"""
Create animated bar chart race showing leaderboard evolution over time
"""
import pandas as pd
import numpy as np
from typing import List, Tuple
import json
import os
def prepare_animation_data(history_df: pd.DataFrame, top_n: int = 15) -> dict:
"""
Prepare data for D3.js bar chart race animation.
Args:
history_df: DataFrame with columns: date, model, rating, rank
top_n: Number of top models to show at each time point
Returns:
Dictionary with animation data
"""
# Convert date column to Timestamp to support ISO date strings and date objects
if history_df is not None and len(history_df) > 0 and 'date' in history_df.columns:
history_df = history_df.copy()
history_df['date'] = pd.to_datetime(history_df['date'])
# Get all unique dates
dates = sorted(history_df['date'].unique())
if len(dates) == 0:
return {
'frames': [],
'total_frames': 0,
'top_n': top_n,
'start_date': None,
'end_date': None,
}
# For each date, get top N models
frames = []
for date in dates:
date_data = history_df[history_df['date'] == date].nlargest(top_n, 'rating')
frame = {
'date': date.strftime('%Y-%m-%d'),
'timestamp': int(date.timestamp()),
'models': []
}
for rank, row in enumerate(date_data.itertuples(), 1):
frame['models'].append({
'rank': rank,
'name': row.model,
'rating': float(row.rating),
'matches': int(row.matches),
'wins': float(row.wins)
})
frames.append(frame)
animation_data = {
'frames': frames,
'total_frames': len(frames),
'top_n': top_n,
'start_date': dates[0].strftime('%Y-%m-%d'),
'end_date': dates[-1].strftime('%Y-%m-%d')
}
return animation_data
def generate_html_animation(animation_data: dict, output_path: str = "leaderboard_animation.html"):
"""
Generate standalone HTML file with D3.js bar chart race animation.
Args:
animation_data: Dictionary from prepare_animation_data
output_path: Path to save HTML file
"""
html_template = """
Model Leaderboard Evolution
🏆 Model Leaderboard Evolution
Loading...
5x
About: This animation shows the evolution of model rankings based on Elo ratings
calculated from Chatbot Arena voting data. Each frame represents a snapshot in time,
with models ranked by their current Elo rating. Bars show the rating value,
and the animation reveals how models compete and evolve over time.
"""
# Keep generated evidence friendly to `git diff --check` and deterministic
# across editors that otherwise strip indentation-only lines.
html_template = "\n".join(line.rstrip() for line in html_template.splitlines()) + "\n"
# Write to file
with open(output_path, 'w', encoding='utf-8') as f:
f.write(html_template)
print(f"Generated animation HTML at: {output_path}")
print(f"Open the file in a web browser to view the animation.")
def create_simple_animation(history_df: pd.DataFrame, output_path: str = "leaderboard_animation.html", top_n: int = 15):
"""
Convenience function to create animation in one step.
Args:
history_df: DataFrame with rating history
output_path: Path to save HTML file
top_n: Number of top models to show
"""
print("Preparing animation data...")
animation_data = prepare_animation_data(history_df, top_n)
print(f"Generating HTML animation with {animation_data['total_frames']} frames...")
generate_html_animation(animation_data, output_path)
return output_path