Fix Evolution Over Time chart

1. Logo positioning: Use data coordinates (xref='x', yref='y') instead of
domain coordinates for precise alignment with data points
2. Pareto frontier: Use dashed lines connecting frontier points directly,
matching the style of other charts

Co-authored-by: openhands <openhands@all-hands.dev>

visualizations.py

@@ -187,7 +187,7 @@ def create_evolution_over_time_chart(df: pd.DataFrame) -> go.Figure:
     
     # Add Pareto frontier line (monotonically increasing best score over time)
     if len(plot_df) > 1:
-        # Compute Pareto frontier: for each date, track the max score seen so far
+        # Compute Pareto frontier: only include points that set a new best score
         frontier_dates = []
         frontier_scores = []
         max_score_so_far = float('-inf')
@@ -198,11 +198,6 @@ def create_evolution_over_time_chart(df: pd.DataFrame) -> go.Figure:
             
             if current_score > max_score_so_far:
                 # This point is on the Pareto frontier
-                if frontier_dates:
-                    # Add a horizontal line segment from previous frontier point to this date
-                    frontier_dates.append(current_date)
-                    frontier_scores.append(max_score_so_far)
-                # Add the new frontier point
                 frontier_dates.append(current_date)
                 frontier_scores.append(current_score)
                 max_score_so_far = current_score
@@ -212,17 +207,15 @@ def create_evolution_over_time_chart(df: pd.DataFrame) -> go.Figure:
                 x=frontier_dates,
                 y=frontier_scores,
                 mode='lines',
-                line=dict(color='#FFE165', width=2),  # primary yellow, solid line
+                line=dict(color='#FFE165', width=2, dash='dash'),  # primary yellow, dashed
                 name='Pareto Frontier',
                 hoverinfo='skip',
                 showlegend=False
             ))
     
-    # Calculate axis ranges for domain coordinate conversion
+    # Calculate axis ranges
     min_date = plot_df['release_date'].min()
     max_date = plot_df['release_date'].max()
-    date_range = (max_date - min_date).total_seconds() if max_date != min_date else 1
-    
     min_score = plot_df[score_col].min()
     max_score = plot_df[score_col].max()
     y_min = min_score - 5 if min_score > 5 else 0
@@ -258,9 +251,9 @@ def create_evolution_over_time_chart(df: pd.DataFrame) -> go.Figure:
         )
     ))
     
-    # Add company logo images for each data point
+    # Add company logo images for each data point using data coordinates
     layout_images = []
-    frontier_labels_data = []
+    labels_data = []
     
     for _, row in plot_df.iterrows():
         model_name = row.get(model_col, '')
@@ -277,47 +270,36 @@ def create_evolution_over_time_chart(df: pd.DataFrame) -> go.Figure:
                     x_val = row['release_date']
                     y_val = row[score_col]
                     
-                    # Convert to domain coordinates (0-1 range)
-                    if date_range > 0:
-                        domain_x = (x_val - min_date).total_seconds() / date_range
-                    else:
-                        domain_x = 0.5
-                    
-                    domain_y = (y_val - y_min) / (y_max - y_min) if (y_max - y_min) > 0 else 0.5
-                    
-                    # Clamp to valid range
-                    domain_x = max(0.02, min(0.98, domain_x))
-                    domain_y = max(0.02, min(0.98, domain_y))
-                    
+                    # Use data coordinates for precise alignment
                     layout_images.append(dict(
                         source=logo_uri,
-                        xref="x domain",
-                        yref="y domain",
-                        x=domain_x,
-                        y=domain_y,
-                        sizex=0.04,
-                        sizey=0.06,
+                        xref="x",
+                        yref="y",
+                        x=x_val,
+                        y=y_val,
+                        sizex=15 * 24 * 60 * 60 * 1000,  # ~15 days in milliseconds
+                        sizey=3,  # score units
                         xanchor="center",
                         yanchor="middle",
                         layer="above"
                     ))
                     
                     # Store label data for annotation
-                    frontier_labels_data.append({
-                        'x': domain_x,
-                        'y': domain_y,
+                    labels_data.append({
+                        'x': x_val,
+                        'y': y_val,
                         'label': model_name
                     })
             except Exception:
                 pass
     
     # Add model name labels above each point
-    for item in frontier_labels_data:
+    for item in labels_data:
         fig.add_annotation(
             x=item['x'],
             y=item['y'],
-            xref="x domain",
-            yref="y domain",
+            xref="x",
+            yref="y",
             text=item['label'],
             showarrow=False,
             yshift=20,