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#!/usr/bin/env python3
"""
Helper functions for analyzing kernelbot submissions.
Usage:
from analyze_submissions import load_submissions, author_progression, top_contestants
"""
import pandas as pd
from pathlib import Path
def format_score(score, unit='us'):
"""
Format score with appropriate units.
Args:
score: Score in seconds
unit: 'us' for microseconds, 'ms' for milliseconds, 'auto' for automatic
Returns:
Formatted string with units
"""
if pd.isna(score):
return 'N/A'
if unit == 'auto':
if score < 0.001: # Less than 1ms, show in microseconds
return f"{score * 1_000_000:.2f} µs"
elif score < 1: # Less than 1s, show in milliseconds
return f"{score * 1_000:.3f} ms"
else:
return f"{score:.4f} s"
elif unit == 'us':
return f"{score * 1_000_000:.2f} µs"
elif unit == 'ms':
return f"{score * 1_000:.3f} ms"
else:
return f"{score:.6f} s"
def load_submissions(parquet_path: str = None) -> pd.DataFrame:
"""Load deduplicated submissions from parquet file."""
if parquet_path is None:
parquet_path = Path(__file__).parent.parent.parent / "nvidia_nvfp4_submissions.parquet"
return pd.read_parquet(parquet_path)
def author_progression(df: pd.DataFrame, user_id: str = None, user_name: str = None,
problem_name: str = None) -> pd.DataFrame:
"""
Get submissions from an author sorted by time to see their progression.
Args:
df: DataFrame of submissions
user_id: Filter by user ID (Discord ID)
user_name: Filter by username (partial match, case-insensitive)
problem_name: Filter by problem name
Returns:
DataFrame sorted by submission_time showing the author's journey
"""
result = df.copy()
if user_id:
result = result[result['user_id'] == user_id]
if user_name:
result = result[result['user_name'].str.contains(user_name, case=False, na=False)]
if problem_name:
result = result[result['problem_name'] == problem_name]
return result.sort_values('submission_time')
def top_contestants(df: pd.DataFrame, problem_name: str = None, n: int = 20,
passing_only: bool = True) -> pd.DataFrame:
"""
Get top contestants sorted by their best score (fastest time).
Args:
df: DataFrame of submissions
problem_name: Filter by problem name (required for meaningful results)
n: Number of top contestants to return
passing_only: Only include passing submissions
Returns:
DataFrame with top contestants and their best scores
"""
result = df.copy()
if problem_name:
result = result[result['problem_name'] == problem_name]
if passing_only:
result = result[result['passed'] == True]
# Filter out rows with NA scores
result = result.dropna(subset=['score'])
if result.empty:
return pd.DataFrame(columns=['user_name', 'user_id', 'score', 'submission_time', 'problem_name'])
# Get best score per user
best_scores = result.loc[result.groupby('user_id')['score'].idxmin()]
return best_scores.sort_values('score').head(n)[
['user_name', 'user_id', 'score', 'submission_time', 'problem_name']
]
def leaderboard_summary(df: pd.DataFrame, score_unit='us') -> pd.DataFrame:
"""
Get summary statistics for each problem.
Args:
df: DataFrame of submissions
score_unit: 'us' for microseconds, 'ms' for milliseconds, 's' for seconds
Returns:
DataFrame with submission counts, unique users, score ranges
"""
summary = df.groupby('problem_name').agg({
'submission_id': 'count',
'user_id': 'nunique',
'score': ['min', 'median', 'max'],
'passed': 'sum'
})
summary.columns = ['submissions', 'unique_users', 'best_score', 'median_score',
'worst_score', 'passing_count']
# Convert scores to specified unit
if score_unit == 'us':
multiplier = 1_000_000
summary['best_score'] = (summary['best_score'] * multiplier).round(2)
summary['median_score'] = (summary['median_score'] * multiplier).round(2)
summary['worst_score'] = (summary['worst_score'] * multiplier).round(2)
elif score_unit == 'ms':
multiplier = 1_000
summary['best_score'] = (summary['best_score'] * multiplier).round(3)
summary['median_score'] = (summary['median_score'] * multiplier).round(3)
summary['worst_score'] = (summary['worst_score'] * multiplier).round(3)
return summary
def user_stats(df: pd.DataFrame, user_id: str = None, user_name: str = None) -> pd.DataFrame:
"""
Get statistics for a specific user across all problems.
"""
result = df.copy()
if user_id:
result = result[result['user_id'] == user_id]
elif user_name:
result = result[result['user_name'].str.contains(user_name, case=False, na=False)]
return result.groupby('problem_name').agg({
'submission_id': 'count',
'score': 'min',
'passed': 'sum'
}).rename(columns={
'submission_id': 'num_submissions',
'score': 'best_score',
'passed': 'passing_count'
})
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