Buckets:
| import json | |
| import os | |
| import fastf1 | |
| import numpy as np | |
| import pandas as pd | |
| import requests | |
| YEAR = 2018 | |
| events = [ | |
| "Bahrain Grand Prix", | |
| "Chinese Grand Prix", | |
| "Azerbaijan Grand Prix", | |
| "Spanish Grand Prix", | |
| "Monaco Grand Prix", | |
| "Canadian Grand Prix", | |
| "French Grand Prix", | |
| "Austrian Grand Prix", | |
| "British Grand Prix", | |
| "German Grand Prix", | |
| "Hungarian Grand Prix", | |
| "Belgian Grand Prix", | |
| "Italian Grand Prix", | |
| "Singapore Grand Prix", | |
| "Mexican Grand Prix", | |
| "Brazilian Grand Prix", | |
| "Abu Dhabi Grand Prix", | |
| "Russian Grand Prix", | |
| "Japanese Grand Prix", | |
| "United States Grand Prix", | |
| ] | |
| def average_race_pace( | |
| year: int, | |
| event: str | int, | |
| session: str, | |
| ) -> any: | |
| f1session = fastf1.get_session( | |
| year, | |
| event, | |
| session, | |
| # backend="fastf1", | |
| # force_ergast=False, | |
| ) | |
| f1session.load(telemetry=False, weather=False, messages=False) | |
| laps = f1session.laps | |
| laps = laps.loc[laps.LapNumber > 1] | |
| laps = laps.pick_track_status( | |
| "1", | |
| ) | |
| laps["LapTime"] = laps.Sector1Time + laps.Sector2Time + laps.Sector3Time | |
| # convert LapTime to seconds | |
| laps["LapTime"] = laps["LapTime"].apply(lambda x: x.total_seconds()) | |
| laps = laps.loc[laps.LapTime < laps.LapTime.min() * 1.07] | |
| df = laps[["LapTime", "Driver"]].groupby("Driver").mean().reset_index(drop=False) | |
| df = df.sort_values(by="LapTime").reset_index(drop=True) | |
| df["LapTime"] = df["LapTime"].round(3) | |
| df["Diff"] = (df["LapTime"] - df["LapTime"].min()).round(3) | |
| teams = laps[["Driver", "Team"]].drop_duplicates().reset_index(drop=True) | |
| # join teams and df | |
| df = df.merge(teams, on="Driver", how="left") | |
| # car_colors = utils.team_colors(year) | |
| # df["fill"] = df["Team"].map(car_colors) | |
| df_json = df.to_dict("records") | |
| return {"racePace": df_json} | |
| # Your list of events | |
| events_list = events | |
| # Loop through each event | |
| for event in events_list: | |
| session = "Race" | |
| race_pace_dict = average_race_pace(YEAR, event, session) | |
| # Specify the file path where you want to save the JSON data | |
| file_path = f"{event}/{session}/average_race_pace.json" | |
| # Save the dictionary to a JSON file | |
| with open(file_path, "w") as json_file: | |
| json.dump(race_pace_dict, json_file) | |
| print(f"Dictionary saved to {file_path}") | |
Xet Storage Details
- Size:
- 2.48 kB
- Xet hash:
- 3200fc08a9b73cd2a1a1f3d474fa791323fd93f9e4506126f9944e37b0fe0827
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