']
+        if not apr_data.empty:
+            logger.info(f"  Adding {len(apr_data)} APR markers for {agent_name}")
+            for idx, row in apr_data.iterrows():
+                logger.info(f"    APR marker: timestamp={row['timestamp']}, apr={row['apr']}")
+            
+                # Use explicit Python boolean for showlegend
+                is_first_point = bool(idx == apr_data.index[0])
+                fig.add_trace(
+                    go.Scatter(
+                        x=[row['timestamp']],
+                        y=[row['apr']],
+                        mode='markers',
+                        marker=dict(
+                            color='blue',  # Force consistent color
+                            symbol='circle',
+                            size=14,       # Make markers larger
+                            line=dict(
+                                width=2,
+                                color='black'
+                            )
+                        ),
+                        name=f'{agent_name} APR',
+                        legendgroup=agent_name,
+                        showlegend=is_first_point,  # Use native Python boolean
+                        hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
+                    )
+                )
+        
+        # Add the combined line AFTER markers for both APR and Performance
         fig.add_trace(
             go.Scatter(
                 x=agent_data['timestamp'],
@@ -600,50 +662,8 @@ def create_combined_time_series_graph(df):
                 hovertemplate='Time: %{x}<br>Value: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
             )
         )
-        
-        # Add scatter points for APR values
-        apr_data = agent_data[agent_data['metric_type'] == 'APR']
-        if not apr_data.empty:
-            logger.info(f"  Adding {len(apr_data)} APR markers for {agent_name}")
-            for idx, row in apr_data.iterrows():
-                logger.info(f"    APR marker: timestamp={row['timestamp']}, apr={row['apr']}")
-            
-            fig.add_trace(
-                go.Scatter(
-                    x=apr_data['timestamp'],
-                    y=apr_data['apr'],
-                    mode='markers',
-                    marker=dict(color=color, symbol='circle', size=10),
-                    name=f'{agent_name} APR',
-                    legendgroup=agent_name,
-                    showlegend=False,
-                    hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>',
-                    visible=True  # Explicitly set visibility
-                )
-            )
-        
-        # Add scatter points for Performance values
-        perf_data = agent_data[agent_data['metric_type'] == 'Performance']
-        if not perf_data.empty:
-            logger.info(f"  Adding {len(perf_data)} Performance markers for {agent_name}")
-            for idx, row in perf_data.iterrows():
-                logger.info(f"    Performance marker: timestamp={row['timestamp']}, apr={row['apr']}")
-            
-            fig.add_trace(
-                go.Scatter(
-                    x=perf_data['timestamp'],
-                    y=perf_data['apr'],
-                    mode='markers',
-                    marker=dict(color=color, symbol='square', size=10),
-                    name=f'{agent_name} Perf',
-                    legendgroup=agent_name,
-                    showlegend=False,
-                    hovertemplate='Time: %{x}<br>Performance: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>',
-                    visible=True  # Explicitly set visibility
-                )
-            )
     
-    # Update layout
+    # Update layout - use simple boolean values everywhere
     fig.update_layout(
         title="APR and Performance Values for All Agents",
         xaxis_title="Time",
@@ -663,33 +683,91 @@ def create_combined_time_series_graph(df):
         hovermode="closest"
     )
     
-    # Force y-axis range to include all data points with padding
-    y_padding = (max_apr - min_apr) * 0.1  # 10% padding
+    # FORCE FIXED Y-AXIS RANGE
     fig.update_yaxes(
         showgrid=True, 
         gridwidth=1, 
         gridcolor='rgba(0,0,0,0.1)',
-        range=[min_apr - y_padding, max_apr + y_padding]  # Force range to include all points
+        range=[min_apr, max_apr],  # Fixed range
+        tickmode='linear',
+        tick0=-100,
+        dtick=50
     )
     
     # Update x-axis
-    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
-    
-    # Save the figure (still useful for reference)
-    graph_file = "modius_apr_combined_graph.html"
-    fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
+    fig.update_xaxes(
+        showgrid=True, 
+        gridwidth=1, 
+        gridcolor='rgba(0,0,0,0.1)'
+    )
     
-    # Also save as image for compatibility
-    img_file = "modius_apr_combined_graph.png"
+    # SIMPLIFIED APPROACH: Do a direct plot without markers for comparison
+    # This creates a simple, reliable fallback plot if the advanced one fails
     try:
-        fig.write_image(img_file)
-        logger.info(f"Combined graph saved to {graph_file} and {img_file}")
+        # Save the figure (still useful for reference)
+        graph_file = "modius_apr_combined_graph.html"
+        fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
+        
+        # Also save as image for compatibility
+        img_file = "modius_apr_combined_graph.png"
+        try:
+            fig.write_image(img_file)
+            logger.info(f"Combined graph saved to {graph_file} and {img_file}")
+        except Exception as e:
+            logger.error(f"Error saving image: {e}")
+            logger.info(f"Combined graph saved to {graph_file} only")
+        
+        # Return the figure object for direct use in Gradio
+        return fig
     except Exception as e:
-        logger.error(f"Error saving image: {e}")
-        logger.info(f"Combined graph saved to {graph_file} only")
-    
-    # Return the figure object for direct use in Gradio
-    return fig
+        # If the complex graph approach fails, create a simpler one
+        logger.error(f"Error creating advanced graph: {e}")
+        logger.info("Falling back to simpler graph")
+        
+        # Create a simpler graph as fallback
+        simple_fig = go.Figure()
+        
+        # Add zero line
+        simple_fig.add_shape(
+            type="line",
+            line=dict(dash="solid", width=1.5, color="black"),
+            y0=0, y1=0,
+            x0=min_time, x1=max_time
+        )
+        
+        # Simply plot each agent's data as a line with markers
+        for i, agent_id in enumerate(unique_agents):
+            agent_data = df[df['agent_id'] == agent_id].copy()
+            agent_name = agent_data['agent_name'].iloc[0]
+            color = colors[i % len(colors)]
+            
+            # Sort by timestamp
+            agent_data = agent_data.sort_values('timestamp')
+            
+            # Add a single trace with markers+lines
+            simple_fig.add_trace(
+                go.Scatter(
+                    x=agent_data['timestamp'],
+                    y=agent_data['apr'],
+                    mode='lines+markers',
+                    name=agent_name,
+                    marker=dict(size=10),
+                    line=dict(width=2)
+                )
+            )
+        
+        # Simplified layout
+        simple_fig.update_layout(
+            title="APR and Performance Values (Simplified View)",
+            xaxis_title="Time",
+            yaxis_title="Value",
+            yaxis=dict(range=[-110, 110]),
+            height=600,
+            width=1000
+        )
+        
+        # Return the simple figure
+        return simple_fig
 
 def save_to_csv(df):
     """Save the APR data DataFrame to a CSV file and return the file path"""