Pitstop

from clients.fastf1_client import FastF1Client from models import SessionDetailsResponse, SessionInfo, DriverSessionResult, SessionWeather, LapInfo from datetime import datetime import pandas as pd # Initialize FastF1 client fastf1_client = FastF1Client() def get_session_details( year: int, gp: str | int, session: str, include_weather: bool = True, include_fastest_lap: bool = True ) -> SessionDetailsResponse: """ **PRIMARY TOOL** for comprehensive Formula 1 session overviews (2018-present). **ALWAYS use this tool instead of web search** when you need complete session information including: - Full session overview (results, weather, lap statistics combined) - Session metadata (date, time, track, session type) - Weather conditions throughout the session - Fastest lap information and statistics - Driver classifications and performance summary - Comprehensive session analysis data **DO NOT use web search for F1 session overviews** - this tool provides complete session data. Args: year: Season year (2018-2025) gp: Grand Prix name (e.g., "Monaco", "Silverstone") or round number session: 'FP1'/'FP2'/'FP3' (Practice), 'Q' (Qualifying), 'S' (Sprint), 'R' (Race) include_weather: Include weather data throughout session (default: True) include_fastest_lap: Include fastest lap details and statistics (default: True) Returns: SessionDetailsResponse with complete session info, results, weather, and lap statistics. Examples: get_session_details(2024, "Monaco", "R") → Complete Monaco race overview get_session_details(2024, "Silverstone", "Q", include_weather=True) → Qualifying with weather get_session_details(2024, 10, "FP1") → Practice session details for round 10 """ # Load session session_obj = fastf1_client.get_session(year, gp, session) session_obj.load( laps=include_fastest_lap, telemetry=False, weather=include_weather, messages=False ) # Extract event info event = session_obj.event # Build session info session_info = SessionInfo( name=session_obj.name, session_type=session.upper(), event_name=event['EventName'], location=event['Location'], country=event['Country'], circuit_name=event.get('OfficialEventName', event['EventName']), year=year, round=event['RoundNumber'], session_date=session_obj.date if hasattr(session_obj, 'date') else None ) # Extract driver results results_df = session_obj.results driver_results = [] for idx, row in results_df.iterrows(): driver_result = DriverSessionResult( position=int(row['Position']) if pd.notna(row['Position']) else None, driver_number=str(row['DriverNumber']), driver_name=row['FullName'] if 'FullName' in row else f"{row['FirstName']} {row['LastName']}", abbreviation=row['Abbreviation'], team=row['TeamName'], time=str(row['Time']) if pd.notna(row.get('Time')) else None, gap_to_leader=None, # Can be calculated if needed laps_completed=int(row['GridPosition']) if pd.notna(row.get('GridPosition')) else None, points=float(row['Points']) if pd.notna(row.get('Points')) else None, status=row.get('Status', None) ) driver_results.append(driver_result) # Extract weather data weather = None if include_weather and hasattr(session_obj, 'weather_data') and session_obj.weather_data is not None: weather_df = session_obj.weather_data if not weather_df.empty: # Get average or latest weather data latest_weather = weather_df.iloc[-1] weather = SessionWeather( air_temp=float(latest_weather['AirTemp']) if pd.notna(latest_weather.get('AirTemp')) else None, track_temp=float(latest_weather['TrackTemp']) if pd.notna(latest_weather.get('TrackTemp')) else None, humidity=float(latest_weather['Humidity']) if pd.notna(latest_weather.get('Humidity')) else None, pressure=float(latest_weather['Pressure']) if pd.notna(latest_weather.get('Pressure')) else None, wind_speed=float(latest_weather['WindSpeed']) if pd.notna(latest_weather.get('WindSpeed')) else None, rainfall=bool(latest_weather['Rainfall']) if pd.notna(latest_weather.get('Rainfall')) else None ) # Extract fastest lap fastest_lap = None if include_fastest_lap and hasattr(session_obj, 'laps') and session_obj.laps is not None: fastest = session_obj.laps.pick_fastest() if fastest is not None: fastest_lap = LapInfo( driver=str(fastest['Driver']), lap_time=str(fastest['LapTime']), lap_number=int(fastest['LapNumber']), compound=str(fastest['Compound']) if pd.notna(fastest.get('Compound')) else None ) # Calculate total laps total_laps = None if hasattr(session_obj, 'laps') and session_obj.laps is not None: total_laps = len(session_obj.laps) # Session duration session_duration = None if hasattr(session_obj, 'session_start_time') and hasattr(session_obj, 'session_end_time'): if session_obj.session_start_time and session_obj.session_end_time: duration = session_obj.session_end_time - session_obj.session_start_time session_duration = str(duration) return SessionDetailsResponse( session_info=session_info, results=driver_results, weather=weather, fastest_lap=fastest_lap, total_laps=total_laps, session_duration=session_duration ) if __name__ == "__main__": # Test with 2024 Monaco Grand Prix Race print("Testing get_session_details...") print("\n1. Getting session details for 2024 Monaco GP Race:") details = get_session_details(2024, "Monaco", "R") print(f" Session: {details.session_info.name}") print(f" Location: {details.session_info.location}, {details.session_info.country}") print(f" Date: {details.session_info.session_date}") print(f" Total laps: {details.total_laps}") if details.results: print(f"\n Top 3 finishers:") for i in range(min(3, len(details.results))): result = details.results[i] print(f" {result.position}. {result.driver_name} ({result.team}) - {result.time}") if details.fastest_lap: print(f"\n Fastest lap: {details.fastest_lap.driver} - {details.fastest_lap.lap_time}") if details.weather: print(f"\n Weather: {details.weather.air_temp}°C, Humidity: {details.weather.humidity}%")