feat: enhance data consistency and logging in combined time series graph

app.py

@@ -481,12 +481,22 @@ def create_combined_time_series_graph(df):
         )
         return fig
     
-    # ADDED: Export full dataframe to CSV for debugging
+    # IMPORTANT: Force data types to ensure consistency
+    df['apr'] = df['apr'].astype(float)  # Ensure APR is float
+    df['metric_type'] = df['metric_type'].astype(str)  # Ensure metric_type is string
+    
+    # CRITICAL: Log the exact dataframe we're using for plotting to help debug
+    logger.info(f"Graph data - shape: {df.shape}, columns: {df.columns}")
+    logger.info(f"Graph data - unique agents: {df['agent_name'].unique().tolist()}")
+    logger.info(f"Graph data - unique metric types: {df['metric_type'].unique().tolist()}")
+    logger.info(f"Graph data - min APR: {df['apr'].min()}, max APR: {df['apr'].max()}")
+    
+    # Export full dataframe to CSV for debugging
     debug_csv = "debug_graph_data.csv"
     df.to_csv(debug_csv)
     logger.info(f"Exported graph data to {debug_csv} for debugging")
     
-    # ADDED: Write detailed data report
+    # Write detailed data report
     with open("debug_graph_data_report.txt", "w") as f:
         f.write("==== GRAPH DATA REPORT ====\n\n")
         f.write(f"Total data points: {len(df)}\n")
@@ -525,8 +535,13 @@ def create_combined_time_series_graph(df):
     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
+    
     # Add background shapes for APR and Performance regions
     min_time = df['timestamp'].min()
     max_time = df['timestamp'].max()
@@ -536,7 +551,7 @@ def create_combined_time_series_graph(df):
         type="rect",
         fillcolor="rgba(230, 243, 255, 0.3)",
         line=dict(width=0),
-        y0=0, y1=1000,
+        y0=0, y1=max_apr,
         x0=min_time, x1=max_time,
         layer="below"
     )
@@ -546,7 +561,7 @@ def create_combined_time_series_graph(df):
         type="rect",
         fillcolor="rgba(255, 230, 230, 0.3)",
         line=dict(width=0),
-        y0=-1000, y1=0,
+        y0=min_apr, y1=0,
         x0=min_time, x1=max_time,
         layer="below"
     )
@@ -567,7 +582,12 @@ def create_combined_time_series_graph(df):
         
         # Sort the data by timestamp
         agent_data = agent_data.sort_values('timestamp')
-        print("agent_data_combined",agent_data)
+        
+        # Log actual points being plotted for this agent
+        logger.info(f"Plotting agent: {agent_name} (ID: {agent_id}) with {len(agent_data)} points")
+        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
         fig.add_trace(
             go.Scatter(
@@ -583,35 +603,43 @@ def create_combined_time_series_graph(df):
         
         # Add scatter points for APR values
         apr_data = agent_data[agent_data['metric_type'] == 'APR']
-        print("apr_data_combined",apr_data)
         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=8),
+                    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>'
+                    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']
-        print("perf_data_combined",perf_data)
         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=8),
+                    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>'
+                    hovertemplate='Time: %{x}<br>Performance: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>',
+                    visible=True  # Explicitly set visibility
                 )
             )
     
@@ -635,9 +663,17 @@ def create_combined_time_series_graph(df):
         hovermode="closest"
     )
     
-    # Update axes
+    # Force y-axis range to include all data points with padding
+    y_padding = (max_apr - min_apr) * 0.1  # 10% padding
+    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
+    )
+    
+    # Update x-axis
     fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
-    fig.update_yaxes(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"
@@ -645,9 +681,12 @@ def create_combined_time_series_graph(df):
     
     # Also save as image for compatibility
     img_file = "modius_apr_combined_graph.png"
-    fig.write_image(img_file)
-    
-    logger.info(f"Combined graph saved to {graph_file} and {img_file}")
+    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